Pub Date : 2026-01-21DOI: 10.1016/j.gloepi.2026.100251
Julie E. Goodman, Denali N. Boon
The International Agency for Research on Cancer (IARC) conducted a quantitative bias analysis (QBA) of talc use and ovarian cancer in Monograph 136. While the inclusion of a QBA was an important improvement compared to prior monographs, it was based on “best guesses” of sensitivity and specificity, rather than available data on talc recall. IARC incorporated some uncertainty in its analysis, but did not consider uncertainty around each sensitivity and specificity value. IARC concluded that a positive association between talc and ovarian cancer was credible but, even setting aside methodological issues, the QBA clearly showed that cohort study results were very similar before and after adjustment with various assumptions about sensitivity and specificity, and that case-control studies results were greatly attenuated. Thus, IARC's conclusions are inconsistent with the analyses presented in the Monograph, which clearly demonstrate that exposure misclassification could fully explain associations in case-control studies, and that epidemiology evidence does not support an association between talc and ovarian cancer. We propose that future IARC QBAs rely on empirical data rather than expert guesses (when possible), are fully transparent, consider all relevant information (including dose-response data), and use probabilistic and Bayesian analyses to address uncertainties.
{"title":"Interpretation of the IARC quantitative bias analysis of talc and ovarian cancer","authors":"Julie E. Goodman, Denali N. Boon","doi":"10.1016/j.gloepi.2026.100251","DOIUrl":"10.1016/j.gloepi.2026.100251","url":null,"abstract":"<div><div>The International Agency for Research on Cancer (IARC) conducted a quantitative bias analysis (QBA) of talc use and ovarian cancer in Monograph 136. While the inclusion of a QBA was an important improvement compared to prior monographs, it was based on “best guesses” of sensitivity and specificity, rather than available data on talc recall. IARC incorporated some uncertainty in its analysis, but did not consider uncertainty around each sensitivity and specificity value. IARC concluded that a positive association between talc and ovarian cancer was credible but, even setting aside methodological issues, the QBA clearly showed that cohort study results were very similar before and after adjustment with various assumptions about sensitivity and specificity, and that case-control studies results were greatly attenuated. Thus, IARC's conclusions are inconsistent with the analyses presented in the Monograph, which clearly demonstrate that exposure misclassification could fully explain associations in case-control studies, and that epidemiology evidence does not support an association between talc and ovarian cancer. We propose that future IARC QBAs rely on empirical data rather than expert guesses (when possible), are fully transparent, consider all relevant information (including dose-response data), and use probabilistic and Bayesian analyses to address uncertainties.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077925","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}
Pub Date : 2026-01-17DOI: 10.1016/j.gloepi.2026.100246
Prashant Ramdas Kokiwar , Amit Singh Pawaiya , Ranjana Roy , Reenoo Jauhari
{"title":"Comment on “do certain blood groups increase COVID-19 severity and mortality?”","authors":"Prashant Ramdas Kokiwar , Amit Singh Pawaiya , Ranjana Roy , 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":"2026-01-17","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}
Pub Date : 2026-01-16DOI: 10.1016/j.gloepi.2026.100250
Cyril Jaksic, Thomas Perneger, Christophe Combescure
Background
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.
Methods
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.
Results
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 <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 (<30%), overestimation was strong (relative bias >1.78), and almost no estimates were roughly accurate. Sign errors became noticeably prevalent only at very low levels of power (<10%). In all situations, the relative bias had a lower magnitude than the type M error.
Conclusion
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.
当低功率分析产生统计上显著的结果时，他们可能高估了真实效果。虽然样本估计值是围绕真实值对称分布的，但那些偶然非常高的样本更有可能实现统计显著性。显著性滤波器引起的偏置随着功率的减小而增大。在这里，我们试图量化与低功率相关的估计偏差，并将其与从不同角度评估相同现象的M型误差进行对比。方法我们使用模拟来量化统计显著结果中与功率相关的估计偏差。我们计算了M型误差、相对偏差（估计的平均差异与真实值的比率），以及不同程度的高估和低估的结果比例。结果对于中等效应量（Cohen’s d = 0.5），在高功率下平均差的高估是中度的（≥80%）：相对偏倚为1.13，约65%的估计大致准确（真实值的0.75至1.25之间），符号误差几乎不存在。相比之下，在低功率（30%）下，高估是强烈的（相对偏差>；1.78），几乎没有估计是大致准确的。只有在非常低的功率水平（<10%）下，符号错误才变得明显普遍。在所有情况下，相对偏差的幅度都低于M型误差。结论低功率统计显著性结果可能完全由幅度误差、符号误差和1型误差组成，且有较高的强高估风险（双效应）。读者应该警惕低功率分析的积极结果。
{"title":"Statistically significant results from low-power analyses: A comedy of errors","authors":"Cyril Jaksic, Thomas Perneger, 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 <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 (<30%), overestimation was strong (relative bias >1.78), and almost no estimates were roughly accurate. Sign errors became noticeably prevalent only at very low levels of power (<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":"2026-01-16","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}
International migration has increased significantly in recent decades, creating major challenges for health systems, particularly in transit and destination countries such as Morocco. Migrants, often living in precarious socio-economic conditions, face multiple obstacles to healthcare access, which heightens their vulnerability. This narrative review aims to identify the main barriers and facilitators to healthcare access for migrants in Morocco, using Levesque's conceptual framework.
