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• Published: 31 March 2015

Genetic linkage analysis in the age of whole-genome sequencing

• Jurg Ott 1 , 2 ,
• Jing Wang 1 &
• Suzanne M. Leal 3  

Nature Reviews Genetics volume  16 ,  pages 275–284 ( 2015 ) Cite this article

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• DNA sequencing
• Genetic linkage study
• Genetic mapping
• Next-generation sequencing

Genetic linkage analysis can be used as a tool for estimating the genetic distance between two loci.

In family data, a small recombination fraction between a hypothesized disease locus and a genetic marker is evidence of short distance between the two loci.

Linkage analysis is contrasted with family-based association analysis, in which unaffected family members serve as control individuals (in family-based association tests).

Single-nucleotide variants (SNVs) generated by whole-genome sequencing (WGS) can be used in linkage analysis.

We describe various linkage algorithms and their properties, as well as their implementations.

A detailed enumeration of the pertinent steps in linkage analysis provides a guideline for non-specialists on procedures and pitfalls.

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data.

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Genome-wide association studies

Benefits and limitations of genome-wide association studies

Multi-resolution localization of causal variants across the genome

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Acknowledgements

This work was supported by the Natural Science Foundation of China grant 31470070 (to J.O.) and the US National Institutes of Health grants R01 DC003594, R01 DC011651 and U54 HG006493 (to S.M.L.).

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Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, 100101, China

Jurg Ott & Jing Wang

Laboratory of Statistical Genetics, Rockefeller University, 1230 York Avenue, New York, 10065, New York, USA

Department of Human and Molecular Genetics, Center for Statistical Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, 77030, Texas, USA

• Suzanne M. Leal

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Powerpoint slide for fig. 1, powerpoint slide for fig. 2, powerpoint slide for fig. 3, powerpoint slide for table 1.

The ordering of loci on a chromosome and the determination of the distances between two adjacent loci. For short distances, the recombination fraction can serve as a measure of genetic distance, with the unit of measurement being the centimorgan (cM); 1 cM = 1% recombination fraction.

A phenomenon whereby two alleles, one each at two different loci, are transmitted together from parents to offspring more often than expected by chance. It leads to a recombination fraction smaller than 0.5.

Individuals that exhibit the phenotype of a Mendelian trait but that are not carriers of a susceptible genotype. Phenocopies were thought to result from non-genetic factors, but genes at locations other than those under current consideration can also lead to (genetic) phenocopies.

The conditional probability of being affected given one of the genotypes at the disease locus, '+ +', '+ d' or 'dd', where 'd' is the disease allele and '+' the non-disease (wild-type) allele. More generally, penetrance is the conditional probability of a phenotype given a genotype.

Two alleles, one from each of two loci, can be inherited from one parent but originate from two different grandparents. If the two marker loci are on the same chromosome, a recombination is the result of an odd number of crossovers between the markers.

A cytogenetic phenomenon that occurs during the formation of human gametes (egg or sperm cells). The salient feature of crossing over is that it occurs semi-randomly along chromosomes, with at least one crossover occurring on each chromosome in meiosis.

(θ). The expected proportion of recombinant children divided by the total number of recombinant and non-recombinant children. For two loci in close proximity to each other, θ is small owing to the randomness of crossing over, but it increases to 0.5 for loci that are far apart.

Z( x ) = log 10 [L( x )/L(∞)] is the logarithm of the likelihood ratio, with the numerator being calculated under the assumption of linkage and the denominator under no linkage. A LOD score of 3.3 or higher has been shown to correspond to a genome-wide significance level of 0.05.

The Mendelian laws of inheritance, when applied to variants, stipulate that an individual carries two copies (alleles) of a given nucleotide and passes one of them at random to each of their offspring. Disease may be the result of a single copy of the allele (dominant inheritance) or of two copies (recessive inheritance) in an individual.

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Ott, J., Wang, J. & Leal, S. Genetic linkage analysis in the age of whole-genome sequencing. Nat Rev Genet 16 , 275–284 (2015). https://doi.org/10.1038/nrg3908

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14.3: Linkage analysis and genome-wide association studies (GWAS)

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• Renee J. LeClair
• Virginia Tech Carilion School of Medicine via Virginia Tech Libraries' Open Education Initiative

There is tremendous interest in finding specific genes that predispose individuals to common disease traits, most of which follow complex inheritance patterns rather than Mendelian (single gene) patterns. Physicians will find frequent references in the medical literature related to the search for genes with high predictive value in common disorders.

While we know the DNA sequence of the vast majority of the coding regions of the genome, we still do not understand the full function of the majority of genes or how they are involved in human health conditions. There are two major approaches to identifying genetic loci, which contribute to disease presentation: linkage analysis and genome-wide association studies.

Linkage analysis

Linkage analysis relies on the fact that disease-causing mutations are inherited jointly (linked) with genetic markers located in their immediate vicinity. In order for a gene and a genetic marker to be linked, they must be syntenic, meaning they must be located on the same chromosome. Most genes or markers within the human genome are inherited independently of one another, and therefore are transmitted together 50 percent of the time.

Linkage between two genes means that they tend to be inherited together more often than expected by chance.

For linkage to occur, two conditions must be met:

• First, the genes must be syntenic.
• Second, they need to reside relatively close to one another.

Syntenic genes may become detached from one another through crossing over (or recombination). For large chromosomes, crossing over is so common that genes at opposite ends of the chromosome are inherited together no more often than if they resided on entirely different chromosomes.

When markers are close enough together on the same chromosome, crossing over fails to separate them frequently enough for them to be inherited independently of one another. This is evidenced by coinheritance of greater than 50 percent.

The unit of measure in linkage studies is “centimorgans." This concept can be confusing because we refer to the “distance” between two traits, but what is measured experimentally is the frequency of coinheritance, not physical distance.

A very small linkage distance means the traits are rarely separated during meiosis. A distance of 0 cM means two traits always stay together, implying that they are extremely close to one another on the same chromosome. If the two traits separate from one another 1 percent of the time during meiosis, they are described as being 1 cM apart; if the two traits separate from one another 5 percent of the time during meiosis, they are described as being 5 cM apart (figure 14.6).

The further apart two genes or markers are on the same chromosome increases the probability of a crossover occurring in between the two markers. Studies to determine linkage require the careful study of large numbers of parents and their offspring. Careful study of the family relationships across three generations allows linkage phases to be determined. In this case, the grandparents' information is required to determine how the genes are initially linked in the parents, and the grandchildren are studied to determine recombination events (crossing over) that separate the genes or markers during meiosis in the parents.

Distance can be expressed in cM as described previously, or in terms of theta (Θ), which are proportions. Remember, both are measures of probability, not physical distance. Linkage determinations are based on the fundamental rules of probability and binomial mathematics. Like any probability issue, a ratio greater than one reflects odds in favor (of linkage), and less than one reflects odds against.

For linkage studies, each family represents an independent estimate of the odds in favor of (or against) linkage. The property within standard probability laws is the concept of joint probability. To determine joint probability, meaning the chance that BOTH of two events will happen, we use what is often called the “AND rule." The AND rule applies whenever the probabilities under study are independent of one another.

Multiplying the results of many families is challenging, and was particularly so before computer resources became readily available. It is simpler mathematically to add numbers. We can move from multiplication to addition if we simply use the log of the probability instead of the probability number itself. Remember that the log of a number that is less than one is a negative number, and for a number greater than one, it is a positive number. Using a log conversion makes it simple to see if the ratio of the odds is favorable (positive) or unfavorable. The term “LOD score” refers to the log (base 10) of the odds of linkage, looking across a series of independent families.

There really are just two things to remember about LOD scores:

• First, it is a convenient system for combining the observations across a large number of families to describe the odds of linkage.
• Second, the values of LOD scores define “proof” that two genes or markers are linked or not linked.
• When the odds reach an LOD score of 3, the two markers are considered to be proven to be linked.
• When odds reach a level of -2, this is taken as conclusive evidence that the two genes or markers are not linked. LOD scores appear in a great deal of medical literature where the identification or location of disease-related genes is being considered.

Genome-wide association studies (GWAS)

Population association is easily confused with the concepts surrounding linkage. These studies look for a statistical association between a marker (often a single nucleotide polymorphism or SNP) and a specific trait. The concept of population association can be exploited to simultaneously study a very large number of detectable genetic markers (SNPs) in patient populations with common disorders.

Much of the power of personalized medicine is derived from such associations. There is an abundance of GWAS that appear in the medical literature. This is a highly sophisticated type of case-control study for which careful study design is required to avoid spurious findings. These studies provide information related to common genetic traits but do not help address genetic manifestations of rare traits in a population (figure 14.7).

Figure 14.7: Schematic of GWAS.

For more information on these types of studies, please see: https://www.genome.gov/20019523/geno...ies-factsheet/ .

References and resources

Clark, M. A.  Biology , 2nd ed. Houston, TX: OpenStax College, Rice University, 2018, Chapter 10: Cell Reproduction, Chapter 12: Mendel's Experiments and Heridity, Chapter 13: Modern Understandings of Inheritance.

Le, T., and V. Bhushan.  First Aid for the USMLE Step 1 , 29th ed. New York: McGraw Hill Education, 2018, 55–59.

Nussbaum, R. L., R. R. McInnes, H. F. Willard, A. Hamosh, and M. W. Thompson.  Thompson & Thompson Genetics  in Medicine , 8th ed. Philadelphia: Saunders/Elsevier, 2016, Chapter 7: Patterns of Single Gene Inheritance, Chapter 9: Genetic Variations in Populations, Chapter 10: Identifying the Genetic Basis for Human Disease.

Grey, Kindred, Figure 14.6 Relationship between centimorgans and recombination frequency. 2021. https://archive.org/details/14.6_20210926. CC BY 4.0 .

Tam, V., Patel, N., Turcotte, M. et al. Figure 14.7 Schematic of GWAS study. Adapted under Fair Use from Benefits and limitations of genome-wide association studies. Nat Rev Genet 20, 467–484 (2019). https://pubmed.ncbi.nlm.nih.gov/31068683/ . Fig. 1: GWAS study design. Added Mitochondrial inheritance by Domaina, Angelito7 and SUM1. CC BY-SA 4.0 . From Wikimedia Commons . Added Genetic similarities between 51 worldwide human populations (Euclidean genetic distance using 289,160 SNPs) by Tiago R. Magalhães, Jillian P. Casey, Judith Conroy, Regina Regan, Darren J. Fitzpatrick, Naisha Shah, João Sobral, Sean Ennis. CC BY 2.5 . From Wikimedia Commons . Added Histopathology of adenosquamous carcinoma of the pancreas by Yeung, Vincent; Palmer, Joshua D.; Williams, Noelle; Weinstein, Jonathan C.; Fortuna, Danielle; Sama, Ashwin; Winter, Jordan; Bar-Ad, Voichita. CC BY 4.0 . From Wikimedia Commons .

Additional resources

Hardy-Weinberg problems:  https://www.k-state.edu/parasitology.../hardwein.html

Practice pedigrees:  https://ocw.mit.edu/courses/biology/...ntals-of-biology-fall-2011/genetics/pedigrees/MIT7_01SCF11_3.3sol1.pdf

Practice pedigrees:  https://www.khanacademy.org/science/high-school-biology/hs-classical-genetics/hs-pedigrees/a/hs-pedigrees-review

Linkage analysis, GWAS, transcriptome analysis to identify candidate genes for rice seedlings in response to high temperature stress

Affiliations.

• 1 Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
• 2 State Key Laboratory of Crop Biology, College of Agriculture, Shandong Agricultural University, Tai'an, 271018, Shandong, China.
• 3 State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, 310006, China.
• 4 Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China. [email protected].
• 5 State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, 310006, China. [email protected].
• 6 Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China. [email protected].
• PMID: 33563229
• PMCID: PMC7874481
• DOI: 10.1186/s12870-021-02857-2

Background: Rice plants suffer from the rising temperature which is becoming more and more prominent. Mining heat-resistant genes and applying them to rice breeding is a feasible and effective way to solve the problem.

Result: Three main biomass traits, including shoot length, dry weight, and fresh weight, changed after abnormally high-temperature treatment in the rice seedling stage of a recombinant inbred lines and the natural indica germplasm population. Based on a comparison of the results of linkage analysis and genome-wide association analysis, two loci with lengths of 57 kb and 69 kb in qDW7 and qFW6, respectively, were associated with the rice response to abnormally high temperatures at the seedling stage. Meanwhile, based on integrated transcriptome analysis, some genes are considered as important candidate genes. Combining with known genes and analysis of homologous genes, it was found that there are eight genes in candidate intervals that need to be focused on in subsequent research.

Conclusions: The results indicated several relevant loci, which would help researchers to further discover beneficial heat-resistant genes that can be applied to rice heat-resistant breeding.

Keywords: GWAS; High-temperature-mediated growth response; Linkage analysis; Rice seedling; Transcriptome analysis.

Publication types

• Comparative Study
• Gene Expression Profiling
• Gene Expression Regulation, Plant
• Genes, Plant*
• Genetic Linkage
• Genetic Variation
• Genome-Wide Association Study
• Hot Temperature*
• Oryza / genetics*
• Oryza / growth & development*
• Seedlings / genetics*
• Seedlings / growth & development*
• Stress, Physiological / genetics*

Grants and funding

• KQTD2016113010482651/Shenzhen Science and Technology Program
• RC201901-05/Projects Subsidized by Special Funds for Science Technology Innovation and Industrial Development of Shenzhen Dapeng New District
• 2019A1515110557/Guangdong Basic and Applied Basic Research Foundation
• 2020M672903/China Postdoctoral Science Foundation

• Open access
• Published: 22 April 2024

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat ( Triticum aestivum L.)

• Xinru Wang 1 ,
• Jiankang Wang 1 ,
• Xianchun Xia 1 ,
• Xiaowan Xu 1 ,
• Lingli Li 1 ,
• Shuanghe Cao 1 ,
• Yuanfeng Hao 1 &
• Luyan Zhang 1  

BMC Plant Biology volume  24 , Article number:  306 ( 2024 ) Cite this article

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Metrics details

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

Peer Review reports

Introduction

Genotyping classifies life individuals to determine the linkage combination of genes, DNA sequences or genetic markers on chromosomes, according to allelic variations. Advances in sequencing-based genotyping technologies have allowed the genotyping for a large number of single nucleotide polymorphisms (SNP) loci in multiple individuals [ 1 ]. With marker number increased greatly, marker density augments accordingly. At the same time, map length is also exaggerated. One important reason for the length expansion is the presence of genotyping errors.

More and more researchers have realized that molecular analysis and manual sampling process are not fully reliable, and each step of genotyping process as well as various factors may produce genotyping errors [ 2 , 3 ]. The major cause of genotyping error is effects of DNA sequence, low quantity or poor quality DNA, biochemical equipment and products, and human factors [ 4 ]. Genotyping errors may vary from experiment to experiment, so it is often overlooked in many scientific studies. However, even a moderate number of genotyping errors may dominate the accuracy of linkage studies [ 5 , 6 , 7 , 8 , 9 ]. For example, genotyping error rate of 1% can result in the loss of 21–58% of the linkage information for the situations simulated by [ 5 ].

Genotyping error may mask the true segregation of alleles, which has a serious impact on genetic studies, such as genetic linkage map construction, gene mapping, genomic selection and prediction. Construction of high-density and accurate linkage maps is an important field of genetic research. As early as the 1990s, it was shown that genotyping error can lead to incorrect map order and map length inflation. Each 1% error in a marker added 2 cM of inflation distance to the map, if there was one marker every 2 cM on average. In other words, an average error rate of 1% would double the map length [ 10 , 11 ]. Effect of genotyping errors on linkage map construction can be explained by the decrease in accuracy of recombination frequency estimation. When a marker is located at both ends of one chromosome, each genotyping error causes one cross event. When a marker is located in the middle of one chromosome, each genotyping error causes two cross events. The more missing markers or genotyping errors a population has, the lower the accuracy of sequencing is observed [ 12 , 13 ]. Quantitative trait locus (QTL) mapping is the process to determine the location of genetic loci for quantitative traits on chromosomes and estimate their genetic effects. Linkage disequilibrium (LD) between a QTL and a marker or a linear combination of markers is an important factor affecting the accuracy of QTL mapping [ 14 ]. Even a low genotyping error rate can have a far-reaching impact on LD measurement. With the increase of genotyping errors, the accuracy of LD estimation will decrease substantially. Effect of genotyping errors on genomic prediction is different under diverse genetic structures. Definitely, genomic prediction accuracy decreases with the increase of genotyping error rate, and the highest accuracy of genomic prediction is observed at error rate of zero and high heritability [ 15 ].

