research on malnutrition in ghana

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• Published: 28 November 2007

Malnutrition and the disproportional burden on the poor: the case of Ghana

• Ellen Van de Poel 1 , 2 ,
• Ahmad Reza Hosseinpoor 3 ,
• Caroline Jehu-Appiah 4 ,
• Jeanette Vega 3 &
• Niko Speybroeck 5  

International Journal for Equity in Health volume  6 , Article number:  21 ( 2007 ) Cite this article

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Malnutrition is a major public health and development concern in the developing world and in poor communities within these regions. Understanding the nature and determinants of socioeconomic inequality in malnutrition is essential in contemplating the health of populations in developing countries and in targeting resources appropriately to raise the health of the poor and most vulnerable groups.

This paper uses a concentration index to summarize inequality in children's height-for-age z-scores in Ghana across the entire socioeconomic distribution and decomposes this inequality into different contributing factors. Data is used from the Ghana 2003 Demographic and Health Survey.

The results show that malnutrition is related to poverty, maternal education, health care and family planning and regional characteristics. Socioeconomic inequality in malnutrition is mainly associated with poverty, health care use and regional disparities. Although average malnutrition is higher using the new growth standards recently released by the World Health Organization, socioeconomic inequality and the associated factors are robust to the change of reference population.

Child malnutrition in Ghana is a multisectoral problem. The factors associated with average malnutrition rates are not necessarily the same as those associated with socioeconomic inequality in malnutrition.

In the developing world, an estimated 230 million (39%) children under the age of five are chronically malnourished and about 54% of deaths among children younger than 5 are associated with malnutrition [ 1 ]. Malnutrition is a major public health and development concern with important health and socioeconomic consequences. In Sub-Saharan Africa, the prevalence of malnutrition among the group of under-fives is estimated at 41% [ 1 ]. It is the only region in the world where the number of child deaths is increasing and in which food insecurity and absolute poverty are expected to increase [ 2 – 4 ]. Malnutrition in early childhood is associated with significant functional impairment in adult life, reduced work capacity and decreasing economic productivity [ 5 – 10 ]. Children who are malnourished not only tend to have increased morbidity and mortality but are also more prone to suffer from delayed mental development, poor school performance and reduced intellectual achievement [ 6 – 8 ].

Chronic malnutrition is usually measured in terms of growth retardation. It is widely accepted that children across the world have much the same growth potential, at least to seven years of age. Environmental factors, diseases, inadequate diet, and the handicaps of poverty appear to be far more important than genetic predisposition in producing deviations from the reference. These conditions, in turn, are closely linked to overall standards of living and the ability of populations to meet their basic needs. Therefore, the assessment of growth not only serves as one of the best global indicators of children's nutritional status, but also provides an indirect measurement of the quality of life of an entire population [ 11 – 13 ].

Large scale development programs such as the Millennium Development Goals (MDGs) have also emphasized the importance of the under-fives' nutritional status as indicators for evaluating progress [ 14 ]. When aiming at reducing childhood malnutrition, it is important not only to consider averages, which can obscure large inequalities across socioeconomic groups. Failure to tackle these inequalities may act as a brake on making progress towards achieving the MDGs and is a cause of social injustice [ 15 , 16 ].

Against this background, Ghana provides an interesting case study. The country experienced remarkable gains in health from the immediate post independence era. Life expectancy improved over the years and the prevention of a range of communicable diseases improved child survival and development. However in the last decade despite increasing investments in health, Ghana has not achieved target health outcomes. There has been no significant change in Ghana's under-five and infant mortality rates between 1993 and 2003. In the last couple of years, under-five mortality was actually slightly increasing. Life expectancy has also fallen from 57 years in 2000 to 56 years in 2005 [ 17 ]. Ghana's Human Development Index (HDI), a measure combing life expectancy, literacy, education and standard of living, has been worsening too; after improving from 0.444 in 1975 to 0.563 in 2001, the HDI dropped to 0.520 in 2005 [ 15 ]. Since 1988, there has been no definite trend in malnutrition (in terms of height-for-age). Apparent gains between 1988 and 1998 were reversed in 2003 [ 18 ]. Although the 2003 Ghana Demographic Health Survey (DHS) final report [ 17 ] recommends caution when using data from the various DHS to assess the trend in the nutritional status, it is noted that there was a trend over the past five years of increased stunting compared to a decrease of wasting and underweight. Further, there has been a trend of continued high values of stunting in the North compared to the South [ 17 , 19 ].

Malnutrition in Ghana has been most prevalent under the form of Protein Energy Malnutrition (PEM), which causes growth retardation and underweight. About 54% of all deaths beyond early infancy were associated with PEM, making this the single greatest cause of child mortality in Ghana [ 20 ].

A paradigm shift in Ghanaian health policy has been taking place in 2006. The theme for the new health policy in Ghana was 'Creating Wealth through Health". One of the fundamental hypotheses of this policy was that improving health and nutritional status of the population would lead to improved productivity, economic development and wealth creation [ 21 ]. Since this policy adopted an approach that addressed the broader determinants of health, it has thus generated interest in socio-economic inequalities in health and malnutrition. It was further recognised that not paying attention to malnutrition inequalities during the early years of life is likely to perpetuate inequality and ill health in future generations and thus defeat the aims of the new health policy.

From the existing evidence it is clear that childhood malnutrition is associated with a number of socioeconomic and environmental characteristics such as poverty, parents' education/occupation, sanitation, rural/urban residence and access to health care services. Also demographic factors such as the child's age and sex, birth interval and mother's age at birth have been linked with malnutrition [ 4 , 5 , 22 – 26 ]. Previous studies have also drawn attention to the disproportional burden of malnutrition among children from poor households [ 27 – 31 ]. However, much less is known on which factors lie behind this disproportional burden. It is important to note that the most important determinants of malnutrition are not necessarily also the most important determinants of socioeconomic inequality in malnutrition. [ 31 ] shows that the poorest-to-richest odds-ratio of stunting is almost halved by controlling for household and child characteristics using Ghanaian data. However, it is not clear how much each of these characteristics is contributing to this reduction. Understanding the nature and determinants of socioeconomic inequality in malnutrition is essential in contemplating the health of populations in developing countries and in targeting resources appropriately to raise the health of the poor and most vulnerable groups. This paper employs a concentration index to summarize inequality across the entire socioeconomic distribution rather than simply comparing extremes as in ratio measures. The concentration index is decomposed using the framework suggested by [ 32 ], allowing to identify the factors that are associated with socioeconomic inequality in malnutrition. This decomposition takes into account that both the association of a determinant with malnutrition as well as its distribution across socioeconomic groups play a role in the extent to which it is contributing to socioeconomic inequality in malnutrition. The usefulness of this approach has already been demonstrated on European data, but has known limited applications on developing countries.

Further, this paper contributes to the literature by delivering evidence on the determinants of malnutrition and socioeconomic inequality in Ghana using the new child growth standards population that has recently been released by the World Health Organization (WHO) [ 33 ]. This reference population includes children from Brazil, Ghana, India, Norway, Oman and the US. The new standards adopt a fundamentally prescriptive approach designed to describe how all children should grow rather than merely describing how children grew in a single reference population at a specified time [ 34 ]. For example, the new reference population includes only children from study sites where at least 20% of women are willing to follow breastfeeding recommendations. To our knowledge this is the first study presenting estimates of malnutrition in Ghana based upon these new standards. To check sensitivity of the results to this change in reference group, the analysis is also done using the US National Center for Health Statistics (NCHS) reference population [ 35 ].

The results are useful from a policy perspective as they can be used in setting policies to reduce malnutrition and the excessive burden on the poor. The results of this study are particularly relevant for Ghanaian policy makers, but can also be generalized to other settings in the sense that they show that malnutrition is associated with a broad range of factors and that the factors related to average malnutrition are not necessarily the same as those related to socioeconomic inequality in malnutrition.

Measuring malnutrition

Nutritional status was measured by height-for-age z-scores. An overview of other nutritional indices and why height-for-age is the most suited for this kind of analysis is provided in [ 36 ]. A height-for-age z-score is the difference between the height of a child and the median height of a child of the same age and sex in a well-nourished reference population divided by the standard deviation in the reference population. The new WHO child growth population is used as reference population [ 33 ]. To construct height-for-age z-scores based upon these standards, we used the software available on the WHO website [ 37 ]. To check sensitivity of the results to this change in reference group, the analysis is also done by using the US National Center for Health Statistics (NCHS) reference population [ 35 ].

