difference between research study and case study

Action Research vs. Case Study

What's the difference.

Action research and case study are both research methodologies used in social sciences to investigate and understand complex phenomena. However, they differ in their approach and purpose. Action research is a collaborative and participatory approach that involves researchers and practitioners working together to identify and solve practical problems in real-world settings. It aims to bring about positive change and improvement in the context being studied. On the other hand, case study is an in-depth and detailed examination of a particular individual, group, or situation. It focuses on understanding the unique characteristics and dynamics of the case being studied and often involves extensive data collection and analysis. While action research emphasizes practical application and problem-solving, case study emphasizes detailed exploration and understanding of a specific case.

Further Detail

Introduction.

Action research and case study are two widely used research methodologies in various fields. While both approaches aim to gain insights and understanding, they differ in their focus, design, and implementation. This article will explore the attributes of action research and case study, highlighting their similarities and differences.

Action Research

Action research is a participatory approach that involves collaboration between researchers and practitioners to address real-world problems. It emphasizes the active involvement of stakeholders in the research process, aiming to bring about practical change and improvement. Action research typically follows a cyclical process, consisting of planning, action, observation, and reflection.

One of the key attributes of action research is its focus on generating knowledge that is directly applicable to the context in which it is conducted. It aims to bridge the gap between theory and practice by actively involving practitioners in the research process. This participatory nature allows for a deeper understanding of the complexities and nuances of the problem being investigated.

Action research often involves multiple iterations, with each cycle building upon the insights gained from the previous one. This iterative approach allows for continuous learning and adaptation, enabling researchers to refine their interventions and strategies based on the feedback received. It also promotes a sense of ownership and empowerment among the participants, as they actively contribute to the research process.

Furthermore, action research is characterized by its emphasis on collaboration and co-learning. It encourages the exchange of ideas and knowledge between researchers and practitioners, fostering a sense of shared responsibility and collective action. This collaborative approach not only enhances the quality of the research but also increases the likelihood of successful implementation of the findings.

In summary, action research is a participatory and iterative approach that aims to generate practical knowledge through collaboration between researchers and practitioners. It focuses on addressing real-world problems and promoting positive change within specific contexts.

Case study, on the other hand, is an in-depth investigation of a particular phenomenon, event, or individual. It involves the detailed examination of a specific case or cases to gain a comprehensive understanding of the subject under study. Case studies can be conducted using various research methods, such as interviews, observations, and document analysis.

One of the key attributes of case study research is its ability to provide rich and detailed insights into complex phenomena. By focusing on a specific case, researchers can delve deep into the intricacies and unique aspects of the subject, uncovering valuable information that may not be easily captured through other research methods.

Case studies are often used to explore and understand real-life situations in their natural settings. They allow researchers to examine the context and dynamics surrounding the case, providing a holistic view of the phenomenon under investigation. This contextual understanding is crucial for gaining a comprehensive and nuanced understanding of the subject.

Furthermore, case studies are particularly useful when the boundaries between the phenomenon and its context are not clearly defined. They allow for the exploration of complex and multifaceted issues, enabling researchers to capture the interplay of various factors and variables. This holistic approach enhances the validity and reliability of the findings.

Moreover, case studies can be exploratory, descriptive, or explanatory in nature, depending on the research questions and objectives. They can be used to generate hypotheses, provide detailed descriptions, or test theoretical frameworks. This versatility makes case study research applicable in various fields, including psychology, sociology, business, and education.

In summary, case study research is an in-depth investigation of a specific phenomenon, providing rich and detailed insights into complex situations. It focuses on understanding the context and dynamics surrounding the case, allowing for a comprehensive exploration of multifaceted issues.

Similarities

While action research and case study differ in their focus and design, they also share some common attributes. Both approaches aim to gain insights and understanding, albeit through different means. They both involve the collection and analysis of data to inform decision-making and improve practice.

Furthermore, both action research and case study can be conducted in naturalistic settings, allowing for the examination of real-life situations. They both emphasize the importance of context and seek to understand the complexities and nuances of the phenomena under investigation.

Moreover, both methodologies can involve multiple data collection methods, such as interviews, observations, and document analysis. They both require careful planning and design to ensure the validity and reliability of the findings.

Additionally, both action research and case study can contribute to theory development. While action research focuses on generating practical knowledge, it can also inform and contribute to theoretical frameworks. Similarly, case studies can provide empirical evidence that can be used to refine and expand existing theories.

In summary, action research and case study share common attributes, including their aim to gain insights and understanding, their focus on real-life situations, their emphasis on context, their use of multiple data collection methods, and their potential contribution to theory development.

Action research and case study are two distinct research methodologies that offer unique approaches to gaining insights and understanding. Action research emphasizes collaboration, participation, and practical change, while case study focuses on in-depth investigation and contextual understanding. Despite their differences, both approaches contribute to knowledge generation and have the potential to inform theory and practice. Researchers should carefully consider the nature of their research questions and objectives to determine which approach is most suitable for their study.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 29 April 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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Difference Between Case Study and Research

Table of Contents

Key Difference

Case studies and research are both valuable tools in academic and professional fields but serve different purposes and methodologies. A case study is a detailed examination of a specific instance, situation, or individual, often used to explore complex issues in real-world settings. It provides in-depth insights into a particular subject.

Research, on the other hand, is a broader term that encompasses the systematic investigation and study of materials and sources to establish facts and reach new conclusions. It often involves hypothesis testing, data collection, and analysis across a wider scope.

Comparative Analysis

• Case Study : Concentrates on a specific instance or example.
• Research : Broader inquiry, exploring a general or specific topic.
• Case Study : Qualitative, narrative analysis, in-depth investigation.
• Research : Can be qualitative or quantitative, involves systematic data collection and analysis.
• Case Study : To provide a detailed understanding, and explore the nuances of a single case.
• Research : To discover, interpret, or revise facts, theories, and applications.
• Case Study : Findings are often not generalizable.
• Research : Aims for findings that can be generalized or applied broadly.
• Case Study : Used in business, psychology, social sciences for practical insights.
• Research : Applied across all scientific and scholarly disciplines.

Table Summary of Case Study vs Research

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Home » Education » Difference Between Case Study and Phenomenology

Difference Between Case Study and Phenomenology

Main difference – case study vs phenomenology.

Case study and phenomenology are two terms that are often used in the field of social science s and research. Both these terms refer to types of research methods ; however, phenomenology is also a concept in philosophical studies. As a research methodology, the main difference between case study and phenomenology is that case study is an in-depth and detailed investigation of the development of a single event, situation, or an individual over a period of time whereas phenomenology is a study that is designed to understand the subjective, lived experiences and perspectives of participants.

In this article, we will be discussing,

     1. What is a Case Study           – Definition, Use, Data Collection, Limitations      2. What is Phenomenology           – Definition, Use, Data Collection, Limitations      3. What is the difference between Case Study and Phenomenology

What is a Case Study

A case study is defined as “an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used” (Yin,1984).  In simple terms, it is an in-depth and detailed investigation of the development of a single event, situation, or an individual over a period of time. Case studies are often used to explore and unearth complex issues such as social issues, medical conditions, etc. Many researchers use case study method to explore social issues like prostitution, drug addiction, unemployment, and poverty. Case studies can be qualitative and/or quantitative in nature.

A case study commences with identifying and defining the research problem; then the researcher has to select the cases and decide techniques for data collection and analysis. This is followed by collecting data in the field and evaluating and analyzing the data. The final step in a case study involves preparing the research report.  Data collection methods in a case study involve observations, questionnaires, interviews, analysis of recorded data, etc. A successful case study is always context-sensitive, holistic, systematic, layered and comprehensive.

Case studies are sometimes classified into three categories known as exploratory, descriptive and explanatory case studies. Ethnographies are also considered as a type of case studies.

Although case studies offer detailed and in-depth information about a particular phenomenon, it is difficult to use this information to form generalization since they only focus on a single phenomenon.

Figure 1: Questionnaires can be used to collect data for case studies.

What is Phenomenology

Phenomenology is both a philosophy and a research method. As a philosophical study, phenomenology refers to the study of the structures of experience and consciousness. In the field of research, it refers to a study that is designed to understand the subjective, lived experiences and perspectives of participants. Phenomenology is based on the principle that a single experience can be interpreted in multiple ways and that reality consists of each participant’s interpretation of the said experience. Thus, phenomenology provides information about unique individual experiences, offering a rich and complete description of human experiences and meanings.

Data is collected in phenomenology through long and intensive, semi-structured or unstructured personal interviews. The researcher may also have to conduct several interview sessions with each participant since phenomenology relies heavily on interviews. However, the information gathered through these interviews may also depend on the interviewing skills of the researcher and the articulate skills of the participants. This is a limitation of this method.

Figure 2: Phenomenology often involves long personal interviews.

Case Study: Case study is an in-depth and detailed investigation of the development of a single event, situation, or an individual over a period of time.

Phenomenology: Phenomenology is a study that is designed to understand the subjective, lived experiences and perspectives of participants.

Data Collection

Case Study: Data collection methods include observations, interviews, questionnaires, etc.

Phenomenology: Interviews are the main method of data collection.

Case Study: Case studies focus on a single incident, event, organization, or an individual.

Phenomenology: Phenomenology focus on various individuals and their experiences.

Limitations

Case Study: The information obtained from a case study cannot be used to make generalizations.

Phenomenology: Information relies heavily on the interviewing skills of the researcher and the articulate skills of the participants.

Reference: 1. Yin, Robert. “Case study research. Beverly Hills.” (1984).

Image Courtesy: 1. “5 Candidates reading a questionnaire Photo Tony Ntumba MONUSCO” by MONUSCO Photos (CC BY-SA 2.0) via Flickr 2. “1702648” (Public Domain) via Pixabay

About the Author: Hasa

Hasanthi is a seasoned content writer and editor with over 8 years of experience. Armed with a BA degree in English and a knack for digital marketing, she explores her passions for literature, history, culture, and food through her engaging and informative writing.

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Difference between Case Study and Action research

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1. Action Research : Action Research is a type of qualitative research. As the name suggests it is more action oriented in order to solve an immediate problem. Action research helps the researcher to improvise its current practices and is applied for researching into issues. It aims to learn through action leading to personal or professional development means focuses on improving and/or refining actions. This type research generally used in field of education to bridge the gap between educational theory and professional practice by improvising their current practices. This helps in observing the problem and identifying the cause and then addressing the issue so mainly it is more focused on immediate addressing to practical problems and in generating knowledge to produce change.

2. Case Study : Case study research refers to an in-depth examination of a particular event or individual or a group of individuals. It is more of a qualitative method of research where it understand complex issues by deeply observing and analyzing the event or situation by collecting and reporting the data related to the event or situation. Case study research is more towards description rather than immediate cause and effect finding. Case study is categorized into three ways i.e., exploratory, explanatory and descriptive based on research method. These studies involve both quantitative and qualitative data. This type of research can be used to address community-based problems like illiteracy, unemployment, poverty, and drug addiction.

Difference between Case study and Action Research :

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Action Research vs Case Study : Know the Key Difference Between Two Qualitative Research Methods

A research method is nothing but a technique of inquiry which proceeds from the underlying philosophical assumptions to research design and data collection. Specific research methods imply various assumptions, skills, research practices and the choice of research approach influences the manner in which the data is collected. 

Among various research methods, the most popular and widely used design is qualitative research. This design consists of many philosophical perspectives and various research methods, of which includes  action research and case study research.

Action Research

Action research is a type of qualitative research, which is adopted by the researcher in order to solve the immediate problem arisen during the particular course of time. It is a way which bridges the gap between educational theory and professional practice by improvising their current practices. This type of research helps the researcher to improvise its current practices and is applied for researching into issues.

The main purpose of action research is to learn through action leading to personal or professional development. It enables researchers not only to suggest appropriate lines of action but also to investigate the actual effects of such actions. Further, this type of research is situation based, is useful in problem-solving and deals with individuals or groups with a common purpose of improving practice.

Action research is conducted in classrooms and organisations, where the practitioner will observe what happens and then identify an issue or problem that they need to address. Further according to the issues, ways to solve the problems are identified and applied by the practitioner in their practices. This approach is applied using qualitative designs to explain what is happening and to understand the effects of some educational intervention. 

Further, this research helps in addressing practical problems and in generating knowledge to produce change.

Methods used in collecting data in action research are:

• Observing individuals or groups
• Using audio and videotape recording
• Using structured or semi-structured interviews
• Taking field notes
• Conducting surveys or questionnaires

Case study research is more of a qualitative method of research where there is an in-depth study of an individual or a group of individuals. It explores a contemporary prodigy within its real-life context and provides an organised way of observing the events, collecting data, analysing information, and reporting the results.

Further, the case study method focuses on the description or exploration of a particular phenomenon, rather than identifying the cause and effect. This method includes both quantitative and qualitative data and allow the researchers to see beyond statistical results and understand human conditions like illiteracy, poverty, etc. 

Case studies is categorised in 3 ways: exploratory, explanatory and descriptive.

Exploratory case studies explore any event in the data which serves as a point of interest to the researcher. For example, a researcher conducting an exploratory case study on an individual’s learning process may ask questions, such as, “Does a student use any strategies when he learns a text?” This type of question results in further examination of the phenomenon. 

On the other hand, the explanatory case study examines the data carefully and explains the phenomenon occurred in the data.

Descriptive case studies describe the natural phenomena which occur within the data. For example, what are the strategies used by the learner?, etc. 

Case studies are useful as they help the researcher to analyse the data at a small level but there is a  tendency for the researcher to be biased at the time of interpreting the data. 

Methods used in collecting the data in the case study method are:

• Interviews, transcript analyses or protocol 
• An exploration of artifacts.
• A review of documents and archived record
• Direct participant observations
• Field studies

Difference between action research and case study

At times people confuse the action research method with that of case study as both are a little bit similar to each other. But in real-time, they are quite different.

• Action research focuses on solving the immediate problem whereas, case studies focus on a particular phenomenon for a longer period of time.
• Action research method emphasis on solving the problem whereas case study method emphasis on observing, analysing and interpreting a particular phenomenon or scenario.
• Researcher at times can also be the part of the action research whereas in case study researcher don’t take part in the research.

Now that you know the difference between the two approaches, choose the method accordingly and accomplish your research.

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• Multiple adverse...

Multiple adverse outcomes associated with antipsychotic use in people with dementia: population based matched cohort study

Linked editorial.