In recently years, researchers have conducted a series of studies to minimize the impact of genotyping errors. For example, genotyping error can be evaluated by genotyping repetitive samples and testing whether they deviate from Hardy-Weinberg equilibrium [ 16 , 17 ]. It can also be determined by checking whether the marker data conforms to the Mendelian inheritance, the double recombination events of closely linked markers, and the consistency of repeated genotypes [ 18 ]. In fact, the real error rate is higher than the estimated value, which may be due to the “Mendelian compatibility” error, i.e., the wrong genotype may still conform to the Mendel’s laws of inheritances. Because of the various error types and different effects of each error type on the results, many algorithms and software packages for genotyping error detection and correction have been developed. For example, Genocheck [ 19 ], Pedcheck [ 20 ], MENDEL [ 21 ], SIMWALK [ 22 ], R/QTL [ 23 ], SOLOMON [ 24 ], GIGI-Check [ 25 ] can be used to detect Mendelian errors. LINKPHASE3 relies on the Mendelian segregation law to reconstruct haplotypes and correct genotyping errors [ 26 ]. ConGenR rapidly determines consensus genotypes and estimates genotyping errors from replicated genetic samples [ 27 ]. Smooth and Smooth-Descent predict genotyping errors, which improve the map quality and correctness of marker sequence [ 28 , 29 ].

Main consequences of genotyping errors on map construction are the incorrect map order and map length expansion. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 (YZ) and Jingshuang 16 × Bainong 64 (JB) using 15 K wheat Affymetrix SNP array in wheat were taken as examples to investigate the effect of genotyping errors on linkage map construction. Accuracy of different software packages for error correction was compared by using the two populations and simulated genotypic data with different levels of random errors. These findings not only specify an effective evaluation system of genotyping quality, but also provide an efficient approach to reduce the adverse effect of genotyping errors on the accuracy and reliability of linkage map construction.

Materials and methods

Plant materials and genotypic data.

The two wheat populations used in this study were YZ F 6 RILs and JB F 6 RILs, which had been reported in Li et al. [ 30 ] and Xu et al. [ 31 ], respectively. The parents and 193 progenies in the YZ population (denoted as YZ1 to YZ193) were planted at Beijing and Shijiazhuang (Hebei Province) in 2011–2012 cropping season, and Gaoyi (Hebei Province) and Xinxiang (Henan Province) in 2019–2020 cropping season [ 30 ]. The parents and 181 progenies in the JB population (denoted as JB1 to JB181) were planted at Beijing and Gaoyi (Hebei Province) in 2019–2020 cropping season [ 31 ]. The samples for genotyping were harvested in Gaoyi 2019–2020 cropping season for both populations. Each population was genotyped twice at the same time by the 15 K wheat Affymetrix SNP array at China GoldenMarker (Beijing) Biotech Co., Ltd. ( http://www.cgmb.com.cn/ ). Quality control was conducted on the genotypic data, by removing heterozygous and non-polymorphic markers in parents, and non-polymorphic markers in progenies. Common markers of the two replications of genotyping after quality control were filtrated and regarded as the original data (Supplemental Data 1 and 2 for the YZ population, and Supplemental Data 3 and 4 for the JB population). The YZ and JB populations had 4273 and 4497 SNP markers, respectively. These two data sets were denoted as data set 1 for YZ and data set 2 for JB, each with two replications (Table  1 ).

Calculation of missing and error rates

Consistent genotypes in the two replications of genotyping were treated as correct genotypes, while inconsistent genotypes were treated as genotyping errors. Missing and error rates of genotypes in the two RIL populations were calculated using R software by the following procedure. Firstly, missing marker points in one replication were also set as missing in the other replication to make missing points consistent between the two replications. Secondly, genotyping errors were classified into three types, i.e., 01, 02 and 12 errors, where the numbers 2, 1 and 0 represent the first parental, hybrid and the second parental genotypes, respectively. Error 01 meant that the genotype was 0 in one replication and 1 in the other replication. Similarly define 02 and 12 errors. Missing rate, error rate of each type, and total error rate were calculated in each population. Then, genotyping errors were replaced by missing values to obtain the non-erroneous genotypes. In other words, two replications of genotyping resulted in one set of non-erroneous genotypes, by replacing all inconsistent genotypes with missing values. The inconsistent genotypes included 01, 02, 12 errors and missing genotypes in one replication of genotyping. This treatment was named by the non-erroneous method for simplification. The resulted data sets were denoted as data set 3 for YZ and data set 4 for JB, each with one set of genotypic data (Table  1 ).

Sampling of repeated genotyping individuals

In the present study, all RILs were genotyped twice and had repeated genotypes. Non-erroneous genotypes were obtained by applying the non-erroneous method on the two replications of genotyping. When the proportion of repeated genotyping individuals was lower, the genotypes achieved by the non-erroneous method still contained some errors. To study the impact of repeated proportion on linkage analysis, the JB population was taken as an example. A plug-in in EXCEL called square grid was used to randomly select 5-50% individuals with a step size of 5%, and each level was repeated for three times. The principle of not-putting-back random sampling was adopted. The sampled individuals were regarded as repeated genotyped, and then the non-erroneous method was applied. Genotypes of the other individuals had no treatment. In other words, 10 groups of genotypic data were generated by randomly sampling 5-50% repeated genotyping individuals. Each group contained three replications of sampling, and each sampling contained two replications of genotyping. The resulted data sets were denoted as data sets 5 to 14, corresponding to the 10 levels of repeated proportion (Table  1 ).

Error detection by software packages

Besides the non-erroneous method, the accuracy of error detection by the two software packages were compared in the two populations. The first package is QTL IciMapping V4.2 [ 32 ]. We implemented an algorithm for error correction in QTL IciMapping, denoted as EC for short. For each marker point, theoretical frequency ( p ) of its genotype is calculated based on the genotypes of its neighboring markers and recombination frequencies between the three markers, which is also related to the population type and marker categories. Then a random number ( rn ) is generated between 0 and 1. If rn is larger than p , this marker point is regarded as a genotyping error, and then is replaced by missing values. Apply the EC method for the two replications of genotyping, respectively. The resulted data sets were denoted as data set 15 for YZ and data set 16 for JB, each with two replications (Table  1 ). The other package is Genotype-Corrector implemented by Python language and denoted as GC for short [ 33 ]. Specify the cutoff_SNP option to delete tags with missing rate higher than 80%. Use the sig_cutoff option to remove markers with severe singular separation. Merge the same homozygous markers in short genome interval of heterozygous region, set the sliding window size at 15, and then enter the process of genotype inference. Apply the GC method for the two replications of genotyping, respectively. The resulted data sets were denoted as data set 17 for YZ and data set 18 for JB, each with two replications (Table  1 ).

Genetic linkage map construction

The MAP functionality in QTL IciMapping was used for linkage map construction on the 18 data sets described above. Method nnTwoOpt proposed by Zhang et al. [ 13 ] was adopted for marker ordering, which was a modifications of the k-Optimal (K-Opt) algorithm for solving the traveling-salesman problem (TSP). The other parameters were set as default. Pearson correlation coefficient between the linkage and physical maps was calculated for each constructed map by R software. Data sets 1 to 4 were also ordered by physical map to reflect the impact of genotyping error on recombination frequency estimation.

• Simulation study

To further explore the influence of genotyping errors on linkage analysis and efficiency of error correction methods, simulation experiments were designed with different levels of error rate. The BIP functionality in QTL IciMapping was used to simulate the genotypic data of one chromosome with markers evenly distributed. The marker density was set at 1 cM. Two marker numbers were considered, i.e. 100 and 200, corresponding to chromosome length of 100 and 200 cM. Five levels of genotyping error were randomly added into the simulated genotypes, i.e., 0.5%, 1%, 2%, 3%, and 5%. The EC and GC methods were adopted for genotyping error detection, respectively. Then the MAP functionality was used for linkage map construction on the simulated chromosome with errors as well as the corrected genotypic data. Each scenario in the simulation was repeated for 10 times, and the resulted map length was averaged from the 10 runs.

Missing and error rates in the two RIL populations

The YZ population had missing rate of 1.18% and error rate of 0.35%, lower than the JB population (Tables S1 , S2 ). In the YZ population, rates of 01, 02 and 12 errors were 0.23, 0.00 and 0.12%, respectively. Error rate was the highest on chromosome 7D, and lowest on chromosome 2D, whereas missing rate was the highest on chromosome 3D, and lowest on chromosome 6B (Table S1 ). Genotypic data of the JB population had missing rate of 1.42% and error rate of 8.47% (Table S2 ). Rates of 01, 02 and 12 errors were 3.09, 2.31 and 3.07%, respectively. Missing rate was the highest on chromosome 6D, and lowest on chromosome 4 A. Error rate was the highest on chromosome 3 A, and lowest on chromosome 1D.

Comparison of genetic maps constructed using original genotypic data and non-erroneous genotype

The distribution of SNPs and linkage map information using the original data and non-erroneous genotypes were given in Table  2 for the YZ population and in Table  3 for the JB population. For population YZ, the full genome ordered by nnTwoOpt was 3940.48, 3930.33 and 3892.17 cM in length for replicate 1, replicate 2 and non-erroneous genotypes (Table  2 ). Chromosome length from the non-erroneous genotypes was always the shortest, except on chromosomes 1D, 3D, 4D, and 5D. When ordered by physical map, the full genome was 5757.77, 4712.47 and 4860.15 cM in length. As the marker orders were the same among the three maps, the difference on map length was caused by the impact of genotyping error on recombination frequency estimation. Replicate 2 formed a much shorter map than did replicate 1, which indicated that data quality of replicate 2 was better than that of replicate 1. The full genome ordered by nnTwoOpt was 4290.51, 4346.14 and 3817.46 cM in length for replicate 1, replicate 2 and non-erroneous genotypes of the JB population, much larger than counterparts of the YZ population (Table  3 ). Chromosome length from the non-erroneous genotypes was also the shortest in the JB population. The difference in map length between the non-erroneous genotypes and replicate 1 or replicate 2 became much larger, because of the higher genotyping error rate in the JB population. Upon being ordered by physical map, the full genome was 16001.36, 16192.33 and 15185.63 cM in length. The high error rate resulted in extremely long maps. Data quality of replicate 1 was better than that of replicate 2, resulting in a relatively shorter map. Although the non-erroneous method was applied, the map was still long, probably due to the marker order difference between the true linkage and physical maps.

Collinearity of marker order between linkage and physical maps was shown in Fig.  1 for the YZ population and in Fig.  2 for the JB population by using R-package ggplot2 [ 34 ]. For population YZ, marker orders in the three linkage maps and physical map had high collinearity across the 21 chromosomes, and the difference among the three linkage maps was minor (Fig.  1 ). The downward trend of the non-erroneous map could still be observed on chromosomes 2 A, 4B, and 7 A, reflecting the shorter map from the non-erroneous genotypes. For population JB, lower collinearity of marker order between linkage and physical maps was observed, especially on chromosomes 1B, 5D, 6B, and 7 A (Fig.  2 ). Improvement of map length by the non-erroneous method was significant on all chromosomes except chromosome 3D.

Collinearity of marker orders between linkage and physical maps in the Yangxiaomai×Zhongyou9507 RIL population. Different colors represent the source data for linkage map constructions, i.e., the first replication of genotyping (green dots), the second replication of genotyping (blue dots), and non-erroneous genotypes (red dots)

Collinearity of marker orders between linkage and physical maps in the Jingshuang16×Bainong64 RIL population. Different colors represent the source data for linkage map constructions, i.e., the first replication of genotyping (green dots), the second replication of genotyping (blue dots), and non-erroneous genotypes (red dots)

Table S3 provided the Pearson correlation coefficient between linkage and physical maps constructed using different genotypic data in the two populations. For population YZ, the average correlation coefficient across all chromosomes was 94.69, 95.92 and 95.18% for replicate 1, replicate 2 and non-erroneous genotypes. Correlation coefficient was always higher than 90% except on chromosomes 1D, 6D, 7B and 7D. Correlation coefficients were much lower in population JB, and the average value across chromosomes was 75.05, 74.26 and 78.95% for replicate 1, replicate 2 and non-erroneous genotypes. The non-erroneous method improved the correlation coefficient, especially on chromosomes 3D, 4 A and 6B.

Linkage maps constructed using different proportions of repeated genotyping individuals

Figure S1 shows the error rate of genotypic data in the A genome of population JB for different proportions of repeated genotyping individuals with a step size of 5%. As the non-erroneous method was applied for the repeated genotypes, rates of 01, 02, 12, and total errors decreased with the increasing of repeated proportion. At the same time, the missing rate increased, because detected errors were replaced by missing values. Similar trend was also observed in the B and D genomes.

Length of linkage maps using genotypic data with different proportions of repeated genotyping individuals was shown in Fig. S2 , averaged from three replications of sampling. The rightmost column corresponded to the non-erroneous map. It could be seen intuitively that the corrected map (i.e., 5 to 50% repeated) was shorter than the original map (i.e., 0% repeated), but longer than the non-erroneous map (i.e., 100% repeated). Interestingly, when the repeated proportion was 30%, map length is the smallest among levels of 5 to 50%, which was closest to length of the non-erroneous map. Although error rate decreased with the increasing of repeated genotyping individuals, the map length expanded when more than 30% individuals were genotyped repeatedly. The reason may be the increasing missing rate with the increased repeated proportion. A high missing rate also decrease map quality, which is consistent with the results of the DH population experiment simulated by [ 12 ]. Therefore, if it is impossible to genotype all individuals repeatedly, 30% is recommended in repeated genotyping, which has a balance between error and missing rates.

Comparisons of genetic maps constructed using genotypic data corrected by the EC and GC methods

The distribution of SNPs and linkage map information using the genotypes corrected by the EC and GC methods were given in Table  4 for the two populations. For population YZ, the full genome corrected by the EC method was 3347.90 and 3371.26 cM in length for replicate 1 (denoted by EC 1) and replicate 2 (denoted by EC 2), respectively, 592.58 and 559.07 cM shorter than the corresponding maps for original genotypic data. The full genome corrected by the GC method was 3189.35 and 2178.68 cM in length for replicate 1 (denoted by GC 1) and replicate 2 (denoted by GC 2), which was 1751.13 and 1751.65 cM shorter than the original map. For population JB, the full genome was 3819.02 and 3807.80 cM in length for EC 1 and EC 2, which was 471.49 and 538.34 cM shorter than the original map. The full genome was 2323.70 and 2311.91 cM in length for GC 1, GC 2, which was 1966.81 and 2034.23 cM shorter than the original maps. Length contraction by GC was much more significant than that by EC, but the map length corrected by EC was closer to the non-erroneous map length.

Pearson correlation coefficient between the corrected map and physical map was given in Table S4 . For population YZ, the correlation coefficient greatly varied from 76.06 to 99.97% for EC 1, from 83.79 to 99.96% for EC 2, from 82.61 to 100% for GC 1, and from 91.13 to 99.99% for GC 2 on different chromosomes. The average correlation coefficient was 95.03, 96.01. 97.51 and 98.12% for EC 1, EC 2, GC 1, and GC 2, respectively. Both the EC and GC methods improved the correlation coefficient between linkage and physical maps, compared with the original genotypic data. Pearson correlation coefficients between different linkage maps and non-erroneous map were given in Table S5 for population YZ and in Table S6 for population JB. The linkage maps included the maps from replicate 1, replicate 2, EC 1, EC 2, GC 1 and GC 2. For population YZ, average correlation coefficient from EC was the highest, followed by GC and the original data sets (Table S5 ). For population JB, map from EC had similar or higher correlation coefficient than the map from the original data except on chromosome 3D. Map from the GC method had similar or lower correlation coefficient than did the original data except on chromosomes 1 A and 6B (Table S6 ). Generally speaking, in both populations, EC had higher correlation coefficient with the non-erroneous map than did GC and the original genotypes.