Generally, children whose height-for-age z-score is below minus two standard deviations of the median of the reference population are considered chronically malnourished or stunted. In the regression models, the negative of the z-score is used as dependent variable ( y ). This facilitates interpretation since it has a positive mean and is increasing in malnutrition [ 32 ]. For the purpose of our analysis, using the z-score instead of a binary or ordinal variable indicating whether the child is (moderately/severely) stunted is preferred as it facilitates the interpretation of coefficients and the decomposition of socioeconomic inequality. However, binary indicators of stunting are also used in the descriptive analysis and to position Ghana within a set of other Sub-Saharan African countries.

The concentration index as a measure of socioeconomic inequality

Assume y i is the negative of the height-for-age z-score of child i . The concentration index (C) of y results from a concentration curve, which plots the cumulative proportion of children, ranked by socioeconomic status, against the cumulative proportion of y . The concentration curve lies above the diagonal if y is larger among the poorer children and vice versa. The further the curve lies from the diagonal, the higher the socioeconomic inequality in nutritional status. A concentration index is a measure of this inequality and is defined as twice the area between the concentration curve and the diagonal. If children with low socioeconomic status suffer more malnutrition than their better off peers the concentration index will be negative [ 38 ]. It should be noted that the concentration index is not bounded within the range of [-1,1] if the health variable of interest takes negative, as well as positive values. Since children with a negative y are better off than children in the reference population, they cannot be considered malnourished. Therefore their z-score is changed into zero, such that the z-scores are restricted to positive values with zero indicating no malnutrition and higher z-scores indicating more severe malnutrition.

Further, the bounds of the concentration index depend upon the mean of the indicator when applied to binary indicators, such as stunting [ 39 ]. This would impede cross-country comparisons due to substantial differences in means across countries. To avoid this problem, we used an alternative but related concentration index that was recently introduced by [ 40 ] and does not suffer from mean dependence, when comparing Ghana with other Sub-Saharan African countries.

Decomposition of socioeconomic inequality

More formally, a concentration index of y can be written as [ 38 ]:

where y i refers to the height-for-age of the i -th individual and R i is its respective fractional rank in the socioeconomic distribution. As will be discussed further in the following section, the present paper uses a continuous wealth variable, developed by principal component analysis, as a measure of socioeconomic status [see e.g. [ 41 ]].

If y i is linearly modelled

[ 32 ] showed that the concentration index of height-for-age can be decomposed into inequality in the determinants of height-for-age as follows:

where μ is the mean of y , x ¯ k MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGafmiEaGNbaebadaWgaaWcbaGaem4AaSgabeaaaaa@2EEF@ is the mean of x k , C k is the concentration index of x k (with respect to socioeconomic status) and GCε is the generalized concentration index of the residuals. The latter term reflects the socioeconomic inequality in height-for-age that is left unexplained by the model and is calculated as G C ε = 2 n ∑ i = 1 n ε i R i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGaem4raCKaem4qam0aaSbaaSqaaGGaciab=v7aLbqabaGccqGH9aqpjuaGdaWcaaqaaiabikdaYaqaaiabd6gaUbaakmaaqahabaGae8xTdu2aaSbaaSqaaiabdMgaPbqabaGccqWGsbGudaWgaaWcbaGaemyAaKgabeaaaeaacqWGPbqAcqGH9aqpcqaIXaqmaeaacqWGUbGBa0GaeyyeIuoaaaa@40B6@ .

As the DHS data have a hierarchical structure, with children nested in households and households nested within communities, we have also considered using multilevel models to estimate the associations of variables with childhood malnutrition (see e.g. [ 42 ]). Allowing for random effects on the household and/or community level yielded coefficients that were similar to the ones from OLS regression corrected for clustering. Because of this similarity and because the use of multilevel models would complicate the decomposition of socioeconomic inequality in malnutrition, the remainder is based on results from linear regression corrected for clustering on the community level.

All estimation takes account of sample weights (provided with the DHS data). Statistical inference on the decomposition results is obtained through bootstrapping with 3000 replications. The bootstrap procedure takes into account the dependence of observations within clusters.

Data is used from the 2003 Ghana Demographic Health Survey (DHS) and are restricted to children under the age of 5. Anthropometric measures are missing for 12.3% of children in this age group. The final sample contains information on 3061 children. We did examine possible selection problems due to the high proportion of missing observations. A logit model explaining the selection in the sample and a Heckman sample selection model (using different exclusion restrictions) were used to check for this [ 43 ]. Both tests did not reveal large sample selection problems, and coefficients in the Heckman model were very similar to those in the model presented here.

The nutritional status of a child is specified to be a linear function of child-level characteristics such as age, sex, duration of breastfeeding, size at birth; maternal characteristics such as education, mother's age at birth, birth interval, marital status, use of health services, occupation and finally household-level characteristics such as wealth, type of toilet facility, access to safe water, number of under-five children in the household, region and urbanization. We preferred not to include information on the type of toilet and water source into the wealth indicator, as these variables can be expected to have a direct relation with children's growth apart from being correlated with household socioeconomic status [ 44 ].

The explanatory variables are described in the last column of Table 1 . All have well documented relevance in the literature [ 5 , 22 – 26 , 31 , 32 , 45 , 46 ].

No information on mother's nutritional status was included in the set of explanatory variables. Since about 10% of women in the dataset were pregnant at the time of interview, their BMI did not provide an accurate measure of their nutritional status. Furthermore, BMI reflects current nutritional status and may not be relevant for children born 5 years prior to the interview. Inclusion of mother's height-for-age had no significant effect on results.

Summary statistics

In the 2003 DHS data for Ghana, 36% of children under the age of 5 are stunted. Stunting is defined as height-for-age being below minus 2 SD from the median of the reference population. The concentration index for stunting in children under the age of 5 was -0.12 (SD = 0.016). This negative value implies that poor children had a higher probability of being stunted than their better off peers. Using the older NCHS reference study showed a lower prevalence of stunting (29%) and slightly higher socioeconomic inequality (C = -0.15, SD = 0.019).

Figure 1 illustrates the strong socioeconomic inequality in childhood stunting. The stunting rate among the poorest 60 percent was more than twice the rate of children in the richest 20 percent.

Distribution of stunting across wealth quintiles.

Figure 2 shows a comparative picture of stunting and socioeconomic inequality in stunting across the Sub-Saharan African region. Stunting and socioeconomic variables are calculated for each country on DHS data in exactly the same way as is described for the Ghana DHS. Summary statistics of all variables are shown in Table 1 .

Average stunting versus socioeconomic inequality in stunting in under-five children in Sub-Saharan Africa. Data from recent Demographic Health surveys. Stunting is measured using the WHO child growth standards. Concentration index as suggested by [40] is used since it is invariant to the mean of the binary variable.

Determinants of malnutrition

The regression coefficients and their significance are shown in the first column of Table 2 . Note that the dependent variable is increasing in malnutrition, such that a negative coefficient should be interpreted as lowering malnutrition.

Malnutrition increased with the child's age in a non-linear way. Children who were very small at birth had a higher probability to be stunted than children with normal size. Male children were more prone to malnutrition than their female peers. Long duration of breastfeeding is associated with higher malnutrition.

With respect to maternal characteristics, the existence of a short birth interval was significantly increasing malnutrition. Children of women that accessed health services more frequently were less prone to being malnourished. Maternal occupation showed no clear effect. Maternal education and household wealth showed a significant association with childhood malnutrition. The presence of two or more under-five children in the household was negatively associated with the child's nutritional status. Sanitation variables however had no significant association on malnutrition. As compared to the Northern region all regions were associated with lower malnutrition, especially the Accra region. The high regional disparities in malnutrition are further illustrated in Figure 3 . The four most deprived regions in Ghana (Northern, Central, Upper East and Western regions) exhibited the greatest burden of malnutrition.

Inequality in stunting by regions (A) and grouped regions (B) (as in [55]).

Decomposition of socioeconomic inequality in malnutrition

Table 2 also shows the concentration index and the relative contributions of each determinant to socioeconomic inequality in childhood malnutrition. For the ease of interpretation, the last column shows the grouped contribution from the categorical variables. A negative contribution to socioeconomic inequality implies that the respective variable is lowering socioeconomic inequality and vice versa. A variable can contribute to socioeconomic inequality in malnutrition both through its association with malnutrition and through its unequal distribution across wealth groups. The extent to which each of the explanatory variables is unequally distributed across wealth is reflected by its C value. A negative C means that the determinant is more prevalent among poorer households.

