Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model

Clustering based on adherence data

Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long follow-up or treatment period, and some measurements may be missing due to death or other reasons. A natural question then is how to describe adherence behavior over the whole period in a simple way. In the literature, measurements over a period are usually combined just by using averages like percentages of compliant days or percentages of doses taken. In the paper we adapt an approach where patient adherence measures are seen as a stochastic process. Repeated measures are then analyzed as a Markov chain with finite number of states rather than as independent and identically distributed observations, and the transition probabilities between the states are assumed to fully describe the behavior of a patient. The patients can then be clustered or classified using their estimated transition probabilities. These natural clusters can be used to describe the adherence of the patients, to find predictors for adherence, and to predict the future events. The new approach is illustrated and shown to be useful with a simple analysis of a data set from the DART (Development of AntiRetroviral Therapy in Africa) trial in Uganda and Zimbabwe

Prevalence, correlates for early neurological disorders and association with functioning among children and adolescents with HIV/AIDS in Uganda.

BACKGROUND: The aim of this study was to determine the prevalence of neurological disorders and their associated correlates and relations with clinical and behavioural problems among children and adolescents with HIV/AIDS (CA-HIV). METHODS: This study involved a sample of 1070 CA-HIV/caregiver dyads who were evaluated at their 6-month follow-up visit as part of their participation in the longitudinal study, 'Mental health among HIV infected CHildren and Adolescents in KAmpala and Masaka, Uganda (the CHAKA study)'. Participants completed an extensive battery of measures that included a standardized DSM-5- referenced rating scale, the parent version (5-18?years) of the Child and Adolescent Symptom Inventory-5 (CASI-5). Using logistic regression, we estimated the prevalence of neurological disorders and characterised their associations with negative clinical and behavioural factors. RESULTS: The overall prevalence of at least one neurological disorders was 18.5% (n?=?198; 95% CI, 16.2-20.8). Enuresis / encopresis was the most common (10%), followed by motor/vocal tics (5.3%); probable epilepsy was the least prevalent (4%). Correlates associated with neurological disorders were in two domains: socio-demographic factors (age, ethnicity and staying in rural areas) and HIV-related factors (baseline viral load suppression). Enuresis/encopresis was associated with psychiatric comorbidity. Neurological disorders were associated with earlier onset of sexual intercourse (adjusted OR 4.06, 95% CI 1.26-13.1, P?=?0.02). CONCLUSIONS: Neurological disorders impact lives of many children and adolescents with HIV/AIDS. There is an urgent need to integrate the delivery of mental and neurological health services into routine clinical care for children and adolescents with HIV/AIDS in sub-Saharan Africa

INSPIRE datahub: a pan-African integrated suite of services for harmonising longitudinal population health data using OHDSI tools

Introduction Population health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources. Methods The INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis. Results The INSPIRE datahub's “On-ramp” services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability. Conclusion The pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools

Towards Trustworthy Artificial Intelligence for Equitable Global Health

Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustworthy AI in global health, we convened a workshop on Fairness in Machine Intelligence for Global Health (FairMI4GH). The event brought together a global mix of experts from various disciplines, community health practitioners, policymakers, and more. Topics covered included managing AI bias in socio-technical systems, AI's potential impacts on global health, and balancing data privacy with transparency. Panel discussions examined the cultural, political, and ethical dimensions of AI in global health. FairMI4GH aimed to stimulate dialogue, facilitate knowledge transfer, and spark innovative solutions. Drawing from NIST's AI Risk Management Framework, it provided suggestions for handling AI risks and biases. The need to mitigate data biases from the research design stage, adopt a human-centered approach, and advocate for AI transparency was recognized. Challenges such as updating legal frameworks, managing cross-border data sharing, and motivating developers to reduce bias were acknowledged. The event emphasized the necessity of diverse viewpoints and multi-dimensional dialogue for creating a fair and ethical AI framework for equitable global health.Comment: 7 page

Adherence to Antiretroviral Therapy (ART) in the DART Trial in Uganda and Zimbabwe: Statistical Analysis for Predictors and Consequences of Poor Adherence