Results in simulated populations

Length of linkage maps using original simulated data and genotypes corrected by the EC and GC methods in simulated chromosomes was given in Table  5 . No matter whether genotypes were corrected or not, map length increased with the increasing of error rate. When simulated length was 100 cM, map using original genotypes ranged from 99.23 to 615.62 cM in length when error rate ranged from 0 to 5%; maps using genotypes corrected by the EC method ranged from 94.19 to 154.19 cM in length; maps using genotypes corrected the GC method ranged from 62.45 to 126.97 cM in length. When simulated length was 200 cM, map using original genotypes ranged from 199.81 to 1357.40 cM when error rate ranged from 0 to 5%; maps using genotypes corrected by the EC method ranged from 189.81 to 353.12 cM in length; maps using genotypes corrected the GC method ranged from 124.67 to 190.54 cM in length. It was concluded that if error correction was not conducted, map length was doubled when error rate was 1%, for both simulated chromosome length of 100 and 200 cM. Both error correction methods reduced the map length, and GC resulted in a shorter map than did EC. But map length from GC was significantly underestimated when error rate was smaller than 2% for map length of 100 cM and 5% for map length of 200 cM. For example, when error rate was 1%, map length from GC was only 73.89 and 136.70 cM, compared with predefined length of 100 and 200 cM. At this error rate, map length from EC was 89.20 and 197.84 cM, which was closer to the true values.

Table S7 provided the Pearson correlation coefficient of marker orders using different genotypic data with the predefined order. No matter genotypes were corrected or not, correlation coefficient decreased with the increasing of error rate. When simulated length was 100 cM, correlation coefficient using original genotypes ranged from 99.9657 to 99.1756% when error rate ranged from 0 to 5%; correlation coefficient using genotypes corrected by the EC method ranged from 99.9505 to 99.9316%; correlation coefficient using genotypes corrected by the GC method ranged from 99.9877 to 99.9874%. When simulated length was 200 cM, correlation coefficient using original genotypes ranged from 99.9975 to 96.6721% when error rate ranged from 0 to 5%; correlation coefficient using genotypes corrected by the EC method ranged from 99.9996 to 99.0349%; correlation coefficient using genotypes corrected by the GC method ranged from 99.9990 to 99.9980%. Both error correction methods improved correlation coefficient, and the difference between EC and GC was minor. Genotyping error had a more obvious impact on correlation coefficient for map length of 200 cM than did map length of 100 cM.

Accuracy of error correction by EC and GC methods

Accuracy of EC and GC in the two actual RIL populations and simulated populations was calculated and shown in Figs.  3 and 4 , representing by true positive, false positive, true negative and false negative rates. For a marker point, if there is a genotyping error, and the error correction method detects it, it is treated as true positive; if the method cannot detect it, it is treated as false negative. If there is no genotyping error, and the method regards it as a true genotype, it is treated as true negative; if the method regards it as an error, it is treated as false positive.

True positive, false positive, true negative and false negative rates of genotyping error correction by the EC and GC methods in two wheat RIL populations. YZ represents the Yangxiaomai×Zhongyou9507 RIL population, and JB represents the Jingshuang16×Bainong64 RIL population. Area of each circle is 2. The left half is the total percentage of true negative (yellow) and false negative (gray), with the area of 1. The right half is the total percentage of true positive (blue) and false positive (red), with the area of 1

True positive, false positive, true negative and false negative rates of genotyping error correction by the EC and GC methods in the two simulated chromosomes at different genotyping error rates. Area of each circle is 2. The left half is the total percentage of true negative (yellow) and false negative (gray), with the area of 1. The right half is the total percentage of true positive (blue) and false positive (red), with the area of 1

In population YZ, the true negative rate of the EC method was 99.9967%, while the true positive rate was 74.29%. The true negative of the GC method maintained well, reaching 99.96%, but the true positive rate was only 23.47%, which was far lower than that of the EC method. In population JB, the true positive rate of the EC method was as high as 98.55%, and the true negative rate was 97.53%. In contrast, the true negative rate of the GC method was 92.82%, but the true positive rate was only 27.74% (Fig.  3 ). In conclusion, for both RIL populations, the EC method had larger true negative and true positive rates than did the GC method. The false negative and false positive rates of EC were lower than that of GC.

For both simulated chromosome lengths and correction methods, true negative and true positive rates decreased with the increasing of error rate, while false negative and false positive rates increased. Difference on true negative and false negative rates between the EC and GC methods was minor, but EC had higher true positive and lower false positive rates than did GC at each error rate (Fig.  4 ). For example, when error rate was 5%, true negative and true positive rates of the EC method were 98.73 and 94.13%, while rates of the GC method were 99.44 and 47.25%. False negative and false positive rates of the EC method were 1.27 and 5.87%, while rates of the GC method were 0.56 and 52.75%. The high false positive rate of the GC method is an important reason of the underestimated map length. In other words, many accurate genotypes are treated as errors by the GC methods, resulting in a shorter map compared with the true map.

Error rate in the two wheat RIL populations

The two populations were both sequenced by the 15 K wheat Affymetrix SNP array, but their data quality was much different, especially in error rate. Total error rate in the whole genome of populations YZ and JB was 0.35 and 8.47% (Tables S1 , S2 ), respectively. One reason of the high error rate in the JB population (F 6 RILs) may be that the population was not completely homozygous, leading to a relatively high heterozygosity of individuals. Heterozygosity of the two replications was 3.95 and 4.95% in the JB population, compared to corresponding values of 2.26 and 2.30% in the YZ population. Another notable finding is that the 01 and 12 errors have higher rates compared to the 02 error, especially in the YZ population. This observation aligns with previous researches where homozygous genotypes were mistakenly classified as heterozygous. It is crucial to address these errors as they can significantly affect the downstream analyses [ 35 ]. Owing to the higher error rate, map quality of population JB was much poorer than that of population YZ, both in map length, correlation coefficient, and collinearity of marker orders between linkage and physical maps (Tables  1 and 2 , S3 , Figs.  1 and 2 ).

Repeated genotyping improves the map quality

The non-erroneous method based on repeated genotyping individuals improved the map quality in both populations, and the degree of improvement was much larger in population JB. Most studies typically perform only one round of genotyping. However, if budget allows, repeated genotyping would be preferable. Find out the loci with inconsistent genotypes and report them as genotyping errors, which will be replaced by missing values, or corrected by reliable error correction software. Pool et al. and Davey et al. also indicated that locus with high error rate can be accommodated as deletion data and reduced by appropriate statistical correction [ 36 , 37 ]. If it is not allowed to conduct repeated sequencing for all individuals, 30% is a recommended proportion for repeated sequencing, which provides a balance between error and missing rates, and results in a relatively reasonable map length (Figs. S1 , S2 ).

Some exception was observed on some chromosomes of population YZ, where the non-erroneous map was slightly longer than the map from one replication, such as chromosomes 1 A, 3D, 4D, and 5D (Table  2 ). An important reason may come from the algorithm of the non-erroneous method. Insistent genotypes between the two replications were replaced by missing. So after error correction, correctly assigned genotypes in one replication may become missing ones, which reduce the map quality to some extent. But this phenomenon disappeared when the error rate was higher, as the positive effect of error correction covered the negative effect of missing data. In population JB, all chromosomes in the non-erroneous map were shorter than those from each replication (Table  3 ). The negative effect from the non-erroneous method can be solved by replacing the error data point by right genotypes. But it is hard to derive the right genotypes from two replications of genotyping, and improvement should be conducted on the non-erroneous method using the linkage information.

Comparison between the EC and GC methods for error correction

Besides the non-erroneous method, this study conducted comparison of efficiency and accuracy for error correction between the EC and GC methods using actual and simulated populations. Both methods shortened the map length and improved the correlation coefficient between linkage and physical maps in all populations, especially when the error rate was high (Tables  3 and 4 , S4 ). Map from the EC method was closer to the non-erroneous map, and GC method resulted in a shorter map. But different from repeated genotyping, error correction software may produce wrong corrections. In the simulation experiment, map length form the GC method was shorter than the predefined length when error rate was low. It hints that the GC method may be too sensitive and conduct hypercorrection. This conclusion was proved by the calculation of true positive, false positive, true negative and false negative rates shown in Figs.  3 and 4 . False positive rate of GC was much higher than that of EC.

Genotyping errors often reduce the power of linkage and association analysis, while current system to detect and correct genotyping errors is not satisfied [ 7 ]. Error correction improves statistical ability, but the correction process itself is prone to mistakes, and if not done well, new errors may occur. Further research and technical improvements are needed to solve the challenges. Firstly, many existing studies only used simulated data or a small number of real samples for verification of the error-correction methods. By applying these methods for large-scale data sets, performance of error correction software can be evaluated, and the room for improvement can be determined. Secondly, more precise and efficient error correction algorithms need to be developed. The current error correction software usually relies on a single site or small fragments, but it is still difficult for large-scale genome data processing. More comprehensive error correction strategies based on global genome information and machine learning are expected to be developed. In addition, we can also consider to optimize the sequencing platform and related equipment to improve the accuracy of genotyping at the technical level. For example, the adoption of more advanced and accurate gene sequencing techniques may significantly reduce the error rate and provide more reliable, accurate and reusable data for genetic analysis.

Strategy for construction of high-quality linkage map

In this study, nnTwoOpt is adopted for marker ordering, which has been proved to be effective no matter the marker number is large or small [ 13 ]. Maps ordered by physical map were compared with those ordered by nnTwoOpt (Tables  1 and 2 ; Figs.  1 and 2 ). For some chromosomes, map length and marker order had small difference between the two methods, for example, on chromosomes 1 A, 1B, 2B in population YZ, and chromosomes 1 A, 2B, 3D in population JB, and so on. But the difference was much larger on some of the other chromosomes, such as chromosomes 1D and 7D in population YZ, and chromosomes 1B, 1D, 2 A in population JB, and so on. This phenomenon was observed in both populations, and the consistence of physical and linkage orders varies among chromosomes and populations. Translocation, inversion, genetic diversity among varieties, and many other reasons will all cause the difference between linkage order and physical order in the reference variety. Therefore, physical map only provides a reference for linkage map construction. It is not recommended to order markers same as the physical map. A speedy and accuracy ordering method is necessary for linkage map construction, especially when the marker number is large.

By repeated genotyping, it is found that the YZ population had lower genotyping error rate than the JB population. Error correction is more urgent and significant in the JB population. But in studies with only one replication of genotyping, it is hard to determine the error rate accurately. Under this circumstance, map length and Pearson correlation coefficient between linkage and physical maps can give us some suggestions. In both actual and simulated populations, map length increased with the increasing of error rate, meanwhile, the correlation coefficient decreased. Researchers should pay more attention to genotyping errors when linkage map is extremely long or Pearson correlation coefficient is low.

Repeated genotyping individuals improve map quality on both map length and consistence with physical map, no matter all individuals or only part of them are sequenced repeatedly. But of cause, more budget is needed. Software packages for genotyping error correction can also improve linkage map to some extent. But false positives and false negatives may be produced during the correction procedure, leading to overcorrection or under-correction on some chromosome segments. It is recommended to conduct genotyping error correction during the process of linkage map construction. The researchers can select repeated genotyping or correction packages depending on their budget and acceptance level of false positives and false negatives in error correction.

Genotyping errors reduce the quality of genetic linkage maps, and in particular lead to inflated map lengths and reduced correlation coefficients with physical maps. The higher the error rate is, the worse the map quality is. By replacing the inconsistent genotypes with missing values, the map length was shortened and the correlation coefficient between linkage and physical maps was improved. Map quality can be improved significantly by error correction software. Map length form the EC method was closer to the non-erroneous map, and the accuracy of EC in actual and simulated populations was more stable, compared with the GC method. Although map from the GC method was shorter than that of the EC method, false positive rate of GC was rather high, leading to too short map compared to the true values.

Data availability

The input files for linkage map construction in the two RIL populations were submitted together with the article as supplementary data sets.

Abbreviations

single nucleotide polymorphisms

Quantitative trait locus

Linkage disequilibrium

recombinant inbred line

Yangxiaomai × Zhongyou 9507

Jingshuang 16 × Bainong 64

traveling-salesman problem

error correction method in QTL IciMapping

Genotype-Corrector

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This work was supported by grants from the STI 2030-Major Projects (Project No. 2023ZD0407501), the National Natural Science Foundation of China (Project No. 32370673), and the Agricultural Science and Technology Innovation Program of CAAS.

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XinruWang conducted data analysis. Jiankang Wang and Luyan Zhang supervised the study and developed error correction method. Xianchun Xia, Xiaowan Xu, Lingli Li, Shuanghe Cao, and Yuanfeng Hao conducted data collection and experimentation. Xinru Wang and Luyan Zhang draft the paper. Jiankang Wang, Shuanghe Cao., Yuanfeng Hao, Luyan Zhang revised the manuscript. All authors read and approved the final manuscript.

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Wang, X., Wang, J., Xia, X. et al. Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat ( Triticum aestivum L.). BMC Plant Biol 24 , 306 (2024). https://doi.org/10.1186/s12870-024-05005-8

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Basic Concepts of Linkage Analysis

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The goal of linkage analysis in human disease gene mapping is to assess whether an observed genetic marker locus is physically linked to the disease locus. This is equivalent to testing the null-hypothesis that the recombination fraction between the marker locus and the disease locus, θ , equals ½. In this case, we say the marker locus and the disease locus are unlinked. It is also possible to estimate θ , which can be used to provide an approximate idea of the location of the DSL relative to observed markers. In this chapter, we discuss the basic concepts of parametric linkage analysis. We explain how linkage between two genetic loci can be utilized to construct long-range mapping approaches that require only a small number of marker loci per chromosome to cover the entire human genome sufficiently. Using fully parameterized statistical models, parametric linkage describes the phenotype as a function of the genetic marker locus and its relative distance to the disease locus, i.e., the recombination fraction (Ott (1999)). The simplest case of parametric linkage analysis uses the method of direct counting , where θ can be estimated by directly counting recombinant and non-recombinant offspring haplotypes (Ott (1979)). Using the method of direct-counting, we outline the principles of parametric linkage analysis. Advanced topics such as non-parametric linkage analysis and multi-point analysis (Kruglyak et al. (1996)) are discussed in Appendix A. While the advanced topics that are included in Appendix A are necessary for a thorough grounding in linkage analysis, they are not required for an introduction to association analysis.

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Laird, N.M., Lange, C. (2011). Basic Concepts of Linkage Analysis. In: The Fundamentals of Modern Statistical Genetics. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7338-2_6

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Exploring health care providers’ engagement in prevention and management of multidrug resistant Tuberculosis and its factors in Hadiya Zone health care facilities: qualitative study

• Bereket Aberham Lajore 1   na1   nAff5 ,
• Yitagesu Habtu Aweke 2   na1   nAff6 ,
• Samuel Yohannes Ayanto 3   na1   nAff7 &
• Menen Ayele 4   nAff5  

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Engagement of healthcare providers is one of the World Health Organization strategies devised for prevention and provision of patient centered care for multidrug resistant tuberculosis. The need for current research question rose because of the gaps in evidence on health professional’s engagement and its factors in multidrug resistant tuberculosis service delivery as per the protocol in the prevention and management of multidrug resistant tuberculosis.

The purpose of this study was to explore the level of health care providers’ engagement in multidrug resistant tuberculosis prevention and management and influencing factors in Hadiya Zone health facilities, Southern Ethiopia.