Wealth accounted for the major part (31%) of socioeconomic inequality. This part of socioeconomic inequality reflects the direct contribution of wealth. The remainder is the wealth-related inequality in malnutrition through other factors. Important contributors were regional variables (23%) and the use of health care services (18%). The age of the child was contributing negatively to socioeconomic inequality (-8%). This means that the combined effect of its coefficient and its distribution by wealth was lowering socioeconomic inequality in malnutrition. Older children were more likely to be stunted and were more prevalent in higher wealth quintiles. The latter is reflected by the positive and significant C of the variable age > 12 months The contribution of the error term only amounted to about 6%, meaning that the decomposition model functioned well in explaining socioeconomic inequality in malnutrition.

Using the older NCHS reference population gave very similar regression and decomposition results are therefore not discussed (results are available upon request.).

Relative to other Sub-Saharan countries, Ghana appeared to have a rather low level of average stunting, combined with relatively high socioeconomic inequality in stunting. The use of the new WHO child growth standards yielded a higher average stunting rate as compared to the older NCHS reference group. [ 47 ] found the same for Bangladesh, Dominican Republic and a pooled sample of North American and European children. However, the variables associated with malnutrition and socioeconomic inequality were very robust to the change of the reference population.

Malnutrition in Ghanaian children rises with the age of the child, which is confirmed by other studies [ 5 , 25 , 32 ]. The higher prevalence of malnutrition among boys as compared to girls, and the negative association of long breastfeeding have also been established in the literature [ 5 , 22 , 32 , 45 ]. Long duration of breastfeeding may be associated with higher malnutrition because it reflects lack of resources to provide children with adequate nutrition [ 31 ]. It is also possible that children who are breastfed for a long time are more reluctant to eat other foods, as was found by [ 22 ] in their study on a cohort of Ghanaian children.

Short birth intervals and the presence of two or more under-five children in the household, affected childhood growth negatively by placing a heavy burden on the mother's reproductive and nutritional resources, and by increasing competition for the scarce resources within the household [ 22 ]. Children of younger mothers could be more prone to malnutrition because of physiological immaturity and social and psychological stress that come with child bearing at young age [ 48 ].

Maternal education was significantly lowering childhood malnutrition. This may reflect education generating the necessary income to purchase food. However, although education is often suggested to be a measure of social status, the coefficient stayed significant after controlling for household wealth and living conditions. A high level of maternal education could also lower childhood malnutrition through other pathways such as increased awareness of healthy behaviour, sanitation practices and a more equitable sharing of household resources in favour of the children [ 4 , 5 , 49 ].

Sanitation in terms of having a toilet and access to safe water did not significantly affect malnutrition. [ 26 ] also reported this result, but they did find a significant association between sanitation and wasting (which reflects current nutritional status). This might suggest that good sanitation can avoid episodes of diarrhoea and hereby affect current nutritional status, while it may not be sufficient for long term child growth.

The higher levels of malnutrition of the population living in the northern regions of Ghana have already been observed more than a decade ago [ 23 ]. This regional pattern reflects ecological constraints, worse general living conditions and access to public facilities in the Northern regions. In addition, the persistence of this regional inequality can point to an intergenerational effect of malnutrition. Since women who were malnourished as children are more likely to give birth to low-birth-weight children, past prevalence of child malnutrition is likely to have an effect on current prevalence.

The high socioeconomic inequality in childhood malnutrition is -apart from wealth itself-mainly associated with regional characteristics and use of health care services. Wealth was responsible for about one third of the socioeconomic inequality in malnutrition. This means that poorer children were more likely to be malnourished, mainly because of their poverty. The regional contribution results from the fact that poorer children are more likely to live in regions with disadvantageous characteristics. Given the strong regional associations with malnutrition, after controlling for a broad range of socioeconomic and demographic covariates, there must be other important regional aspects. The regional inequality in Ghana originates from both geographical and historical reasons. Much of the North is characterized by lower rainfall, savannah vegetation, periods of severe drought and remote and inaccessible location. Further, the colonial dispensation ensured that northern Ghana was a labor reserve for the southern mines and forest economy and the post-colonial failed to break the established pattern [ 19 ].

Health services use was also responsible for a substantial proportion of socioeconomic inequality in malnutrition. This derives from the combined effect of the positive associations between health services use and childhood growth and the unequal use across socioeconomic groups. The reason for the lower health care use amongst the poor may be due to several barriers including the cost of care, cost of transportation and lower awareness on health promoting behavior [ 50 ]. User fees were introduced in Ghana in 1985 as a cost-sharing mechanism at all public health facilities. To ensure access to health care services for the poor and vulnerable the government introduced fee exemptions. Then again in 2003, a new policy for exempting deliveries from user fees in the four most deprived regions of the country, namely Central, Northern, Upper East and Upper West regions were introduced. To further bridge the inequality a key recommendation of the Ghana Poverty Reduction Strategy [ 51 ] was to allocate 40% of the non-wage recurrent budget to the deprived regions. However, experience to date indicates that Ghana has not been able to implement an efficient exemption mechanism or commit to the 40% budgetary allocation to achieve the principal purpose. In addition to these financial hurdles, poorer people are often also located further from health centers. The ratios of population to nurses and doctors are the highest the poorest regions of Ghana. For example the ratio of population to doctors in the northern region is 1:81338 compared to the national average of 1:17733. Trends show that since 1995 the Northern region has had the lowest average number of outpatient visits per capita in the country [ 52 ]. Also partly related to the use of health services is the contribution of the number of under-fives in the household. Poor women are more likely to have more children and these, in turn, are therefore more likely to be malnourished. The higher parity among poorer women may be related to difficult access to or knowledge on family planning services. The much lower use and knowledge of modern contraception among poor women is documented in the Ghana DHS 2003 final report [ 17 ].

The negative contribution of age comes from the combined facts that older children are more likely to be malnourished and at the same time more prevalent in the richer wealth quintiles. The latter could be related to higher child and infant mortality rates amongst poorer households that cause the proportion of older children to be lower among poor households as compared to richer households.

Combining the results from the analysis on the determinants of malnutrition and socioeconomic inequality demonstrates that variables that are associated with average malnutrition are not necessarily also related to socioeconomic inequality. Although bio-demographic variables such as a risky birth interval, size at birth, duration of breastfeeding and the sex of the child are quite strongly associated with a child's nutritional status, they do not contribute to socioeconomic inequality in malnutrition. This is because of their relatively equal distribution across socioeconomic groups. Other variables such as urban/rural location, having a toilet, access to clean water and maternal occupation are very unequally distributed across socioeconomic groups, but still do not contribute to socioeconomic inequality in malnutrition because they are not significantly associated with malnutrition. A third group of variables such as regions, health care use and wealth are both very strongly related to average and socioeconomic inequality in malnutrition.

Considerations and limitations

There exist some limitations of this study. First, DHS only collects information on the recent food consumption of the youngest child under three years of age living with the mother. Restricting the sample to these children would substantially reduce the number of observations. However, the analysis was also conducted on this sub sample, using food consumption as one of the determinants of malnutrition (indices were created similar to [ 25 , 45 ]). Since the regression and decomposition results did not differ much, these are not presented in this paper (but are available from the authors upon request). Second, one has to bear in mind that, although commonly used, the construction of an asset index to capture socioeconomic status has its shortcomings and e.g. is sensitive to the assets included [ 44 ]. However, in the absence of reliable information on income or expenditure, the use of such an asset index is generally a good alternative to distinguish socioeconomic layers within a population [ 53 ]. Finally, it is important to note that this paper is showing the factors that are associated with malnutrition and socioeconomic inequality in malnutrition and the magnitude of these associations. These results are subject to the usual caveats regarding the causal interpretation of cross-sectional results. Focusing on child health avoids much of the direct feedback of income and health that is usually present in microeconomic studies. To gain some insight into the severity of endogeneity problems we also did the analysis excluding possible endogenous variables such as birth interval, breastfeeding, the number of children in the household and use of health care services. Again, wealth and regional characteristics were contributing most to socioeconomic inequality, followed by maternal education. To avoid endogeneity of health care use, it would be better to use data on proximity/availability of care. However, no such data were available in the 2003 Ghana DHS. Another option would be to predict health care use, but we were not able to find strong predictors for health care.