Epidemiologisissa tutkimuksissa huono hoitomyönteisyys peittää ja laimentaa terapeuttisten hoitojen vaikutusta muuten hyvin suunnitelluissa kokeissa. Kliinissä kokeissa harvoin saavutetaan optimaalista tilannetta, ja siksi huonon hoitomyönteisyyden syyt ja seuraukset muodostavat kiinnostavan tutkimuskohteen. Hoitomyönteisyyden mittaaminen ja analysointi on suuri haaste tilastollisten menetelmien kehittäjille. Tässä työssä analysoidaan 3316 HIV-potilaan muodostamaa kohorttia. Potilaat, jotka aloittivat ART-hoidon kolmessa hoitoklinikassa Ugandassa ja Zimbabwessa, arvottiin kahteen hoitoryhmään, (i) laboratorio- ja klinikkamonitorointi (LCM) ja (ii) pelkkä klinikkamonitorointi (CDM). Hoitosuunnitelman mukaisesti potilaat vierailivat klinikoissa 4 viikon välein, ja hoitomyönteisyyttä mitattiin tällöin muun muassa palautettujen lääkkeiden määrän (drug possession ratio, DPR) ja kyselylomakkeen avulla. Keskimääräinen hoitomyönteisyys parani huomattavasti ensimmäisen vuoden aikana, jonka jälkeen se heikkeni useasta syystä hitaasti 5-vuotisen seurannan loppuun asti. Tässä väitöskirjatyössä tarkastellaan erilaisia tapoja kuvata ja analysoida hoitomyönteisyysmittauksia pitkän seuranta-ajan aikana. Perinteinen tapa on keskiarvoistaa yksilön havainnot, ja sen jälkeen luokitella potilaat keskiarvokäyttäytymisen avulla. Tämä lähestymistapa kuitenkin unohtaa ilmiön dynaamisuuden. Olettaen, että yksilön mittaukset muodostavat Markovin ketjun, tutkimuksessa luokitellaan yksilöt myös käyttäen yksilöllisiä estimoituja siirtymistodennäköisyyksiä. Erilaisia tapoja luokitella potilaat vertaillaan tutkimuksessa käyttäen ristiintaulukointia, ja taustamuuttujien ja luokittelujen väliset yhteydet selvitettiin tilastollisesti (yhteensopivuustestit, yleistetyt estimoivat yhtälöt). Erilaisten hoitomyönteisyysmittreiden kykyä ennustaa potilaan kuolema ja alhainen CD4-arvo seurannan aikana oli myös tarkastelun kohteena (Coxin suhteellisen vaaran malli). Myös dynaamista logistista mallia käytettiin mallittamaan kuolemanvaaraa hoitomyönteisyyshistorian avulla, jolloin voitiin myös estimoida hoitomyönteisyyden vaikutus populaatiotasolla (populaatiosyyosuus). ART-hoitojen kannalta tärkeimmät tutkimustulokset olivat: Alhainen CD4, ja matala koulutus muun muassa ennustivat huonoa hoitomyönteisyyttä. Kuuluminen huonoimpaan Markovin ketjuihin perustuva potilasryhmän ja kuuluminen huonoimpaan DPR-ryhmään ennustivat molemmat kuolemaa toisistaan riippumatta. Huonon hoitomyönteisyyden dynaamiseen logistiseen malliin perustuva estimoitu populaatiosyyosuus 5 vuoden seurannan aikan oli 16.0 % ja 33.1 % LCM- ja CDM-ryhmissä.The aim of this doctoral thesis was to explore existing statistical methods and develop new tools to analyse adherence data. In addition to the development and description of statistical methods, this research tries to find answers to several important epidemiological questions. Analysis and understanding of adherence data is a big challenge for investigators and researchers. Poor medication adherence, for example, can lead to under-reporting of both therapeutic and adverse effects and undermine the results of the otherwise well-designed studies. In some clinical trials, optimal adherence cannot often be reached, and therefore adherence has a dual role in data analysis as an outcome and an important explanatory variable. In this work, we analyse the data from a large cohort (n = 3316) of previously untreated African individuals initiating ART in rural and urban centres in Uganda and Zimbabwe. Participants were randomly assigned to receive laboratory and clinical monitoring (LCM), or clinically driven monitoring (CDM). We observed excellent clinic attendance over the first year on antiretroviral therapy (ART). Our follow-up included 93% of those enrolled. Adherence measured by drug possession ratio (DPR) was high at each visit. Only 12% of patients maintained consistently high adherence over the course of the first year. Most patients had high adherence most of the time, with only one or two visits with less than 95% adherence, and less than 1% of the participants never achieved high adherence during the first year. Regardless of the measure, adherence increases over the first year. In this work we first explore different methods of summarising adherence data collected over a time interval. We consider traditional averaging approaches and quantile based classifications or groups of patients based on these. We also consider adherence data as a realization of a Markov chain, and use the estimated transition probabilities calculated separately for each individual as summary measures. Hierarchical clustering using these summary statistics is then used to classify the patients. Different classifications are compared by their interpretations and by cross-tabulations, the associations between group memberships and the relevant background variables are described, and the group memberships are used to predict the mortality and CD4 failures. Generalized estimating equations (GEE) were used to model for optimal adherence during the first 48 weeks (12 visits). The impact of adherence during the first 48 weeks separately on time to death and time to CD4 failure was modeled with Cox proportional hazard models. Four different adherence classifications were used as explaining factors, and comparisons were made between the models. Finally, a dynamic logistic model was used to study the association between adherence and mortality. The model allows that the probability of dying between two clinic visits is explained by recent adherence history before the latest visit (assessed again at scheduled 4-weekly clinic visits) as well as by other (time dependent or baseline) covariates. In addition to the estimates of effects at the individual level, the approach also allows for the estimation of the population attributable fraction (PAF) a population level measure of the effect of adherence on mortality. Based on our findings, a group of individuals (those with low CD4, reporting sexual partners 3 months prior to ART initiation, and low education) could be targeted for adherence-enhancing interventions both at ART initiation and in those not adhering well after a year on ART. Worst adherence class based on Markov chain (MC) approach seems to predict mortality and CD4 failure independently of the worst class based on drug possession ratio (DPR).Whilst MC modeling is best suited to a research setting, DPR can be directly calculated from late return to clinic and self reports of 4-day/weekend a simple (does not require calculation) measure are therefore most suited to a clinical setting. The estimated population attributable fractions (PAF) based on the dynamic logistic regression model, that is, the estimated proportions of deaths that could have been avoided with optimal adherence in the LCM and CDM groups during the 5 years follow-up period were 16.0% (90% CI -0.7,31.6)) and 33.1% (20.5,44.8), respectively. The estimated proportions of deaths on long-term ART that could be delayed at a population level (by eliminating non-optimal adherence) are similar to benefits from CD4 cell count monitoring of ART. In the absence of CD4 or viral load monitoring, individuals with optimal adherence experienced similar survival to those with customary adherence with CD4 monitoring suggesting that an alternative potential role of CD4 monitoring would be to reinforce adherence