Descriptive phenomenological qualitative study design was employed between 02 May and 09 May, 2019. We conducted a key informant interview and focus group discussions using purposely selected healthcare experts working as directly observed treatment short course providers in multidrug resistant tuberculosis treatment initiation centers, program managers, and focal persons. Verbatim transcripts were translated to English and exported to open code 4.02 for line-by-line coding and categorization of meanings into same emergent themes. Thematic analysis was conducted based on predefined themes for multidrug resistant tuberculosis prevention and management and core findings under each theme were supported by domain summaries in our final interpretation of the results. To maintain the rigors, Lincoln and Guba’s parallel quality criteria of trustworthiness was used particularly, credibility, dependability, transferability, confirmability and reflexivity.

Total of 26 service providers, program managers, and focal persons were participated through four focus group discussion and five key informant interviews. The study explored factors for engagement of health care providers in the prevention and management of multidrug resistant tuberculosis in five emergent themes such as patients’ causes, perceived susceptibility, seeking support, professional incompetence and poor linkage of the health care facilities. Our findings also suggest that service providers require additional training, particularly in programmatic management of drug-resistant tuberculosis.

The study explored five emergent themes: patient’s underlying causes, seeking support, perceived susceptibility, professionals’ incompetence and health facilities poor linkage. Community awareness creation to avoid fear of discrimination through provision of support for those with multidrug resistant tuberculosis is expected from health care providers using social behavioral change communication strategies. Furthermore, program managers need to follow the recommendations of World Health Organization for engaging healthcare professionals in the prevention and management of multidrug resistant tuberculosis and cascade trainings in clinical programmatic management of the disease for healthcare professionals.

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Introduction

Mycobacterium tuberculosis, the infectious agent that causes multi-drug resistant tuberculosis (MDR-TB), is resistant to at least rifampicin and isoniazid. Direct infection can cause the disease to spread, or it can develop secondary to improper management of tuberculosis among drug susceptible tuberculosis cases and associated poor adherence [ 1 ].

Multidrug-resistant strains of mycobacterium tuberculosis have recently emerged, which makes achieving “End TB Strategy” more difficult [ 2 ]. Multi drug resistant tuberculosis (MDR-TB) has been found to increasingly pose a serious threat to global and Ethiopian public health sector. Despite the fact that a number of risk factors for MDR-TB have been identified through various research designs, the epidemiology of this disease is complex, contextual, and multifaceted [ 1 ]. Quantitative studies demonstrate that prior treatment history [ 3 , 4 , 5 , 6 , 7 ], interrupted drug supply [ 8 ], inappropriate treatments and poor patient compliance [ 3 , 7 , 9 ], poor quality directly observed treatment short course (DOTS), poor treatment adherence [ 10 ], age [ 5 ], and malnutrition [ 11 ] were factors associated with multi drug resistant TB.

Globally, an estimated 20% of previously treated cases and 3.3% of new cases are thought to have MDR-TB; these levels have essentially not changed in recent years. Globally, 160,684 cases of multidrug-resistant TB and rifampicin-resistant TB (MDR/RR-TB) were notified in 2017, and 139,114 cases were enrolled into treatment in 2017 [ 12 ]. A systematic review in Ethiopia reported 2% prevalence of MDR-TB [ 3 ] that is higher than what is observed in Sub-Saharan Africa, 1.5% [ 13 ]. The prevalence of MDR-TB, according to the national drug-resistant tuberculosis (DR-TB) sentinel report, was 2.3% among newly diagnosed cases of TB and 17.8% among cases of TB who had already received treatment,. This suggests a rising trend in the prevalence of TB drug resistance compared to the results of the initial drug-resistant TB survey carried out in Ethiopia from 2003 to 2005 [ 14 ].

Ethiopia has placed strategies into place that emphasize political commitment, case finding, appropriate treatment, a continuous supply of second-line anti-TB medications of high quality, and a recording system. Due to other competing health priorities, the nation is having difficulty accelerating the scale-up of the detection, enrollment and treatment of drug-resistant TB patients [ 15 , 16 ]. To address these issues, the nation switched from a hospital-based to a clinic-based ambulatory model of care, which has allowed MDR-TB services to quickly decentralize and become more accessible. Accordingly, the nation has set up health facilities to act as either treatment initiating centers (TIC) or treatment follow-up centers (TFC) or both for improved referral and communication methods [ 15 ].

One of the key components of the “End TB strategy” is engagement of health care professionals in the prevention and management of multidrug resistant tuberculosis [ 17 ]. Inadequate engagement of healthcare providers is one aspect of the healthcare system that negatively influences MDR-TB prevention and control efforts [ 17 ]. This may be manifested in a number of ways, including inadequate understanding of drug-resistant tuberculosis, improper case identification, failure to initiate treatment again, placement of the wrong regimens, improper management of side effects and poor infection prevention [ 1 ]. These contributing factors are currently being observed in Ethiopia [ 18 ], Nigeria [ 7 , 19 , 20 ] and other countries [ 21 , 22 ]. According to a study conducted in Ethiopia, MDR-TB was linked to drug side effects from first-line treatments, being not directly observed, stopping treatment for at least a day, and retreating with a category II regimen [ 17 ].

This may be the result of a synergy between previously investigated and other contextual factors that have not yet been fully explored, such as professional engagement, beliefs, and poor preventive practices. The engagement of health professionals in MDR-TB prevention and control is assessed using a number of composite indicators. Health professionals may interact primarily inside the healthcare facilities. Typically, they play a significant role in connecting healthcare services with neighborhood-based activities [ 17 ]. One of the main research areas that have not sufficiently addressed is evidence indicating the status of healthcare professionals’ engagement and contextual factors in MDR-TB prevention and management.

It is increasingly urgent to identify additional and existing factors operating in a particular context that contribute to the development of the disease in light of the epidemic of drug resistance, including multi-drug resistance (MDR-TB) and extensively drug resistant TB (XDR-TB) in both new and previously treated cases of the disease [ 23 ]. In order to develop and implement control measures, it is therefore essential to operationally identify a number of contextual factors operating at the individual, community, and health system level.

Therefore, the overall purpose of this study was to explore the level of engagement of health care providers and contextual factors hindering/enabling the prevention and provision of patient-centered care for MDR-TB in health facilities, DOTS services centers and MDR-TB treatment initiation center [TIC], in Hadiya Zone, Southern Ethiopia.

Qualitative approach and research paradigm

Descriptive phenomenological qualitative study design was employed to explore factors influencing engagement of health professionals in MDR-TB prevention and management and thematic technique was employed for the analysis of the data.

Researchers’ characteristics and reflexivity

Three Principal investigators conducted this study. Two of them had Masters of public health in Epidemiology and Reproductive health and PhD candidates and the third one had Bachelor’s degree in public health with clinical experience in the area of Tuberculosis prevention and management and MPH in Biostatistics. The principal investigators have research experience with published articles in different reputable journals. There were no prior contacts between researchers and participants before the study whereas researchers have built positive rapport with study participants during data collection to foster open communication and trust and had no any assumptions and presuppositions about the research topic and result.

Context/ study setting and period

The study was conducted between 2 and 9 May, 2019 in Hadiya Zone with more than 1.7 million people residing in the Zone. There are 300 health posts, 63 health centers, 3 functional primary hospitals and 1 comprehensive specialized hospital in the Zone. Also, there are more than 350 private clinics and 1 private hospital in the Zone. All of the public health facilities and some private health facilities provide directly observed short course treatment (DOTS) service for tuberculosis patients. There are more than eight treatment initiation centers (TICs) for MDR-TB patients in Hadiya Zone. MDR-TB (Multidrug-resistant tuberculosis) treatment initiation centers are specialized facilities that provide comprehensive care, diagnosis and treatment initiation, psychosocial support, and follow up services to individuals with MDR-TB. The linkage between MDR-TB treatment initiation centers and other healthcare facilities lies in the coordination of care, referral pathways, and collaboration to ensure comprehensive and integrated care for individuals with MDR-TB. Overall, healthcare providers play a crucial role in the management of MDR-TB by providing specialized care, ensuring treatment adherence, monitoring progress and outcomes, and supporting individuals in achieving successful treatment outcomes and improved health.

Units of study and sampling strategy

Our study participants were health care professionals working in MDR-TB TICs in both private and public health facilities, and providing DOTS services, MDR-TB program leaders in treatment initiation centers, as well as TB focal persons, disease prevention and health promotion focal person, and project partners from district health offices. The study involved four focus group discussion (FGDs) and five key informants’ interview (KII) with a total of 26 participants to gather the necessary information. Expert purposive sampling technique was employed and sample size was determined based on the saturation of idea required during data collection process.

Data collection methods and instruments

Focus group discussion and face to face key informants’ interviews were employed to collect the data. We conducted a total of four FGD and five key informants’ interviews with participants chosen from DOTS providing health facilities and MDR-TB program leaders in treatment initiation centers, as well as TB focal persons and project partners from district health offices and disease prevention and health promotion focal person. One of the FGDs was conducted among health professionals from the public MDR-TB treatment initiation centers. Three FGDs were conducted among disease prevention and health promotion focal persons, TB focal persons and DOTS providers in public health facilities (health centers).

An observation checklist was developed to assess the general infection prevention and control measures used by specific healthcare facilities in the study area. We used unstructured FGD guide, key informant interview guide, observation checklist and audio recorders to collect primary data and it was collected using local language called Amharic. Prior to data collection, three people who are not among principal investigators with at least a master’s degree in public health and prior experience with qualitative research were trained by principal investigators. Three of them acts as a tape recorder, a moderator, and as a note taker alternatively. The length of FGD ranged from 58 to 82 min and that of key informants’ interview lasted from 38 to 56 min.

Data processing and data analysis

Memos were written immediately after interviews followed by initial analysis. Transcription of audio records was performed by principal investigators. The audio recordings and notes were refined, cleaned and matched at the end of each data collection day to check for inconsistencies, correct errors, and modify the procedures in response to evolving study findings for subsequent data collection. Transcribed interviews, memos, and notes from investigator’s observation were translated to English and imported to Open Code 4.02 [ 2 ] for line by line coding of data, and categorizing important codes (sub theming). The pre-defined themes for MDR-TB prevention and control engagement were used to thematize the line-by-line codes, categories, and meanings using thematic analysis. Finally, the phenomenon being studied was explained by emerging categories and themes. Explanations in themes were substantiated by participants’ direct quotations when necessary.

Trustworthiness

Phone calls and face to face briefing were requested from study participants when some expressions in the audio seems confusing while transcripts were performed. To ensure the credibility of the study, prolonged engagement was conducted, including peer debriefing with colleagues of similar status during data analysis and inviting available study participants to review findings to ensure as it is in line with their view or not. Memos of interviews and observation were crosschecked while investigator was transcribing to ensure credibility of data as well as to triangulate investigator’s categorizing and theming procedures. For transferability, clear outlines of research design and processes were provided, along with a detailed study context for reader judgment. Dependability was ensured through careful recording and transcription of verbal and non-verbal data, and to minimize personal bias, scientific procedures were followed in all research stages. Conformability was maintained by conducting data transcription, translation, and interpretation using scientific methods. Researchers did all the best to show a range of realities, fairly and faithfully. Finally, an expert was invited to put sample of codes and categories to emerged corresponding categories and themes respectively.

Demographic characteristics of study participants

Four focus group discussions and five key informants’ interviews were conducted successfully. There were 26 participants in four focus group discussions, and key informants’ interview. Ages of participants ranges from 20 to 50 years with an average age of 33.4  ±  6.24 SD years. Participants have five to ten years of professional experience with DOTS services (Table  1 ).

Emergent themes and subthemes

The study explored how health care providers’ engagement in MDR-TB prevention and management was influenced. The investigation uncovered five major themes. These themes were the patient’s underlying causes, seeking support, perceived susceptibility, healthcare providers’ incompetence, and poor linkage between health facilities. Weak community TB prevention, health system support, and support from colleagues were identified subthemes in the search for help by health professionals whereas socioeconomic constraints, lack of awareness, and fear of discrimination were subthemes under patients underlying factors (Fig.  1 ).

Themes and subthemes emerged from the analysis of health professionals’ engagement in MDR-TB prevention and management study in Hadiya zone’s health facilities, 2019

The patient’s underlying causes

This revealed why TB/MDR-TB treatment providers believe health professionals are unable to provide standard MDR-TB services. The subthemes include TB/MDR-TB awareness, fear of discrimination, and patients’ socioeconomic constraints.

Socioeconomic constraints

According to our research, the majority of healthcare professionals who provided directly observed short-course treatment services mentioned socioeconomic constraints as barriers to engage per standard and provide MDR-TB prevention and management service. More than half of the participants stated that patients’ primary reasons include lack of money for house rental close to the treatment centers, inability to afford food and other expenses, and financial constraints to cover transportation costs.

In addition to this, patients might have additional responsibilities to provide food and cover other costs for their families’ need. The majority of health care professionals thought that these restrictions led to their poor engagement in MDR-TB prevention and management. One of the focus groups’ discussants provided description of the scenario in the following way:

“…. I have many conversations with my TB/MDR-TB patients. They fail to complete DOTS or treatment intensive care primarily as a result of the requirement of prolonged family separation. They might provide most of the family needs, including food and other expenses” (FGD-P01).

Lack of awareness about MDR-TB

This subtheme explains how MDR-TB patients’ knowledge of the illness can make it more difficult for health professionals to provide DOTS or TICs services. The majority of DOTS providers stated that few TB or MDR-TB patients were aware of how MDR-TB spreads, how it is treated, and how much medication is required. Additionally, despite the fact that they had been educated for the disease, majority of patients did not want to stop contact with their families or caregivers. A health care provider stated,

“…. I provided health education for MDR-TB patients on how the disease is transmitted and how they should care for their family members. They don’t care; however, give a damn about their families .” (FGD-P05).

Some healthcare professionals reported that some patients thought that MDR-TB could not be cured by modern medication. One medical professional described the circumstance as follows:

“…. I noticed an MDR-TB patient who was unwilling to be screened. He concluded that modern medication is not effective and he went to spiritual and traditional healers” (FGD-P02).

As a result, almost all participants agreed on the extent to which patient knowledge of TB and MDR-TB can influence a provider’s engagement to MDR-TB services. The majority suggested that in order to improve treatment outcomes and preventive measures, the media, community leaders, health development armies, one-to-five networks, non-governmental organizations, treatment supporters, and other bodies with access to information need to put a lot of efforts.

Fear of discrimination

According to our research, about a quarter of healthcare professionals recognized that patients’ fear of discrimination prevents them from offering MDR-TB patients the DOTS services they need, including counseling index cases and tracing contact histories.

HEWs, HDAs, and 1-to-5 network members allegedly failed to monitor and counsel the index cases after their immediate return to their homes, according to the opinions from eight out of twenty-six healthcare professionals. The patients began to engage in routine social and political activities with neighbors while hiding their disease status. A healthcare professional described this situation as follows:

“…. I understood from my MDR-TB patient’s words that he kept to himself and avoided social interaction. He made this decision as a result of stigmatization by locals, including health extension workers. As a result, the patient can’t attend social gatherings. …. In addition, medical professionals exclude MDR-TB patients due to fear of exposures. As a result, patients are unwilling to undergo early screening” (FGD-P04).

Professionals’ perceived risk of occupational exposure

This theme highlights the anxiety that healthcare workers experience because of MDR-TB exposure when providing patient care. Our research shows that the majority of health professionals viewed participation as “taking coupons of death.” They believed that regardless of how and where they engaged in most healthcare facilities, the risk of exposure would remain the same. According to our discussion and interview, lack of health facility’s readiness takes paramount shares for the providers’ risk of exposures and their susceptibility.

According to the opinion from the majority of FGD discussants and in-depth interviewees, participants’ self-judgment score and our observation, the majority of healthcare facilities that offer DOTS for DS-TB and MDR-TB did not create or uphold standards in infection prevention in the way that could promote better engagement. These include poor maintenance of care facilities, lack of personal protective equipment, unsuitable facility design for service provision, lack of patient knowledge regarding the method of MDR-TB transmission, and lack of dedication on the part of health care staff.

