Global Epidemiology最新文献

{"title":"Magnitude and correlates of overweight or obesity among adults with diabetes mellitus duration of five or more years in rural Uganda: A cross-sectional study","authors":"Boniface Amanee Elias Lumori ,&nbsp;Lodiong Jackson Dumo Lodiong ,&nbsp;Ucama Ufoymungu Patrick ,&nbsp;Jonathan Izudi","doi":"10.1016/j.gloepi.2026.100254","DOIUrl":"10.1016/j.gloepi.2026.100254","url":null,"abstract":"<div><h3>Background</h3><div>Overweight and obesity are major risk factors for micro and macrovascular diseases, with diabetes mellitus exacerbating these burdens. We determined the prevalence of overweight or obesity and its associated factors among adults with diabetes mellitus with a duration of ≥5 years at Mbarara Regional Referral Hospital in Southwestern Uganda.</div></div><div><h3>Methods</h3><div>This analytic cross-sectional study was conducted over 5 months, from November 2017 to March 2018. We collected demographic and clinical data and measured body mass index (BMI). The outcome was overweight or obesity, measured as the proportion of individuals with a BMI of 25 kg/m² or more. We used binary logistic regression to estimate associations of a priori selected variables with the outcome.</div></div><div><h3>Results</h3><div>Of 189 participants, 138 (73%) were female, the mean age was 61.5 ± 11.1 years, and the median duration of diabetes mellitus since diagnosis was 10 years (interquartile range, 7–15). Overall, 122 (64.5%) participants were overweight or obese. In the multivariable logistic regression analysis, former cigarette smoking (adjusted odds ratio (AOR) 0.2, 95% confidence interval (CI) 0.1–0.6), every 1-year increase in the duration of diabetes mellitus (AOR 1.1, 95% CI 1.0–1.1), and hypertension (AOR 2.8, 95% CI 1.2–6.5) were independently associated with overweight or obesity.</div></div><div><h3>Conclusion</h3><div>Overweight/obesity is prevalent among adults with diabetes mellitus duration of 5 years and over, in a rural Ugandan population. Former cigarette smokers have a decreased likelihood of being overweight or obese, while hypertension and every 1-year increase in the duration of diabetes mellitus are associated with a higher likelihood of being overweight or obese.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147378863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistically significant results from low-power analyses: A comedy of errors","authors":"Cyril Jaksic,&nbsp;Thomas Perneger,&nbsp;Christophe Combescure","doi":"10.1016/j.gloepi.2026.100250","DOIUrl":"10.1016/j.gloepi.2026.100250","url":null,"abstract":"<div><h3>Background</h3><div>When low-power analyses yield statistically significant results, they likely overestimate the true effect. Although sample estimates are symmetrically distributed around the true value, those that are by chance very high are more likely to achieve statistical significance. The bias induced by the significance filter increases as power decreases. Here we sought to quantify the estimation bias associated with low power and to contrast it with the type M error, which assesses the same phenomenon from a different perspective.</div></div><div><h3>Methods</h3><div>We used simulations to quantify estimation bias in relation to power among statistically significant results. We computed the type M error, relative bias (ratio of the estimated mean differences and the true value), and proportions of results with various levels of over- and under-estimation.</div></div><div><h3>Results</h3><div>For a medium effect size (Cohen's d of 0.5), overestimation of the mean difference was moderate at high power (≥80%): relative bias was &lt;1.13, about 65% of estimates were roughly accurate (between 0.75 and 1.25 of the true value), and sign errors were virtually absent. In contrast, at low power (&lt;30%), overestimation was strong (relative bias &gt;1.78), and almost no estimates were roughly accurate. Sign errors became noticeably prevalent only at very low levels of power (&lt;10%). In all situations, the relative bias had a lower magnitude than the type M error.</div></div><div><h3>Conclusion</h3><div>Low-power statistically significant results may consist entirely of magnitude errors, sign errors, and type 1 errors with high risk of strong overestimation (double effect). Readers should beware positive results from low-power analyses.