Methods
A literature search was conducted in PubMed, Web of Science, Scopus and Google Scholar, as well as in institutional sources (WHO, IOM, UNHCR, HCP). Relevant publications published between 2013 and 2025 in French or English were critically reviewed. The analysis was structured according to the five dimensions of Levesque's framework: accessibility, acceptability, availability, affordability and appropriateness.
Results
Twenty-two publications were included. Findings reveal that migrants in Morocco face economic, geographical, linguistic, sociocultural, administrative and structural barriers. Financial constraints, lack of health coverage, and regional disparities are among the most significant obstacles. However, several facilitators were also identified, including NGO initiatives, community-based support, and inclusive public policies.
Conclusion
Migrant access to healthcare in Morocco remains shaped by complex and multidimensional challenges. Despite notable progress, greater coordination, sustainability, and cultural sensitivity are required to ensure equitable and universal access to healthcare for all migrants, regardless of legal or economic status.
近几十年来，国际移徙显著增加，给卫生系统带来了重大挑战，特别是在摩洛哥等过境国和目的地国。移徙者往往生活在不稳定的社会经济条件下，在获得医疗保健方面面临多重障碍，这加剧了他们的脆弱性。本叙述性审查旨在利用Levesque的概念框架，确定摩洛哥移民获得医疗保健的主要障碍和促进因素。方法在PubMed、Web of Science、Scopus、b谷歌Scholar以及机构来源（WHO、IOM、UNHCR、HCP）进行文献检索。对2013年至2025年间以法文或英文出版的相关出版物进行了严格审查。根据Levesque框架的五个维度进行分析：可及性、可接受性、可获得性、可负担性和适当性。结果共纳入文献22篇。调查结果显示，摩洛哥的移民面临经济、地理、语言、社会文化、行政和结构障碍。财政限制、缺乏医疗保险和区域差异是最重要的障碍。然而，也确定了几个促进因素，包括非政府组织倡议、社区支持和包容性公共政策。结论：摩洛哥移民获得医疗保健的机会仍然受到复杂和多方面挑战的影响。尽管取得了显著进展，但需要加强协调、可持续性和文化敏感性，以确保所有移徙者，无论其法律或经济地位如何，都能公平和普遍地获得医疗保健。
{"title":"Barriers and facilitators to healthcare access for migrants in Morocco: A narrative review","authors":"Amaghdour Chaimaa , Farhan Houssein Ali , Belouali Radouane , Hassouni Kenza","doi":"10.1016/j.gloepi.2026.100249","DOIUrl":"10.1016/j.gloepi.2026.100249","url":null,"abstract":"<div><h3>Background</h3><div>International migration has increased significantly in recent decades, creating major challenges for health systems, particularly in transit and destination countries such as Morocco. Migrants, often living in precarious socio-economic conditions, face multiple obstacles to healthcare access, which heightens their vulnerability. This narrative review aims to identify the main barriers and facilitators to healthcare access for migrants in Morocco, using Levesque's conceptual framework.</div></div><div><h3>Methods</h3><div>A literature search was conducted in PubMed, Web of Science, Scopus and Google Scholar, as well as in institutional sources (WHO, IOM, UNHCR, HCP). Relevant publications published between 2013 and 2025 in French or English were critically reviewed. The analysis was structured according to the five dimensions of Levesque's framework: accessibility, acceptability, availability, affordability and appropriateness.</div></div><div><h3>Results</h3><div>Twenty-two publications were included. Findings reveal that migrants in Morocco face economic, geographical, linguistic, sociocultural, administrative and structural barriers. Financial constraints, lack of health coverage, and regional disparities are among the most significant obstacles. However, several facilitators were also identified, including NGO initiatives, community-based support, and inclusive public policies.</div></div><div><h3>Conclusion</h3><div>Migrant access to healthcare in Morocco remains shaped by complex and multidimensional challenges. Despite notable progress, greater coordination, sustainability, and cultural sensitivity are required to ensure equitable and universal access to healthcare for all migrants, regardless of legal or economic status.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023151","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}
Pub Date : 2026-01-14DOI: 10.1016/j.gloepi.2026.100248
Aly Lamuri , Spyros Balafas , Eelko Hak , Jens H. Bos , Frederike Jörg , Talitha L. Feenstra
Background:
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.