As one of our key informant interviewees [District Disease Prevention Head], described health professionals’ low engagement has been due to fear of perceived susceptibility. He shared with us what he learned from a community forum he moderated.

Community forum participant stated that “… There was a moment a health professional run-away from the TB unit when MDR-TB patient arrived. At least they must provide the necessary service, even though they are not willing to demonstrate respectful, compassionate, or caring attitude to MDR-TB patients” (KII-P01). Besides , one of the FGD discussants described the circumstance as follows:

“…. Emm…. Because most health facilities or MDR-TB TIC are not standardized, I am concerned about the risk of transmission. They are crammed together and poor ventilation is evident as well as their configuration is improper. Other medical services are causing the TICs to become overcrowded. Most patients and some medical professionals are unconcerned with disease prevention ” (FGD-P19).

Participants’ general fear of susceptibility may be a normal psychological reaction and may serve as a motivation for taking preventative actions. However, almost all participants were concerned that the main reasons for their fear were brought up by the improper application of programmatic management and MDR-TB treatment standards and infection prevention protocols in healthcare facilities.

Health care providers’ incompetence

This theme illustrates how professionalism and dedication impact participation in MDR-TB prevention and management. The use of DS-TB prevention and management by health professionals was also taken into account because it is a major factor in the development of MDR-TB. This theme includes the participants’ perspectives towards other healthcare workers involved in and connected to MDR-TB.

Nearly all of the participants were aware of the causes and danger signs of MDR-TB. The majority of the defined participants fit to the current guidelines. However, participants in focus groups and key informant interviews have brought up shortcomings in MDR-TB service delivery practice and attitude. We looked at gaps among healthcare professionals’ knowledge, how they use the national recommendations for programmatic management and prevention of MDR-TB, prevent infections, take part in community MDR-TB screenings, and collaborate with other healthcare professionals for better engagement.

More than half of the participants voiced concerns about their attitudes and skill sets when using MDR-TB prevention and management guideline. When asked about his prior experiences, one of the focus group participants said:

“…. Ok, let me tell you my experience, I was new before I attended a training on MDR-TB. I was unfamiliar with the MDR-TB definition given in the recommendations. When I was hired, the health center’s director assigned me in the TB unit. I faced difficulties until I received training” (FGD-P24). Furthermore , one of the key informant interview participants shared a story: “…. In my experience, the majority of newly graduated health professionals lack the required skill. I propose that pre-service education curricula to include TB/MDR-TB prevention and management guideline trainings” (KII-P01).

The majority of participants mentioned the skill gap that was seen among health extension workers and laboratory technicians in the majority of healthcare facilities. Some of the participants in the in-depth interviews and FGD described the gaps as follows:

“…. According to repeated quality assurance feedbacks, there are many discordant cases in our [ District TB Focal Person ] case. Laboratory technicians who received a discrepant result (KII-P01) are not given training which is augmented by shared story from FGD discussants, “According to the quality assurance system, laboratory technicians lack skill and inconsistent results are typical necessitating training for newly joining laboratory technicians” (FGD-P20).

Through our discussions, we explored the level of DOTS providers’ adherence to the current TB/MDR-TB guideline. As a result, the majority of participants pointed out ineffective anti-TB management and follow-up care. One of the participants remembered her practical experience as follows:

“…. In my experience, the majority of health professionals fail to inform patients about the drug’s side effects, follow-up procedures, and other techniques for managing the burden of treatment. Only the anti-TB drug is provided, and the patient is left alone. The national treatment recommendation is not properly implemented by them” (FGD-P04).

Many barriers have been cited as reasons that might have hindered competencies for better engagement of health professionals. Training shortage is one of the major reasons mentioned by many of the study participants. One of discussants from private health facility described the problem as

“…. We are incompetent, in my opinion. Considering that we don’t attend update trainings. Many patients who were diagnosed negative at private medical facilities turned out to be positive, and vice versa which would be risky for drug resistance” (FGD-P14) which was supported by idea from a participant in our in-depth interview: “…. We [Program managers] are running short of training for our health care providers at different health centers and revealed that four out of every five healthcare professionals who work in various health centers are unaware of the TB/MDR-TB new guideline” (KII-P02).

Seeking support

This theme focuses on the significance and effects of workplace support in the engagement of MDR-TB prevention and control. This also explains the enabling and impeding elements in the engagement condition of health professionals. Three elements make up the theme: coworkers (other health professionals) in the workplace, support from community TB prevention actors, and a healthcare system.

Support from community TB prevention actors

This subtheme includes the assistance provided to study participants by important parties such as community leaders, the health development army, and other stakeholders who were involved in a community-based TB case notification, treatment adherence, and improved patient outcomes.

Many of the study participants reported that health extension workers have been poorly participating in MDR-TB and TB-related community-based activities like contact tracing, defaulter tracing, community forums, health promotion, and treatment support. One study participant described their gap as follows:

“…. I understood that people in the community were unaware of MDR-TB. The majority of health extension workers do not prioritize raising community awareness of MDR-TB” (FGD-P13). This was supported by idea from a district disease prevention head and stated as: “…. There is no active system for contacts tracing. Health educators send us information if they find suspected cases. However, some patients might not show up as expected. We have data on three family members who tested positive for MDR-TB” (KII-P3).

Support from a health system

The prime focus of this subtheme is on the enabling elements that DOTS providers require assistance from the current healthcare system for better engagement. All study participants expressed at least two needs to be met from the health system in order for them to effectively participate in MDR-TB prevention, treatment, and management. All study participants agreed that issues with the health system had a negative impact on their engagement in the prevention, treatment, diagnosis, and management of MDR-TB in almost all healthcare facilities. Poor conditions in infrastructure, resources (supplies, equipment, guidelines, and other logistics), capacity building (training), supportive supervision, establishment of public-private partnerships, and assignment of motivated and trained health professionals are some of the barriers that needs to be worked out in order to make them engage better. One of the participants pronounces supplies and logistics problems as:

“…. The health center I worked in is listed as a DOTS provider. However, it lacks constant electricity, a working microscope, lab supplies, medications, etc, and we refer suspected cases to nearby health centers or district hospitals for AFB-examination and, “Sometimes we use a single kit for many patients and wait for the medication supply for three or more weeks and patients stops a course of therapy that might induce drug resistance” (FGD-PI04) which was augmented by statement from FGD participant who works at a treatment initiation center: “…. We faced critical shortage of supplies and hospital administrators don’t care about funding essential supplies for patient care. For instance, this hospital (the hospital in which this FGD was conducted) can easily handle N-95 masks. Why then they (hospital administrators working in some TIC) can’t do it?” (FGD-P18).”

Regarding in-service training on MDR-TB, almost all participants pointed out shortage of on-job training mechanisms. One of our FGD participants said:

“…. I missed the new training on MDRTB programmatic management guidelines. I’ve heard that new updates are available. I still work using the old standard” (FGD-PI05). A health professional working in private clinic heightens the severity of training shortage as: “…. We have not participated in TB/MDR-TB guidelines training. You know, most of for-profit healthcare facilities do not provide any training for their staff. I’m not sure if I’m following the (TB/MDR-TB) guideline” (FGD-P14). One of our key informant interview participants; MDR-TB center focal person suggested the need for training as: “…. I’ve received training on the MDR-TB services and public-private partnership strategy. It was crucial in my opinion for better engagement. It is provided for our staff [MDRTB center focal person]. However, this has not yet been expanded to other health facilities” (KII-P04).

Concerning infrastructures, transportation problem was one of the frequently mentioned obstacles by many participants that hinder engagement in MDR-TB/TB service. This factor had a negative impact to both sides (health professionals and patients). One of discussants said:

“…. I face obstacles such as transport cost to perform effective TB/MDR-TB outreach activities like health education, tracing family contacts and defaulters and community mobilization. Rural kebeles are far apart from each other. How can I support 6 rural Kebeles?” (FGD-P01). One of the participants; MDR-TB treatment centers supervisor/program partner seconded the above idea as: “…. I suggest government must establish a system to support health professionals working in remote health care facilities in addition to MDR-TB centers. I guess there are more than 30 government health centers and additional private clinics. We can’t reach them all due to transportation challenges” (KII-P05). One of the participants , a district disease prevention head added: “…. Our laboratory technicians take sample from MDR-TB suspects to the post office and then, the post office sends to MDR-TB site. Sometimes, feedback may not reach timely. There is no any system to cover transportation cost. That would make case detection challenging” (FGD-P02).

Support from colleagues

Study participants stated the importance of having coworker with whom they could interconnect. However, eight participants reported that they were discriminated by their workmates for various reasons, such as their perceived fear of exposure to infection and their perception as if health professionals working in TB/MDR-TB unit get more training opportunities and other incentives. One of the focus group discussants said:

“…. My colleagues [health professional working out of MDR-TB TICs] stigmatize us only due to our work assignment in MDR-TB clinic. I remember that one of my friends who borrowed my headscarf preferred to throw it through a window than handing-over it back safely. Look, how much other health professionals are scared of working in MDR-TB unit. This makes me very upset. I am asking myself that why have I received such training on MDR-TB?” (FGD-P04).

Some of the participants also perceived that, health professionals working in MDR-TB/TB unit are the only responsible experts regarding MDR-TB care and treatment. Because, other health professionals consider training as if it is an incentive to work in such units. One of the FGD discussants described:

“… Health professionals who work in other service units are not volunteer to provide DOTS if TB focal person/previously trained staffs are not available. Patients wait for longer time” (FGD-P11).

Health facilities’ poor linkage

This theme demonstrates how various healthcare facilities, including private and public healthcare facilities such as, health posts, health care centers and hospitals, and healthcare professionals working at various levels of the healthcare system in relation to TB/MDR-TB service, are inter-linked or communicating with one another.

Many study participants noted a lack of coordination between higher referral hospitals, TB clinics, health posts, and health centers. Additionally, the majority of the assigned healthcare professionals had trouble communicating with patients and their coworkers. A focus group discussant also supported this idea as

“…. There is a lack of communication between us [DOTS providers at treatment initiation centers] and health posts, health centers, and private clinics. We are expected to support about 30 public health facilities. It’s of too much number, you know. They are out of our reach. We only took action when a problem arose” (FGD-P16).

Significant number of participants had raised the problem of poor communication between health facilities and treatment initiation centers. One of the interviewees [program manager] said:

“…. I see that one of our challenges is the weak referral connections between treatment initiation centers and health centers. As a result, improper sample transfer to Gene- Xpert sites and irregular postal delivery are frequent” . “Our; DOTS staff at the MDR-TB center, DOTS staff at the health center, and health extension workers are not well connected to one another. Many patients I encountered came to this center [MDR-TB center] after bypassing both health post and health center. Poor linkage and communication, in my opinion, could be one of the causes. The same holds true for medical facilities that are both public and private ” (KII-P02).

Engagement of individual healthcare providers is one of the peculiar interventions to achieve the goal of universal access to drug resistance tuberculosis care and services [ 17 ]. Healthcare providers engagement in detecting cases, treating and caring for multidrug resistant tuberculosis (MDR-TB) may be influenced by various intrinsic (individual provider factors) and extrinsic (peer, health system, political and other factors) [ 15 ]. Our study explored engagement of individual DOTS providers and factors that influence their engagement in MDR-TB prevention and management service. This is addressed through five emergent themes and subthemes as clearly specified in our results section.

The findings showed patients’ socioeconomic constraints were important challenges that influence health professionals’ engagement, and provision of MDR-TB prevention and management services. Although approaches differ, studies in Ethiopia [ 24 ], South Africa [ 25 ] and India [ 26 , 27 ] documented that such factors influence health providers’ engagement in the prevention and management of multi drug resistant tuberculosis. Again, the alleviation of these factors demands the effort from patients, stakeholders working on TB, others sectors, and the healthcare system so that healthcare providers can deliver the service more effectively in their day-to-day activities and will be more receptive to the other key factors.

We explored participants’ experiences on how patients’ awareness about drug sensitive or multi drug resistant tuberculosis influenced their engagement. Accordingly, participants encountered numerous gaps that restricted their interactions with TB/MDR-TB patients. In fact, our study design and purposes vary, studies [ 28 , 29 , 30 ] indicated that patients awareness influenced providers decision in relation to MDR-TB services and patients’ awareness status is among factors influencing healthcare providers’ decision making about the care the MDR-TB patient receives. As to our knowledge, patients’ perceived fear of discrimination was not documented whether it had direct negative impact on reducing providers’ engagement. Therefore, patients’ awareness creation is an important responsibility that needs to be addressed by the community health development army, health extension workers, all other healthcare providers and stakeholder for better MDR-TB services and patient outcomes.

Our study indicates that healthcare providers perceived that they would be exposed to MDR-TB while they are engaged. Some of the participants were more concerned about the disadvantages of engagement in providing care to MDR-TB patients which were predominantly psychological and physical pressure. In this context, the participants emphasized that engagement in MDR-TB patient care is “always being at risk” and expressed a negative attitude. This finding is similar to what has been demonstrated in a cross-sectional study conducted in South Africa in which majority of healthcare providers believed their engagement in MDR-TB services would risk their health [ 21 ].

However, majority of the healthcare providers demonstrated perceived fear of exposures mainly due to poor infection prevention practices and substandard organization of work environment in most TB/MDR-TB units. This is essentially reasonable fear, and needs urgent intervention to protect healthcare providers from worsening/reducing their effective engagement in MDR-TB patient care. On the other side of the coin, perceived risk of occupational exposure to infection could be source for taking care of oneself to combat the spread of the infection.

In our study, healthcare provider’s capability (competence) also had an impact on their ability to engage in prevention and management of MDR-TB. Here, participants had frequently raised their and other healthcare providers’ experience regarding skill gaps, negative attitude towards the service unit they were working in, ineffective use of MDR-TB guideline, poor infection prevention practices and commitment. In addition, many health professionals report serious problems regarding case identification and screening, drug administration, and side effect management. These findings were supported by other studies in Ethiopia [ 7 ] and in Nigeria [ 19 , 20 ]. This implies an urgent need for training of health care worker on how to engage in prevention and management of multidrug resistant TB.

Moreover, our findings provide insights into the role of community TB prevention actors, currently functioning health system, and colleagues and other stakeholders’ regarding healthcare providers’ engagement. Participants emphasized that support from community TB prevention actors is a key motivation to effectively engage on management and prevention roles towards MDR-TB. Evidence shows that community TB prevention is one of the prominent interventions that study participants would expect in DOTS provision as community is the closest source of information regarding the patients [ 31 , 32 ].

Similarly, all participants had pointed out the importance of support from a health system directly or indirectly influence their engagement in the prevention, diagnosis, treatment, and management of MDR-TB. Researches indicated that health system supports are enabling factors for healthcare providers in decision making regarding TB/MDR-TB prevention and treatment [ 33 ]. This problem is documented by the study done in Ethiopia [ 22 ]. In addition, support from colleagues and other stakeholders was also a felt need to engage in MDR-TB which was supported by the World Health Organization guideline which put engagement in preventing MDR-TB and providing patients centered care needs collaborative endeavor among healthcare providers, patients, and other stakeholders [ 17 ].

Participants showed that there were poor linkage among/within DOTS providers working in health post (extension workers), health centers, hospitals and MDRTB treatment initiation centers. Our finding is consistent with a research in South Africa which shows poor health care attitude is linked to poor treatment adherence [ 34 ]. Our study implies the need for further familiarization especially on clinical programmatic management of drug resistant tuberculosis. Moreover, program managers need to follow health professionals’ engagement approaches recommended by the World Health Organization: End TB strategy [ 17 ].

Limitations of the study

There are some limitations that must be explicitly acknowledged. First, participants from private health facilities were very few, which might have restricted the acquisition and incorporation of perspectives from health care providers from private health care facilities. Second, healthcare providers’ engagement was not measured from patient side given that factors for engagement may differ from what has been said by the healthcare provides. Third, power relationships especially among focus group discussant in MDR-TB treatment initiation centers might have influenced open disclosures of some sensitive issues.