Use of antipsychotics in adults with dementia

• Related content
• Peer review
• Pearl L H Mok , research fellow 1 2 ,
• Matthew J Carr , research fellow 1 2 3 ,
• Bruce Guthrie , professor 4 ,
• Daniel R Morales , Wellcome Trust clinical research fellow 5 ,
• Aziz Sheikh , professor 6 7 ,
• Rachel A Elliott , professor 3 8 ,
• Elizabeth M Camacho , senior research fellow 8 ,
• Tjeerd van Staa , professor 9 ,
• Anthony J Avery , professor 3 10 ,
• Darren M Ashcroft , professor 1 2 3
• 1 Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, University of Manchester, Manchester, M13 9PT, UK
• 2 Manchester Academic Health Science Centre, Manchester, UK
• 3 NIHR Greater Manchester Patient Safety Research Collaboration, University of Manchester, Manchester, UK
• 4 Advanced Care Research Centre, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
• 5 Population Health and Genomics, University of Dundee, Dundee, UK
• 6 Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
• 7 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
• 8 Manchester Centre for Health Economics, Division of Population Health, Manchester, UK
• 9 Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
• 10 Centre for Primary Care, School of Medicine, University of Nottingham, Nottingham, UK
• Correspondence to: P L H Mok pearl.mok{at}manchester.ac.uk
• Accepted 29 February 2024

Objective To investigate risks of multiple adverse outcomes associated with use of antipsychotics in people with dementia.

Design Population based matched cohort study.

Setting Linked primary care, hospital and mortality data from Clinical Practice Research Datalink (CPRD), England.

Population Adults (≥50 years) with a diagnosis of dementia between 1 January 1998 and 31 May 2018 (n=173 910, 63.0% women). Each new antipsychotic user (n=35 339, 62.5% women) was matched with up to 15 non-users using incidence density sampling.

Main outcome measures The main outcomes were stroke, venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury, stratified by periods of antipsychotic use, with absolute risks calculated using cumulative incidence in antipsychotic users versus matched comparators. An unrelated (negative control) outcome of appendicitis and cholecystitis combined was also investigated to detect potential unmeasured confounding.

Results Compared with non-use, any antipsychotic use was associated with increased risks of all outcomes, except ventricular arrhythmia. Current use (90 days after a prescription) was associated with elevated risks of pneumonia (hazard ratio 2.19, 95% confidence interval (CI) 2.10 to 2.28), acute kidney injury (1.72, 1.61 to 1.84), venous thromboembolism (1.62, 1.46 to 1.80), stroke (1.61, 1.52 to 1.71), fracture (1.43, 1.35 to 1.52), myocardial infarction (1.28, 1.15 to 1.42), and heart failure (1.27, 1.18 to 1.37). No increased risks were observed for the negative control outcome (appendicitis and cholecystitis). In the 90 days after drug initiation, the cumulative incidence of pneumonia among antipsychotic users was 4.48% (4.26% to 4.71%) versus 1.49% (1.45% to 1.53%) in the matched cohort of non-users (difference 2.99%, 95% CI 2.77% to 3.22%).

Conclusions Antipsychotic use compared with non-use in adults with dementia was associated with increased risks of stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury, but not ventricular arrhythmia. The range of adverse outcomes was wider than previously highlighted in regulatory alerts, with the highest risks soon after initiation of treatment.

Introduction

Dementia is a clinical syndrome characterised by progressive cognitive decline and functional disability, with estimates suggesting that by 2050 around 152.8 million people globally will be affected. 1 Behavioural and psychological symptoms of dementia are common aspects of the disease and include features such as apathy, depression, aggression, anxiety, irritability, delirium, and psychosis. Such symptoms can negatively impact the quality of life of patients and their carers and are associated with early admission to care. 2 3 Antipsychotics are commonly prescribed for the management of behavioural and psychological symptoms of dementia, despite longstanding concerns about their safety. 4 5 6 During the covid-19 pandemic, the proportion of people with dementia prescribed antipsychotics increased, possibly owing to worsened behavioural and psychological symptoms of dementia linked to lockdown measures or reduced availability of non-pharmaceutical treatment options. 7 According to guidelines from the UK’s National Institute for Health and Care Excellence, antipsychotics should only be prescribed for the treatment of behavioural and psychological symptoms of dementia if non-drug interventions have been ineffective, if patients are at risk of harming themselves or others or are experiencing agitation, hallucinations, or delusions causing them severe distress. 8 Antipsychotics should at most be prescribed at the lowest effective dose and for the shortest possible time. Only two antipsychotics, risperidone (an atypical, or second generation, antipsychotic) and haloperidol (a typical, or first generation, antipsychotic), are licensed in the UK for the treatment of behavioural and psychological symptoms of dementia, 9 although others have been commonly prescribed off-label. 5 10

Based on evidence from clinical trials of risperidone, the US Food and Drug Administration (FDA) first issued a warning in 2003 about the increased risks of cerebrovascular adverse events (eg, stroke, transient ischaemic attack) associated with use of atypical antipsychotics in older adults with dementia. 11 A meta-analysis of 17 trials among such patients subsequently found a 1.6-1.7-fold increased risk of mortality with atypical antipsychotics compared with placebo, which led the FDA to issue a “black box” warning in 2005 for all atypical antipsychotics. 11 This warning was extended to typical antipsychotics in 2008, after two observational studies reported that the risk of death associated with their use among older people might be even greater than for atypical antipsychotics. 12 13 14 The increased risks for stroke and mortality have been consistently reported by many observational studies and meta-analyses since, 11 15 16 17 18 19 20 21 and they have led to regulatory safety warnings and national interventions in the UK, US, and Europe, aiming to reduce inappropriate prescribing of these drugs for the treatment of behavioural and psychological symptoms of dementia. 8 11 22 23 24 25 26 Other adverse outcomes have also been investigated in observational studies, 27 28 29 although, with the exception of pneumonia, 14 30 31 32 the evidence is less conclusive or is more limited among people with dementia. For example, inconsistent or limited evidence has been found for risks of myocardial infarction, 33 34 ventricular arrhythmia, 35 36 venous thromboembolism, 37 38 39 40 fracture, 41 42 43 and acute kidney injury. 44 45 46 Most studies also reported only one outcome or type of outcomes. Examining multiple adverse events in a single cohort is needed to give a more comprehensive estimate of the total potential harm associated with use of antipsychotics in people with dementia.

Using linked primary and secondary care data in England, we investigated the risks of a range of adverse outcomes potentially associated with antipsychotic use in a large cohort of adults with dementia—namely, stroke, venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury. We report both relative and absolute risks.

Data sources

The study used anonymised electronic health records from Clinical Practice Research Datalink (CPRD). In the UK, residents are required to be registered with a primary care general practice to receive care from the NHS. The NHS is a publicly funded healthcare service, free at the point of use. More than 98% of the UK population are registered with a general practice, and their electronic health records are transferred when they change practice. 47 48 Community prescribing is most often done by the general practitioner, including antipsychotic treatment recommended by specialists. CPRD data are sourced from more than 2000 general practices covering around 20% of the UK population, and include information on diagnoses, primary healthcare contacts, prescribed drugs, laboratory test results, and referrals to secondary healthcare services. 47 48 CPRD contains two databases: Aurum and GOLD. CPRD Aurum includes data from contributing general practices in England that use the EMIS Web patient management software, and CPRD GOLD consists of patient data from practices across all four UK nations that use the Vision system. Both datasets are broadly representative of the UK population. 47 48 49 Primary care data from general practices in England can be linked to other datasets, including hospital admissions in Hospital Episode Statistics, and mortality and index of multiple deprivation data from the Office for National Statistics (ONS). Individual patients can opt-out of sharing their records with CPRD, and individual patient consent was not required as all data were deidentified.

Study population

We delineated two cohorts, one each from Aurum and GOLD. For the latter, we included patients from English practices only because linkage to hospital admission and mortality data were required in our analyses. To ensure that the study dataset would not contain any duplicate patient records, we used the bridging file provided by CPRD to identify English practices that have migrated from the GOLD to the Aurum dataset, and removed such practices from the GOLD dataset. For both cohorts, we included patients who had a first dementia diagnosis code between 1 January 1998 and 31 May 2018. Dementia was identified from Read, SNOMED, or EMIS codes used in the databases (see supplementary appendix). We defined the date of first dementia diagnosis as the date of first dementia code. Patients needed to be aged 50 years or over at the time of dementia diagnosis, have been registered with the CPRD practice for at least a year, not be prescribed an antipsychotic in the 365 days before their first dementia code, and have records that were eligible for linkage to Hospital Episodes Statistics, mortality, and index of multiple deprivation data. In addition, because anticholinesterases (such as donepezil, rivastigmine, and galantamine) may sometimes be prescribed to patients showing signs of dementia before their first dementia code, we excluded patients with an anticholinesterase prescription before their first dementia code. Supplementary figures S1 and S2 show how the two cohorts for Aurum and GOLD, respectively, were delineated.

Study design

Matched cohort design —We implemented a matched cohort design. Supplementary figure S3 shows the study design graphically. 50 For the Aurum and GOLD cohorts separately, patients who used antipsychotics were defined as patients in each cohort issued with an antipsychotic prescription after (or on the same day as) the date of their first dementia diagnosis, with the date of first antipsychotic prescription being the index date after which outcomes were measured. For each outcome, follow-up began from the date of the first antipsychotic prescription (the index date) and ended on the earliest of date of first diagnosis of outcome (ie, the earliest recording of the outcome whether it was from the patient’s primary or secondary care or mortality records), death, transfer out of the general practice, last data collection date of the general practice, two years from the date of antipsychotics initiation, or 31 May 2018. Because patients who have experienced an outcome were potentially at higher risk of subsequently experiencing the same event, which could confound any risks associated with antipsychotic use, we excluded those with a history of the specific outcome under investigation before the index date from the analysis of that outcome. For example, we excluded patients with a record of stroke before the index date from the analysis of stroke, but they would still be eligible for the study of other outcomes. For the analysis of acute kidney injury, patients with a diagnosis of end stage kidney disease before the index date were also excluded, and a diagnosis of end stage kidney disease after the index date was an additional condition for end of follow-up. 44

Matched comparators —Each patient who used antipsychotics on or after the date of their first dementia diagnosis was matched using incidence density sampling with up to 15 randomly selected patients who had the same date of first dementia diagnosis (or up to 56 days after) and who had not been prescribed an antipsychotic before diagnosis. Incidence density sampling involves matching on sampling time, with each antipsychotic user in our study being matched to one or more comparators who were eligible for an antipsychotic but had not become a user at the time of matching. 51 The selection of comparators was done with replacement—that is, an individual could be used as a comparator in multiple matched sets. In our study, this meant that patients were eligible to be a non-user matched comparator up to the date of their first antipsychotic prescription. We excluded matched comparators with a history of the specific outcome under investigation before the index date from the analysis of that event. For each outcome, follow-up of matched comparators began on the same day as the patient to whom they were matched (the index date) and ended on the earliest of date of their first antipsychotic prescription (if any), or date of one of the end of follow-up events described earlier for the antipsychotic users.

Use of antipsychotics

We included both typical and atypical antipsychotics, identified by product codes in Aurum and GOLD (see supplementary appendix for list of drugs included). Senior author DMA (pharmacist) reviewed the code lists. As previous studies have shown a temporal association between antipsychotic use and development of adverse outcomes, 30 31 52 we treated use of antipsychotics as a time varying variable, classified as current, recent, and past use. Current use was defined as the first 90 days from the date of an antipsychotic prescription, recent use as up to 180 days after current use ended, and past use as the time after the recent use period had ended. If a patient was issued another prescription during the 90 days after their last prescription, their current use period would be extended by 90 days from the date of their latest prescription. For example, if a patient had two prescriptions and the second was issued 60 days after the first, their current use period would be a total of 150 days: 60 days after the first prescription plus 90 days after the second. At the end of the 150 days current use period, the next 180 days would be the recent use period, and the time after this recent use period would be past use. As patients could have multiple prescriptions over time, they could move between the three antipsychotic use categories during follow-up, and they could therefore be defined as current, recent, or past users more than once. See the supplementary appendix for further information on how this definition is applied.

In post hoc analyses, we also investigated typical versus atypical antipsychotics, and specific drug substances: haloperidol, risperidone, quetiapine, and other antipsychotics (as a combined category).

Outcomes were stroke, venous thromboembolism (including deep vein thrombosis and pulmonary embolism), myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury. With the exceptions of pneumonia and acute kidney injury, outcomes were identified by Read, SNOMED, or EMIS codes in the primary care records, and by ICD-10 (international classification of diseases, 10th revision) codes from linked secondary care data from Hospital Episodes Statistics, and cause of death data from the ONS mortality records. For pneumonia and acute kidney injury, we only included those that were diagnosed in hospitals or as a cause of death, ascertained from Hospital Episodes Statistics and ONS data.

We also investigated appendicitis and cholecystitis combined as an unrelated (negative control) outcome to detect potential unmeasured confounding. 53 These outcomes were chosen because evidence of an association with antipsychotic use is lacking from the literature. We identified appendicitis and cholecystitis from Read, SNOMED, EMIS, and ICD-10 codes. Clinicians (BG, AJA, DRM) checked all code lists (see supplementary appendix).

We used propensity score methods to control for imbalances in measurable patient characteristics between antipsychotic users and their matched non-users, with personal characteristics, lifestyle, comorbidities, and prescribed drugs included in the propensity score models. A counterfactual framework for causal inference was applied to estimate the average treatment effect adjusting for inverse probability of treatment weights generated from the propensity score models. 54 55 Selection of covariates was informed by the literature, based on their potential associations with antipsychotic initiation and study outcomes. 31 34 44 56 57 All variables were assessed before the index date (see supplementary figure S3). Variables for personal characteristics included sex, age at dementia diagnosis, age at start of follow-up, ethnicity, and index of multiple deprivation fifths based on the location of the general practice. Comorbidities were derived as dichotomous variables and included a history of hypertension, types 1 and 2 diabetes mellitus, chronic obstructive pulmonary disease, rheumatoid arthritis, moderate or severe renal disease, moderate or severe liver disease, atrial fibrillation, cancer, and serious mental illness (bipolar disorders, schizophrenia, schizoaffective disorders, and other psychotic disorders). Lifestyle factors included smoking status and alcohol use. Medication covariates were represented as dichotomous indicators, defined by at least two prescriptions for each of the following drugs in the 12 months before the index date: antiplatelets, oral anticoagulants, angiotensin converting enzyme inhibitors or angiotensin II receptor blockers, alpha blockers, beta blockers, calcium channel blockers, diuretics, lipid lowering drugs, insulin and antidiabetic drugs, non-steroidal anti-inflammatory drugs, antidepressants, benzodiazepines, and lithium. We also included the following potential confounders for the investigations of venous thromboembolism and fracture: prescriptions for hormone replacement therapy and selective oestrogen receptor modulators (for venous thromboembolism), 58 59 a history of inflammatory bowel disease (for pneumonia and fracture), 60 61 and prescriptions for immunosuppressants, oral corticosteroids, and inhaled corticosteroids (for pneumonia). 62 63

Statistical analysis

For each patient included in the study, we derived a propensity score representing the patient’s probability of receiving antipsychotic treatment. Propensity scores were estimated using multivariable logistic regression, with antipsychotic use as the dependent variable. Predictors included personal characteristics, lifestyle, comorbidities, and prescribed drugs. Patients with missing information on ethnicity, index of multiple deprivation, smoking, or alcohol use were grouped into an unknown category for each of these variables and included in the propensity score models. We used the Hosmer-Lemeshow test and likelihood ratio test to test the fit of the models, and interaction terms were included to improve the model fit. 64 The derived scores were used as inverse probability of treatment weights to reweigh the data, balancing the distribution of baseline covariates between antipsychotic users and non-users (matched comparators)—that is, standardised differences <0.1 after weighting. 65 Propensity score models were run for each outcome, and for the Aurum and GOLD cohorts separately. For further information, see the supplementary appendix section on propensity score methods to control for potential confounding.