Objective:
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.
Methods:
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 () quantified relative nodal importance. Weekly-aggregated data included number of dispensing () and eigenvector centrality, which were decomposed using a singular-spectrum approach.
Results:
Antidepressants (: 0.09, : 28,993) and anxiolytics (: 0.05, : 14,061) had high eigenvector centrality, demonstrating frequent co-prescription. Other ATC groups with high centrality included those for the alimentary tract and metabolism (A01-A16), blood and blood-forming organs (B01-B06), cardiovascular system (C01-C10), respiratory system (R01-R07), and analgesics (N02).
Discussion:
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.
{"title":"A temporal network analysis of drug co-prescription during antidepressants and anxiolytics dispensing in the Netherlands from 2018 to 2022","authors":"Aly Lamuri , Spyros Balafas , Eelko Hak , Jens H. Bos , Frederike Jörg , 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":"2026-01-14","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}
Pub Date : 2026-01-13DOI: 10.1016/j.gloepi.2026.100247
Montana Kekaimalu Hunter , Anthony James Russell , George Maldonado , Igor Burstyn
Misinterpretation of null-hypothesis tests (p-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 p-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 p-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 p-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 p-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 p-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 p-values where appropriate.
{"title":"Exploring the proper use of p-values and confidence intervals in leading epidemiology journals","authors":"Montana Kekaimalu Hunter , Anthony James Russell , George Maldonado , 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":"2026-01-13","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}
Pub Date : 2026-01-07DOI: 10.1016/j.gloepi.2026.100244
Ricardo Perea-Jacobo , Jose Luis Soto-Ledesma , Rafael Laniado-Laborín , Dora-Luz Flores , Rogelio Zapata-Garibay , J. Eduardo González-Fagoaga , Luis Alberto García-Sánchez , Javier Robles-Flores , Liliana Guadalupe Villa-Aviles , Raquel Muñiz-Salazar
Objective
To systematically review and synthesize the available literature on the prevalence and incidence of tuberculosis in incarcerated populations in Latin America and the Caribbean, identifying regional patterns, data gaps, and key challenges and providing recommendations for strengthening TB control strategies within prison settings.
Methods
A systematic review was conducted across databases and reports using the following search terms: “tuberculosis”, “prisons”, “prisoners”, “Latin America”, “Caribbean”, “inmates”, and “social readaptation center”. The review focused on prevalence rates, country-specific studies, total prison population studied by country, diagnostic methods used, and the frequency of TB reporting.
Results
A total of 45 studies met the inclusion criteria, with most conducted in Brazil (60%), Colombia (13%), and Paraguay (9%). TB prevalence and incidence in prisons were found to be up to ten times higher than in the general population, with substantial variation in study design, diagnostic tools, and reporting standards. Structural risk factors such as overcrowding, HIV coinfection, and limited access to molecular diagnostics were frequently reported. Notably, no studies were found from Caribbean countries, and only one study was identified in Mexico, revealing significant regional data gaps.
Conclusion
TB in Latin American prisons represents a serious but underreported public health crisis. The combination of elevated incidence, limited diagnostics, and fragmented surveillance highlights the urgent need for standardized, prison-specific TB control strategies and expanded research in underrepresented regions.