The study showed how healthcare provider’s engagement in MDR-TB management and prevention was influenced. Accordingly, patient’s underlying causes, seeking support, perceived occupational exposure, healthcare provider’s incompetence and health facilities poor linkage were identified from the analysis. Weak community TB prevention efforts, poor health system support and support from colleagues, health care providers’ incompetence and health facilities poor linkage were among identified factors influencing engagement in MDR – TB prevention and management. Therefore, measures need to be in place that avert the observed obstacles to health professionals’ engagement including further quantitative studies to determine the effects of the identified reasons and potential factors in their engagement status.

Furthermore, our findings pointed out the need for additional training of service providers, particularly in clinical programmatic management of drug-resistant tuberculosis. Besides, program managers must adhere to the World Health Organization’s recommendations for health professional engagement. Higher officials in the health sector needs to strengthen the linkage between health facilities and service providers at different levels. Community awareness creation to avoid fear of discrimination including provision of support for those with MDR-TB is expected from health experts through implementation of social behavioral change communication activities.

Abbreviations

Directly observed treatment short-course

Drug susceptible tuberculosis

Millennium development goals

Multidrug resistant tuberculosis

Sustainable development goals

Tuberculosis

Treatment initiation center

World Health Organization

Extensively drug resistant TB

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Acknowledgements

We would like to acknowledge Hosanna College of Health Sciences Research and community service directorate for providing us the opportunity and necessary fund to conduct this research. Our appreciation also goes to heads of various health centers, hospitals, district health and Hadiya Zone Health office for unreserved cooperation throughout data collection.

The authors declare that this study received funding from Hosanna College of Health Sciences. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

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Bereket Aberham Lajore & Menen Ayele

Present address: Hossana College of Health Sciences, Hosanna, SNNPR, Ethiopia

Yitagesu Habtu Aweke

Present address: College of Health Sciences, School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia

Samuel Yohannes Ayanto

Present address: College of Health Sciences, Institute of Public Health, Department of -Population and Family Health, Jimma University, Jimma, Ethiopia

Bereket Aberham Lajore, Yitagesu Habtu Aweke and Samuel Yohannes Ayanto contributed equally to this work.

Authors and Affiliations

Department of Family Health, Hossana College of health sciences, Hossana, Ethiopia

Bereket Aberham Lajore

Department of Health informatics, Hossana College of Health Sciences, Hossana, Ethiopia

Department of Midwifery, Hossana College of Health Sciences, Hossana, Ethiopia

Department of Clinical Nursing, Hossana College of Health Sciences, Hossana, Ethiopia

Menen Ayele

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Bereket Aberham Lajore, Yitagesu Habtu Aweke, and Samuel Yohannes Ayanto conceived the idea and wrote the proposal, participated in data management, analyzed the data and drafted the paper and revised the analysis and subsequent draft of the paper. Menen Ayele revised and approved the proposal, revised the analysis and subsequent draft of the paper. Yitagesu Habtu and Bereket Aberham Lajore wrote the main manuscript text and prepared all tables. All authors reviewed and approved the final manuscript.

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Correspondence to Bereket Aberham Lajore .

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Ethical approval was obtained from Institutional review board [IRB] of Hossana College of health sciences after reviewing the protocol for ethical issues and provided a formal letter of permission to concerned bodies in the health system. Accordingly, permission to conduct this study was granted by respective health facilities in Hadiya zone. Confidentiality of the information was assured and participants’ autonomy not to participate or to opt-out at any stage of the interview were addressed. Finally, informed consent was obtained from the study participants after detailed information.

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Lajore, B.A., Aweke, Y.H., Ayanto, S.Y. et al. Exploring health care providers’ engagement in prevention and management of multidrug resistant Tuberculosis and its factors in Hadiya Zone health care facilities: qualitative study. BMC Health Serv Res 24 , 542 (2024). https://doi.org/10.1186/s12913-024-10911-6

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• Volume 109, Issue 3
• Paediatric intensive care admissions of preterm children born
• Article Text
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• http://orcid.org/0000-0001-6157-1083 Tim J van Hasselt 1 ,
• http://orcid.org/0000-0003-0707-876X Chris Gale 2 ,
• http://orcid.org/0000-0002-2898-553X Cheryl Battersby 2 ,
• Peter J Davis 3 ,
• Elizabeth Draper 1 ,
• http://orcid.org/0000-0001-8711-4817 Sarah E Seaton 1
• On behalf of the United Kingdom Neonatal Collaborative and the Paediatric Critical Care Society Study Group (PCCS-SG)
• 1 Department of Population Health Sciences , University of Leicester , Leicester , UK
• 2 Neonatal Medicine, School of Public Health, Faculty of Medicine , Imperial College London , London , UK
• 3 Paediatric Intensive Care Unit, Bristol Royal Hospital for Children , University Hospitals Bristol NHS Foundation Trust , Bristol , UK
• Correspondence to Dr Tim J van Hasselt, Department of Population Health Sciences, University of Leicester, Leicester LE1 7RH, UK; t.vanhasselt{at}nhs.net

Objective Survival of babies born very preterm (<32 weeks gestational age) has increased, although preterm-born children may have ongoing morbidity. We aimed to investigate the risk of admission to paediatric intensive care units (PICUs) of children born very preterm following discharge home from neonatal care.

Design Retrospective cohort study, using data linkage of National Neonatal Research Database and the Paediatric Intensive Care Audit Network datasets.

Setting All neonatal units and PICUs in England and Wales.

Patients Children born very preterm between 1 January 2013 and 31 December 2018 and admitted to neonatal units.

Main outcome measures Admission to PICU after discharge home from neonatal care, before 2 years of age.

Results Of the 40 690 children discharged home from neonatal care, there were 2308 children (5.7%) with at least one admission to PICU after discharge. Of these children, there were 1901 whose first PICU admission after discharge was unplanned.

The percentage of children with unplanned PICU admission varied by gestation, from 10.2% of children born <24 weeks to 3.3% born at 31 weeks.

Following adjustment, unplanned PICU admission was associated with lower gestation, male sex (adjusted OR (aOR) 0.79), bronchopulmonary dysplasia (aOR 1.37), necrotising enterocolitis requiring surgery (aOR 1.39) and brain injury (aOR 1.42). For each week of increased gestation, the aOR was 0.90.

Conclusions Most babies born <32 weeks and discharged home from neonatal care do not require PICU admission in the first 2 years. The odds of unplanned admissions to PICU were greater in the most preterm and those with significant neonatal morbidity.

• intensive care units, neonatal
• intensive care units, paediatric
• paediatrics
• neonatology

Data availability statement

Data may be obtained from a third party and are not publicly available. Data may be obtained from a third party and are not publicly available. PICANet data may be requested from the data controller, the Healthcare Quality Improvement Partnership (HQIP). A Data Access Request Form can be obtained from https://www.hqip.org.uk/national-programmes/accessing-ncapop-data/%23.XQeml_lKhjU .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/archdischild-2023-325970

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Children born very preterm (<32 weeks gestational age) are at increased risk of morbidity in childhood.

For such children, survival has increased, particularly for babies born <28 weeks.

It is not known what proportion of very preterm babies discharged from neonatal units will need further intensive care.

WHAT THIS STUDY ADDS

2308 babies (5.7%) born very preterm and discharged home from neonatal care were subsequently admitted to paediatric intensive care units (PICUs) before the age of 2 years, and the majority of these admissions were unplanned.

The percentage of children with unplanned PICU admission following neonatal discharge varied by gestation, from 10.2% of babies born <24 weeks to 3.3% born at 31 weeks.

Most unplanned PICU admissions of children born very preterm and discharged home occur in the first few months after neonatal discharge.

Major neonatal morbidities (brain injury, severe necrotising enterocolitis and bronchopulmonary dysplasia) were associated with unplanned PICU admission after neonatal discharge.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Neonatal healthcare professionals may be able to identify babies at highest risk of being admitted to PICU when planning neonatal discharge.

Appropriate, sensitive and tailored discussion with families may improve understanding of this risk; we plan to work with families to develop infographics to assist these discussions.

In recent decades, survival following very preterm birth (<32 weeks gestation) has increased, both within the UK 1 and in other high-income countries. 2 Despite improvements in survival, very preterm-born children experience ongoing morbidity 3 and increased frequency of hospitalisation. 4 A proportion of children born preterm become critically ill after discharge from neonatal care, and require admission to paediatric intensive care units (PICUs), particularly due to respiratory viruses in the first years of life. 5 Previous estimates using Office for National Statistics (ONS) data suggest that up to 20%–25% of babies born <25 weeks in the UK may be admitted to PICU in the first 2 years of life, 6 however to our knowledge no study has been published within the UK or internationally using data linkage of national neonatal and PICU datasets to examine PICU admissions of preterm-born children.

The risk of requiring PICU admission after neonatal discharge for very preterm-born children is uncertain, as are factors that affect this risk. Understanding these may be helpful for counselling families of very preterm children, and for neonatal discharge planning. We aimed to use a novel linkage of the National Neonatal Research Database (NNRD) and Paediatric Intensive Care Audit Network (PICANet) datasets to investigate the risk of, and risk factors associated with, admission to PICU after neonatal discharge for babies born at 22–31 weeks gestational age.

We identified all babies born at <32 weeks gestational age from 1 January 2013 to 31 December 2018, and admitted for neonatal care in England and Wales. All babies born at this gestation should all receive neonatal care and therefore have associated NNRD data. We excluded babies born <22 weeks, and those whose neonatal admissions were recorded as occurring after day 1. Each child was followed up until 2 years of age (a period when children are particularly susceptible to respiratory infections) to investigate if they were admitted to PICUs in England and Wales.

We identified children discharged home from neonatal units, and classified subsequent PICU admission in two ways: (1) ‘PICU admission from home’ was identified as PICU admission at least 24 hours following neonatal discharge home; (2) ‘unplanned PICU admissions’, a subset of (1), excluding elective PICU admissions such as those for planned surgery.

Previous PICU admissions during the neonatal stay, with return to the neonatal unit, were not examined, although these children were included within the cohort if they were subsequently discharged home from the neonatal unit.

Information about the care the babies received in the neonatal unit was provided by the NNRD, which captures demographic and clinical data related to neonatal unit admissions, daily clinical care and discharges from all neonatal units in England since 2012 and Wales since 2013. 7 This was linked with PICU admissions provided by the PICANet, a national audit database of demographic and clinical data collected from every PICU admission across the UK and Ireland, with complete coverage for England and Wales from 2003. 8 Data are submitted to PICANet within three months of a child’s discharge, and subsequently data are subject to validation before any analysis.

Personal identifiers for babies and children (NHS number; date of birth; surname; postcode) were provided by NNRD and PICANet to NHS Digital (now NHS England) who identified children common to both cohorts and provided pseudonymised linked data. Over 99% of children had unique NHS numbers in both datasets, for whom probabilistic linkage was not required. Linked information on deaths in the first 2 years of life was provided by the ONS.

Statistical analysis

We performed descriptive statistics of the cohort, presenting frequencies with percentages for categorical variables, mean with SD for parametric variables and median with IQR for non-parametric variables. We calculated postmenstrual age (PMA) at neonatal discharge and at first unplanned PICU admission.

Our primary analysis was a logistic regression model predicting unplanned PICU admission using characteristics from the neonatal period. Variables were selected for inclusion by a multidisciplinary advisory panel ( online supplemental table 1 ). The selected variables were: gestation, sex, small for gestational age (SGA) and the major neonatal morbidities of bronchopulmonary dysplasia (BPD) requiring oxygen at 36 weeks, severe necrotising enterocolitis (NEC) requiring surgery 9 and brain injury. Brain injury included grade III/IV intraventricular haemorrhage, periventricular leukomalacia, hypoxic ischaemic encephalopathy, meningitis and seizures after exclusions for congenital or inherited causes. 10 The major neonatal morbidities also reflected the core outcome set for neonatal research. 11

Supplemental material

Implausible birth weights over 3 SD from the median for gestation and sex, 12 or <300 g/>2500 g, were excluded from multivariable models. SGA was defined as birth weight <10th centile for gestation and sex using centiles defined elsewhere. 13 14

Gestational age in completed weeks was modelled linearly, with sensitivity analysis re-fitting the model using categorisation of gestation at birth to investigate the robustness of this approach. The primary model and sensitivity analyses were compared using the Akaike Information Criterion (AIC) for model quality, 15 Hosmer-Lemeshow 16 and link tests 17 for model fit, Brier’s score 18 and c-statistic for predictive ability 19 and variance inflation factor for multicollinearity. 20

Repeat PICU admission in the first 2 years was explored graphically using a Sankey diagram, created using SankeyMATIC (sankeymatic.com).

There were 46 698 children born <32 weeks between 2013 and 2018 and admitted to neonatal units within England and Wales. After exclusions for neonatal admission after day 1 (n=13), and gestation <22 weeks (n=1), there were 46 684 children ( figure 1 ). In total, 3929 babies died in neonatal care and 2065 were discharged to receive ongoing care in other settings. Of the 40 690 children discharged home from neonatal care, 2308 children (5.7%) had at least one admission to PICU after discharge, comprising 3270 PICU admissions in total.

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Study flowchart. PICU, paediatric intensive care unit.

All PICU admissions

The subgroup of preterm-born children admitted to PICU had a greater proportion of males (60.0% vs 54.1%), lower birth weight (mean 1131 g vs 1246 g), lower gestational age at birth (median 28 weeks vs 29 weeks), and a greater proportion with neonatal morbidity such as brain injury (11.4% vs 6.0%) compared with the overall cohort of babies discharged home ( table 1 ).

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Birth and neonatal characteristics of cohort: children born <32 weeks between 2013 and 2018 in England and Wales and discharged home from neonatal care

The observed percentage of children who were admitted to PICU after discharge varied by gestational age at birth ( table 2 ), from 13.6% of children born <24 weeks to 3.7% of children born at 31 weeks.

Number and percentage of children with one or more PICU admissions after discharge home from the neonatal unit, and PMA at neonatal discharge and at first unplanned PICU admission, by gestational age at birth

Examining the first PICU admission after neonatal discharge home ( figure 2 , a Sankey diagram in which the width of the connections represents the number of children), the primary diagnosis on admission was most commonly respiratory disease (n=1436, 62.2%) followed by infection (n=223, 9.7%) and cardiovascular disease (n=196, 8.5%).

Sankey diagram showing diagnostic category of first and subsequent paediatric intensive care unit admissions after neonatal discharge home of children born <32 weeks.

Overall, 507 (22.0%) children admitted to PICU from home had at least one further PICU admission before the age of 2, this increased to 31.9% for children born <24 weeks. Respiratory conditions were consistently the most common cause of admission ( figure 2 ).

The observed mortality within PICU for children of all gestations was 2.4% (n=56).

Unplanned PICU admissions

The majority of first admissions to PICU following neonatal discharge were unplanned (n=1901, 82.4%). The percentage of children discharged home who had subsequent unplanned PICU admissions varied by gestation, from 10.2% of children discharged born <24 weeks to 3.3% of those born 31 weeks ( table 2 ). As gestational age at birth increased, neonatal discharge occurred at an earlier PMA, as did the first unplanned PICU admission ( table 2 ). Among unplanned admissions, 431 (22.7%) children had further PICU admissions (including planned or unplanned), again this was highest in those born at <24 weeks (33.3%).

A total of 40 290 children were included in logistic regression analysis after exclusions for missing variable data (0.98% missing) ( table 1 ). Following adjustment, unplanned PICU admission was associated with lower gestation at birth, male sex, BPD, severe NEC and brain injury ( table 3 ). Of the neonatal morbidities, brain injury had the greatest increase in adjusted OR (aOR 1.42) followed by severe NEC (aOR 1.39) then BPD (aOR 1.37).