Adherence to Antiretroviral Therapy (ART) in the DART Trial in Uganda and Zimbabwe: Statistical Analysis for Predictors and Consequences of Poor Adherence

Epidemiologisissa tutkimuksissa huono hoitomyönteisyys peittää ja laimentaa terapeuttisten hoitojen vaikutusta muuten hyvin suunnitelluissa kokeissa. Kliinissä kokeissa harvoin saavutetaan optimaalista tilannetta, ja siksi huonon hoitomyönteisyyden syyt ja seuraukset muodostavat kiinnostavan tutkimuskohteen. Hoitomyönteisyyden mittaaminen ja analysointi on suuri haaste tilastollisten menetelmien kehittäjille. Tässä työssä analysoidaan 3316 HIV-potilaan muodostamaa kohorttia. Potilaat, jotka aloittivat ART-hoidon kolmessa hoitoklinikassa Ugandassa ja Zimbabwessa, arvottiin kahteen hoitoryhmään, (i) laboratorio- ja klinikkamonitorointi (LCM) ja (ii) pelkkä klinikkamonitorointi (CDM). Hoitosuunnitelman mukaisesti potilaat vierailivat klinikoissa 4 viikon välein, ja hoitomyönteisyyttä mitattiin tällöin muun muassa palautettujen lääkkeiden määrän (drug possession ratio, DPR) ja kyselylomakkeen avulla. Keskimääräinen hoitomyönteisyys parani huomattavasti ensimmäisen vuoden aikana, jonka jälkeen se heikkeni useasta syystä hitaasti 5-vuotisen seurannan loppuun asti. Tässä väitöskirjatyössä tarkastellaan erilaisia tapoja kuvata ja analysoida hoitomyönteisyysmittauksia pitkän seuranta-ajan aikana. Perinteinen tapa on keskiarvoistaa yksilön havainnot, ja sen jälkeen luokitella potilaat keskiarvokäyttäytymisen avulla. Tämä lähestymistapa kuitenkin unohtaa ilmiön dynaamisuuden. Olettaen, että yksilön mittaukset muodostavat Markovin ketjun, tutkimuksessa luokitellaan yksilöt myös käyttäen yksilöllisiä estimoituja siirtymistodennäköisyyksiä. Erilaisia tapoja luokitella potilaat vertaillaan tutkimuksessa käyttäen ristiintaulukointia, ja taustamuuttujien ja luokittelujen väliset yhteydet selvitettiin tilastollisesti (yhteensopivuustestit, yleistetyt estimoivat yhtälöt). Erilaisten hoitomyönteisyysmittreiden kykyä ennustaa potilaan kuolema ja alhainen CD4-arvo seurannan aikana oli myös tarkastelun kohteena (Coxin suhteellisen vaaran malli). Myös dynaamista logistista mallia käytettiin mallittamaan kuolemanvaaraa hoitomyönteisyyshistorian avulla, jolloin voitiin myös estimoida hoitomyönteisyyden vaikutus populaatiotasolla (populaatiosyyosuus). ART-hoitojen kannalta tärkeimmät tutkimustulokset olivat: Alhainen CD4, ja matala koulutus muun muassa ennustivat huonoa hoitomyönteisyyttä. Kuuluminen huonoimpaan Markovin ketjuihin perustuva potilasryhmän ja kuuluminen huonoimpaan DPR-ryhmään ennustivat molemmat kuolemaa toisistaan riippumatta. Huonon hoitomyönteisyyden dynaamiseen logistiseen malliin perustuva estimoitu populaatiosyyosuus 5 vuoden seurannan aikan oli 16.0 % ja 33.1 % LCM- ja CDM-ryhmissä.The aim of this doctoral thesis was to explore existing statistical methods and develop new tools to analyse adherence data. In addition to the development and description of statistical methods, this research tries to find answers to several important epidemiological questions. Analysis and understanding of adherence data is a big challenge for investigators and researchers. Poor medication adherence, for example, can lead to under-reporting of both therapeutic and adverse effects and undermine the results of the otherwise well-designed studies. In some clinical trials, optimal adherence cannot often be reached, and therefore adherence has a dual role in data analysis as an outcome and an important explanatory variable. In this work, we analyse the data from a large cohort (n = 3316) of previously untreated African individuals initiating ART in rural and urban centres in Uganda and Zimbabwe. Participants were randomly assigned to receive laboratory and clinical monitoring (LCM), or clinically driven