Analyses for estimating harms were then conducted after combining (appending) the Aurum and GOLD datasets. We used Cox regression survival analyses to estimate the risks of each outcome associated with antipsychotic use relative to the comparator cohort, and we report the results as hazard ratios. Use of an antipsychotic was treated as a time varying variable. To account for the matched design, we fitted stratified models according to the matched sets and used robust variance estimation. In all models, we also included a covariate indicating whether the patient was from the Aurum or GOLD cohort and calculated hazard ratios with adjustments for inverse probability of treatment weights. Cox regression assumes proportional hazards—that is, the relative hazard of the outcome remains constant during the follow-up period. 66 We assessed this assumption using the Grambsch-Therneau test based on the Schoenfeld residuals. 67 Because this assumption did not hold for all outcomes examined, in addition to reporting the hazard ratios pertaining to the whole follow-up period, we estimated hazard ratios separately for the several time windows: the first seven days, 8-30 days, 31-180 days, 181-365 days, and 366 days to two years (see supplementary appendix for an illustration of stratification of follow-up time). For each outcome, we calculated the incidence rate and the number needed to harm (NNH) over the first 180 days as well as two years after start of follow-up. The NNH represents the number of patients needed to be treated with an antipsychotic for one additional patient to experience the outcome compared with no treatment. We also calculated cumulative incidence percentages (absolute risks) for each outcome accounting for competing mortality risks based on previous recommendations. 68 These were calculated at 90 days, 180 days, 365 days, and two years after start of follow-up for antipsychotic users and their matched comparators separately. We also reported the difference in cumulative incidence between antipsychotic users and their matched comparators at these time points. Analyses were conducted using Stata/MP v16.1.

Sensitivity analyses

We investigated two other definitions of antipsychotic use as sensitivity analyses: the first 60 days as current use followed by 120 days of recent use, and a current use period of 30 days followed by a recent use period of 60 days. We also conducted the following post hoc sensitivity analyses. Firstly, as levomepromazine is often prescribed in palliative care to treat distressing symptoms in the last days of life, 69 we censored individuals at the time of their first levomepromazine prescription. Secondly, we used Fine-Gray subdistribution hazard regression models to estimate the hazard of each adverse outcome, accounting for the competing risks of death. 70 These results were reported as subhazard ratios. Thirdly, we compared the incidence rates and hazards of adverse outcomes for male versus female individuals. For these sex specific analyses, we modified the existing matched cohort by excluding non-user comparators who were of a different sex from the antipsychotic user to whom they were matched. We then derived a new propensity score for each individual by excluding sex as a covariate in the propensity score models. Incidence rate ratios and corresponding 95% confidence intervals (CIs) for male versus female individuals were calculated using the ‘iri’ command in Stata. To investigate whether hazards of each adverse outcome associated with antipsychotic use differed by sex, we fitted Cox regression models with sex, antipsychotic use, and their interaction as covariates. Sex specific hazard ratios and ratios of male to female hazard ratios were reported.

Patient and public involvement

This study is part of a National Institute of Health and Care Research funded programme (RP-PG-1214-20012): Avoiding patient harm through the application of prescribing safety indicators in English general practices (PRoTeCT). Two patient and public involvement members in the project team contributed to the study design and protocol of this study. Our study was not, however, coproduced with people with dementia or their carers.

Characteristics of study population

A total of 173 910 adults (63.0% women) with dementia were eligible for inclusion in the study: 139 772 (62.9% women) in the Aurum dataset and 34 138 (63.4% women) in GOLD. The mean age at dementia diagnosis for individuals in both cohorts was 82.1 years (standard deviation (SD) 7.9 years), and the median age was 83 years (interquartile range (IQR) 78-88 years in Aurum and 78-87 years in GOLD). A total of 35 339 individuals (62.5% women; 28 187 in Aurum, 62.6% women; 7152 in GOLD, 62.5% women) were prescribed an antipsychotic during the study period, and a matched set was generated for each of these individuals. The mean number of days between first dementia diagnosis and date of a first antipsychotic prescription was 693.8 ((SD 771.1), median 443 days) in Aurum and 576.6 ((SD 670.0), median 342 days) in GOLD. A total of 544 203 antipsychotic prescriptions (433 694 in Aurum, 110 509 in GOLD) were issued, of which 25.3% were for a typical antipsychotic and 74.7% for an atypical antipsychotic. The most prescribed antipsychotics were risperidone (29.8% of all prescriptions), quetiapine (28.7%), haloperidol (10.5%), and olanzapine (8.8%), which together accounted for almost 80% of all prescriptions (see supplementary table S1).

Since we excluded people with a history of the event before the start of follow-up, the number of individuals and matched sets included in analysis varies by outcome. Table 1 shows the baseline characteristics of patients for the analysis of stroke, before and after inverse probability of treatment weighting. Antipsychotic users were more likely than their matched comparators to have a history of serious mental illness and to be prescribed antidepressants or benzodiazepines in the 12 months before start of follow-up. After inverse probability of treatment weighting, standardised differences were <0.1 for all covariates. Baseline characteristics of individuals included in the analyses of other outcomes were similar to those reported for stroke (see supplementary tables S2-S9).

Baseline characteristics of antipsychotic users and matched comparators included in the analysis of stroke (CPRD Aurum and GOLD combined data). Values are number (percentage) unless stated otherwise

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Incidence rates and relative hazards of adverse outcomes

All antipsychotics.

In the two years after initiation of antipsychotics, the highest incidence rates of adverse outcomes were for pneumonia, fracture, and stroke, and ventricular arrhythmias were rare ( table 2 ). Figure 1 shows the hazard ratios of adverse outcomes associated with current, recent, past, and any use of antipsychotics versus non-use (ie, matched comparators). Except for ventricular arrhythmia, any use of antipsychotics was associated with increased risks for all adverse outcomes, ranging from a hazard ratio of 2.03 (95% CI 1.96 to 2.10) for pneumonia to 1.16 (1.09 to 1.24) for heart failure. Current use (ie, prescribed in the previous 90 days) was associated with high risks for pneumonia (2.19, 2.10 to 2.28), acute kidney injury (1.72, 1.61 to 1.84), venous thromboembolism (1.62, 1.46 to 1.80), and stroke (1.61, 1.52 to 1.71). Recent antipsychotic use (ie, in the 180 days after current use ended) was also associated with increased risk for these outcomes, as well as for fracture, but past use of antipsychotics (ie, after recent use ended) was not associated with increased risks of the adverse outcomes examined, except for pneumonia. For the negative control outcome (appendicitis and cholecystitis), no significant associations were found with current, recent, or any antipsychotic use, but a statistically significant association was observed with past use (1.90, 1.01 to 3.56).

Incidence rate (per 10 000 person years) and number needed to harm of adverse outcomes associated with antipsychotic use during the first 180 days and two years of follow-up period

Hazard ratios (adjusted for inverse probability of treatment weights) of adverse outcomes associated with current, recent, and past antipsychotic use; with current use being defined as the first 90 days from the date of an antipsychotic prescription, recent use as up to 180 days after current use ended, and past use as after recent use. CI=confidence interval

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Table 2 shows that the NNH ranged from 9 (95% CI 9 to 10) for pneumonia to 167 (116 to 301) for myocardial infarction during the first 180 days after initiation of antipsychotics, and from 15 (14 to 16) for pneumonia to 254 (183 to 413) for myocardial infarction after two years. These figures suggest that over the 180 days after drug initiation, use of antipsychotics might be associated with one additional case of pneumonia for every nine patients treated, and one additional case of myocardial infarction for every 167 patients treated. At two years, there might be one additional case of pneumonia for every 15 patients treated, and one additional case of myocardial infarction for every 254 patients treated.

Table 3 shows hazard ratios stratified by follow-up time (except for ventricular arrhythmia and the negative control where the number of patients was very low). For almost all outcomes, relative hazards were highest in the first seven days after initiation of antipsychotic treatment. Risks for pneumonia were particularly increased in the first seven days (9.99, 8.78 to 11.40) and remained substantial afterwards (3.39, 3.04 to 3.77, 8-30 days). No increased risks for heart failure were found for current users after 180 days from start of treatment, nor for myocardial infarction one year after drug initiation. However, risks for stroke, venous thromboembolism, fracture, pneumonia, and acute kidney injury remained increased among continuous antipsychotic users up to two years after initiation of treatment.

Hazard ratios (adjusted for IPT weights) of adverse outcomes associated with current, recent, and past antipsychotic use stratified by follow-up period

Types of antipsychotics

During the current use period of 90 days after a prescription, both typical and atypical antipsychotics were associated with increased risks of all adverse outcomes compared with non-use, except for ventricular arrhythmia and the negative control (see supplementary table S10). Hazards were higher when current use of typical antipsychotics was directly compared with atypical antipsychotics for stroke (1.23, 1.09 to 1.40), heart failure (1.18, 1.01 to 1.39), fracture (1.22, 1.08 to 1.38), pneumonia (1.92, 1.77 to 2.08), and acute kidney injury (1.22, 1.05 to 1.42), but no significant differences between the two types of drug were found for the risks of venous thromboembolism or myocardial infarction.

Supplementary table S11 shows the risks of adverse outcomes associated with haloperidol (the most prescribed typical antipsychotic) and with risperidone and quetiapine (the two most prescribed atypical antipsychotics). Current use of risperidone and haloperidol compared with non-use was associated with increased risks of all adverse outcomes except for ventricular arrhythmia and the negative control. Current use of quetiapine compared with non-use was associated with increased risks for fracture, pneumonia, and acute kidney injury. Among current users of haloperidol or risperidone, risks for fracture, pneumonia, and acute kidney injury were higher for haloperidol versus risperidone, but risks for stroke, venous thromboembolism, myocardial infarction, and heart failure were similar for both drugs. With the exceptions of myocardial infarction, ventricular arrhythmia, and the negative control, risks of all adverse outcomes were higher for haloperidol than for quetiapine, especially for pneumonia (2.53, 2.21 to 2.89) and venous thromboembolism (1.99, 1.33 to 2.97). Among current users of quetiapine compared with risperidone, there were no significant differences in risks for myocardial infarction, heart failure, or fracture. However, risks for stroke (0.64, 0.53 to 0.78), venous thromboembolism (0.49, 0.36 to 0.68), pneumonia (0.72, 0.63 to 0.81), and acute kidney injury (0.81, 0.67 to 0.96) were lower for quetiapine than for risperidone.

Absolute risks of adverse outcomes

Cumulative incidence for all outcomes examined was higher for antipsychotic users versus matched comparators, except for ventricular arrhythmia and the negative control ( table 4 ). The absolute risk, as well as risk difference, was particularly large for pneumonia. In the 90 days after initiation of an antipsychotic, the cumulative incidence of pneumonia among antipsychotic users was 4.48% (95% CI 4.26% to 4.71%) v 1.49% (1.45% to 1.53%) in the matched cohort of non-users (difference 2.99%, 95% CI 2.77% to 3.22%). At one year, this increased to 10.41% (10.05% to 10.78%) for antipsychotic users compared with 5.63% (5.55% to 5.70%) for non-users (difference 4.78%, 4.41% to 5.16%).

Cumulative incidence of adverse outcomes associated with antipsychotic use at 90, 180, and 365 days and at two years after start of follow-up

Similar results were found in sensitivity analysis using two other definitions of antipsychotic use (see supplementary figures S4 and S5). Of the 544 203 antipsychotic prescriptions issued, 1.3% were for levomepromazine (see supplementary table S1). Results remained similar when patients were censored at the time of their first levomepromazine prescription (see supplementary figure S6). Results of the Fine-Gray models accounting for the competing risks of death also showed broadly similar patterns of hazards to those from the Cox models (see supplementary table S12 and figure S7). Sex specific analyses showed that male patients had higher incidence rates of all adverse outcomes than female patients, except for fracture and venous thromboembolism where incidence was higher for female patients than for male patients (see supplementary table S13). Compared with female antipsychotic users, male users had increased hazards for pneumonia and acute kidney injury (male to female hazard ratio 1.16, 95% CI 1.08 to 1.25 for pneumonia and 1.22, 1.08 to 1.37 for acute kidney injury), but lower hazards for stroke (0.81, 0.73 to 0.91). No significant differences were found by sex in the hazards for venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, or fracture (see supplementary table S14).

In this population based cohort study of adults (≥50 years) with dementia, use of antipsychotics compared with non-use was associated with increased risks for stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury. Increased risks were observed among current and recent users and were highest in the first week after initiation of treatment. In the 90 days after a prescription, relative hazards were highest for pneumonia, acute kidney injury, stroke, and venous thromboembolism, with increased risks ranging from 1.5-fold (for venous thromboembolism) to twofold (for pneumonia) compared with non-use. No increased risk was found for ventricular arrhythmia or the negative control outcome (appendicitis and cholecystitis). Absolute risk differences between antipsychotic users and their matched comparators were substantial for most adverse events, and largest for pneumonia. In the 90 days after a prescription, risks of stroke, heart failure, fracture, pneumonia, and acute kidney injury were higher for typical antipsychotics versus atypical antipsychotics, whereas no significant differences between these two drug classes were found for risks of venous thromboembolism or myocardial infarction. Haloperidol was associated with higher risks for fracture, pneumonia, and acute kidney injury than risperidone, but no significant differences between the two drugs were found for the other outcomes. Risks of almost all adverse outcomes were higher for haloperidol than for quetiapine. No significant differences were found between risperidone and quetiapine for risks of myocardial infarction, heart failure, or fracture, but risks for stroke, venous thromboembolism, pneumonia, and acute kidney injury were lower for quetiapine versus risperidone.