Logistic regression analysis for unplanned PICU admission for children discharged home from neonatal care, using gestation as continuous variable (n=40 290)

We compared model predictions for unplanned PICU admission by gestation and morbidity with overall observed percentages ( online supplemental figure 1 ). Increases in predicted risk of unplanned PICU admission appeared to relate to the total number of neonatal morbidities, increasing to 16.8% (95% CI 12.9% to 20.8%) for children born <24 weeks with BPD, NEC and brain injury.

Results remained consistent in planned sensitivity analyses in which gestational age was modelled as a category; and after exclusion of the 760 (1.9%) children with any congenital anomaly ( online supplemental tables 2–4 ). Values for AIC showed little change, indicating similar model quality. Tests of the primary model showed moderate predictive ability (c-statistic 0.614, Brier score 0.042) and acceptable model fit and collinearity ( online supplemental table 5 ).

In this work, we explored the probability of PICU admission for children who were born very preterm and admitted for neonatal care in England and Wales between 2013 and 2018. The observed percentage of children admitted to PICU after neonatal discharge home was highly correlated with gestational age; ranging from 13.6% in those born at <24 weeks gestational age to 3.7% of those born at 31 weeks gestational age. The majority (82%) of these PICU admissions were unplanned, and hence unexpected for the families of these children.

Between a quarter and third of babies born extremely preterm develop BPD, 21 which can result in respiratory impairment 22 and susceptibility to respiratory infections into childhood. 5 We found that BPD was associated with unplanned PICU admission. Given that the largest percentage of PICU admissions and readmissions were for respiratory disease, the continued high rates of BPD in very preterm survivors is concerning. 23 Unplanned PICU admission was also associated with neonatal brain injury and severe NEC. This is consistent with previous studies that have demonstrated that the number of neonatal morbidities is associated with death and disability in preterm-born children. 24 25

Respiratory admissions to PICU are most commonly due to viral illnesses, such as bronchiolitis. 26 Selective passive immunisation to respiratory syncytial virus (RSV) has been recommended since 2010 in the UK, 27 however our results may prompt neonatal services to consider whether additional measures around neonatal discharge planning may help families prepare for and potentially prevent respiratory viral infections. This may include guidance on how best to access healthcare, 28 open access to paediatric assessment units or neonatal outreach nurse services. The first unplanned PICU admission tended to occur relatively soon after neonatal discharge; healthcare professionals and families may wish to know that the first few months after going home are the highest risk for very preterm-born children.

We observed that a relatively high proportion of children admitted to PICU required subsequent PICU readmission before the age of 2 years (22.0%), and this percentage increased further for the most preterm children. Previous studies have described readmission rates of 15.5% of children readmitted to PICU within 1 year, 29 increasing to 21.7% for children with complex chronic disease, 30 which was similar to that observed in our study.

The mortality rate within PICU was relatively low (2.4%), and similar to the rate previously observed using PICANet data (2.8%) for preterm-born children admitted with respiratory failure. 26

The majority of PICU admissions (82% of first admissions) after neonatal discharge were unplanned. Both neonatal and PICU care are associated with symptoms of post-traumatic stress in parents, 31–33 and unexpected admissions to another intensive care environment may exacerbate this. Moreover, families may find the PICU environment very different to the neonatal environment they had been used to. 34 We are not aware of any literature examining the effects of preparing families for the possibility of PICU admission, however information from our work regarding the potential for PICU admission could be made available to clinicians to share with families who wish to know this.

Strengths and limitations

The major strength of this study is the use of a novel, large, linked national dataset. Given the data quality and completeness of the NNRD and PICANet, and the use of NHS numbers, this should allow high levels of linkage success, 8 35 although we cannot quantify this.

Limited research has explored this area previously. Aggregate data from NNRD and PICANet been used to estimate PICU admission during the neonatal stay for children born very preterm (1.7%–5.5% of children), 36 however PICU admissions after neonatal discharge were not examined. A conference abstract reported estimates of extreme preterm-born children requiring PICU admission between birth and 2 years using PICANet and ONS summary data (up to 20%–25% of children born <25 weeks). 6 However, 30% of PICU admissions had missing data for gestation, leading to a high degree of uncertainty regarding these estimates, in addition this study included children admitted to PICU during their neonatal stay, unlike our study. The use of patient-level data linkage in our study enables us to identify patient journeys of preterm-born children so provides more robust estimates, and allows adjustment for neonatal factors.

While the datasets we have used cover a wide geographical area, there may be some children whose PICU admission was outside of England or Wales and therefore missed. In addition, a small number of children (0.4%) died outside of neonatal care or PICUs within England and Wales.

While tests of model fit and multicollinearity were satisfactory, our model had only moderate predictive ability for unplanned PICU admission despite a large dataset and selection of significant neonatal morbidity. Therefore, there may have been unmeasured factors which affect the risk of respiratory infections requiring PICU admission, such as the season of discharge, individuals’ RSV prophylaxis or smoking within the home. 37

Implications for future research

Our dataset does not include babies born since the 2019 changes in UK neonatal management of babies born 22–23 weeks. 38 Future work using more recent data may increase understanding of the intensive care needs of this relatively small population, and any effect on overall PICU admissions.

This study did not examine children who were discharged directly from neonatal units to ongoing care in inpatient wards, high dependency units or PICUs (n=2065). These children, particularly those requiring ongoing critical care, are likely to have a greater degree of morbidity, and we intend to study this important group further.

Future research could also examine the impact of early neonatal discharge, and the season of discharge, particularly with regard to subsequent respiratory admissions, and assess the effectiveness of interventions to prevent such illnesses such as RSV prophylaxis for high-risk children.

Conclusions

The majority of babies born <32 weeks and discharged home from neonatal care do not require PICU admission in the first 2 years of life. However, unexpected admissions to PICU are more common in the most preterm-born children, and especially those with brain injury, severe NEC or BPD. More work is required to understand the impact of morbidity and multimorbidity in the very preterm population.

The main driver of PICU admissions is respiratory illness, mostly occurring in the first few months following neonatal discharge, while a considerable proportion of children require multiple PICU admissions for respiratory disease in the first 2 years.

Our results provide data to support neonatal healthcare professionals in identifying babies with the greatest risk of PICU admission after neonatal discharge. We plan to work with families and healthcare professionals to develop resources to aid discussions around this risk.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Research ethical approval was provided by the East of England committee (reference: 20/EE/0220) and the Confidentiality Advisory Group (20/CAG/0110). This study made use of routinely collected data and therefore no participants were actively recruited.

Acknowledgments

The authors thank all UK Neonatal Collaborative neonatal units that agreed to the inclusion of data in the National Neonatal Research Database (NNRD) from their patients to be used in this work. The NNRD is a UK Health Research Authority-approved research database; the Chief Investigator for the NNRD is Professor Neena Modi. The authors would like to thank all paediatric intensive care units in England and Wales who allowed their data to be used in this study. PICANet is commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical Audit and Patient Outcomes Programme (NCAPOP). HQIP is led by a consortium of the Academy of Medical Royal Colleges, the Royal College of Nursing and National Voices. Its aim is to promote quality improvement in patient outcomes, and in particular, to increase the impact that clinical audit, outcome review programmes and registries have on healthcare quality in England and Wales. HQIP holds the contract to commission, manage and develop the NCAPOP, comprising around 40 projects covering care provided to people with a wide range of medical, surgical and mental health conditions. The programme is funded by NHS England, the Welsh Government and, with some individual projects, other devolved administrations and crown dependencies www.hqip.org.uk/national-programmes. Support with the data extraction and linkage was kindly provided by Kayleigh Ougham (NNRD), Lee Norman (PICANet) and NHS Digital. The authors would like to thank the other members of the study advisory group (Professor Jennifer J Kurinczuk, Dr Jonathan Cusack, Dr Patrick Davies, Dr Nicola Mackintosh and Dr Joseph Manning). The authors would also like to acknowledge and thank the support of Bliss, the charity for babies born premature or sick, ADAPT Prem Babies—Leicestershire, the local charity supporting parents and families with premature and poorly babies and The Smallest Things, the volunteer-run charity promoting health and well-being for preterm babies and their families. In particular, the authors would like to acknowledge the children who have contributed data to our research and their families; and the support and input from the families who have contributed so much to this project during the patient and public involvement meetings.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

X @DrCGale, @DrCBattersby

Collaborators Matthew Babirecki (Airedale General Hospital), Rebecca Kettle (Alder Hey), Anand Kamalanathan (Arrowe Park Hospital), Clare Cane (Barnet Hospital), Kavi Aucharaz (Barnsley District General Hospital), Rathod Poorva (Basildon Hospital), Maninder Bal (Basingstoke & North Hampshire Hospital), L M Wong (Bassetlaw District General Hospital), Anita Mittal (Bedford Hospital), Penny Broggio (Birmingham City Hospital), Pinki Surana (Birmingham Heartlands Hospital), Matt Nash (Birmingham Women's Hospital), Sam Wallis (Bradford Royal Infirmary), Ahmed Hassan (Broomfield Hospital, Chelmsford), Karin Schwarz (Calderdale Royal Hospital), Shu-Ling Chuang (Chelsea & Westminster Hospital), Penelope Young (Chesterfield & North Derbyshire Royal Hospital), Ramona Onita (Colchester General Hospital), Mani Kandasamy (Conquest Hospital), Stephen Brearey (Countess of Chester Hospital), Morris/Siramhatia (Croydon University Hospital), Yee Aung (Cumberland Infirmary), Bharath Gowda (Darent Valley Hospital), Mehdi Garbash (Darlington Memorial Hospital), Alex Allwood (Derriford Hospital), Pauline Adiotomre (Diana Princess of Wales Hospital), Nigel Brooke (Doncaster Royal Infirmary), Claire Hollinsworh (Dorset County Hospital), Toria Klutse (East Surrey Hospital), Sonia Spathis (Epsom General Hospital), Sathish Krishnan (Frimley Park Hospital), Samar Sen (Furness General Hospital), Alaa Ghoneem (George Eliot Hospital), Jennifer Holman (Gloucester Royal Hospital), Daniel Dogar (Good Hope Hospital), Girish Gowda (Great Western Hospital), Karen Turnock (Guy's & St Thomas' Hospital), Sobia Balal (Harrogate District Hospital), Cath Seagrave (Hereford County Hospital), Tristan Bate (Hillingdon Hospital), Hilary Dixon (Hinchingbrooke Hospital), Narendra Aladangady (Homerton Hospital), Hassan Gaili (Hull Royal infirmary), Matthew James (Ipswich Hospital), M Lal (James Cook University Hospital), Oluseun Tayo (James Paget Hospital), Abraham Isaac (Kettering General Hospital), Carolina Zorro (Kings College Hospital), Dhaval Dave (King's Mill Hospital), Jonathan Filkin (Kingston Hospital), Savi Sivashankar (Lancashire Women and Newborn Centre), Hannah Shore (Leeds General Infirmary), Jo Behrsin (Leicester General Hospital), Jo Behrsin (Leicester Royal Infirmary), Michael Grosdenier (Leighton Hospital), Ruchika Gupta (Lincoln County Hospital), Ather Ahmed (Lister Hospital), Alison Bedford Russell (Liverpool Women's Hospital), Jennifer Birch (Luton & Dunstable Hospital), Surendran Chandrasekaran (Macclesfield District General Hospital), Ashok Karupaiah (Manor Hospital), Ghada Ramadan (Medway Maritime Hospital), I Misra (Milton Keynes General Hospital), Chris Knight (Musgrove Park Hospital), Richard Heaver (New Cross Hospital), Mohammad Alam (Newham General Hospital), Prakash Thiagarajan (Nobles Hospital), Muthukumar (Norfolk & Norwich University Hospital), Tiziana Fragapane (North Devon District Hospital), Bivan Saha (North Manchester General Hospital), Cheentan Singh (North Middlesex University Hospital), Nick Barnes (Northampton General Hospital), Sangeeta Tiwary (Northumbria Specialist Emergency Care Hospital), Richard Nicholl (Northwick Park Hospital), Dush Batra (Nottingham City Hospital), Dush Batra (Nottingham University Hospital (QMC)), Victoria Nesbitt (Ormskirk District General Hospital), Amit Gupta (Oxford University Hospitals, John Radcliffe Hospital), Katharine McDevitt (Peterborough City Hospital), Ruchika Gupta (Pilgrim Hospital), David Gibson (Pinderfields General Hospital), Peter Mcewan (Poole General Hospital), Sanath Reddy (Princess Alexandra Hospital), Mark Johnson (Princess Anne Hospital), Aesha Mohammedi (Princess Royal Hospital), Patrica Cowley (Princess Royal Hospital), Rashmi Gandhi (Princess Royal University Hospital), Charlotte Groves (Queen Alexandra Hospital), Lidia Tyszcuzk (Queen Charlotte's Hospital), Shilpa Ramesh (Queen Elizabeth Hospital, Gateshead), Salamatu Jalloh (Queen Elizabeth Hospital, King's Lynn), Julia Croft (Queen Elizabeth Hospital, Woolwich), Bushra Abdul-Malik (Queen Elizabeth the Queen Mother Hospital), Dominic Muogbo (Queen's Hospital, Burton on Trent), Ambalika Das (Queen's Hospital, Romford), Khalid Mannan (Queen's Hospital, Romford), Rajiv Chaudhary (Rosie Maternity Hospital, Addenbrookes), Soma Sengupta (Rotherham District General Hospital), Christos Zipitis (Royal Albert Edward Infirmary), Kemy Naidoo (Royal Berkshire Hospital), Archana Mishra (Royal Bolton Hospital), Chris Warren (Royal Cornwall Hospital), Nigel Ruggins (Royal Derby Hospital), Chrissie Oliver (Royal Devon & Exeter Hospital), Lucinda Winckworth (Royal Hampshire County Hospital), Joanne Fedee (Royal Lancaster Infirmary), Anitha Vayalakkad (Royal Oldham Hospital), Richa Gupta (Royal Preston Hospital), Lee Abbott (Royal Stoke University Hospital), Ben Obi (Royal Surrey County Hospital), Aesha Mohammedi (Royal Sussex County Hospital), Rebecca Winterson (Royal United Hospital), Naveen Athiraman (Royal Victoria Infirmary), Anjali Pektar (Russells Hall Hospital), Jim Baird (Salisbury District Hospital), Adedayo Owoeye (Scarborough General Hospital), Umapathee Majuran (Scunthorpe General Hospital), Richard Lindley (Sheffield Children's Hospital), Vineet Gupta (Southend Hospital), Faith Emery (Southmead Hospital), Donovan Duffy (St George's Hospital), Salim Yasin (St Helier Hospital), Hannah Shore (St James University Hospital), Akinsola Ogundiya (St Mary's Hospital, IOW), Lidia Tyszcuzk (St Mary's Hospital, London), Ngozi Edi-Osagie (St Mary's Hospital, Manchester), Pamela Cairns (St Michael's Hospital), Vennila Ponnusamy (St Peter's Hospital), Victoria Sharp (St Richard's Hospital), Carrie Heal (Stepping Hill Hospital), Sanjay Salgia (Stoke Mandeville Hospital), Imran Ahmed (Sunderland Royal Hospital), Helen Purves (Tameside General Hospital), Porus Bastani (The Jessop Wing, Sheffield), Eleanor Bond (The Royal Free Hospital), Divyen Shah (The Royal London Hospital—Constance Green), Esther Morris (Torbay Hospital), Se-Yeon Park (Tunbridge Wells Hospital), Giles Kendall (University College Hospital), Puneet Nath (University Hospital Coventry), Igor Fierens (University Hospital Lewisham), Mehdi Garbash (University Hospital of North Durham), Hari Kumar (University Hospital of North Tees), Peter Curtis (Victoria Hospital, Blackpool), Delyth Webb (Warrington Hospital), Bird (Warwick Hospital), Sankara Narayanan (Watford General Hospital), Yee Aung (West Cumberland Hospital), Eleanor Hulse (West Middlesex University Hospital), Tayyaba Aamir (West Suffolk Hospital), Angela Yannoulias (Wexham Park Hospital), Caroline Sullivan (Whipps Cross University Hospital), Ros Garr (Whiston Hospital), Wynne Leith (Whittington Hospital), Shaveta Mulla (William Harvey Hospital), Anna Gregory (Worcestershire Royal Hospital), Edward Yates (Worthing Hospital), Abijeet Godhamgaonkar (Wythenshawe Hospital), Megan Eaton (Yeovil District Hospital), Sundeep Sandhu (York District Hospital), Arun Ramachandran (Singleton Hospital), Abby Parish (Princess of Wales Hospital), Anitha James (The Grange University Hospital), Ian Barnard (Glan Clwyd Hospital), Artur Abelian (Wrexham Maelor Hospital), Shakir Saeed (Ysbyty Gwynedd), Nitin Goel (University Hospital of Wales), David Deekollu (Prince Charles Hospital), Prem Pitchaikani (Glangwili General Hospital).