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of social support, risk behaviors, and HIV health service use among adolescent and young males and females in Lusaka, Zambia","authors":"Sanjana Batabyal ,&nbsp;Ronald Mungoni ,&nbsp;Drosin Mulenga ,&nbsp;Nachela Chelwa ,&nbsp;Michael Mbizvo ,&nbsp;Laura Nyblade ,&nbsp;Yevgeniya Kaganova ,&nbsp;Sonja Hoover ,&nbsp;Sujha Subramanian","doi":"10.1016/j.gloepi.2025.100240","DOIUrl":"10.1016/j.gloepi.2025.100240","url":null,"abstract":"<div><h3>Background</h3><div>Human immunodeficiency virus (HIV) remains the leading cause of death in Zambia. While females are disproportionately affected by HIV, males – especially young males – are vulnerable to the disease due to a variety of risk factors. This study aimed to understand what, if any, sex-related differences exist between young females and males on social support, risk behavior, and HIV healthcare utilization issues.</div></div><div><h3>Methods</h3><div>Baseline survey responses from an implementation trial (<span><span>NCT03995953</span><svg><path></path></svg></span>) were examined for 863 females and 302 males affected by HIV between ages 15 and 26. We created summary statistics related to peer and familial support, risk factors (i.e., physical safety, economic security, mental health, substance abuse, and sexual behavior), and HIV healthcare utilization. Summary statistics were evaluated for statistical significance through Pearson Chi-Square testing.</div></div><div><h3>Findings</h3><div>Females and males, regardless of HIV status, have higher average confidence in familial support (67 %) than peer support (40 %). Across HIV status, females and males had similar rates of physical safety risk. Regardless of HIV status, about half the participants reported worrying about running out of food. Substance abuse risk is higher among males; 15 % of males at risk of HIV and 7 % of males living with HIV report drug usage other than alcohol or marijuana compared to just 1 % of all females. Among individuals at risk of HIV, there are differences in rates of HIV testing by sex: 27.7 % among males vs. 6.7 % among females.</div></div><div><h3>Interpretations</h3><div>While there are some differences, the many similarities between young females and males suggest that joint interventions which incorporate familial support could be beneficial to address shared risk factors. These joint interventions can be supplemented with sex-specific interventions related to substance abuse for males and HIV testing for females.</div></div><div><h3>Funding</h3><div>Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute Of Child Health &amp; Human Development of the National Institutes of Health under Award Number UH3HD096908. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the proper use of p-values and confidence intervals in leading epidemiology journals","authors":"Montana Kekaimalu Hunter ,&nbsp;Anthony James Russell ,&nbsp;George Maldonado ,&nbsp;Igor Burstyn","doi":"10.1016/j.gloepi.2026.100247","DOIUrl":"10.1016/j.gloepi.2026.100247","url":null,"abstract":"<div><div>Misinterpretation of null-hypothesis tests (<em>p</em>-values) and confidence intervals has been a longstanding issue in epidemiology. Despite efforts by leading journals to discourage or ban such practices, the extent of misinterpretations in modern epidemiologic literature remains unclear. We examined papers published in 2022 in three leading epidemiology journals (International Journal of Epidemiology, Epidemiology, and American Journal of Epidemiology) to assess the frequency and types of misinterpretations of <em>p</em>-values and confidence intervals. We randomly sampled 64 papers that assessed exposure-outcome relationships. Two authors independently reviewed the selected papers, cataloging misinterpretations according to guidelines published in 2016. While concerns about <em>p</em>-value misuse persist in scientific literature, our review of recent epidemiological studies reveals encouraging progress: outright statistical misinterpretations were not observed in the leading journals. We identified subtle opportunities to enhance reporting, including reducing reliance on binary “significant” vs. “non-significant” language, more consistently pairing <em>p</em>-values with effect sizes, and fuller interpretations of confidence intervals. In a sense, our concerns relate to the suitability of null hypothesis testing framework in epidemiology, rather than its correct application. Notably, we highlight examples of commendable practices where studies successfully integrated statistical results with clinical and public health context. Modern epidemiological research shows improved statistical reporting, while some concerns persist. Importantly, the findings of this review apply only to the