Conclusion and policy implications

The regression results show that malnutrition is associated with a broad range of factors. However in Ghana it often falls through the cracks since it has no institutional home. Tackling malnutrition therefore calls for a shared vision and should be viewed and addressed in a broader context [ 54 ]. Therefore special attention needs to be given to policies aimed at reducing malnutrition based on the magnitude and nature of determinants of malnutrition, such as poverty, education, health care and family planning services and regional characteristics. Currently in Ghana, various interventions are being implemented to reduce both PEM and micro nutrient deficiencies. These include the Infant and Young Child Feeding Strategy (IYCF) and Community Based Nutrition and Food Security project among others. Notwithstanding the positive effects of these programs, they address only the symptoms of malnutrition and therefore are most likely not sufficient to have a sustained impact in the long term as they do not deal with a lot of the root causes of malnutrition.

The results also suggest that factors strongly associated with average malnutrition are not necessarily also contributing to socioeconomic inequality in malnutrition. The distinction between these groups of variables can be quite important, as it suggests that policies trying to reduce average malnutrition rates can be different from those aiming at lowering socioeconomic inequality in malnutrition. If equity goals are to be achieved, health policies in Ghana should further be directed at strategies/interventions to reduce poverty and to improve the use of health care and family planning services among the poorer population groups. Furthermore, regional disparities should further be tackled to narrow the gap in malnutrition between the poor and the rich. A starting point could be for policy makers to include under-five malnutrition differentials to set criteria to guide resource allocation to regions. Moreover, the strong regional contributions to socioeconomic inequality, even after controlling for other factors such as household wealth and education, bring forward the issue of geographical targeting. Further targeting public programs towards the central and northern regions would substantially reduce socioeconomic inequality in malnutrition and is administratively easier than targeting the poor. The latter argument is relevant for Ghana, where pro-poor policies (redistribution schemes and exemption policies) are not having the aimed effect because of problems in identifying the poor [ 55 , 56 ]. Geographic targeting reduces leakage of program benefits to the non-needy compared to untargeted programs, although under coverage of the truly needy can increase. "Fine-tuning" the targeting by basing it on smaller geographic units increases efficiency, but in some circumstances may be costly and politically unacceptable [ 57 ].

With respect to Ghana, regional averages should be interpreted with caution as there is large heterogeneity between districts in each region and indeed among socio-economic groups within districts. In this case, polices aimed at reducing child malnutrition based on regional averages may lead to under coverage of those in need. [ 58 ] exposes some important limitations of geographic targeting if used to place poverty-alleviation or nutrition interventions within cities. Using data from Abidjan (Cote d'Ivoire) and Accra (Ghana), they found significant clustering in housing conditions; however they did not find any sign of geographic clustering of nutritional status in either city. This implies that geographic targeting of nutrition interventions in these and similar cities has important limitations. Geographic targeting would probably lead to a significant under coverage of the truly needy and, unless accompanied by additional targeting mechanisms, would also result in significant leakage to non-needy populations. Nonetheless, there is a need for additional research to further decompose regional malnutrition inequalities to generate valuable information for policy making decisions. The Ghana Growth and Poverty Reduction Strategy for 2006 – 2009 [ 59 ] states that one of the strategies to be implemented is developing and implementing high impact yielding strategies for malnutrition. This would mean targeting areas at the greatest risks of malnutrition, replicate best practices and expand coverage. This then should result in decreasing malnutrition rates among children particularly in rural areas and northern Ghana.

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Acknowledgements

Many thanks to Tom Van Ourti, Owen O'Donnell, Eddy van Doorslaer and participants at the UNU-WIDER conference on Advancing Health Equity for useful comments. Ellen Van de Poel acknowledges the University of Antwerp and the World Health Organization for support and funding.

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Van de Poel, E., Hosseinpoor, A.R., Jehu-Appiah, C. et al. Malnutrition and the disproportional burden on the poor: the case of Ghana. Int J Equity Health 6 , 21 (2007). https://doi.org/10.1186/1475-9276-6-21

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Open Mapping towards Sustainable Development Goals pp 47–56 Cite as

Rural Household Food Insecurity and Child Malnutrition in Northern Ghana

• Kwaku Antwi 3 ,
• Conrad Lyford 4 &
• Patricia Solís 5  
• Open Access
• First Online: 29 November 2022

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Part of the book series: Sustainable Development Goals Series ((SDGS))

Close to 750 million or nearly one in ten people in the world are exposed to severe levels of food insecurity, and 2 billion people do not have regular access to safe, nutritious, and sufficient food. A critical but overlooked question is how to properly locate the most food insecure and malnourished households within geographical areas that have been generally identified as food insecure and malnourished – especially where such areas are poorly mapped. The YouthMappers approach served as a baseline to inform our spatial analysis of food insecurity and underpinned household surveys on malnutrition. The resulting maps paint a powerful picture of the spatial variation of factors affecting different places, knowledge which can better inform interventions that are tailored to households in need and ultimately help to meet the goals of SDG 2, Zero Hunger.

• Food insecurity
• Rural development
• Spatial analysis

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1 What We Need to Know About Hunger

Food insecurity and malnutrition continue to persist as relevant development challenges due to the growing number of the world’s population that suffer from these two undesired conditions. Despite the reduction in the prevalence of hunger worldwide, pockets of places persist where increases in hunger levels have been observed. Currently, the realization of the 2030 targets (SDG 2) by many African countries seems unattainable because many developing countries have been thrown out of track to achieving zero hunger in the next decade. If recent trends continue, the number of people affected by hunger would surpass 840 million by 2030. Food production and consumption are not aligned with where hunger is happening, implicating SDG 12 as well. Consequently, an increase in food insecurity and hunger are directly linked with rising incidence of malnutrition levels in sub-Saharan African countries including Ghana where incidence of malnutrition is currently above 20% (FAO 2020 ).

A lot of research has been undertaken to address the problem of food insecurity and malnutrition, particularly among households in Africa and in Ghana. Most of these studies have dwelt on the determinants of household food insecurity and malnutrition, and data has been analyzed using econometric models, accompanied by recommendations to inform policy decisions. However, the missing middle in most of these research studies has been how to properly locate the most food insecure and malnourished households in geographical areas that have been generally identified as food insecure and having malnourished populations.

2 Seeking Answers with YouthMappers

The question of “where” inspired me to seek answers through my doctoral dissertation, which was focused on agricultural economics, but I had very little background in spatial science. Through my mentors, I got introduced and became part of the YouthMappers Chapter of Texas Tech University, one of the co-founding universities of the network. Serendipitously, the University of Cape Coast in Ghana, my home country, became the first international inaugural chapter to join that year.

Through this experience, I also met members of other YouthMappers chapters in many countries including Kenya, India, and Columbia. That summer, we visited the YouthMappers chapter at the University of Cape Coast in Ghana where we enlisted local support of geographers and others for mapping and input to the research. I then learned more about mapping and spatial analysis, taking a service-learning course that falls to better understand not only how to create but also how to use the open spatial data from OpenStreetMap (OSM).

2.1 Locating Food Insecure and Malnourished Regions

In this research, we explore how a combination of both econometric analysis and spatial technologies brings out the status of household food insecurity and malnutrition in some selected communities in the northern region of Ghana. The study communities are found in the Central Gonja (8°57′N 0°13′W), East Mamprusi (10°26′N 0°37′W), Gushiegu (9°55′N 0°13′W), Mion (9°44′N 0°00′W), Tolon (9°26′N 1°4′W), and Zabzugu (9°17′N 0°22′E) Districts in the northern region. According to the Ghana Ministry of Food and Agriculture (MoFA), it is estimated that about 70% of the population in these communities live in rural communities and their main livelihood activity is agriculture, which mainly involves the production of crops and livestock.

2.2 Laying Baseline Data in Poorly Mapped Food Insecure Regions

Using OSM procedures, my OSM study group members and I at Texas Tech University were able to map out some agricultural-related infrastructure such as roads, houses in farming communities, and water bodies in the northern region of Ghana. I used this approach during my dissertation research to provide geographical location to the households that were found to be food insecure and malnourished in the northern region of Ghana. Figures 4.1 and 4.2 demonstrate the extensive mapping performed with the work of many mappers from Texas, Ghana, and beyond, depicting food insecure, poorly mapped Northern Ghana before and after this work, and the close example of mapping done in rural Gushiegu, respectively. The study presented here takes that baseline information as a starting point to locate the important factors for this particular region (Fig. 4.3 ).