Comparison with other studies

A population based study in Wales reported no increased risks for non-fatal acute cardiac events associated with antipsychotic use in patients with all cause dementia, although those with Alzheimer’s disease showed increased risks. 37 Systematic reviews and meta-analyses of studies not limited to patients with dementia have also reported inconsistent evidence for myocardial infarction, or lack of robustness of these data. 33 34 71 Our findings for myocardial infarction were similar to those in a study that first documented a modest and time limited increase in risk of this outcome associated with antipsychotic use among patients with dementia. 56 In a study of nursing home residents in the US, users of typical, but not atypical, antipsychotics were more likely than non-users to be admitted to hospital for ventricular arrhythmia or cardiac arrest, 35 and a study not limited to older people reported increased risks for ventricular arrhythmia or sudden cardiac death associated with both typical and atypical antipsychotics. 36 We did not find any association with ventricular arrhythmia, but the number of events was low and we did not examine cardiac arrest or sudden death.

Increased risks of venous thromboembolism associated with antipsychotic use have been reported in the general population, 38 but meta-analyses found increased risks of venous thromboembolism only among younger users. 39 40 Our findings are consistent with those of the Welsh study, which reported increased risks of venous thromboembolism in the 12 months after drug initiation (prior event rate ratio 1.95, 95% CI 1.83 to 2.0). 37 In absolute terms, however, these risks were relatively low compared with other outcomes examined in this study.

We found that both the relative and the absolute risks for pneumonia were highest among all outcomes examined. Current users of antipsychotics had a twofold increased risk compared with non-users ( fig 1 ), and although this magnitude of increased risk was comparable to previous reports, 14 31 32 we additionally observed that risks were greater in the first week after drug initiation. One study also reported a particularly high risk for patients with hospital diagnosed pneumonia in the first week, but the magnitude of increase (odds ratio 4.5, 95% CI 2.8 to 7.3) was much lower than our observation. 30 The mechanisms linking antipsychotic use and development of pneumonia is not well understood, and substantial heterogeneity exists among the drug substances, but antipsychotic induced extrapyramidal symptoms, sedation, xerostomia (dry mouth), and dyskinesia or impaired swallowing are commonly considered as potential risk factors. 72 In addition, because elderly people with pneumonia may be less likely than younger patients to present with respiratory symptoms but more likely to show signs of delirium, 73 it is possible that reverse causality might have contributed to the high risks observed in the early days after drug initiation, as delirium from the onset of pneumonia might have been treated with antipsychotics before pneumonia was diagnosed. 30 However, although causality cannot be inferred, the particularly high increased risks observed for a range of outcomes and not only for pneumonia in the early days after drug initiation are consistent with other studies. 28 This could be partly explained by further prescriptions being given only to patients who tolerated the first days of drug use.

The use of atypical antipsychotics in older adults (≥65 years) has been shown to be associated with increased risk of acute kidney injury. 44 45 46 Two studies reported significantly increased risks in users compared with non-users in the 90 days after initiation of atypical antipsychotics. 44 45 In contrast, another study observed no increased risks from use of the broad category of atypical antipsychotics, although a significantly increased risk was found with olanzapine. 46 In our study, we found increased risks of acute kidney injury with both typical and atypical antipsychotics, with risks being higher for haloperidol than for risperidone and quetiapine.

In a meta-analysis of observational studies, antipsychotic use was associated with increased risks of hip fracture among people with dementia. 41 A self-controlled case series study of older adult patients (≥65 years) also reported increased risks of falls and fracture after initiation of antipsychotics, but incidence was found to be even higher in the 14 days before treatment started. 43 Similar findings were also reported in another study, suggesting that the risks observed during the treatment periods might not be attributable to the antipsychotics alone. 42 Although we cannot eliminate confounding in our study, we minimised this risk by adjusting for a large number of both clinical and non-clinical characteristics that might have influenced treatment assignment. We also found no increased risks associated with current or recent antipsychotic use for the negative control outcome (appendicitis and cholecystitis).

Our study found that the risks of stroke and heart failure were higher for typical antipsychotics than for atypical antipsychotics, but risks of venous thromboembolism and myocardial infarction were similar between the two drug classes. We also found no significant differences between haloperidol and risperidone in risks of these four outcomes, but significantly increased risks for stroke, venous thromboembolism, and heart failure for haloperidol versus quetiapine. Previous studies of elderly patients have reported similar risks for cardiovascular or cerebrovascular events associated with use of typical and atypical antipsychotics, 17 74 75 76 but risks of these outcomes and of all cause mortality were increased with haloperidol versus risperidone. 21 76 For fracture and pneumonia, we found that risks were higher in association with typical antipsychotics than atypical antipsychotics and for haloperidol versus risperidone or quetiapine. The findings from previous studies comparing these risks by antipsychotic types have been inconsistent. 30 31 32 74 75

Strengths and limitations of this study

A key strength of this study was the investigation of a wide range of adverse events in a large population based cohort, and the reporting of both relative and absolute risk differences over multiple periods. Previous studies commonly focused on a single outcome or type of outcome, such as cerebrovascular events, and on the reporting of relative risks. By examining the same cohort at risk, we were able to directly compare the hazards of multiple outcomes without differential biases between the cohorts. In addition, we only included patients with a clinician recorded diagnosis of dementia, and we adjusted for many variables that might have influenced the probability of antipsychotic initiation, seeking to minimise confounding by indication. CPRD is one of the largest primary care databases in the world, and it is broadly representative of the UK population. 47 48 49 The database includes all prescriptions issued in participating primary care practices in the UK, and it is recognised as a high quality resource to support international pharmacovigilance. 77 The longitudinal nature of CPRD, with linked data from secondary care and mortality records, enabled us to capture the study outcomes from multiple sources, as well as information on prescribing and comorbidities. 78 79 Our findings were also robust to different classifications of usage periods and we found no associations between current and recent antipsychotic use with the development of the negative control outcome (appendicitis and cholecystitis). However, a significant association with past use was observed that we are unable to explain.

As with all observational studies, residual confounding cannot be excluded. For example, polypharmacy is common among elderly people, which could lead to drug-drug interactions and potentially confound our findings. 80 81 We also did not have information on indications for antipsychotics treatment. We minimised the risk of confounding using propensity score methods to control for imbalances in measurable patient characteristics between antipsychotic users and their matched comparators. However, unlike randomised control trials, which, if properly conducted, could account for both observed and unobserved differences between treated and untreated groups, the propensity score method can only adjust for the observed differences between two groups. Additionally, our choice of covariates was based on the literature and discussions with clinical experts and was not formally structured using, for example, a directed acyclic graph. Although the strong associations with pneumonia in the first seven days of antipsychotic initiation may partially be attributed to reverse causality, however, it is less likely to explain associations over longer periods. We also found no increased risk for appendicitis and cholecystitis during current and recent use—our negative control outcome that was included to detect potential unmeasured confounding. 53 Another limitation of our study is that although prescriptions issued in primary care are reliable in CPRD, information on dosage is not well recorded and information on drug adherence or prescriptions issued while patients are in hospital is not available. 48 Misclassification of drug use is therefore a potential problem. As with other electronic health data that are routinely collected for administrative rather than research purposes, potential issues exist with coding errors, missing or incomplete information, and variations in data quality between practices and healthcare settings. Although the data undergo quality checks before being released and our use of the linked data would have helped to deal with such problems, we were restricted to data coded in patients’ electronic health records. In addition, despite the representativeness of the CPRD data, care should be taken in making inferences beyond the population studied. Our sex specific investigations were also conducted as post hoc analyses. By using existing matched sets but restricting the comparators to those of the same sex as the antipsychotic user to whom they were matched, the number of comparators was greatly reduced. Although we found some evidence of differences in hazards for stroke, pneumonia, and acute kidney injury between male and female antipsychotic users, further research is needed to validate these findings.

Policy implications

The mechanisms underlying the links between antipsychotics and the outcomes in our study are not fully understood. In the UK, US, and Europe, current regulatory warnings for using antipsychotics to treat behavioural and psychological symptoms of dementia were mostly based on evidence of increased risks for stroke and mortality. 8 11 22 23 24 25 26 We found a considerably wider range of harms associated with antipsychotic use in people with dementia, and the risks of harm were highest soon after initiation. Our findings must be seen in the context of trial evidence of at best modest benefit on behavioural and psychological symptoms of dementia. The efficacy of antipsychotics in the management of behavioural and psychological symptoms of dementia remains inconclusive. 82 83 84 85 Atypical antipsychotics, including risperidone, which is one of two antipsychotics licensed in the UK for the treatment of behavioural and psychological symptoms of dementia, have the strongest evidence base, but the benefits are only modest. 82 85

Any potential benefits of antipsychotic treatment therefore need to be weighed against the risk of serious harm across multiple outcomes. Although there may be times when an antipsychotic prescription is the least bad option, clinicians should actively consider the risks, considering patients’ pre-existing comorbidities and living support. The NNH reported in this study can help to inform clinical judgements on the appropriateness of treatments, taking account of the modest potential benefits reported in clinical trials. When prescriptions of such drugs are needed, treatment plans should be reviewed regularly with patients and their carers to reassess the need for continuing treatment. 9 In addition, given the higher risks of adverse events in the early days after drug initiation, clinical examinations should be taken before, and clinical reviews conducted shortly after, the start of treatment. Our study reaffirms that these drugs should only be prescribed for the shortest period possible. 9 Although regulators have made efforts to limit the use of these drugs to people with the most severe behavioural and psychological symptoms of dementia, 8 82 86 antipsychotic prescribing in dementia remains common and has even increased in recent years. 4 5 87 88 If such trends continue, further communication on the associated risks could be considered by guideline developers or regulators after a review of the totality of evidence. Greater accountability and monitoring in the use of these drugs may be called for, and additional legal reforms may be required to regulate adherence. 89 In recent years, other psychotropic drugs such as antidepressants, benzodiazepines, mood stabilisers, and anticonvulsants have been prescribed instead of antipsychotics for the treatment of behavioural and psychological symptoms of dementia. 28 90 91 These drugs, however, also pose their own risks. Further research is needed into safer drug treatment of behavioural and psychological symptoms of dementia and more efficacious, easy to deliver, initial non-drug treatments.

Conclusions

Antipsychotic use is associated with a wide range of serious adverse outcomes in people with dementia, with relatively large absolute risks of harm for some outcomes. These risks should be considered in future regulatory decisions, alongside cerebrovascular events and mortality. Any potential benefits of antipsychotic treatment need to be weighed against risk of serious harm, and treatment plans should be reviewed regularly. The effect of antipsychotics on behavioural and psychological symptoms of dementia is modest at best, but the proportion of people with dementia prescribed antipsychotics has increased in recent years. Our finding that antipsychotics are associated with a wider range of risks than previously known is therefore of direct relevance to guideline developers, regulators, and clinicians considering the appropriateness of antipsychotic prescribing for behavioural and psychological symptoms of dementia.

What is already known on this topic

Despite safety concerns, antipsychotics continue to be frequently prescribed for the management of behavioural and psychological symptoms of dementia

Current regulatory warnings for the treatment of behavioural and psychological symptoms of dementia using antipsychotics are based on evidence of increased risks of stroke and death

Evidence for other adverse outcomes is less conclusive or is more limited among people with dementia, and comparisons of risks for multiple adverse events are also difficult owing to different study designs and populations

What this study adds

Antipsychotic use in people with dementia was associated with increased risks of stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury, compared with non-use, but not ventricular arrhythmia

Relative hazards were highest for pneumonia, acute kidney injury, stroke, and venous thromboembolism, and absolute risk and risk difference between antipsychotic users and their matched comparators was largest for pneumonia

Risks of these wide ranging adverse outcomes need to be considered before prescribing antipsychotic drugs to people with dementia

Ethics statements

Ethical approval.

This study was approved by the Clinical Practice Research Datalink’s (CPRD) independent scientific advisory committee (protocol 18_168). CPRD also has ethical approval from the Health Research Authority to support research using anonymised patient data (research ethics committee reference 21/EM/0265). 92 Individual patient consent was not required as all data were deidentified.

Data availability statement

Electronic health records are, by definition, considered sensitive data in the UK by the Data Protection Act and cannot be shared via public deposition because of information governance restriction in place to protect patient confidentiality. Access to Clinical Practice Research Datalink (CPRD) data is subject to protocol approval via CPRD’s research data governance process. For more information see https://cprd.com/data-access . Linked secondary care data from Hospital Episodes Statistics, mortality data from the Office for National Statistics, and index of multiple deprivation data can also be requested from CPRD.

Acknowledgments

We thank Hayley Gorton and Thomas Allen for their contribution to the protocol development, Evan Kontopantelis for his statistical advice, and members of our patient and public involvement team, Antony Chuter and Jillian Beggs, for their contributions to this project. This study is based on data from the Clinical Practice Research Datalink obtained under licence from the UK Medicines and Healthcare Products Regulatory Agency (MHRA). The data are provided by patients and collected by the NHS as part of its care and support. Hospital Episode Statistics and Office for National Statistics mortality data are subject to Crown copyright (2022) protection, reused with the permission of The Health and Social Care Information Centre, all rights reserved. The interpretation and conclusions contained in this study are those of the authors alone, and not necessarily those of the MHRA, National Institute of Health and Care Research, NHS, or Department of Health and Social Care. The study protocol was approved by Clinical Practice Research Datalink’s independent scientific advisory committee (reference: 18_168). We would like to acknowledge all the data providers and general practices who make anonymised data available for research.