primary results as reported in published manuscripts and do not extend to the broader analytic process that generates those results. Such assumptions are not secondary to hypothesis testing; rather, they contribute as much to the resulting <em>p</em>-value as the target hypothesis itself and overlooking them can lead to overly optimistic interpretations. Recognizing this distinction is essential for contextualizing our conclusions and for situating <em>p</em>-values and confidence intervals within the broader inferential framework. We recommend targeted refinements: avoiding binary language, mandating effect size reporting, and developing methods to interpret confidence intervals beyond null-hypothesis testing. These steps will align the field with evolving standards while preserving the utility of <em>p</em>-values where appropriate.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling seizure-related predictors of epilepsy diagnostic gap in two urban informal settlements of Nairobi using machine learning","authors":"Daniel Mwanga ,&nbsp;Frederick Murunga Wekesah ,&nbsp;Frank Ouma ,&nbsp;Symon M. Kariuki ,&nbsp;Joan Kinuthia ,&nbsp;Peter Otieno ,&nbsp;Thomas Kwasa ,&nbsp;Quincy Mongare ,&nbsp;Abigael Machuka ,&nbsp;Steve Cygu ,&nbsp;Samuel Iddi ,&nbsp;Gabriel Davis Jones ,&nbsp;Arjune Sen ,&nbsp;Charles R. Newton ,&nbsp;Gershim Asiki ,&nbsp;Damazo T. Kadengye ,&nbsp;for the EPInA Study Group","doi":"10.1016/j.gloepi.2025.100241","DOIUrl":"10.1016/j.gloepi.2025.100241","url":null,"abstract":"<div><h3>Background</h3><div>There is a wide gap in epilepsy diagnosis, particularly in low- and middle-income countries. We used machine learning models to identify seizure-related factors associated with the epilepsy diagnostic gap within the Nairobi Urban Health and Demographic Surveillance System (NUHDSS), Kenya, to inform effective community-level interventions.</div></div><div><h3>Methods</h3><div>Data were drawn from a two-stage, population-based census. In Stage-I, 56,425 residents of NUHDSS were screened for possible convulsive and non-convulsive epilepsy using a standardized questionnaire. In Stage-II, individuals who screened positive were invited for clinical assessment and diagnostic confirmation by neurologists. We used latent class analysis to classify symptom patterns. Seven machine learning models were trained, with extreme gradient boost and random forest models achieving the highest area under the receiver operating characteristic curve (98 %).</div></div><div><h3>Results</h3><div>A total of 528 individuals were diagnosed with epilepsy, among whom 80 % (<em>n</em> = 420) had not been previously diagnosed. The epilepsy diagnostic gap was 100 % (<em>n</em> = 160/160) in persons with non-convulsive epilepsy, meaning that none of them had been diagnosed before the survey. Among those with convulsive epilepsy, the diagnostic gap was 71 % (<em>n</em> = 260/368). Experiencing fewer types of seizure symptoms, non-convulsive seizures, or seizures with subtle features, such as those involving only one body part and those whose first experience of a seizure was recent, were associated with a wider epilepsy diagnostic gap.</div></div><div><h3>Conclusion</h3><div>There is critically huge diagnostic gap for epilepsy in Nairobi's informal settlements. People with subtle, fewer or less obvious seizure types are more likely to be undiagnosed. These findings highlight the importance of seizure symptom characteristics in understanding patterns of underdiagnosis. Thus, approaches to reducing the diagnostic gap should take into consideration subtle and non-convulsive seizure presentations, such as training on symptom recognition and timely care-seeking.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial distribution and multilevel analysis of full vaccination coverage among children aged 12–23 months in Democratic Republic of Congo","authors":"Nigussie Adam Birhan ,&nbsp;Abebew Aklog Asmare ,&nbsp;Kefale Tilahun Getahun ,&nbsp;Gedif Mulat Alemayehu ,&nbsp;Zelalem Meraf Wolde ,&nbsp;Denekew Bitew Belay","doi":"10.1016/j.gloepi.2026.100256","DOIUrl":"10.1016/j.gloepi.2026.100256","url":null,"abstract":"<div><h3>Background</h3><div>Vaccination is one of the most cost-effective public health interventions for reducing child morbidity and mortality. However, full vaccination coverage remains suboptimal in many low- and middle-income countries, including Democratic Republic Congo (DRC). This study aimed to assess spatial distribution and risk factors of full vaccination coverage among children aged 12–23 months in DRC.