A heat map of new edits demonstrates the extensive contributions of YouthMappers to OSM in Ghana

Prior to this collaborative project, the extent of edits on OSM from YouthMappers throughout the country of Ghana were limited

One example of the detailed contributions of YouthMappers is visible in the before image of Gushiago [sic] (left) compared to Gushiegu (right)

3 Building the Case to Measure and Map Hunger

TheUnited States Department of Agriculture (USDA) defines household food as having the means to always access enough food by all members for an active, healthy life (USDA 2006 ). The Food and Agriculture Organization (FOA) also defines food security as a state that exists when all people, at all times, have physical and economic access to sufficient, safe, and nutritious food that meet their dietary needs and food preferences for an active and healthy life (World Food Summit, 1996 ). The achievement of food security among households, particularly those in rural communities, directly contributes to the attainment of SDG 2 and indirectly to SDG 12. It is noteworthy that across many regions, the overall prevalence of hunger and food insecurity has fallen since the millennium from 14.8% in 2000 to 10.8% in 2018. Despite these reductions in the prevalence of hunger worldwide, increases in hunger levels in particular areas remain high or have increased, where currently the realization of the 2030 targets (SDG 2) by many African countries seems far from being reached.

3.1 Tracking Hunger Through the SDGs

The world is not on track to achieve zero hunger by 2030. If recent trends continue, the number of people affected by hunger would surpass 840 million by 2030. The patterns of food insecurity and hunger are directly linked with rising incidence of malnutrition levels in sub-Saharan African countries, including Ghana, where incidence of malnutrition is currently above 20% (FAO 2020 ).

Malnutrition or malnourishment is a condition that results from continuous eating of diet in which nutrients are either not enough or are too much, which causes health problems and manifested in conditions such as stunting and obesity, particularly among children. The majority of the world’s undernourished 381 million are still found in Asia, while more than 250 million live in Africa, where the number of undernourished is growing faster than anywhere in the world. In 2019, close to 750 million or nearly one in ten people in the world were exposed to severe levels of food insecurity. An estimated 2 billion people in the world did not have regular access to safe, nutritious, and sufficient food in 2019.

According to the World Health Organization (WHO), lack of access to highly nutritious foods is the main cause of malnutrition, a condition that is estimated to contribute to 45% of child deaths. The WHO estimates that 162 million children under 5 years around the world are identified as stunted, 51 million are underweight, while 44 million are overweight or obese because of poor feeding. According to the Ghana Health and Demographic Survey (GHDS), in Northern Ghana, 33% of children are stunted, 11% are underweight, and 6.3% are wasted as compared to the national averages of 19%, 11%, and 5% for stunting, underweight, and wasting, respectively.

Poor nutrition has implications for a child’s development, since a lack of adequate calories and nutrients to sustain normal growth puts children at a greater risk of being vulnerable to diseases and has adverse effects on their physical, cognitive, and mental development. There has been a global effort to achieve food security and improved nutrition, particularly among rural households in developing countries. Despite these efforts, rural household food insecurity and undernourishment have been major developmental challenges to governments of many developing countries including Ghana. In Ghana, the northern region has been identified as the home of many of Ghana’s food insecure and malnourished households, and research has shown that more than 70% of rural households in the northern region of Ghana are food insecure and malnourished.

3.2 Current Indicators of Causes of Food Insecurity and Malnutrition

The measurement of household food insecurity status has been done over the years by food security experts using different indicators. Commonly used indicators of household food insecurity status include the food consumption score (FCS), household dietary diversity score (HDDS), coping strategies index (CSI), household hunger scale (HHS), and household food insecurity and access scale (HFIAS). The HDDS and the FCS are metrics that assess the number of different types of food or food groups that individuals or households consume and the frequency at which they are eaten. The idea here is that the more diverse a meal is, the more likely it is to contain essential calories and nutrients. The difference between HDDS and FCS has been found to be the reference time. While the HDDS uses a 24-h recall for the measurement of food security, the FCS uses 7-day recall for food security measurement. The CSI is an indirect measure of food security and measures the consumption behaviors of households while assessing the frequency and severity of households’ behaviors during the period they do not have enough food or enough resources to purchase food. The HFIAS and the HHS are measures that assess households’ behaviors that show a lack of sufficient and quality food and anxiety over future insecure access to food.

The body mass index (BMI), which is defined as the ratio of the weight (kg) and the square of the height (m), BMI = weight (m)/height 2 (m), has been used by the WHO and other health organizations as the main anthropometric measure of humans. This anthropometric measure is used to indirectly assess the nutritional status of children between the ages of two years and five years. It is used as an indicator of child malnutrition within the sampled households. Research has shown that the assessment of the nutritional status of any population using anthropometric data is the most popular indirect approach since the BMI is mainly sensitive to changes in the food security situation of a household. Furthermore, it is less subject to systematic measurement errors, it can be disaggregated to provide individual-level information, and it is well suitable for monitoring and evaluating program interventions.

The weight-for-height, height-for-age, and weight-for-age outcomes when determined are compared with the World Health Organization (WHO) standards to determine the level of wasting , stunting , and underweight in the sample. The level of wasting within a population provides an indication of acute malnutrition, stunting provides information on chronic malnutrition, and the level of underweight provides indication of both acute and chronic malnutrition, and the three indicators provided information on the prevalence of malnourishment in the population.

3.3 Going Beyond Goals and Indicators Through the Map

Proper identification and targeting of malnourished and food-insecure households in any population are an important step in solving the problem of malnutrition and food insecurity in order to achieve SDG 2. But there is the need to go beyond identifying the causes of food insecurity and malnutrition to properly identify the physical locations and distribution/spread of the food insecure and undernourished households to effectively target them with intervention strategies that make the most sense for the context where they are happening.

This study explores and demonstrates the need to add a spatial dimension to complement the already-existing quantitative and econometric analysis to efficiently tackle the problem of food insecurity and undernutrition. The development and presentation of a spatial visualization of food insecurity and undernourishment are vital to providing insights including how these phenomena are geographically distributed, which otherwise would have been missed by policymakers, development partners, and implementers of food security intervention programs without spatial visualization of these patterns. Spatial visualization of food insecurity and undernourishment using Geographical Information System (GIS) technology is important for the advancement and sustainability of food production and distribution.

Spatial patterns of food insecurity and malnutrition have been widely used in numerous policy and research applications ranging from targeting emergency food aid and food security intervention programs to the assessment of causes of food insecurity and malnourishment (Davies 2003 ). In addition, analyzing food insecurity and undernourishment using spatial visualization provides information that can be more accessible to a wider range of users. A combination of food insecurity estimates and GIS tools to analyze food insecurity and undernourishment is a way of displaying implicit information that may not be apparent from conventional statistical tables.

4 Assessing Household Food Security Status

In this study, we used the household dietary diversity score (HDDS) to measure rural households’ food security status. We used this indicator because the HDDS provides us with a direct outcome food security indicator, which is related to food and nutritional security. In determining the household dietary diversity score, we categorized the food items that households responded to have eaten over the last 24 h into ten food categories including cereals and grains; roots and tubers; vegetables; fruits; meat; eggs; fish and seafood; legumes, nuts, and seeds; milk and milk products; and oils and fats. We scored one for a particular food group if members of that household had consumed any food item belonging to that food category. We scored 0 for a particular category if members of that household had not consumed any food item belonging to that food category during the past 24 h. In this study, we categorized the sampled households into food-secure households and food-insecure households based on the household dietary diversity score obtained by the household.

We used a multistage sampling approach, which included random stratified sampling combined with probability proportionate to size procedure to select households that were used for data collection. At the first stage of sampling, we randomly sampled six out of the ten districts for the data collection. At the final stage, we used the probability proportionate to size procedure to select representative households in each of the district. We used this procedure to select 504 households from the selected communities for the data collection. We collected the data through personal interviews using a semi-structured questionnaire in two periods. We did that to ensure that seasonal variations in household food consumption did not influence data analysis and interpretation. The head of the household and/or partner and the person responsible for preparing meals for the household were the main respondents to the survey questions.