monitoring (CDM). We observed excellent clinic attendance over the first year on antiretroviral therapy (ART). Our follow-up included 93% of those enrolled. Adherence measured by drug possession ratio (DPR) was high at each visit. Only 12% of patients maintained consistently high adherence over the course of the first year. Most patients had high adherence most of the time, with only one or two visits with less than 95% adherence, and less than 1% of the participants never achieved high adherence during the first year. Regardless of the measure, adherence increases over the first year. In this work we first explore different methods of summarising adherence data collected over a time interval. We consider traditional averaging approaches and quantile based classifications or groups of patients based on these. We also consider adherence data as a realization of a Markov chain, and use the estimated transition probabilities calculated separately for each individual as summary measures. Hierarchical clustering using these summary statistics is then used to classify the patients. Different classifications are compared by their interpretations and by cross-tabulations, the associations between group memberships and the relevant background variables are described, and the group memberships are used to predict the mortality and CD4 failures. Generalized estimating equations (GEE) were used to model for optimal adherence during the first 48 weeks (12 visits). The impact of adherence during the first 48 weeks separately on time to death and time to CD4 failure was modeled with Cox proportional hazard models. Four different adherence classifications were used as explaining factors, and comparisons were made between the models. Finally, a dynamic logistic model was used to study the association between adherence and mortality. The model allows that the probability of dying between two clinic visits is explained by recent adherence history before the latest visit (assessed again at scheduled 4-weekly clinic visits) as well as by other (time dependent or baseline) covariates. In addition to the estimates of effects at the individual level, the approach also allows for the estimation of the population attributable fraction (PAF) a population level measure of the effect of adherence on mortality. Based on our findings, a group of individuals (those with low CD4, reporting sexual partners 3 months prior to ART initiation, and low education) could be targeted for adherence-enhancing interventions both at ART initiation and in those not adhering well after a year on ART. Worst adherence class based on Markov chain (MC) approach seems to predict mortality and CD4 failure independently of the worst class based on drug possession ratio (DPR).Whilst MC modeling is best suited to a research setting, DPR can be directly calculated from late return to clinic and self reports of 4-day/weekend a simple (does not require calculation) measure are therefore most suited to a clinical setting. The estimated population attributable fractions (PAF) based on the dynamic logistic regression model, that is, the estimated proportions of deaths that could have been avoided with optimal adherence in the LCM and CDM groups during the 5 years follow-up period were 16.0% (90% CI -0.7,31.6)) and 33.1% (20.5,44.8), respectively. The estimated proportions of deaths on long-term ART that could be delayed at a population level (by eliminating non-optimal adherence) are similar to benefits from CD4 cell count monitoring of ART. In the absence of CD4 or viral load monitoring, individuals with optimal adherence experienced similar survival to those with customary adherence with CD4 monitoring suggesting that an alternative potential role of CD4 monitoring would be to reinforce adherence