</div></div><div><h3>Methods</h3><div>A cross sectional secondary data analysis on 2023/24 DRC Demographic and Health Survey data with a total weighted sample of 3897 children aged 12–23 months was used. Moran's I and Getis-Ord Gi* statistics was used to identify clustering patterns of full vaccination coverage. Multilevel analysis was used to examine factors associated with full vaccination coverage.</div></div><div><h3>Result</h3><div>The prevalence of full vaccination was 14.9% (95% CI: 13.8, 16.0). Moran's I 0.4 (<em>p</em> value = 0.01) indicated spatial clustering of full vaccination coverage. Full vaccination coverage was associated with maternal age 25–34 years (AOR = 1.54, 95% CI: 1.06–2.24), secondary and above education (AOR = 1.84, 95% CI: 1.18–2.88), being married (AOR = 1.55, 95% CI: 1.15–2.09), rich household wealth (AOR = 1.64, 95% CI: 1.06–2.51), 1–7 antenatal care visits (AOR = 2.25, 95% CI: 1.40–3.62), health facility delivery (AOR = 3.34, 95% CI: 1.87–5.97), and rural residence (AOR = 0.59, 95% CI: 0.39–0.89).</div></div><div><h3>Conclusion</h3><div>Full vaccination coverage in DRC is low and unevenly distributed; with cold spots in Mongala, part of Bas Uele and Tshuapa regions. Hence, targeted interventions focusing on identified cold spot areas, improving maternal education, expanding healthcare access, and promoting antenatal care and institutional delivery are essential to increase vaccine coverage.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on “do certain blood groups increase COVID-19 severity and mortality?”","authors":"Prashant Ramdas Kokiwar ,&nbsp;Amit Singh Pawaiya ,&nbsp;Ranjana Roy ,&nbsp;Reenoo Jauhari","doi":"10.1016/j.gloepi.2026.100246","DOIUrl":"10.1016/j.gloepi.2026.100246","url":null,"abstract":"","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A temporal network analysis of drug co-prescription during antidepressants and anxiolytics dispensing in the Netherlands from 2018 to 2022","authors":"Aly Lamuri ,&nbsp;Spyros Balafas ,&nbsp;Eelko Hak ,&nbsp;Jens H. Bos ,&nbsp;Frederike Jörg ,&nbsp;Talitha L. Feenstra","doi":"10.1016/j.gloepi.2026.100248","DOIUrl":"10.1016/j.gloepi.2026.100248","url":null,"abstract":"<div><h3>Background:</h3><div>Drug prescription networks (DPNs) model the temporal dynamics of medication co-prescription within a population. Understanding these networks can provide insights into polypharmacy and prescribing behaviors.</div></div><div><h3>Objective:</h3><div>This study assesses the structural characteristics of temporal DPNs derived from daily co-prescriptions of antidepressants, anxiolytics, and other therapeutic drug classes. By analyzing these networks using eigenvector centrality, we identify influential medications and prescribing patterns.</div></div><div><h3>Methods:</h3><div>We utilized the IADB.nl database, including prescriptions from 128 Dutch pharmacies (2018–2022). A cohort of patients prescribed antidepressants/anxiolytics was extracted. Medications were classified using the Anatomical Therapeutic Chemical (ATC) system into 24 therapeutic classes. Time-varying DPNs were constructed as undirected graphs using symmetric daily dose-adjusted co-prescriptions. Eigenvector centrality (<span><math><msub><mrow><mi>c</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span>) quantified relative nodal importance. Weekly-aggregated data included number of dispensing (<span><math><msub><mrow><mi>n</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>) and eigenvector centrality, which were decomposed using a singular-spectrum approach.</div></div><div><h3>Results:</h3><div>Antidepressants (<span><math><msub><mrow><mi>c</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span>: 0.09, <span><math><msub><mrow><mi>n</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>: 28,993) and anxiolytics (<span><math><msub><mrow><mi>c</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span>: 0.05, <span><math><msub><mrow><mi>n</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>: 14,061) had high eigenvector centrality, demonstrating frequent co-prescription. Other ATC groups with high centrality included those for the alimentary tract and metabolism (<span>A01-A16</span>), blood and blood-forming organs (<span>B01-B06</span>), cardiovascular system (<span>C01-C10</span>), respiratory system (<span>R01-R07</span>), and analgesics (<span>N02</span>).</div></div><div><h3>Discussion:</h3><div>DPNs revealed key co-prescription patterns. High-centrality medications highlight potential targets for drug monitoring, such as identifying co-prescription trends that may warrant evaluation for safety, appropriateness, or policy oversight. This approach aids in identifying influential medications and refining prescribing oversight.