We collected household demographic data and data on households’ meal consumption a day before the household was interviewed. Furthermore, we collected data on the weight and height of children between the ages of two years and five years. Anthropometric measurements on everyone including height and weight were repeatedly measured, and the average values were used for the data analysis. In situations where a household had more than one eligible child, following the WHO guidelines and standards, the average or mean anthropometric Z-scores were calculated and used during the analysis. To ensure that the human rights of the respondents were protected and respected during the data collection, the entire study was reviewed and approved by the Institutional Review Board of Texas Tech University, Lubbock, Texas, with protocol number IRB2017-646. Per the guidelines established, we sought respondents’ consent by getting approval from respondents after we read the respondents’ consent forms to them.

4.1 Determining Malnutrition Status

We used the anthropometric measurements of children between the ages of two and five years as an indicator of the undernourishment status of children of sampled households. Anthropometric indicators are widely used to understand the demographic dynamics of nutritional status within a household, particularly of mothers and infants as they are among the most vulnerable in society. We derived the Z-score for weight-for-age (WAZ), height-for-age (HAZ), and weight-for-height (WHZ) for the sampled children using the 2006 WHO growth standard with ENA software. We used the Z-scores we obtained to determine the prevalence of underweight, stunting, and wasting among the sampled households. In this study, we classified stunting prevalence rate of <20 as low, 20–29 as medium, 30–39 as high, and > = 40 as very high. Similarly, we classified underweight prevalence rate of <10 as low, 10–19 as medium, 20–29 as high, and > = 30 as very high. Similarly, we classified wasting prevalence rate of <5 as low, 5–9 as medium, 10–19 as high, and > = 20 as very high.

4.2 Data Analysis

Based on the household food insecurity estimates (HDDS), we grouped all households into 2 categories including food secure and food insecur e households. We made this categorization because HDDS, unlike the other indicators of household food security status, defines a household as being either food insecure or food secure. We further categorized the dependent variable (HDDS) as 0 for any household that was found to be food insecure and 1 for households that were categorized as food secure. The relationship between the categorical variables including sex of household head, marital status of household head, whether the household owns livestock, whether the household has access to land, and whether the household has other farms, and food insecurity status was determined. We employed the chi-square test to determine the level of significance of the relationship between household food security status and associated categorical variables. We visualized the spatial distribution of food insecurity and malnutrition using ArcGIS 10.1. The household points regarding latitude and longitude that we obtained during the data collection and information of the central point of each household were created. Food insecurity and malnutrition (stunting, wasting, and underweight) were matched to the point of the household.

4.3 Findings

The overall prevalence of food insecurity in the study area was 82%, which signifies that more than half of all the households in the study area were found to be food insecure (Table 4.1 ).

Figure 4.4 shows the spatial distribution of food insecurity among households in the selected communities where we collected the data.

Food insecurity (red) across the region is prevalent across most of the communities in the study

We found the overall prevalence of stunting, wasting, and underweight among the children is 30.2%, 5.8%, and 21.1%, respectively, which is an indication of high prevalence rate of child malnutrition in the study (Table 4.2 ).

The results further show that the sex of the household head, household access to land, livestock ownership status of the household, years of formal education of household head, and household size were found as significant variables associated with household food security status in the study area. The results in Table 4.3 show that we found a positive significant relationship between household access to agricultural land and household food security ( p  = 0.043) suggesting that households with enhanced access to agricultural lands are more likely to be food secure. Further, the results show that households that own livestock are more likely to be food secure ( p  = 0.031). The chi-square test results further show significant relationship between household food security status and stunting, but there is no significant relation between household food security status and underweight and wasting.

The food insecurity map (Fig. 4.4 ) shows that households in the study area are generally not food secure, which can easily be seen from the map without any complex statistical interpretations. Though the situation is generally similar across the study area, the map shows that food insecurity varies from one geographical area to another. Hence, planning for interventions using aggregated data at the district level may reduce the effectiveness of such programs. Spatial analysis of food insecurity can improve food insecurity intervention coverage effectiveness through the identification of specific geographical locations that needs assistance. The map generated can provide valuable information about spatial disparity of household food insecurity that may be relevant to policymakers, development partners, and institutions that work to reduce the incidence of food insecurity among households. Similarly, the level of stunting, underweight, and wasting is high among the East Mamprusi, Tolon, and Zabzugu Districts. This is because household food insecurity has been found to be highly related to the nutritional status of household members.

5 Conclusion

We found that the overall prevalence of household food insecurity and malnutrition is high in places that matter. The results of the analysis show that rural households’ food security status in the northern region of Ghana is negatively influenced by contextual factors including household size. The negative effect of these factors on households in the short term is not in doubt, and in the long run, the life and survival of rural households could be threatened if measures are not put in place to curb the negative effects of these factors.

We further found that spatial variations of food insecurity and malnutrition exist in the study area. Designing food insecurity intervention programs and plans using regional-level evidence and government administration units might mask the true picture of spatial distribution of the problem in local context as shown by the district variation in the food insecurity status revealed by this study. It is, thus, important that program-level planning should consider district-based microlevel variation in allocating resources for intervention to address food insecurity.

A next step in this type of work would be to actively utilize spatial mapping in food security and malnutrition research, refining the methods we innovated here, based upon what is most effective to generate new insights for the region in question. Further study on spatial distribution of food insecurity and malnutrition using time series data at a microlevel is recommended. This will bring out the temporal variation in spatial disparity of household food insecurity under different seasons that will be relevant for further research on the topic and an even greater refinement of policies that promise to reduce hunger and align production and consumption to the needs and demands.

Finally, additional research that treats food security as a continuum rather than the binary model of HDDS that is in wide use by researchers and practitioners would shed further light on additional questions that this work raises. Studying the varied spatial patterns over time would further shed light on the dynamics of food security in this region, both drawing from and building upon both the open spatial data created through this work. In the end, these results will provide more information to recommend how to best tailor food security policies to the varying needs of different households and communities across different districts, generating a missing link between global goals like SDG 2 and SDG 12 to local realities.

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The lancet series nutritional interventions in Ghana: a determinants analysis approach to inform nutrition strategic planning

• A.E. Yawson 1 ,
• E.O. Amoaful 2 ,
• L.K. Senaya 3 ,
• A.O. Yawson 4 ,
• P.K. Aboagye 2 ,
• A.B. Mahama 5 ,
• L. Selenje 5 &
• V. Ngongalah 5  

BMC Nutrition volume  3 , Article number:  27 ( 2017 ) Cite this article

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Malnutrition is a leading cause of mortality and morbidity among children in low- and middle-income countries. Ghana is one of 36 countries with the highest burden of stunting, globally. The aim of this work is to use data driven planning methods to conduct in-depth analysis on the Lancet series nutrition interventions in Ghana to inform nutritional strategic planning.

A mixed methods approach was employed for this national nutritional assessment conducted in May 2016. Quantitative data on nutritional interventions were generated by application of the Determinants Analysis Tool and phenomenological approach was employed to explain the causes of barriers identified. Outputs from the tool were analyzed by simple descriptive statistics and data from group discussions were assessed by thematic content analysis. The base line years for this assessment were 2014 and 2015.

Overall in Ghana, 21.0% of frontline health workers are trained on lactation management and breastfeeding counselling and support, 56.6% of mothers of children 0–2 years initiated breastfeeding within one hour of birth, and 59.4% of mothers with children 0–5 months took iron folate supplementation for 90 or more days during pregnancy. In addition, only 19.9% of children 12–59 months received two doses of vitamin A supplementation in a calendar year, and 32.5% of children 6–59 months with severe acute malnutrition were admitted for treatment at health facilities. In all, among infants 6–8 months old, 6.9% were fed with minimum dietary diversity, 50.6% received age appropriate meal frequency and 21.6% received iron rich diet. Inadequate pre-service and in-service training for staff, low prioritization and coordination (at higher levels) and weak integration of services (at lower levels) were key barriers to nutrition coverage in Ghana.

Data driven analysis and planning based on proven nutritional interventions in Ghana demonstrated gaps and barriers and garnered workable strategies to improve nutrition services.