Contributors: All authors conceived and designed the study and acquired, analysed, or interpreted the data. BG, DRM, TvS, AJA, and DMA reviewed the clinical codes. PLHM conducted the statistical analyses and wrote the first draft of the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final version. AJA, DMA, RAE, BG, DRM, AS, and TvS obtained the funding. PLHM is the guarantor. The corresponding author (PLHM) attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This study was funded by the National Institute for Health and Care Research (NIHR, RP-PG-1214-20012). MJC, AJA, and DMA were supported by the NIHR Greater Manchester Patient Safety Translational Research Centre (PSTRC-2016-003) at the time of this study and are now supported by the NIHR Greater Manchester Patient Safety Research Collaboration (NIHR204295). The funders had no role in the study design; in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the article for publication. PLHM has full access to all data and all authors have full access to the statistical reports and tables in the study. PLHM takes responsibility for the integrity of the data and the accuracy of the data analysis.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: BG reports research grants from the National Institute for Health and Care Research (NIHR). DRM was awarded a Wellcome Trust Clinical Research Development Fellowship (214588/Z/18/Z). AS reports a research grant from the NIHR. RAE reports research grants from the NIHR and NHS England, and travel costs to attend a roundtable dinner discussion on medication errors, House of Commons, Westminster, on 29 March 2022. TvS reports research grants from the NIHR. AJA is national clinical director for prescribing for NHS England and reports research grants from the NIHR. DMA reports research grants from the NIHR, AbbVie, Almirall, Celgene, Eli Lilly, Janssen, Novartis, UCB, and the Leo Foundation. All other authors declare no support from any organisation for the submitted work (except those listed in the funding section); no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (PLHM: the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: This study used anonymised electronic health records from the CPRD and it is therefore not possible to disseminate the findings directly to individuals whose data we used. This study is part of a National Institute for Health and Care Research (NIHR) funded programme (RP-PG-1214-20012): Avoiding patient harm through the application of prescribing safety indicators in English general practices (PRoTeCT). We have experienced patient and public involvement members aligned to the programme who we will consult in the results dissemination. In addition, senior author DMA is director of NIHR Greater Manchester Patient Safety Research Collaboration (GMPSRC), and co-authors MJC and AJA are affiliated with it. The Patient Safety Research Collaboration has a community of public contributors including patients, carers, and people accessing health and social care services. The authors will work with this network to disseminate findings.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

• GBD 2019 Dementia Forecasting Collaborators
• McKeith I ,
• Stocks SJ ,
• Kontopantelis E ,
• Ashcroft DM
• Szczepura A ,
• Collings SL ,
• ↵ National Institute for Health and Care Excellence. Dementia: assessment, management and support for people living with dementia and their carers. NICE guideline NG97, 20 June 2018. www.nice.org.uk/guidance/ng97
• ↵ National Institute for Health and Care Excellence. Dementia: antipsychotics. Clinical knowledge summaries. December 2022. https://cks.nice.org.uk/topics/dementia/prescribing-information/antipsychotics/
• Marston L ,
• Nazareth I ,
• Petersen I ,
• Walters K ,
• Bronskill SE ,
• Normand SL ,
• Schneeweiss S ,
• Setoguchi S ,
• Brookhart A ,
• Dormuth C ,
• Dzahini O ,
• Schneider LS ,
• Dagerman KS ,
• Douglas IJ ,
• Sacchetti E ,
• Turrina C ,
• Valsecchi P
• Seyfried LS ,
• Kheirbek RE ,
• Little JT ,
• ↵ Medicines Healthcare Products Regulatory Agency. New advice issued on risperidone and olanzapine 2004. https://webarchive.nationalarchives.gov.uk/20141205150130/http://mhra.gov.uk/printpreview/defaultsp/con002047
• ↵ Medicines Healthcare Products Regulatory Agency. Antipsychotics: use in elderly patients with dementia. Drug safety update March 2009;2:8. https://webarchive.nationalarchives.gov.uk/20091114175357/http://mhra.gov.uk/Publications/Safetyguidance/DrugSafetyUpdate/CON041211
• ↵ Banerjee S. The use of antipsychotic medication for people with dementia: time for action. An independent report commissioned and funded by the Department of Health 2009.
• ↵ European Medicines Agency. Priorities for Drug Safety Research: Safety aspects of antipsychotics in demented patients. 2010. https://www.ema.europa.eu/en/documents/other/emea-2010-priorities-drug-safety-research-safety-aspects-antipsychotics-demented-patients_en.pdf
• Hessol NA ,
• Lexchin J ,
• Harrington C
• Lapeyre-Mestre M
• Rogowska M ,
• Thornton M ,
• van Marum RJ ,
• Jansen PA ,
• Souverein PC ,
• Schobben AF ,
• Trifirò G ,
• Gambassi G ,
• Tolppanen AM ,
• Koponen M ,
• Tanskanen A ,
• Zivkovic S ,
• Liperoti R ,
• Lapane KL ,
• Jönsson AK ,
• Spigset O ,
• Ostuzzi G ,
• Thabane L ,
• Brännström J ,
• Lövheim H ,
• Gustafson Y ,
• Nordström P
• Schuemie MJ ,
• Ramcharran D ,
• Brøgger Kristiansen S ,
• Baltzer Houlind M ,
• Herrett E ,
• Gallagher AM ,
• Bhaskaran K ,
• Campbell J ,
• Mahadevan P ,
• Fordyce S ,
• Rassen JA ,
• Rothman KJ ,
• Greenland S ,
• Kleijer BC ,
• Egberts AC ,
• Heerdink ER
• Lipsitch M ,
• Tchetgen Tchetgen E ,
• Austin PC ,
• Pariente A ,
• Fourrier-Réglat A ,
• Ducruet T ,
• Rigler SK ,
• Shireman TI ,
• Cook-Wiens GJ ,
• Balkwill A ,
• Million Women Study Collaborators
• Vinogradova Y ,
• Coupland C ,
• Hippisley-Cox J
• Sandler RS ,
• Kappelman MD
• Spoorenberg SMC ,
• Snijders D ,
• Ovidius Study Group ,
• Capisce Study Group ,
• STEP Study Group
• Koivula I ,
• ↵ Lunt M. Propensity analysis in Stata, revision: 1.1. 2014. University of Manchester. http://personalpages.manchester.ac.uk/staff/mark.lunt/propensity_guide.pdf
• Grambsch PM ,
• Therneau TM
• Gooley TA ,
• Leisenring W ,
• Crowley J ,
• Schmitz A ,
• Gastaldon C ,
• Sultana J ,
• Calabró M ,
• Garcia-Serna R ,
• Huybrechts KF ,
• Silliman RA ,
• Brookhart MA ,
• Schneeweiss S
• Gerhard T ,
• Sahlberg M ,
• Gislason GH ,
• Torp-Pedersen C ,
• Andersson C
• Crellin E ,
• Donegan K ,
• Padmanabhan S ,
• Cameron E ,
• Williams R ,
• Strongman H
• Nørgaard A ,
• Jensen-Dahm C ,
• Wimberley T ,
• Hansen ES ,
• Gitlin LN ,
• Lyketsos CG
• Goodarzi Z ,
• Veroniki AA ,
• Frederiksen KS ,
• Frisoni GB ,
• El Helou ML
• McDermid J ,
• Ballard C ,
• Schnier C ,
• McCarthy A ,
• Morales DR ,
• CVD-COVID-UK/COVID-IMPACT Consortium
• ↵ CPRD - Safeguarding patient data https://cprd.com/safeguarding-patient-data

Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India

• Research Article
• Published: 01 May 2024

Cite this article

• Meena Kumari Kolli 1 , 2 &
• Pennan Chinnasamy   ORCID: orcid.org/0000-0002-3184-2134 1 , 2 , 3 , 4 , 5 , 6  

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance \(({R}_{{\text{rs}}})\) in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms—Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( \({T}_{{\text{s}}}\) ) algorithm—to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the \({T}_{{\text{s}}}\) , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R 2  = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R 2  = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.

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Ahmed U, Mumtaz R, Anwar H, Shah AA, Irfan R, García-Nieto J (2019) Efficient water quality prediction using supervised machine learning. Water 11:2210

CAS   Google Scholar  

Alka S, Sushma P, Singh TS, Patel JG, Tanwar H (2014) Wetland information system using remote sensing and GIS in Himachal Pradesh, India. Asian J Geoinform 14(4):13–22

Google Scholar  

Anderson CW (2006) Turbidity (ver. 2.1): US Geological survey techniques of water-resources investigations. Book 9, chap. A6., sec. 6.7., from: http://pubs.water.usgs.gov/twri9A6/ . Accessed 10 Jul 2022

Bosire AS, Nyantika D, Mamboleo D (2021) Evaluation of the effects of human activities on water resources in Masaba North, Nyamira County. Int J Res Sch Commun 4(2):85–98

Bui DT, Khosravi K, Tiefenbacher J, Nguyen H, Kazakis N (2020) Improving prediction of water quality indices using novel hybrid machine-learning algorithms. Sci Total Environ 721:137612

Bustamante J, Pacios F, Diaz-Delgado R, Aragones D (2009) Predictive models of turbidity and water depth in the Donana marshes using LANDSAT TM and ETM + images. J Environ Manage 90:2219–2225

Chander S, Gujrati A, Hakeem KA, Garg V, Issac AM, Dhote PR, Kumar V, Sahay A (2019) Water quality assessment of River Ganga and Chilika lagoon using AVIRIS-NG hyperspectral data. Curr Sci 116(7):1172–1181

Chen Z, Muller-Karger F, Hu C (2007) Remote sensing of water clarity in Tampa Bay. Remote Sens Environ 109:249–259

Chen X, Liu L, Zhang X, Li J, Wang S, Liu D, Duan H, Song K (2021) An assessment of water color for inland water in China using a Landsat 8-Derived Forel-Ule Index and the Google Earth Engine platform. IEEE J Sel Top Appl Earth Obs Remote Sens 14:5773–5785

Chinnasamy P, Honap VU (2023) Spatiotemporal variations in soil loss across the biodiversity hotspots of Western Ghats Region, India. J Earth Syst Sci 132(2):90

Chinnasamy P, Shah Z, Shahid S (2023) Impact of lockdown on air quality during COVID-19 pandemic: a case study of India. J Indian Soc Remote Sens 51(1):103–120

Choubey VK (1992) Correlation of turbidity with Indian Remote Sensing Satellite-1A data. Hydrol Sci 37(2):129–140

DeLuca N, Zaitchik B, Curriero F (2018) Can multispectral information improve remotely sensed estimates of total suspended solids? A statistical study in Chesapeake bay. Rem Sens 10:1393

Dogliotti AI, Ruddick KG, Nechad B, Doxaran D, Knaeps E (2015) A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sens Environ 156:157–168

Garg V, Kumar AS, Aggarwal SP, Kumar V, Dhote PR, Thakur PK, Nikam BR, Sambare RS, Siddiqui A, Muduli PR, Rastogi G (2017) Spectral similarity approach for mapping turbidity of an inland waterbody. J Hydrol 550:527–537

Garg V, Aggarwal SP, Chauhan P (2020) Changes in turbidity along Ganga River using Sentinel-2 satellite data during lockdown associated with COVID-19. Geomat Nat Hazards Risks 11:1175–1195

Gholizadeh MH, Melesse AM, Reddi L (2016) A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors 16:1298

Gorelick N, Hancher M, Dixon M, Ilyushchhenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27

Hasmadi MI, Norsaliza U (2010) Analysis of SPOT-5 data for mapping turbidity level of River Klang, Peninsular Malaysia. Appl Remote Sens J 1:14–18

Hussain J, Husain I, Arif M, Gupta N (2017) Studies on heavy metal contamination in the Godavari river basin. Appl Water Sci 7:4539–4548

Islam M, Sado K (2006) Analyses of ASTER and Spectroradiometer data with in situ measurements for turbidity and transparency study of lake Abashri. Int J Geoinf 2:31–45

Konik M, Kowalczuk P, Zablocka M, Makarewicz A, Meler J, Zdun A, Darecki M (2020) Empirical relationships between remote-sensing reflectance and selected inherent optical properties in Nordic Sea surface water for the MODIS and OLCI Ocean Colour Sensors. Remote Sens 12:2774

Lacaux JP, Tourre YM, Vignolles C, Ndione JA, Lafaye M (2007) Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley fever epidemics in Senegal. Remote Sens Environ 106(1):66–74

Lobo FDL, Nagel GW, Maciel DA, de Carvalho LAS, Martins VS, Barbosa CCF, de Moraes Novo EML (2021) AlgaeMAp: algae bloom monitoring application for inland waters in Latin America. Remote Sens 13:2874

Lonare A, Maheshwari B, Chinnasamy P (2022) Village level identification of sugarcane in Sangali, Maharashtra using open source data. J Agrometeorol 24(3):249–254

Luis KM, Rheuban JE, Kavanaugh MT, Glover DM, Wei J, Lee Z, Doney SC (2019) Capturing coastal water clarity variability with Landsat 8. Mar Pollut Bull 145:96–104

Mahato LL, Pathak AK, Kapoor D, Patel N, Murthy M (2004) Surface water monitoring and evaluation of Indravati reservoir using the application of principal component analysis using satellite remote sensing technology. In: Proceedings of Map Asia, Beijing, China, 26–29 August 2004

Maltese A, Capodici F, Ciraolo G, La Loggia G (2013) Coastal zone water quality: calibration of a water-turbidity equation for MODIS data. Eur J Remote Sens 46:333–347

Markert KN, Schmidt CM, Griffin RE, Flores AI, Poortinga A, Saah DS, Muench RE, Clinton NE, Chishtie F, Kityuttachai K, Someth P, Anderson ER, Aekakkararungroj A, Ganz DJ (2018) Historical and operational monitoring of surface sediments in the Lower Mekong Basin using Landsat and Google Earth Engine Cloud Computing. Remote Sens 10:909

Martinez E, Gorgues T, Lengaigne M, Fontana C, Sauzede R, Menkes C, Uitz J, Di Lorenzo E, Fablet R (2020) Reconstructing global chlorophyll-a variations using a non-linear statistical approach. Front Mar Sci 7:464

McCarthy FMG, Riddick NL, Volik O, Danesh DC, Krueger AM (2018) Algal palynomorphs as proxies of human impact on freshwater resources in the Great Lakes region. Anthropocene 21:16–31

McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432

Nechad B, Ruddick KG, Park Y (2010) Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sens Environ 114:854–866

Nechad B, Ruddick KG, Neukermans G (2009) Calibration and validation of a generic multisensor algorithm for mapping of turbidity in coastal waters. Proc. SPIE 7473, Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2009, 74730H. https://doi.org/10.1117/12.830700

Chapter   Google Scholar  

Papoutsa C, Retalis A, Toulios L, Hadjimitsis DG (2014) Defining the Landsat TM/ETM+ and CHRIS/PROBA spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy. Int J Remote Sens 35:1674–1692

Peterson KT, Sagan V, Sloan JJ (2020) Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. Giscience Remote Sens 57:510–525

Pote SK, Singal SK (2012) Srivastava DK (2012) Assessment of surface water quality of Godavari River at Aurangabad. Asian J Water Environ Pollut 9(1):117–122

Potes M, Costa MJ, Salgado R (2012) Satellite remote sensing of water turbidity in Aiqueva reservoir and implications on lake modeling. Hydrol Earth Syst Sci Discuss 16:1623–1633

Quang NH, Sasaki J, Higa H, Huan NH (2017) Spatiotemporal variation of turbidity based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam. Water 9(8):570

Rodríguez-López L, Duran-Llacer I, González-Rodríguez L, Cardenas R, Urrutia R (2021) Retrieving water turbidity in Araucanian lakes (South-Central Chile) based on Multispectral Landsat Imagery. Remote Sens 13:3133

Rodriguez-Perez J, Leigh C, Liquet B, Kermorvant C, Peterson E, Sous D, Mengersen K (2020) Detecting technical anomalies in high-frequency water-quality data using artificial neural networks. Environ Sci Technol 54:13719–13730