Contributors TJvH designed the study, and undertook analysis under the supervision of SES, CG and ED. CB and PJD provided clinical interpretation and review of manuscript. All authors contributed to the interpretation, revised the manuscript critically and approved the final version for submission. SES, as supervisor of TJvH, had access to all data and responsibility for the project including decision for publication, and is the guarantor for this paper.

Funding SES (Advanced Fellowship: NIHR300579), CB (Advanced Fellowship: NIHR300617) and TJvH (Doctoral Research Fellowship: NIHR301761) are funded by the National Institute for Health Research (NIHR) for this research project. The views expressed in this publication are those of the authors and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care.CG is supported by the Medical Research Council through a Clinician Scientist Fellowship (MR/N008405/1, MR/V036866/1) and this supported his salary over the time spent on this study. He has also received grants and funding from the NIHR, Action Medical Research, Chiesi Pharmaceuticals and the Canadian Institute for Health Research (CIHR).

Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Linked Articles

• Highlights from this issue Fantoms Ben J Stenson Archives of Disease in Childhood - Fetal and Neonatal Edition 2024; 109 229-229 Published Online First: 18 Apr 2024. doi: 10.1136/archdischild-2024-327233

Read the full text or download the PDF:

The independent source for health policy research, polling, and news.

A New Use for Wegovy Opens the Door to Medicare Coverage for Millions of People with Obesity

Juliette Cubanski , Tricia Neuman , Nolan Sroczynski , and Anthony Damico Published: Apr 24, 2024

The FDA recently approved a new use for Wegovy (semaglutide), the blockbuster anti-obesity drug, to reduce the risk of heart attacks and stroke in people with cardiovascular disease who are overweight or obese. Wegovy belongs to a class of medications called GLP-1 (glucagon-like peptide-1) agonists that were initially approved to treat type 2 diabetes but are also highly effective anti-obesity drugs. The new FDA-approved indication for Wegovy paves the way for Medicare coverage of this drug and broader coverage by other insurers. Medicare is currently prohibited by law from covering Wegovy and other medications when used specifically for obesity. However, semaglutide is covered by Medicare as a treatment for diabetes, branded as Ozempic.

What does the FDA’s decision mean for Medicare coverage of Wegovy?

The FDA’s decision opens the door to Medicare coverage of Wegovy, which was first approved by the FDA as an anti-obesity medication. Soon after the FDA’s approval of the new use for Wegovy, the Centers for Medicare & Medicaid Services (CMS) issued a memo indicating that Medicare Part D plans can add Wegovy to their formularies now that it has a medically-accepted indication that is not specifically excluded from Medicare coverage . Because Wegovy is a self-administered injectable drug, coverage will be provided under Part D , Medicare’s outpatient drug benefit offered by private stand-alone drug plans and Medicare Advantage plans, not Part B, which covers physician-administered drugs.

How many Medicare beneficiaries could be eligible for coverage of Wegovy for its new use?

Of these 3.6 million beneficiaries, 1.9 million also had diabetes (other than Type 1) and may already have been eligible for Medicare coverage of GLP-1s as diabetes treatments prior to the FDA’s approval of the new use of Wegovy.

Not all people who are eligible based on the new indication are likely to take Wegovy, however. Some might be dissuaded by the potential side effects and adverse reactions . Out-of-pocket costs could also be a barrier. Based on the list price of $1,300 per month (not including rebates or other discounts negotiated by pharmacy benefit managers), Wegovy could be covered as a specialty tier drug, where Part D plans are allowed to charge coinsurance of 25% to 33%. Because coinsurance amounts are pegged to the list price, Medicare beneficiaries required to pay coinsurance could face monthly costs of $325 to $430 before they reach the new cap on annual out-of-pocket drug spending established by the Inflation Reduction Act – around $3,300 in 2024, based on brand drugs only, and $2,000 in 2025. But even paying $2,000 out of pocket would still be beyond the reach of many people with Medicare who live on modest incomes . Ultimately, how much beneficiaries pay out of pocket will depend on Part D plan coverage and formulary tier placement of Wegovy.

Further, some people may have difficulty accessing Wegovy if Part D plans apply prior authorization and step therapy tools to manage costs and ensure appropriate use. These factors could have a dampening effect on use by Medicare beneficiaries, even among the target population.

When will Medicare Part D plans begin covering Wegovy?

Some Part D plans have already announced that they will begin covering Wegovy this year, although it is not yet clear how widespread coverage will be in 2024. While Medicare drug plans can add new drugs to their formularies during the year to reflect new approvals and expanded indications, plans are not required to cover every new drug that comes to market. Part D plans are required to cover at least two drugs in each category or class and all or substantially all drugs in six protected classes . However, facing a relatively high price and potentially large patient population for Wegovy, many Part D plans might be reluctant to expand coverage now, since they can’t adjust their premiums mid-year to account for higher costs associated with use of this drug. So, broader coverage in 2025 could be more likely.

How might expanded coverage of Wegovy affect Medicare spending?

The impact on Medicare spending associated with expanded coverage of Wegovy will depend in part on how many Part D plans add coverage for it and the extent to which plans apply restrictions on use like prior authorization; how many people who qualify to take the drug use it; and negotiated prices paid by plans. For example, if plans receive a 50% rebate on the list price of $1,300 per month (or $15,600 per year), that could mean annual net costs per person around $7,800. If 10% of the target population (an estimated 360,000 people) uses Wegovy for a full year, that would amount to additional net Medicare Part D spending of $2.8 billion for one year for this one drug alone.

It’s possible that Medicare could select semaglutide for drug price negotiation as early as 2025, based on the earliest FDA approval of Ozempic in late 2017 . For small-molecule drugs like semaglutide, at least seven years must have passed from its FDA approval date to be eligible for selection, and for drugs with multiple FDA approvals, CMS will use the earliest approval date to make this determination. If semaglutide is selected for negotiation next year, a negotiated price would be available beginning in 2027. This could help to lower Medicare and out-of-pocket spending on semaglutide products, including Wegovy as well as Ozempic and Rybelsus, the oral formulation approved for type 2 diabetes. As of 2022, gross Medicare spending on Ozempic alone placed it sixth among the 10 top-selling drugs in Medicare Part D, with annual gross spending of $4.6 billion, based on KFF analysis . This estimate does not include rebates, which Medicare’s actuaries estimated to be  31.5% overall in 2022  but could be as high as  69%  for Ozempic, according to one estimate.

What does this mean for Medicare coverage of anti-obesity drugs?

For now, use of GLP-1s specifically for obesity continues to be excluded from Medicare coverage by law. But the FDA’s decision signals a turning point for broader Medicare coverage of GLP-1s since Wegovy can now be used to reduce the risk of heart attack and stroke by people with cardiovascular disease and obesity or overweight, and not only as an anti-obesity drug. And more pathways to Medicare coverage could open up if these drugs gain FDA approval for other uses . For example, Eli Lilly has just reported clinical trial results showing the benefits of its GLP-1, Zepbound (tirzepatide), in reducing the occurrence of sleep apnea events among people with obesity or overweight. Lilly reportedly plans to seek FDA approval for this use and if approved, the drug would be the first pharmaceutical treatment on the market for sleep apnea.

If more Medicare beneficiaries with obesity or overweight gain access to GLP-1s based on other approved uses for these medications, that could reduce the cost of proposed legislation to lift the statutory prohibition on Medicare coverage of anti-obesity drugs. This is because the Congressional Budget Office (CBO), Congress’s official scorekeeper for proposed legislation, would incorporate the cost of coverage for these other uses into its baseline estimates for Medicare spending, which means that the incremental cost of changing the law to allow Medicare coverage for anti-obesity drugs would be lower than it would be without FDA’s approval of these drugs for other uses. Ultimately how widely Medicare Part D coverage of GLP-1s expands could have far-reaching effects on people with obesity and on Medicare spending.

• Medicare Part D
• Chronic Diseases
• Heart Disease
• Medicare Advantage

news release

• An Estimated 1 in 4 Medicare Beneficiaries With Obesity or Overweight Could Be Eligible for Medicare Coverage of Wegovy, an Anti-Obesity Drug, to Reduce Heart Risk

Also of Interest

• An Overview of the Medicare Part D Prescription Drug Benefit
• FAQs about the Inflation Reduction Act’s Medicare Drug Price Negotiation Program
• What Could New Anti-Obesity Drugs Mean for Medicare?
• Medicare Spending on Ozempic and Other GLP-1s Is Skyrocketing

ORIGINAL RESEARCH article

Multi-level bivariate analysis of the association between high-risk fertility behaviors of birth and stunting with associated risk factors in ethiopia provisionally accepted.

• 1 Bahir Dar University, Ethiopia
• 2 Bahir Dar University, Ethiopia

The final, formatted version of the article will be published soon.

Currently, the linkage between high-risk fertility behavior of birth and stunting among under five children continues as a public health problem in developing countries, including Ethiopia. This issue poses a threat to the health and overall well-being of children. Thus, the main objective of this study was to examine the association between high-risk fertility behavior of birth and stunting status of children. Method: The data used for this study was extracted from the recent Ethiopian Mini Demographic and Health Survey data 2019. A total weighted sample of 4969 under five children were included in this study and the relevant data was extracted from those samples. The multilevel bivariate analysis was used to assess the association between high-risk fertility behavior of birth and the stunting status of under-five children in Ethiopia. Results: Among 4997 under-five children in the study, 24% of under-five children were experienced stunting as a result of high-risk fertility behavior of birth. Our study also revealed that, an intra-class correlation of 0.2, indicating that 20% of the variability in both high-risk fertility behaviors of birth and stunting can be attributed to differences between communities. Furthermore, there was a statistically significant association between high-risk fertility behavior of birth and the stunting status of children under the age of five [AOR = 8.5, 95% CI: (5.58, 18.70)]. Similarly, the stunting status of birth among males were 1.36 times greater than to the estimated odds of stunting status of birth among females [AOR = 1.36, 95% CI :( 1.19, 1.55)]. Conclusions: This study found that, there was a significant statistical association between high-risk fertility behavior of birth and stunting status of under five children. Specifically, children born to mothers under 18 years and in households with high parity were identified as the main risk factors for child stunting. Furthermore, health-related education, improved access to maternal healthcare, and training interventions were associated with high-risk fertility behavior during birth and child stunting. The study suggests that regular health assessments and early interventions for infants born to mothers with high-risk reproductive characteristics are crucial to reducing the impact of child stunting under five age.

Keywords: High risk fertility behavior, stunting, Ethiopian, multi-level, bivariate

Received: 14 Dec 2023; Accepted: 01 May 2024.

Copyright: © 2024 Ayana and Fenta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Melkamu Ayana, Bahir Dar University, Bahir Dar, Ethiopia

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Housing and Children's Healthy Development: Research Design, Data Collection, and Analysis Plan

Report Acceptance Date: July 2019 (45 pages)

Posted Date: May 02, 2024

The Housing and Children’s Healthy Development (HCHD) study was designed to advance our understanding of the contribution of children’s residential context to their well-being. The HCHD study emanates from a multi-year effort of the MacArthur Foundation’s “How Housing Matters for Families with Children” (“Housing and Children” for short) Research Network. The Housing and Children Research Network developed a consensus about the gaps in this topic area and the best research approach to fill them, including the following:

• How does housing affect children net of other important influences on children’s lives, including families, neighborhoods, and schools?
• What features of housing matter most?
• For whom and in what circumstances does housing matter?
• How do families with children make choices about housing, neighborhood, and schools and what are the effects of these choices?

The RDDCAP describes the motivations and theoretical framework guiding the study. It explains how the study design will answer the research questions by implementing the first ever randomized controlled trial of the impact of a housing choice voucher on children’s development. It details the innovative measures of outcomes of interest used in the study. And it discusses potential contributions to knowledge afforded by the research design’s inclusion of a complementary survey sample of the general population of families with children. The study has received funding from the MacArthur Foundation, the National Institute of Child Health and Human Development, HUD, and the Robert Wood Johnson Foundation.

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UPDATED 11:38 EDT / MAY 02 2024

Extreme Connect event analysis: Pushing connectivity and AI boundaries

by Victor Dabrinze

A networking-focused conference, Extreme Connect usually pulls a diverse group of like-minded professionals, executives and product leaders. This year’s edition spanned two days and hosted informative keynotes and presentations , educational sessions and networking opportunities.

Fort Worth, Texas, played host to the event, and the agenda was chock full of hard-hitting, technical discourse. Addressing a technically inclined audience, Enterprise Networks Inc.’s Chief Product Officer Nabil Bukhari took to the stage on day one, highlighting key themes centered around connectivity, security and AI.

“There was also a fourth theme that emerged as the event progressed, and it was about being future-focused,” according to  Bob Laliberte , principal analyst at theCUBE.

In his event analysis, Laliberte unearthed key themes from the two days at Extreme Connect, covering new product announcements, integrations and an increased ecosystem focus.

Day one: Groundbreaking advances in connectivity and security

During his day one keynote at Extreme Connect, Bukhari explored several themes on connectivity and AI , imploring the industry to remain future-focused in its approach to innovation. A barrage of announcements followed the keynote, the first being Extreme Networks’ approval for standard power outdoor for Wi-Fi 6E and 7. Also, the company lifted the lid on its Universal Zero Trust Network Access, now in limited availability.

“The approval for Extreme to leverage AFC, or automated frequency control, enables organizations to activate the standard power setting and the 6GHz frequency for outdoor and indoor use,” Laliberte wrote in his analysis. “Before this, Low Power Indoor (LPI) has been used in Wi-Fi 6E solutions.”

The addition of automated frequency control has several ramifications for the enterprise. First, U.S. and Canada-based organizations that are already on Extreme Networks’ 6E-enabled outdoor access points can access the new capability at the flick of a switch (with more regions to follow, according to the company).

Second, AFC certification means there’s no interference or frequency overlap with existing providers, ensuring smooth operations for current 6GHz spectrum users, according to Laliberte.

“Using standard power indoors requires additional engineering support as GPS sensors don’t always work deep inside a building,” he said. “To overcome that, Extreme leverages [access points] near the exterior and positioning technology to report on those [access point] locations.”

Day two: Bringing the networking community together

Kicking off with a keynote by Extreme Networks Chief Marketing Officer, Monica Kumar , the event’s second day focused on the larger ecosystem, customer success and artificial intelligence.

First, the company unveiled Extreme Labs, its initiative to foster innovation and integration across the larger partner ecosystem. The first fruit of this broad collaboration is AI Expert, a generative AI solution that is underpinned by an Intel Corp. Network Interface Card and drives visibility from the network endpoint upward.

“Extreme demonstrated the power of the forthcoming gen-AI solution that leverages all of its manuals, technical documents and TAC use cases, as well as the ability to leverage customer environment-specific information,” Laliberte said. “This solution will enable organizations to query issues and quickly search the entire Extreme data lake. One of the important features that CTO of EMEA Markus Nispel highlighted was that it was a multimodal generative AI solution. So, it wasn’t just to bring back text-based answers, but also video and images.”

The essence of Extreme Networks as a networking player was captured at the event. From its key announcements, themes and customer testimonials, there is proof of the company’s commitment to innovation and customer success in the competitive networking space, Laliberte concluded.

Read the full analysis here .

Image: ktsimage/Getty Images

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