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Malnutrition is a leading cause of mortality and morbidity among children in low- and middle-income countries [ 1 , 2 ]. In Sub-Saharan Africa, under nutrition constitutes a leading cause of death in children under five years old [ 3 , 4 ]. Multiple interventions and multi-sectoral approach are needed to prevent different aspects of nutritional depletion and deprivation such as severe malnutrition, anaemia, wasting and stunting [ 2 , 5 , 6 ].

The implications and consequences of these nutritional deprivation in later adult life make it imperative for national nutritional programmes to focus on sufficient feeding for individuals, families and communities as well as a responsive health care delivery system [ 3 , 7 , 8 ]. Child malnutrition within a household is greatly influenced by issues at national and regional levels [ 9 ].

Ghana is beset with under nutrition among children, and is one of 36 countries with the highest burden of stunting, globally [ 4 ]. In Ghana 19% of children are stunted, 5% of children are wasted and 11% of children are underweight [ 10 ]. Anaemia as a result of under nutrition among children is another key national challenge, 66% of children aged 6–59 months have some level of anaemia, 37% have moderate levels of anaemia and 2% have severe anaemia [ 10 , 11 ]. National coverage on interventions is relatively low with inadequate engagement of the health sector and other sectors to address food security, food safety and hygiene for sustained improvement [ 12 ].

Current national efforts to reduce nutritional deprivations in Ghana include building capacity of service providers and volunteers in nutritional counselling, and improving knowledge and skill of service providers in the management of severe acute malnutrition [ 12 , 13 ]. In addition, national scale up of infant and young child feeding activities include strengthening health care practices through the Baby-Friendly Hospital Initiative and creating demand for services through participation of local leaders and communities on sociocultural practices around malnutrition [ 12 , 13 ].

Progress has been made over the past decade, as demonstrated in the 2014 Demographic and Health Survey (GDHS) [ 10 ]. Infant mortality declined from 77 deaths per 1,000 live births in 1988 to 41 in 2014 and under-5 mortality declined substantially from 155 to 60 death per 1,000 live births over the same period. These improvements at the national level notwithstanding, inequities exist in status of children by regional and geographical location in the country [ 10 , 11 ]. Under-5 mortality in 2014 ranged from 47 deaths per 1,000 live births in Greater Accra (the capital region) to 111 deaths per 1,000 live births in Northern region (a predominantly rural region), while stunting ranged from 10.4% in Greater Accra to 33.1% in the Northern Region. The improvements in anaemia, stunting and wasting among children in Ghana have generally been uneven, with wide geographical disparities and among different wealth quintiles [ 10 ].

Interestingly, the burden of nutrition in Ghana is not limited to children because adult men and women are affected as well. In adults under- and over nutrition are both demonstrated through national surveys to be a health challenge. Overall, among men and women aged 15–49 years, 6% of women and 10% of men are underweight (Body Mass Index, BMI <18.5), while 40% of women and 16% of men are overweight (BMI > =25.0) [ 10 ].

Major causes and determinants of malnutrition in Ghana include, inadequate diet diversity (insufficient food/nutrient intake and inadequate consumption of foods rich in micronutrients and protein including eggs and legumes); poor environmental, household and individual hygiene including hand hygiene practices; as well as generally poor infant and young child feeding practices [ 12 ].

This assessment of national nutrition interventions based on The Lancet Series-Maternal and Child Nutrition [ 14 , 15 ] was thus intended to determine the status of these proven nutritional interventions in Ghana, identify barriers to service provision and develop workable strategies. It used data driven planning methods to conduct in-depth analysis needed to guide programme scale up and targeting, to improve coverage and scope of proven nutritional interventions.

Mixed methods approach were employed for this national nutritional assessment conducted in May 2016. Quantitative data on nutritional interventions was generated by application of the Determinants Analysis Tool and phenomenological approach was employed to explain the causes of barriers identified. Four participants (Regional Deputy Director of Public Health, Regional Public Health Nurse, Regional Nutrition Officer and Regional Health Information Officer) were purposively selected from each of the ten administrative regions in the Ghana due to their background and role in nutrition services. In addition local facilitators (Nutrition Unit of the Ghana Health Service) and representatives from some development partners (including UNICEF, WHO and World Food Programme (WFP) participated. Ghana has ten administrative regions, and officers from all ten regions were included to provide a nationwide representation. The national assessment was undertaken in the Ashanti Region of Ghana for three days to assess barriers to coverage of nutrition services and included desk review, group discussions and key informants interviews. Facilitators assisted in the desk reviews, conduct of interviews and provided mentoring and coaching on capturing major issues and recording of key points from the group discussion.

Main causes of identified bottlenecks to the nutritional interventions were thematically analysed through a phenomenological approach under service delivery factors and enabling environment factors through group discussions. Phenomenological approach was used to allow a deeper understanding of an event or phenomenon by looking at the story of the group who experienced a shared lived experience or phenomenon [ 16 ]. Six teams made up of regional, national and international personnel were formed in line with the key interventions to provide qualitative explanation and assessment of the outputs from the tool.

The determinants analysis framework

The Determinants Analysis framework is premised on the notion that effective coverage of services is influenced by four main factors or determinants namely: supply, demand, quality and environment. The Ten Determinant Model Tool produces a graphical output (Fig.  1 ) that facilitates identification of the key bottlenecks.

Graphical presentation of the determinants analysis framework. Description of figure: The figure describes the supply side, demand side and effective coverage determinants and how bottlenecks are determined

Overall, the Ten Determinant Model used for this nutrition assessment in Ghana had FOUR Domains with TEN Determinants: Enabling Environment (Social Norms, Management/Coordination, Legislation/Policy and Budget/Expenditures); Supply (Commodities, Human Resource and Access); Demand (Initial Utilisation, Continuous Utilisation/Knowledge) and Effective Coverage/Behaviour [ 17 , 18 ].

Identification of the low bars on the supply-determinant side of the graph as well as a sharp drop from one bar to the next on the demand and quality-determinant side of the graph enables managers to identify and prioritize bottlenecks to the effective health service coverage.

Data collection

The Ten Determinant Model is based on Microsoft Excel sheets linked together with sections for data inputs linked to outputs. Input data produce simple bar graphs that display the determinants of health service coverage and thus facilitate identification of key bottlenecks that influence effective coverage (illustrated in Fig.  1 ). The section for data input is fully linked with the section that displays the outputs. The tool directly utilised data from District Health Information Management System (DHIMS), Multi-Indicator Cluster Survey (MICS) Reports [ 19 ], Demographic and Health Surveys (DHS) [ 20 ] and other sources of data including programme monitoring and review reports. The DHIMS is a tool for collection, validation, analysis, and presentation of aggregate statistical data, tailored to integrated health information management activities in the Ghana Health Service.

Outcome measures and data analysis

Six key nutrition tracer interventions based on The Lancet Series-Maternal and Child Nutrition [ 15 ], were selected as, Community Management of Acute Malnutrition (CMAM), Complementary Feeding, Early Initiation to Breastfeeding, Exclusive Breastfeeding, Vitamin A Supplementation, and Iron-Folic Acid Supplementation . Each intervention was assessed based on a particular delivery platform (as individualized facility based services or schedulable and outreach services or as family oriented practices and community based services) to enable accurate identification of barriers and bottlenecks. In this analysis, minimum acceptable diet for complementary feeding was assessed using two sub components-minimum dietary diversity and age appropriate meal frequency as specified in the Ministry of Health National Nutrition Policy of Ghana [ 12 ]. Each of the six thematic groups had a moderator and a recorder. Data from group discussions were organized and described using manual thematic content analysis for qualitative data. Each group worked on a different theme to eliminated the issue of duplication and repetition of information among the groups. Quantitative data were analyzed with simple descriptive statistics (i.e. proportions and percentages) by Microsoft Excel 2013.

The base line years for this assessment were 2014 and 2015 and national coverage targets were used as the benchmark to assess these coverage indicators. Outputs from the tool were analyzed by simple descriptive statistics such as frequency, proportions and ratios. The determinants analysis principles were applied to identify bottlenecks. Causality analysis were conducted to identify barriers resulting in these bottleneck. This formed the basis in determining strategies to remove the bottlenecks on nutritional deprivation in Ghana.

Ethical Issues

All data used was aggregated data at national level and had no link to individuals. In all cases, documentations and computerized records were kept secure and accessible to persons directly involved in developing the operational plans. The Ghana Health Service Ethics Review Committee and office of the Policy, Planning, Monitoring and Evaluation Division of the Ghana Health Service gave approval for documentation and dissemination of the Determinant Analysis process in Ghana.