Effect of justification of wife-beating on experiences of intimate partner violence among men and women in Uganda: A propensity-score matched analysis of the 2016 Demographic Health Survey data.

IntroductionIn some communities, rationalization of men's controlling attitudes is associated with the justification of gender norms such as wife-beating as a method of correcting spouse behaviour. In this quasi-experimental study, we investigate the causal effects of the acceptability of gender norms justifying wife-beating on experiences of sexual, emotional, and physical intimate partner violence (IPV) among Ugandan men and women.Methods and materialsWe analysed the 2016 Uganda Demographic and Health Survey data using propensity-score matching. The exposure variable is the acceptability of gender norms justifying wife-beating measured on a binary scale and the outcomes are the respondent's lifetime experiences of sexual, physical, and emotional IPV. We matched respondents who accepted gender norms justifying wife-beating with those that never through a 1:1 nearest-neighbour matching with a caliper to achieve comparability on selected covariates. We then estimated the causal effects of acceptability of gender norms justifying wife-beating on the study outcomes using a logistic regression model.ResultsResults showed that a total of 4,821 (46.5%) out of 10,394 respondents reported that a husband is justified in beating his wife for specific reasons. Among these, the majority (3,774; 78.3%) were women compared to men (1,047; 21.7%). Overall, we found that men and women who accept gender norms justifying wife-beating are more likely to experience all three forms of IPV. In the sub-group analysis, men who justify wife-beating were more likely to experience emotional and physical IPV but not sexual IPV. However, women who justify wife-beating were more likely to experience all three forms of IPV.ConclusionsIn conclusion, the acceptability of gender norms justifying wife-beating has a positive effect on experiences of different forms of IPV by men and women in Uganda. There is, therefore, a need for more research to study drivers for acceptance of gender norms justifying wife-beating to enable appropriate government agencies to put in place mechanisms to address the acceptability of gender norms justifying wife-beating at the societal level

Odds ratio estimates of the effect of justification of wife-beating on experiences of different forms of IPV.

Odds ratio estimates of the effect of justification of wife-beating on experiences of different forms of IPV.</p

Covariate balance before and after PSM.

IntroductionIn some communities, rationalization of men’s controlling attitudes is associated with the justification of gender norms such as wife-beating as a method of correcting spouse behaviour. In this quasi-experimental study, we investigate the causal effects of the acceptability of gender norms justifying wife-beating on experiences of sexual, emotional, and physical intimate partner violence (IPV) among Ugandan men and women.Methods and materialsWe analysed the 2016 Uganda Demographic and Health Survey data using propensity-score matching. The exposure variable is the acceptability of gender norms justifying wife-beating measured on a binary scale and the outcomes are the respondent’s lifetime experiences of sexual, physical, and emotional IPV. We matched respondents who accepted gender norms justifying wife-beating with those that never through a 1:1 nearest-neighbour matching with a caliper to achieve comparability on selected covariates. We then estimated the causal effects of acceptability of gender norms justifying wife-beating on the study outcomes using a logistic regression model.ResultsResults showed that a total of 4,821 (46.5%) out of 10,394 respondents reported that a husband is justified in beating his wife for specific reasons. Among these, the majority (3,774; 78.3%) were women compared to men (1,047; 21.7%). Overall, we found that men and women who accept gender norms justifying wife-beating are more likely to experience all three forms of IPV. In the sub-group analysis, men who justify wife-beating were more likely to experience emotional and physical IPV but not sexual IPV. However, women who justify wife-beating were more likely to experience all three forms of IPV.ConclusionsIn conclusion, the acceptability of gender norms justifying wife-beating has a positive effect on experiences of different forms of IPV by men and women in Uganda. There is, therefore, a need for more research to study drivers for acceptance of gender norms justifying wife-beating to enable appropriate government agencies to put in place mechanisms to address the acceptability of gender norms justifying wife-beating at the societal level.</div