Sebastiá-Frasquet MT, Aguilar-Maldonado JA, Santamaría-Del-Ángel E, Estornell J (2019) Sentinel 2 analysis of turbidity patterns in a coastal lagoon. Remote Sens 11(24):2926

Simpson ZP, Haggard BE (2018) Optimizing the flow adjustment of constituent concentrations via LOESS for trend analysis. Environ Monit Assess 190:103. https://doi.org/10.1007/s10661-018-6461-5

Article   Google Scholar  

Singh N, Nalgire SM, Gupta M, Chinnasamy P (2022) Potential of open source remote sensing data for improved spatiotemporal monitoring of inland water quality in India: case study of Gujarat. Photogramm Eng Remote Sens 88:155–163

Traganos D, Poursanidis D, Aggarwal B, Chrysoulakis N, Reinartz P (2018) Estimating satellite-derived bathymetry (SDB) with the Google Earth Engine and Sentinel-2. Remote Sens 10:859

Union E (2008) Directive 2008/56/EC of the European Parliament and of the council of June 17 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). Off J Eur Union 164:19–40

Vos K, Splinter KD, Harley MD, Simmons JA, Turner IL (2019) CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environ Model Softw 122:104528

Wang L, Xu M, Liu Y, Liu H, Beck R, Reif M, Emery E, Young J, Wu Q (2020) Mapping freshwater chlorophyll-a concentrations at a regional scale integrating multi-sensor satellite observations with Google Earth Engine. Remote Sens 12:3278

Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York, New York, USA. https://ggplot2.tidyverse.org/ . Accessed 22 Aug 2022

Wu JL, Ho CR, Huang CC, Srivastav AL, Tzeng JH, Lin YT (2014) Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: empirical relationship between reflectance and turbidity and total solids. Sensors 14:22670–22688

Yin F, Lewis PE, Gomez-Dans J, Wu Q (2019) A sensor-invariant atmospheric correction method: application toSentinel-2/MSI and Landsat 8/OLI. https://doi.org/10.31223/osf.io/ps957

Yunus AP, Masago Y, Hijioka Y (2020) COVID-19 and surface water quality: improved lake water quality during the lockdown. Sci Total Environ 731:139012

Yunus AP, Masago Y, Hijioka Y (2021) Analysis of long-term (2002–2020) trends and peak events in total suspended solids concentrations in the Chesapeake Bay using MODIS imagery. J Environ Manage 299:113550

Zhang C, Di L, Yang Z, Lin L, Hao P (2020) AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine. Environ Model Softw 129:104694

Zheng G, DiGiacomo PM (2022) A simple water-clarity turbidity index for the Great Lakes. J Great Lakes Res 48:684–694

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Acknowledgements

This work is partially supported by the Institute Post-doctoral Fellowship at IIT Bombay. The authors would like to thank the European Space Agency for free access to the Sentinel-2 data. We are grateful to Google for the GEE platform, which provided an efficient and powerful computing platform.

This research has been supported by the Water Productivity Improvement in Practice (Water-PIP) project, which is funded by the IHE Delft Water and Development Partnership Programme (WDPP) under the programmatic cooperation between the Directorate General for International Cooperation (DGIS) of the Ministry of Foreign Affairs of the Netherlands and IHE Delft (ID: DGIS Activity DME0121369).

Award Number: 111349 (WATERPIP project) | Recipient: Pennan Chinnasamy, Ph.D.

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Centre for Technology Alternatives for Rural Areas, Indian Institute of Technology (IITB), Powai, Mumbai, Maharashtra, 400076, India

Meena Kumari Kolli & Pennan Chinnasamy

Rural Data Research and Analysis Lab (RuDRA), IIT Bombay, Mumbai, India

Interdisciplinary Programme in Climate Studies (IDPCS), IIT Bombay, Mumbai, India

Pennan Chinnasamy

Centre for Machine Intelligence and Data Science(C‑MInDS), IIT Bombay, Mumbai, India

Ashank Desai Centre for Policy Studies, IIT Bombay, Mumbai, India

Nebraska Water Center, University of Nebraska, Lincoln, NE, USA

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Ideation, discussion, data sources identification, draft preparation, editing, and finalization—Prof. Pennan Chinnasamy.

Data collection, GIS, analysis, draft preparation, writing, and maps—Meena Kumari Kolli.

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Correspondence to Pennan Chinnasamy .

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Kolli, M.K., Chinnasamy, P. Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33344-4

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Social isolation and loneliness Print this page

• Stress and trauma 14 Feb 2024
• Physical health of people with mental illness 14 Feb 2024
• Prevalence and impact of mental illness

On this page:

Who experiences social isolation and loneliness?

Preventing and reducing social isolation and loneliness, where can i go for more information.

Loneliness and social isolation were concerns before the onset of the COVID-19 pandemic but have been exacerbated in the subsequent years.

In 2022, males aged 15–24 tended to experience more social isolation and loneliness than females.

Social isolation and loneliness are among the many factors that can be detrimental to a person’s wellbeing.

Social isolation and loneliness can harm both mental and physical health and may affect life satisfaction. They are concerning issues in Australia due to the impact they have on peoples’ lives and wellbeing.

Loneliness has been linked to premature death, poor physical and mental health (Holt-Lunstad et al. 2015), greater psychological distress (Manera et al. 2022) and general dissatisfaction with life (Schumaker et al. 1993). Loneliness among Australians was already a concerning issue before the COVID-19 pandemic, to the extent that in 2022 it has been described as one of the most pressing public health priorities in Australia (Ending Loneliness Together 2022).

Social isolation has been linked to mental illness, emotional distress, suicide, the development of dementia, premature death and poor health behaviours (smoking, physical inactivity and poor sleep) – as well as biological effects, including high blood pressure and impaired immune function (Cacioppo et al. 2002 and Grant et al. 2009 in Holt-Lunstad et al. 2015). Social isolation is also associated with psychological distress (Manera et al. 2022) and sustained decreases in feelings of wellbeing (Shankar et al. 2015). Conversely, more frequent social contact is associated with better overall health (Botha 2022).

The difference between social isolation and loneliness

Social isolation ‘means having objectively few social relationships or roles and infrequent social contact’ (Badcock et al. 2022:7). It differs from loneliness, which is a ‘subjective unpleasant or distressing feeling of a lack of connection to other people, along with a desire for more, or more satisfying, social relationships’ (Badcock et al. 2022:7). The 2 concepts may, but do not necessarily, coexist (Badcock et al. 2022; Relationships Australia 2018) – a person may be socially isolated but not lonely, or socially connected but feel lonely.

Social isolation

In 2022, almost 1 in 7 (15%) Australians (18% of males and 12% of females) were experiencing social isolation. Compared to just before the pandemic (2019) the proportion of young people aged 15–24 experiencing social isolation increased markedly over 2020 and 2021. During the later years of the pandemic (2021 to 2022) the proportion of young females (15–24 years) experiencing social isolation decreased (23% in 2021 down to 17% in 2022), while the proportion of young males continued to increase (from 22% to 25% over this time). The 35–44 year age group was the only one for whom social isolation continued to increase from 2021 (16% in 2021 to 17% in 2022) (Figure SIL.1).

Figure SIL 1: How has social isolation changed over time?

Line graph and butterfly chart showing the per cent of males and females of various age groups experiencing social isolation, from 2001 to 2022. The proportion of males aged 15–24 experiencing social isolation from 2001 to 2019 remained relatively steady between 11% and 15%, before increasing to 19% in 2020 and continuing to increase to 22% in 2021, then dropped to 21% in 2022.    

Source: AIHW analysis of Household and Labour Dynamics in Australia (HILDA) data, waves 1–22.

In 2022, just over 1 in 6 (16%) Australians were experiencing loneliness. As of 2022, about 1 in 5 (17%) males and 1 in 6 (15%) females aged 15–24 were experiencing loneliness. An increasing number of people aged 15–24, have reported experiencing loneliness since 2012. In contrast, the frequency of people aged 65 and over reporting loneliness has been steadily declining since 2001 (Figure SIL 2).

Figure SIL 2: Per cent of people aged 15 and over experiencing loneliness, by sex and age group, 2001–2022

Line graph and butterfly chart showing the per cent of males and females of various age groups experiencing loneliness, from 2001 to 2022. In 2001, 15% of people aged 15–24 were lonely, compared to 16% in 2022. The proportion of people aged 65 and over who are lonely has decreased from 20% in 2001 to 16% in 2022. 

Australia’s available data on loneliness do not allow for reliable international comparisons. In a recent systematic review of loneliness in 113 countries led by Australian researchers, Australian data could not be compared with those of other countries due to a lack of comparable prevalence data – except for the adolescent age group (Surkalim et al. 2022). To date, the Organisation for Economic Co-operation and Development has not reported comparable data for Australia on its measures of ‘people feeling lonely’ and ‘people feeling left out of society’ (OECD 2022, 2023).

Domestic and family violence

Family, domestic and sexual violence is a major health and welfare issue in Australia, occurring across all socioeconomic and demographic groups, but predominantly affecting women and children (AIHW 2022). 

Social isolation is a well-recognised tactic of coercive control used by perpetrators to control their victims (Boxall and Morgan 2021). It ensures the victim does not hear other people’s perspectives: perpetrators control the information the victim receives, reduce their help-seeking opportunities, and control the victim’s ability to leave the abusive relationship (Stark 2007). Recent studies on the impact of the COVID-19 pandemic on Australians are identifying some adverse outcomes of stay-at-home orders associated with increased social isolation that put some women and children at higher risk of experiencing family violence (Morgan and Boxall 2020; Pfitzner et al. 2022). 

An online survey of 166 practitioners conducted in Victoria during the 2020 lockdowns revealed that women’s experiences of intimate partner violence worsened because of their increased social isolation, which reduced their ability to seek external help and support (Pfitzner et al. 2022). This trend was also identified in other cities and countries, with perpetrators using the social isolation provided by the stay-at-home orders to increase abusive behaviours towards victims within their homes (Piquero et al. 2021). An Australian study suggests the combination of increased social isolation and economic stress associated with the COVID-19 pandemic did increase the risks of domestic and family violence for women in current cohabiting relationships (Morgan and Boxall 2020). 

For more information, refer to Family, domestic and sexual violence .       

Engaging in volunteer work and maintaining active memberships of sporting or community organisations are also associated with reduced social isolation (Flood 2005). Participating in paid work and caring for others have been proposed as safeguards against loneliness. However, it is unclear whether community engagement can consistently act as a protective factor against loneliness. For example:

• one study found that loneliness is lower in people who spend at least some time each week volunteering (Flood 2005)
• another study found no relationship between loneliness and volunteering, or between loneliness and socialising and participating in sport and community organisations (Baker 2012).

For people aged 25 to 44, being in a relationship is a greater protective factor against loneliness for men than for women (Baker 2012). Women living with others and women living alone report similar levels of loneliness, while men living alone report higher levels of loneliness than men living with others (Flood 2005).

The role of social media

Whether social media has potential benefits or negative impacts on people’s experiences of social isolation has been discussed since the advent of this medium. There is no straightforward relationship however, between social media use and experiences of social isolation and loneliness, whether positive or negative. 

Researchers have identified some positive impacts of how social media can help people feel socially connected, especially adolescents (aged 11–19) who are looking for peers online to boost their psychosocial wellbeing, discuss identity development and encourage a sense of belonging (Allen et al. 2014). Other research has showed that using social media benefited young people (aged under 21) who experienced higher levels of social anxiety by increasing their ability to socialise, reducing their feelings of social isolation (Lin et al. 2017). 

Even though adolescents can use social media to create supportive communities, research shows that the relationship between its use and loneliness can work both ways. When it is used to escape physical social interactions, feelings of loneliness were found to increase. People experiencing loneliness may benefit from external support with the use of the Internet to ensure they engage in existing friendships and learn how to develop new ones online to reduce feelings of loneliness and social isolation (Nowland et al. 2017). 

More research has emerged since the pandemic started that investigates the use of social media by people of all ages and their experiences of social isolation, but findings are not always positive. For example, a study of people living in Norway, the United States of America, the United Kingdom and Australia looked at the impact of people’s use of social media during the pandemic. The researchers found an association between emotional distress and more frequent use of social media (Geirdal et al. 2021). 

Another international study investigating current research between online social networking and mental health outcomes for people aged 50 and over found that social media enhanced communication with family and friends, provided greater independence and self-efficacy, aided in the creation of new communities online, helped to form positive associations with wellbeing and life satisfaction, and was associated with decreased depressive symptoms (Chen et al. 2021). 

As more studies are conducted through the pandemic and beyond, an understanding of how social media affects feelings of social isolation and loneliness may become clearer. 

Although social isolation and loneliness are now well-recognised public health concerns, major gaps remain in understanding what works to resolve them (Smith and Lim 2020). Due to our diverse social needs, preferences and resources, there is no ‘one size fits all’ solution (Ending Loneliness Together 2022). 

Companion animals

Pets can play an integral part in people’s lives, regardless of the person’s culture, profession or age. Companion animals are one source of external support that can bring both physical and mental health benefits (Brooks et al. 2016). All types of companion animals may contribute to reducing social isolation and feelings of loneliness (Brooks et al. 2018; Kretzler et al. 2022). 

Multiple studies have found an association between pet ownership and lower experiences of social isolation, particularly for children (Christian et al. 2020; Hartwig and Signal 2020; Kretzler et al. 2022). Further, research suggests that companion animals may positively influence experiences for older people (aged 60 and over) by increasing their sense of purpose and meaning, facilitating increased social interaction, reducing loneliness and improving emotional resilience (Gan et al. 2019), as well as being potentially a protective factor against suicide (Young et al. 2020a). Owning a pet increases the opportunity for people to get to know their neighbours and for social interactions and forming friendships (Wood et al. 2015). 

Brooks and colleagues (2018) systematically reviewed 17 studies that investigated the relationship between companion animals, specifically domestic animals, and the assistance these animals provided in helping people to manage their mental health conditions. The quantitative studies produced mixed findings, with people experiencing positive, negative and neutral impacts of their companion animal on their personal mental health. 

Qualitative studies suggest, however, that people with mental health conditions may benefit from the direct support their companion animals provide. This support includes helping their owners to manage their mental health condition, reducing people’s stress and regulating emotions – particularly beneficial during times of crisis, improving people’s quality of life, providing a consistent source of comfort, and aiding social and community interactions. Companion animals were found to help mitigate feelings of social isolation and loneliness by providing physical warmth and companionship, and opportunities for non-judgemental communication for their owners. Further, they may offer a distraction or disruption when their owners experience panic attacks and other symptoms of mental illness (Brooks et al. 2018). On the other hand, negative impacts included difficulties with the daily commitment of pet ownership and the psychological stress when losing a companion pet. 