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On misconceptions about the Brier score in binary prediction models","authors":"Linard Hoessly","doi":"10.1016/j.gloepi.2025.100242","DOIUrl":"10.1016/j.gloepi.2025.100242","url":null,"abstract":"<div><div>The Brier score is a widely used metric in epidemiological and clinical research for evaluating the accuracy of probabilistic predictions for binary outcomes, such as disease occurrence, treatment response, and screening performance. Despite its popularity, the Brier score is frequently misunderstood, leading to flawed interpretation of prediction models and potentially misguided public health and clinical decisions. This study aims to didactically clarify common misconceptions about realised Brier scores and to provide practical, statistically rigorous guidance for its correct interpretation in epidemiologic and public health prediction models. We analytically examined its statistical properties and conducted simulation studies across diverse scenarios, varying the distribution of true outcome probabilities, prediction accuracy, sample size, and event prevalence. Five prevalent misconceptions were identified, including the mistaken belief that a Brier score of zero indicates a perfect model. Analytic arguments and simulations demonstrated that even perfectly specified models yield non-zero Brier scores under realistic conditions. The Brier score was shown to reflect not only prediction accuracy but also the underlying distribution of true risks and random variation in outcomes. Comparisons across different populations or disease settings can therefore be misleading, and the Brier score does not directly measure calibration. We recommend restricting comparisons to the same population and complementing the Brier score with calibration metrics and measures of clinical or public health utility. Adopting these practices will improve the validity and interpretability of risk prediction in epidemiologic research and enhance decision-making in population health.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Socioeconomic and regional determinants of optimal antenatal care utilization among women in South and Central Somalia","authors":"Mohamed Abdirahim Omar ,&nbsp;Yahye Sheikh Abdulle Hassan ,&nbsp;Abdirasak Sharif Ali ,&nbsp;Mohamed Mustaf Ahmed","doi":"10.1016/j.gloepi.2025.100239","DOIUrl":"10.1016/j.gloepi.2025.100239","url":null,"abstract":"<div><h3>Background</h3><div>Somalia faces one of the world's highest maternal mortality ratios, and fewer than one in sixteen pregnant women obtain the previously recommended minimum of four antenatal care (ANC) visits. Understanding the drivers of low ANC use is essential for responsive policy. We investigated the prevalence and determinants of optimal ANC use (≥4 visits) among women in South and Central Somalia using nationally representative survey data.</div></div><div><h3>Methods</h3><div>We conducted a cross-sectional analysis of the 2020 Somali Health and Demographic Survey. The weighted analytic sample comprised 4124 women aged 15–49 years with complete data. Survey-adjusted descriptive statistics characterized ANC use. Bivariate associations and multivariable survey logistic regression identified independent predictors; adjusted odds ratios (aORs) with 95 % confidence intervals (CIs) are reported.</div></div><div><h3>Results</h3><div>Only 5.7 % of women (95 % CI 4.3–7.6) had four or more ANC visits. After adjustment, secondary education (aOR 2.33, 95 % CI 1.01–5.40) and higher education (aOR 5.36, 95 % CI 1.58–18.15) were associated with optimal ANC. Household wealth showed a graded increase, with the richest quintile having nearly 30 times the odds compared with the poorest (aOR 29.66, 95 % CI 8.51–101.27). Home delivery was associated with lower odds of optimal ANC (aOR 0.29, 95 % CI 0.18–0.47). Regional disparities persisted: women in Bay (aOR 4.07, 95 % CI 1.22–13.60) and Galgaduud (aOR 2.74, 95 % CI 1.13–6.64) had higher odds than those in Mudug.</div></div><div><h3>Conclusion</h3><div>Optimal ANC coverage in South and Central Somalia remains critically low. Priorities include reducing financial and geographic barriers to care, strengthening facility-based services, and promoting female education to improve maternal and neonatal outcomes.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100239"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}