The assessment provided quantitative values on the performance of each indicator from outputs of the tool, while qualitative explanation and assessment of identified bottlenecks was through causality analysis and group discussions. Details of the analysis are indicated in a comprehensive descriptive Table (Table  1 ).

Maternal and child undernutrition, consisting of stunting, wasting, and deficiencies of essential vitamins and minerals, was the subject of a Series of papers in The Lancet in 2008 [ 4 , 7 , 14 ]. It was suggested then that, long-term consequences of undernutrition could be reduced through high and equitable coverage of proven nutrition interventions. Included in these were the nutrition specific interventions [ 4 , 7 , 14 ]. Lancet series have been in existence close to eight years and have provided guidance to countries in their quest to improve health and nutrition of children under five years of age and Ghana is no exception. Six of the key nutrition specific interventions were selected for assessment at the national level in Ghana.

The analysis readily highlighted gaps in service coverage quantitatively (from service and survey data) and provided a platform for qualitatively analyzing barriers and bottlenecks in the continuum of care for the newborn. These discussions included key service providers and policy makers with sufficient knowledge of the context of nutritional service delivery in Ghana.

The current national efforts to reduce nutritional deprivations in Ghana outlined in the National Nutrition Policy of 2013 include building capacity of service providers and volunteers in nutritional counselling, improving knowledge and skill of service providers in the management of severe acute malnutrition and a national scale up of infant and young child feeding activities by strengthening health care practices through the Baby-Friendly Hospital Initiative [ 12 , 13 ]. In addition, a number of initiatives and frameworks have been developed and implemented by the national Ministry of Health to address the problem of high under five mortality including the Child Health policy, Millennium Accelerated Framework, Accelerated immunization with introduction of new and additional vaccines, as well as the Global Funded programmes for Malaria, Tuberculosis and HIV [ 12 ].

The challenge has been major gaps in access and utilization of these interventions and the reality however is that, most of these interventions and frameworks have been centrally developed with little attention paid to data driven approaches and causal analysis of barriers at the service delivery level.

To overcome these challenges, the current analysis based the national nutritional strategies on the framework developed by Bezanson and Isenman in 2010 [ 21 ] to provide guidance as Ghana implements the national nutrition policy. The ‘Framework for Action for Scaling Up Nutrition’ in 2010 was based on a broad collaborative effort of the World Bank, United Nations Children Fund (UNICEF), World Health Organization (WHO), World Food Programme (WFP) and a wide range of other international partners from developing countries, with the objective to catalyse actions to move undernutrition toward the centre stage of international discourse [ 21 ].

The action oriented framework from a national discourse (key service providers, policy managers and health managers) is aimed to garner local and governmental support to improving stunting, wasting and anaemia, and reduce inequities- in a setting situated among the lowest 32 countries with such health indices [ 4 ].

A summary of the key elements of the framework is provided as a template and shown in Table  2 .

Key lessons learned

Good quality data are essential for effective planning and analysis, and data validation to improve data quality and accuracy is a core activity that should be undertaken by all levels of the health system. The Determinant Analysis process enables health managers and frontline health workers to readily identify gaps, undertake a causal analysis of the barriers and identify solutions. It garners a data driven approach to implementation even at the lowest level.

Study limitation

The purpose of the study was to assess nutrition services and therefore role of service providers at the basic level (street level bureaucrats) is essential and should have been involved in the assessment. However, critical issues from the basic level were captured by regional officers who work directly with these street level bureaucrats. In addition, the Ten Determinant Model used for the assessment did not explicitly capture indicators on policy, legal, social norms and budget-related factors that shape the determinants of health service coverage. However, these cross-cutting factors were systematically considered as part of analyzing each identified bottleneck during the causal analysis.

Overall in Ghana, there were gaps and insufficient coverage for the six Lancet nutrition specific interventions assessed. Inadequate pre-service and in-service training for staff, low prioritization and coordination (at higher levels) and weak integration of services (at lower levels) were key barriers to nutrition coverage in Ghana. Data driven analysis and planning based on proven nutritional interventions demonstrated gaps and barriers and garnered workable strategies (framework to guide implementation) to improve nutrition services.

Specific recommendations for improving nutrition services include, districts, sub-districts and community level systems being supported with basic commodities to provide the full complement of services. Involvement of all health care workers at districts, sub-districts and community levels and community based volunteers, as well as involvement of health facility managers, stakeholders and partners will be key. Dissemination of all existing and new policies and field guides to the lower levels will be essential. National and sub-national levels should improve data validation activities through regular monitoring and coordination.

Abbreviations

Community based volunteers

Community Health Workers

Community-based Health Planning Services

District Health Information Management System

Exclusive breast feeding

Iron and Folate Supplementation

Infant and young child feeding

Severe acute malnutrition

United Nations Children Fund

Vitamin A Supplementation

World Food Programme

World Health Organization

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Acknowledgement

A special gratitude is extended to all who contributed towards this process. Acknowledgement is specifically made of the support from UNICEF, and officers and staff of Family Health Division of the Ghana Health Service. We acknowledge the contribution of all the regional directors of health and national and international facilitators, for the effort expended in making the assessment successful.

No source of funding was available for the research.

Availability of data and materials

Available Data used for the analysis and assessment have been included and presented as a supplementary material during submission.

Authors' contributions

AEY, LKS and AOY developed the concept, AEY, and LKS are members of the National Determinant Analysis Facilitation Team for Ghana Health Service and UNICEF and were involved in the analysis in Ghana. AEY, AOY, PKA, EOA, LKS, ABM, LS and VN contributed to the writing and reviewing of various sections of the manuscript. All the authors reviewed the final version of the manuscript before submission. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interest. The views expressed in this paper are those of the authors. No official endorsement by the Ministry of Health of Ghana/Ghana Health Service is intended or should be inferred.

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All data used were aggregated data at regional and national and had no link to individuals. In all cases, documentations and computerized records were kept secure and accessible to persons directly involved in the analysis. The Ghana Health Service Ethics Review Committee and office of the Policy, Planning, Monitoring and Evaluation Division of the Ghana Health Service gave approval for documentation and dissemination of the Determinant Analysis process in Ghana.

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L.K. Senaya

Department of Child Health, Korle-Bu Teaching Hospital, Accra, Ghana

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Yawson, A., Amoaful, E., Senaya, L. et al. The lancet series nutritional interventions in Ghana: a determinants analysis approach to inform nutrition strategic planning. BMC Nutr 3 , 27 (2017). https://doi.org/10.1186/s40795-017-0147-1

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DOI : https://doi.org/10.1186/s40795-017-0147-1

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Childhood Malnutrition and Its Determinants among Under-Five Children in Ghana

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• PMID: 26332093
• DOI: 10.1111/ppe.12222

Background: Childhood malnutrition adversely affects short- and long-term health and economic well-being of children. Malnutrition is a global challenge and accounts for around 40% of under-five mortality in Ghana. Limited studies are available indicating determinants of malnutrition among children. This study investigates prevalence and determinants of malnutrition among children under-five with the aim of providing advice to policymakers and other stakeholders responsible for the health and nutrition of children.

Methods: The study used data from the 2008 Ghana Demographic and Health Survey (GDHS). Analyses were conducted on 2083 children under 5 years old nested within 1641 households with eligible anthropometric measurements, using multilevel regression analysis. Results from the multilevel models were used to compute probabilities of malnutrition.

Results: This study observed that 588 (28%), 276 (13%), and 176 (8%) of the children were moderately 'stunted', moderately 'underweight', and moderately 'wasted' respectively. Older ages are associated with increased risk of stunting and underweight. Longer breast-feeding duration, multiple births, experience of diarrhoeal episodes, small size at birth, absence of toilet facilities in households, poor households, and mothers who are not covered by national health insurance are associated with increased risk of malnutrition. Increase in mother's years of education and body mass index are associated with decreased malnutrition. Strong residual household-level variations in childhood nutritional outcomes were found.

Conclusion: Policies and intervention strategies aimed at improving childhood nutrition and health should address the risk factors identified and the need to search for additional risk factors that might account for the unexplained household-level variations.

Keywords: childhood malnutrition; developing countries; epidemiology; malnutrition determinants; multilevel modelling; nutritional status; public health; under-five children.

© 2015 John Wiley & Sons Ltd.

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