Since the start of the COVID-19 pandemic, studies have mostly shown that the association between pet ownership, loneliness and social isolation has strengthened (Kretzler et al. 2022). One study found that cats gave people an outlet for stress through the strong bonds they had established with owners, and the affection and comfort they provided, thus acting as a buffer to the social isolation created by the lockdowns (Currin-McCulloch et al. 2021). Dogs provided people with daily reinforcement of positive behaviours such as routine, exercise and play, which all contributed to decreased feelings of social isolation (Bussolari et al. 2021). 

It is not yet clear whether this strong relationship between people and their pets at the levels seen in the early years of the COVID-19 pandemic will persist in the future (Hughes et al. 2021; Young et al. 2020b). 

For more information about social isolation and loneliness, see:

• Bankwest Curtin – Stronger together: loneliness and social connectedness in Australia
• Measuring what matters
• Ending Loneliness Together

AIHW (Australia Institute of Health and Welfare) (2022) Family, domestic and sexual violence data in Australia , AIHW website, accessed 9 February 2024.

Allen KA, Ryan T, Gray DL, McInerney DM and Waters L (2014) ‘ Social media use and social connectedness in adolescents: the positives and potential pitfalls ’, The Educational and Developmental Psychologist , 31(1):18–31, doi:10.1017/edp.2014.2, accessed 9 February 2024. 

Badcock JC, Holt-Lunstad J, Garcia E, Bombaci P and Lim MH (2022) Position statements on addressing social isolation and loneliness and the power of human connection , Global Initiative on Loneliness and Connection, accessed 9 February 2024.

Baker D (2012) All the lonely people: loneliness in Australia, 2001–2009 , The Australia Institute, Canberra, Institute paper no. 9, accessed 9 February 2024.

Botha F (2022) ‘Social connection and social support’, in Wilkins et al., The Household, Income and Labour Dynamics in Australia Survey: selected findings from waves 1 to 20 , Melbourne Institute: Applied Economic and Social Research, Melbourne.

Boxall H and Morgan A (2021) Statistical Bulletin 30 – experiences of coercive control among Australian women , Australian Institute of Criminology, Canberra. 

Brooks HL, Rushton K, Lovell K, Bee P, Walker L, Grant L and Rogers A (2018) ‘ The power of support from companion animals for people living with mental health problems: a systematic review and narrative synthesis of the evidence ’, BMC Psychiatry , 18(31), doi:10.1186/s12888-018-1613-2, accessed 9 February 2024.

Brooks H, Rushton K, Walker S, Lovell K and Roger A (2016) ‘ Ontological security and connectivity provided by pets: a study in the self-management of the everyday lives of people diagnosed with a long-term mental health condition ’, BMC Psychiatry , 16(409), doi:10.1186/s12888-016-1111-3, accessed 9 February 2024.  

Bussolari C, Currin-McCulloch J, Packman W, Kogan L and Erdman P (2021) ‘“I couldn’t have asked for a better quarantine partner!”: experiences with companion dogs during Covid-19 ’, Animals , 11(2):330, doi:10.3390/ani11020330.

Chen E, Wood D and Ysseldyk R (2021) ‘ Online social networking and mental health among older adults: a scoping review ’, Canadian Journal on Aging / La Revue Canadienne Du Vieillissement , 41(1):26–39, doi:10.1017/S0714980821000040.

Christian H, Mitrou F, Cunneen R and Zubrick SR (2020) ‘ Pets are associated with fewer peer problems and emotional symptoms, and better prosocial behaviour: findings from the longitudinal study of Australian children ’, The Journal of Paediatrics , 220:200–206, doi:10.1016/j.peds.2020.01.012.

Currin-McCulloch J, Bussolari C, Packman W, Kogan L and Erdman P (2021) ‘ Grounded by purrs and petting: experiences with companion cats during Covid-19 ’, Human-Animal Interaction Bulletin , doi:10.1079/hai.2021.0009.

Ending Loneliness Together (2022) Social connection to accelerate social recovery white paper , WayAhead, Sydney, accessed 9 February 2024.

Flood M (2005) Mapping loneliness in Australia , The Australia Institute, Canberra, accessed 9 February 2024.

Gan GZH, Hill A, Yeung P, Keesing S and Netto JA (2019) ‘ Pet ownership and its influence on mental health in older adults ’, Aging and Mental Health , 24(10), 1605–1612, doi:10.1080/13607863.2019.1633620, accessed 9 February 2024.

Geirdal AO, Ruffolo M, Leung J, Thygesen H, Price D, Bonsaksen T and Schoultz M (2021) ‘ Mental health, quality of life, wellbeing, loneliness and use of social media in a time of social distancing during the COVID-19 outbreak. A cross-country comparative study ’, Journal of Mental Health , 30(2):148–155, doi:10.1080/09638237.2021.1875413, accessed 9 February 2024.

Hartwig E and Signal T (2020) ‘ Attachment to companion animals and loneliness in Australian adolescents ’, Australian Journal of Psychology ,   72(4):337–346, doi:10.1111/ajpy.12293, accessed 9 February 2024.

Holt-Lunstad J, Smith TB, Baker M, Harris T and Stephenson D (2015) ‘ Loneliness and social isolation as risk factors for mortality: a meta-analytic review ’, Perspectives on Psychological Science ,   10(2):227–237, doi:10.1177/1745691614568352, accessed 9 February 2024.

Hughes AM, Braun L, Putnam A, Martinez D and Fine A (2021) ‘ Advancing human-animal interaction to counter social isolation and loneliness in the time of Covid-19: a model for an interdisciplinary public health consortium ’, Animals ,   11 ( 8):2325. https://doi.org/10.3390/ani11082325, accessed 9 February 2024.

Kretzler B, Konig H and Hajek A (2022) ‘ Pet ownership, loneliness, and social isolation: a systematic review ’, Social Psychiatry and Psychiatric Epidemiology ,   57 : 1935–1957, doi 10.1007/s00127-022-02332-9, accessed 9 February 2024.

Manera KE, Smith BJ, Owen KB, Phongsavan P and Lim MH (2022) ‘ Psychometric assessment of scales for measuring loneliness and social isolation: an analysis of the household, income and labour dynamics in Australia (HILDA) survey ’, Health and Quality of Life Outcomes , 20:40, doi:10.1186/s12955-022-01946-6, accessed 9 February 2024.

Morgan P and Boxall H (2020) ‘Social isolation, time spent at home, financial stress and domestic violence during the COVID-19 pandemic’ , Trends & Issues in Crime and Criminal Justice , 609, Australian Institute of Criminology, Australian Government, Canberra.

Nowland R, Necka EA and Cacioppo J (2017) ‘ Loneliness and social Internet use: pathways to reconnection in a digital world? ’, Perspectives on Psychological Science , 13(1), doi:10.1177/1745691617713052, accessed 9 February 2024.

OECD (Organisation for Economic Co-operation and Development) (2022) COVID-19 and well-being: life in the pandemic – Australia , OECD website, accessed 9 February 2024.

OECD (2023) Measuring well-being and progress: well-being research , OECD website, accessed 9 February 2024.

Pfitzner N, Fitz-Gibbon K and True J (2022) ‘ When staying home isn’t safe: Australian practitioner experiences of responding to intimate partner violence during COVID-19 restrictions ’, Journal of Gender-Based Violence , 6(2):297–314, accessed 9 February 2024.

Piquero AR, Jennings WG, Jemison E, Kaukinen C and Knaul FM (2021) ‘ Domestic violence during the COVID-19 pandemic: evidence from a systematic review and meta-analysis ’, Journal of Criminal Justice , 74,   doi:10.1016/j.crimjus.2021.101806, accessed 9 February 2024.

Relationships Australia (2018) Is Australia experiencing an epidemic of loneliness? Findings from 16 waves of the Household Income and Labour Dynamics of Australia Survey , Relationships Australia website, accessed 9 February 2024.

Schumaker JF, Shea JD, Monfries MM and Growth-Marnat G (1993) ‘Loneliness and life satisfaction in Japan and Australia’, Journal of Psychology , 127(1):65–71.

Shankar A, Rafnsson SB and Steptoe A (2015) ‘ Longitudinal associations between social connections and subjective wellbeing in the English Longitudinal Study of Ageing ’, Psychology & Health , 30(6):686–698, doi:10.1080/08870446.2014.979823, accessed 9 February 2024. 

Smith B and Lim M (2020) ‘How the COVID-19 pandemic is focusing attention on loneliness and social isolation’, Public Health Research & Practice , 30(2):e3022008.

Stark E (2007) Coercive control: how men entrap women in personal life , Oxford University Press, New York.

Surkalim DL, Luo M, Eres R, Gebel K, van Buskirk J, Bauman A and Ding D (2022) ‘ The prevalence of loneliness across 113 countries: systematic review and meta-analysis ’, BMJ, 376:e067068, doi:10.1136/bmj-2021-067068, accessed 9 February 2024.

Wood L, Martin K, Christian H, Nathan A, Lauritsen C, Houghton S, Kawachi I and McCune S (2015) ‘ The pet factor – companion animals as a conduit for getting to know people, friendship formation and social support ’ PLoS ONE , 10(4), doi:10.1371/journal.pone.0122085, accessed 9 February 2024.

Young J, Bowen-Salter H, O’Dwyer L, Stevens K, Nottle C and Baker A (2020a) ‘ A qualitative analysis of pets as suicide protection for older people ’, Anthrozoos, 33 (2), 191–205, doi:10.1080/08927936.2020.1719759, accessed 9 February 2024.

Young J, Pritchard R, Nottle C and Banwell H (2020b) ‘Pets, touch, and COVID-19: health benefits from non-human touch through times of stress’, Journal of Behavioural Economics for Policy , 4(2), 25–33.

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• How Hispanic Americans Get Their News

U.S.-born Latinos overwhelmingly prefer to get their news in English; about half of immigrant Latinos prefer it in Spanish

Table of contents.

• Where do Hispanic Americans get news?
• Major differences between U.S.-born Latinos and immigrants
• Bilingual Hispanics much more likely to get news in English than Spanish
• U.S.-born Hispanics less likely to engage with Hispanic news outlets
• Latinos with lower levels of income more likely to get news from Hispanic outlets and about origin countries
• Who gets news from Hispanic outlets and about origin countries?
• Acknowledgments
• The American Trends Panel survey methodology
• How family income tiers are calculated

Pew Research Center conducted this study to understand Hispanic Americans’ habits around news and information, including the languages in which they consume news and their engagement with Hispanic media outlets.

Most of the questions in this report are from Pew Research Center’s 2023 National Survey of Latinos, a survey of 5,078 U.S. Hispanic adults conducted Nov. 6-19, 2023. This includes 1,524 Hispanic adults on the Center’s  American Trends Panel  (ATP) and 3,554 Hispanic adults on Ipsos’ KnowledgePanel . Respondents on both panels are recruited through national, random sampling of residential addresses. Recruiting panelists by phone or mail ensures that nearly all U.S. adults have a chance of selection. This gives us confidence that any sample can represent the whole population, or in this case the whole U.S. Hispanic population. (For more information, watch our  Methods 101 explainer  on random sampling.)

To further ensure the survey reflects a balanced cross-section of the nation’s Hispanic adults, the data is weighted to match the U.S. Hispanic adult population by age, gender, education, nativity, Hispanic origin group and other categories. Read more about the  ATP’s methodology . Refer to the topline for the questions used for our National Survey of Latinos , along with responses, and to methodology for more details.

The questions about how often people get news from various platforms, which platforms they prefer for getting news, and which social media sites people get news from are from an ATP survey of 8,842 U.S. adults, including 1,193 Hispanic adults, conducted Sept. 25-Oct. 1, 2023. Refer to the topline for t he questions used for this survey , along with responses, and to the methodology for more details.

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. This is the latest report in Pew Research Center’s ongoing investigation of the state of news, information and journalism in the digital age, a research program funded by The Pew Charitable Trusts, with generous support from the John S. and James L. Knight Foundation.

The terms  Hispanic  and  Latino  are used interchangeably in this report.

Hispanic/Latino Americans, Hispanic/Latino adults , and Hispanics/Latinos are used interchangeably in this report to refer to survey respondents who self-identify as Hispanic or Latino in the United States. They include those who say their race is White, Black, Asian or some other race and those who identify as multiracial. Hispanic/Latino Americans live in the U.S. but are not necessarily U.S. citizens.

U.S. born  refers to people born in the 50 states or the District of Columbia.

Immigrant  refers to people born outside the 50 states or D.C. For the purposes of this report, immigrants include those born in Puerto Rico or another U.S. territory. Although individuals born in Puerto Rico are U.S. citizens by birth, they are grouped with immigrant respondents because they were born into a Spanish-dominant culture and because on many points their attitudes, views and beliefs more closely resemble those of Hispanics born outside the U.S. than Hispanics born in the 50 states or D.C., and even U.S.-born Hispanics who identify as being of Puerto Rican origin.

Second generation  refers to people born in the 50 states or D.C. who have at least one parent born in a different country, Puerto Rico or another U.S. territory.

Third generation   or higher refers to people born in the 50 states or D.C. who have two parents born in the 50 states or D.C.

Language dominance  is a composite measure based on self-described assessments of speaking and reading abilities.  Spanish-dominant  people are more proficient in Spanish than in English (i.e., they speak and read Spanish “very well” or “pretty well” but rate their English ability lower).  Bilingual  refers to people who are proficient in both English and Spanish.  English-dominant  people are more proficient in English than in Spanish.

“Middle income” is defined here as two-thirds to double the median annual family income for panelists on the American Trends Panel. “Lower income” falls below that range; “upper income” falls above it. Refer to the methodology for more details.

Hispanic news outlets are those outlets that focus on providing news and information specifically to Hispanic audiences. These can include newspapers, radio or TV stations, podcasts, or social media accounts created for and by Hispanic people. Their content could be in Spanish, English, both languages or another language.

Country of origin refers to the country that survey respondents, their parents or their Hispanic ancestors came from.

Just over half of U.S. Hispanic adults (54%) get their news mostly in English – far higher than the share who get their news mostly in Spanish (21%). About a quarter of Hispanic Americans (23%) say they consume news in both languages about equally.

There is an almost identical pattern on the question of preferred language for news: 51% prefer to get their news in English, 24% prefer Spanish and 23% say they do not have a preference.

But a new Pew Research Center survey of adults who identify as Hispanic or Latino finds major differences in news consumption habits between U.S.-born Hispanics and those who immigrated from other countries .

While U.S.-born Latinos overwhelmingly get their news in English, and prefer it in English, those born outside the United States have much more varied habits: 41% get their news mostly in Spanish, 26% get it primarily in English and 31% do both about equally. Similarly, 47% of Latino immigrants prefer to get their news in Spanish, while 22% prefer English and 31% do not express a preference.

Among Latino immigrants, those who have spent more years in the U.S. are less inclined than more recent arrivals to get news in Spanish, and more inclined to get it in English. There is little difference in the shares who get news in both languages about equally.

Jump to more information on the languages in which U.S. Latinos consume news.

We asked these questions to better understand how a group that makes up nearly one-in-five Americans stays informed, especially as its demographics and use of Spanish continue to change. Immigrants are declining as a share of all U.S. Hispanics , and the share of Hispanics who speak Spanish at home has also dropped – even though the number of Hispanics who speak Spanish at home has increased due to overall growth in the Hispanic population.

Other key findings about Hispanics’ news consumption include:

Most Latino adults prefer digital devices for news

Latinos get their news from a variety of sources, but most say they prefer to use digital devices over other platforms. Nearly nine-in-ten (87%) say they get news from digital devices at least sometimes, and 65% say they prefer this form of news over TV, radio or print. Digital devices have become an increasingly common source for news among Latinos – and among Americans overall – in recent decades, a shift driven by the rise of the internet .

Latinos are more likely than White Americans (55%) and Black Americans (50%) to prefer getting news from digital devices. Latinos also are more likely than White and Black adults to get news from social media, at least in part because Latino adults tend to be younger than other groups, and young adults are more inclined to use social media for news.

Nearly three-quarters of Latino adults under 50 (73%) prefer to get their news on digital devices, including 27% who prefer social media specifically.

Jump to more information on the platforms where U.S. Latinos get news.

Attention to news is declining among U.S. Latinos

About one-in-five Latino adults (22%) say they follow the news all or most of the time, while an additional 36% follow the news some of the time. The share of Latinos who follow the news all or most of the time has fluctuated in recent years but has dropped by 9 percentage points between 2020 (31%) and 2023 (22%), similar to a pattern seen across the general U.S. public .

In recent years, Hispanic Americans have followed the news less closely than Black and White Americans. Again, the high share of young adults within the Hispanic population plays a role, because young people are less likely to follow the news closely. Among Hispanic adults ages 18 to 29, just 10% say they follow the news all or most of the time – far below the share of Hispanics ages 65 and older who do so (44%).

Jump to more information on U.S. Hispanics’ news consumption habits.

Half of Hispanic adults get news from Hispanic news outlets

Half of U.S. Hispanic adults say they at least sometimes get news from Hispanic news outlets – those that specifically cater to Hispanic audiences. This includes 21% who say they do this extremely or very often. Just over half of Hispanics (54%) get news about their or their family’s country of origin at least sometimes, including 24% who do this often. 

Hispanic immigrants are much more likely than U.S.-born Hispanics to get news from Hispanic outlets and about their origin country. In both cases, about seven-in-ten immigrants say they at least sometimes get these types of news: 69% get news from Hispanic outlets and 72% get news about their country of origin. Among Hispanic adults who were born in the U.S., 33% at least sometimes get news from Hispanic outlets, and 38% get news about their family’s country of origin.

There are further differences among U.S.-born Hispanics: Those whose parents were also born in the U.S. are even less likely than those with one or more immigrant parent to get these types of news.

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New study finds genetic markers that explain up to 12% of the differences between two people's blood pressure

by NIH/National Human Genome Research Institute

National Institutes of Health researchers and collaborators have discovered over 100 new regions of the human genome, also known as genomic loci, that appear to influence a person's blood pressure. Results of the study also point to several specific genomic loci that may be relevant to iron metabolism and a type of cellular receptor known as adrenergic receptors.

The study, published in Nature Genetics , is one of the largest such genomic studies of blood pressure to date, including data from over 1 million participants and laying the groundwork for researchers to better understand how blood pressure is regulated. Such insights could point to potential new drug targets.

"Our study helps explain a much larger proportion of the differences between two people's blood pressure than was previously known," said Jacob Keaton, Ph.D., staff scientist in the Precision Health Informatics Section within the National Human Genome Research Institute's (NHGRI) Intramural Research Program and first author of the study. "Our study found additional genomic locations that together explain a much larger part of the genetic differences in people's blood pressure. Knowing a person's risk for developing hypertension could lead to tailored treatments, which are more likely to be effective."

To understand the genetics of blood pressure, the researchers combined four large datasets from genome-wide association studies of blood pressure and hypertension. After analyzing the data, they found over 2,000 genomic loci linked to blood pressure, including 113 new regions. Among the newly discovered genomic loci, several reside in genes that play a role in iron metabolism, confirming previous reports that high levels of accumulated iron can contribute to cardiovascular disease.

The researchers also confirmed the association between variants in the ADRA1A gene and blood pressure. ADRA1A encodes a type of cell receptor, called an adrenergic receptor, that is currently a target for blood pressure medication, suggesting that other genomic variants discovered in the study may also have the potential to be drug targets to alter blood pressure.

"This study shows that these big genome-wide association studies have clinical relevance for finding new drug targets and are needed to discover more drug targets as we go forward," said Dr. Keaton.

From these analyses, the researchers were able to calculate a polygenic risk score , which combines the effects of all genomic variants together to predict blood pressure and risk for hypertension. These risk scores consider which genomic variants confer risk for hypertension and reveal clinically meaningful differences between people's blood pressure.

Polygenic risk scores have potential to serve as a useful tool in precision medicine, but more diverse genomic data is needed for them to be applicable broadly in routine health care. While the collected data was mostly from people of European ancestry (due to limited availability of diverse datasets when the study was started), the researchers found that the polygenic risk scores were also applicable to people of African ancestry, which was confirmed through analyzing data from NIH's All of Us Research Program, a nationwide effort to build one of the largest biomedical data resources and accelerate research to improve human health.

Nearly half of adults in the United States have high blood pressure , known as hypertension. High blood pressure often runs in families, meaning that there is a genetic component to developing the condition in addition to environmental contributions such as a high-salt diet, lack of exercise, smoking and stress. When blood pressure is consistently too high, it can damage the heart and blood vessels throughout the body, increasing a person's risk for heart disease, kidney disease, stroke and other conditions.

The project was led by researchers at NHGRI in collaboration with Queen Mary University of London, Vanderbilt University Medical Center, Nashville, Tennessee, the University of Groningen in the Netherlands and other institutions, as part of the International Consortium of Blood Pressure. Over 140 investigators from more than 100 universities, institutes and government agencies contributed to this international study.

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New Study Bolsters Idea of Athletic Differences Between Men and Trans Women

Research financed by the International Olympic Committee introduced new data to the unsettled and fractious debate about bans on transgender athletes.

By Jeré Longman

A new study financed by the International Olympic Committee found that transgender female athletes showed greater handgrip strength — an indicator of overall muscle strength — but lower jumping ability, lung function and relative cardiovascular fitness compared with women whose gender was assigned female at birth.

That data, which also compared trans women with men, contradicted a broad claim often made by proponents of rules that bar transgender women from competing in women’s sports. It also led the study’s authors to caution against a rush to expand such policies, which already bar transgender athletes from a handful of Olympic sports.

The study’s most important finding, according to one of its authors, Yannis Pitsiladis, a member of the I.O.C.’s medical and scientific commission, was that, given physiological differences, “Trans women are not biological men.”

Alternately praised and criticized, the study added an intriguing data set to an unsettled and often politicized debate that may only grow louder with the Paris Olympics and a U.S. presidential election approaching.

The authors cautioned against the presumption of immutable and disproportionate advantages for transgender female athletes who compete in women’s sports, and they advised against “precautionary bans and sport eligibility exclusions” that were not based on sport-specific research.

Outright bans, though, continue to proliferate. Twenty-five U.S. states now have laws or regulations barring transgender athletes from competing in girls and women’s sports, according to the Movement Advancement Project , a nonprofit that focuses on gay, lesbian, bisexual and transgender parity. And the National Association of Intercollegiate Athletics , the governing body for smaller colleges, this month barred transgender athletes from competing in women’s sports unless their sex was assigned female at birth and they had not undergone hormone therapy.

Two of the most visible sports at this summer’s Paris Games — swimming and track and field — along with cycling have effectively barred transgender female athletes who went through puberty as males. Rugby has instituted a total ban on trans female athletes, citing safety concerns, and those permitted to participate in other sports often face stricter requirements in suppressing their levels of testosterone.

The International Olympic Committee has left eligibility rules for transgender female athletes up to the global federations that govern individual sports. And while the Olympic committee provided financing for the study — as it does on a variety of topics through a research fund — Olympic officials had no input or influence on the results, Dr. Pitsiladis said.

In general, the argument for the bans has been that profound advantages gained from testosterone-fueled male puberty — broader shoulders, bigger hands, longer torsos, and greater muscle mass, strength, bone density and heart and lung capacity — give transgender female athletes an inequitable and largely irreversible competitive edge.

The new laboratory-based, peer-reviewed and I.O.C.-funded study at the University of Brighton, published this month in the British Journal of Sports Medicine , tested 19 cisgender men (those whose gender identity matches the sex they were assigned at birth) and 12 trans men, along with 23 trans women and 21 cisgender women.

All of the participants played competitive sports or underwent physical training at least three times a week. And all of the trans female athletes had undergone at least a year of treatment suppressing their testosterone levels and taking estrogen supplementation, the researchers said. None of the participants were athletes competing at the national or international level.

The study found that transgender female participants showed greater handgrip strength than cisgender female participants but lower lung function and relative VO2 max, the amount of oxygen used when exercising. Transgender female athletes also scored below cisgender women and men on a jumping test that measured lower-body power.

The study acknowledged some limitations, including its small sample size and the fact that the athletes were not followed over the long term as they transitioned. And, as previous research has indicated, it found that transgender female athletes did retain at least one advantage over cisgender female athletes — a measurement of handgrip strength .

But it is a combination of factors, not a single parameter, that determines athletic performance, said Dr. Pitsiladis, a professor of sport and exercise science.

Athletes who grow taller and heavier after going through puberty as males must “carry this big skeleton with a smaller engine” after transitioning, he said. He cited volleyball as an example, saying that, for transgender female athletes, “the jumping and blocking will not be to the same height as they were doing before. And they may find that, overall, their performance is less good.”

But Michael J. Joyner, a doctor at the Mayo Clinic who studies the physiology of male and female athletes, said that, based on his research and the research of others, science supports the bans in elite sports, where events can be decided by the smallest of margins.

“We know testosterone is performance enhancing,” Dr. Joyner said. “And we know testosterone has residual effects.” Additionally, he added, declines in performance by trans women after taking drugs to suppress their testosterone levels do not fully reduce the typical differences in athletic performance between men and women.

Supporters of transgender athletes, and some scientists who disagree with bans, have accused governing bodies and lawmakers of enacting solutions for a problem that doesn’t exist. There are few elite trans female athletes, they have noted. And there has been limited scientific study of presumed unalterable advantages in strength, power and aerobic capacity gained by experiencing puberty as a male.

For those who have competed in the Olympics, results have varied widely. At the 2021 Tokyo Games, Quinn , a soccer player who is trans nonbinary and was assigned female at birth, helped Canada’s team win a gold medal. But Laurel Hubbard , a transgender weight lifter from New Zealand, failed to complete a lift in her event.

“The idea that trans women are going to take over women’s sports is ludicrous,” said Joanna Harper, a leading researcher of trans athletes and a postdoctoral scholar at Oregon Health & Science University.

Dr. Harper, who is transgender, said it was important for sports to consider physiological differences between transgender women and cisgender women and that she supported certain restrictions, such as requiring the suppression of testosterone levels. But she called blanket bans “unnecessary and unjustified” and said she welcomed the I.O.C.-funded study.

“This fear that trans women aren’t really women, that they’re men who are invading women’s sports, and that trans women will carry all of their male athleticism, their athletic capabilities, into women’s sports — neither of those things are true,” Dr. Harper said.

Sebastian Coe, the president of World Athletics, which governs global track and field, acknowledged that the science remains unresolved. But the organization decided to bar transgender female athletes from international track and field, he said, because “I’m not going to take a risk on this.”

“We think this is in the best interest of preserving the female category,” Mr. Coe said.

In at least two prominent cases, the fight over transgender bans has moved to the courts. The former University of Pennsylvania swimmer Lia Thomas is challenging a ban imposed by World Aquatics, swimming’s global governing body, after she won the 500-yard freestyle race at the 2022 N.C.A.A. championships. That victory made Thomas, who had been among the best men’s swimmers in the Ivy League, the first known trans athlete to win a women’s championship event in college sports’ top division.

Thomas did not dominate all of her races, though, finishing tied for fifth in a second race and eighth in a third. Her winning time in the 500 was more than nine seconds slower than the N.C.A.A. record. Her case, filed at the Swiss-based Court of Arbitration for Sport, is not expected to be resolved before the Paris Olympics begin in July.

Meanwhile, more than a dozen current and former U.S. college athletes, including at least one who competed against Thomas, sued the N.C.A.A. last month . They claimed that, by letting Thomas participate in the national championships, the organization had violated their rights under Title IX, the law that prohibits sex discrimination at institutions that receive federal funding. (Title IX has also been relied upon to argue in favor of transgender female athletes.)

Outsports , a website that reports on L.G.B.T.Q. issues, hailed the I.O.C.-funded study as a “landmark” that concluded that “blanket sports bans are a mistake.” But some scientists and athletes called the study deeply flawed in an article in The Telegraph , which labeled the suggestion that transgender women are at a disadvantage in sports a “new low” for the I.O.C.

So heated is the debate that Dr. Pitsiladis said he and his research team have received threats. That, he warned, could lead other scientists to shy away from pursuing research on the topic.

“Why would any scientist do this if you’re going to get totally slammed and character-assassinated?” he said. “This is no longer a science matter. Unfortunately, it’s become a political matter.”

Jeré Longman covers international sports, focusing on competitive, social, cultural and political issues around the world. More about Jeré Longman

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In brief: what types of studies are there.

Last Update: September 8, 2016 ; Next update: 2024.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

• What is the cause of the condition?
• What is the natural course of the disease if left untreated?
• What will change because of the treatment?
• How many other people have the same condition?
• How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

• Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

• How long the study should last
• How many participants are needed
• How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes .

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

• Is the new drug A better than the standard treatment for medical condition X?
• Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
• Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

• How does high blood pressure affect heart health?
• Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

• Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

• Do HPV infections increase the risk of cervical cancer ?
• Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

• Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

• How tall are German men and women at age 20?
• How many people have cancer screening?
• Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

• How do women experience a Cesarean section?
• What aspects of treatment are especially important to men who have prostate cancer ?
• How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs , are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

• German Network for Evidence-based Medicine. Glossar: Qualitative Forschung.  Berlin: DNEbM; 2011. 
• Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003. 
• Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
• Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
• Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. informedhealth.org can provide support for talks with doctors and other medical professionals, but cannot replace them. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

• Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. In brief: What types of studies are there? [Updated 2016 Sep 8].

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