{"title":"Systematic review of the tuberculosis burden in prisons: Regional patterns and gaps in Latin America and the Caribbean","authors":"Ricardo Perea-Jacobo , Jose Luis Soto-Ledesma , Rafael Laniado-Laborín , Dora-Luz Flores , Rogelio Zapata-Garibay , J. Eduardo González-Fagoaga , Luis Alberto García-Sánchez , Javier Robles-Flores , Liliana Guadalupe Villa-Aviles , Raquel Muñiz-Salazar","doi":"10.1016/j.gloepi.2026.100244","DOIUrl":"10.1016/j.gloepi.2026.100244","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically review and synthesize the available literature on the prevalence and incidence of tuberculosis in incarcerated populations in Latin America and the Caribbean, identifying regional patterns, data gaps, and key challenges and providing recommendations for strengthening TB control strategies within prison settings.</div></div><div><h3>Methods</h3><div>A systematic review was conducted across databases and reports using the following search terms: “tuberculosis”, “prisons”, “prisoners”, “Latin America”, “Caribbean”, “inmates”, and “social readaptation center”. The review focused on prevalence rates, country-specific studies, total prison population studied by country, diagnostic methods used, and the frequency of TB reporting.</div></div><div><h3>Results</h3><div>A total of 45 studies met the inclusion criteria, with most conducted in Brazil (60%), Colombia (13%), and Paraguay (9%). TB prevalence and incidence in prisons were found to be up to ten times higher than in the general population, with substantial variation in study design, diagnostic tools, and reporting standards. Structural risk factors such as overcrowding, HIV coinfection, and limited access to molecular diagnostics were frequently reported. Notably, no studies were found from Caribbean countries, and only one study was identified in Mexico, revealing significant regional data gaps.</div></div><div><h3>Conclusion</h3><div>TB in Latin American prisons represents a serious but underreported public health crisis. The combination of elevated incidence, limited diagnostics, and fragmented surveillance highlights the urgent need for standardized, prison-specific TB control strategies and expanded research in underrepresented regions.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100244"},"PeriodicalIF":0.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023137","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}
Pub Date : 2026-01-07DOI: 10.1016/j.gloepi.2026.100245
Merga Abdissa Aga
Background
Pulse pressure (PP) is an important marker of arterial stiffness and cardiovascular risk during pregnancy, yet its longitudinal determinants remain insufficiently characterized, particularly in low-resource settings.
Objective
To identify determinants of longitudinal pulse pressure among pregnant women using machine learning approaches and to compare their predictive performance with a conventional mixed-effects modeling framework.
Methods
We conducted a retrospective cohort study of 549 pregnant women attending public antenatal care services at Bishoftu General Hospital, Oromia region, Ethiopia, comprising 2760 repeated pulse pressure measurements. Pulse pressure was modeled as a continuous longitudinal outcome. Predictors included maternal sociodemographic characteristics, clinical measurements, obstetric history, and gestational age at each visit. A generalized linear mixed model, random forest regression, and XGBoost regression were applied. Participant-level data partitioning was used for model training and evaluation, and predictive performance was assessed using root mean squared error (RMSE) and mean absolute error (MAE).
Results
Tree-based machine learning models showed improved predictive performance compared with the mixed-effects model, indicating the presence of nonlinear and time-dependent relationships between predictors and pulse pressure trajectories. Maternal age, body weight, gestational age, and pulse pressure values from previous visits consistently contributed to pulse pressure prediction.
Conclusion
Machine learning methods applied to longitudinal antenatal data provide a flexible and effective framework for modeling pulse pressure dynamics during pregnancy. This approach enhances understanding of key clinical and temporal determinants and may support improved cardiovascular risk assessment in maternal health care settings.
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Pub Date : 2026-01-07DOI: 10.1016/j.gloepi.2025.100242
Linard Hoessly
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.
{"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":"2026-01-07","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}
Pub Date : 2026-01-06DOI: 10.1016/j.gloepi.2026.100243
Abdul Muyeed , Ratul Rahman , Sumaiya Islam Suchi , Kawsar Ahmed , Tahmina Akter Tithi
Background
Poor sleep quality and psychological distress are common in medical students worldwide. Understanding the relationship between sleep quality and psychological distress is crucial for enhancing student well-being and academic achievement. This study aimed to assess the prevalence and influencing factors of poor sleep quality and psychological distress among Bangladeshi medical students, and to explore sex and institutional differences.
Methods
A cross-sectional study was conducted among 378 medical students using a structured questionnaire. Data were collected using the Depression, Anxiety, and Stress Scale (DASS-21) and the Pittsburgh Sleep Quality Index (PSQI). Statistical analyses including confirmatory factor analysis (CFA), independent samples t-tests, and a bivariate test of association were conducted.
Results
The prevalence rates of poor sleep quality (67.2 %), depression (55.8 %), anxiety (58.7 %), and stress (38.6 %) were significantly high among medical students in Bangladesh. The CFA test recommended a three-factor model for DASS-21 and a two-factor model for PSQI. A moderately positive association was found between sleep quality and depression, anxiety, and stress. Independent samples t-tests showed that male students reported lower PSQI and DASS-21 scores. Additionally, depression (AOR = 2.61, 95 % CI: 1.37–4.99) and stress (AOR = 2.77, 95 % CI: 1.25–6.14) were found as the most significant predictors of sleep quality.
Conclusions
Psychological distress, excessive time spent on social media, and online games negatively influence sleep quality, while being a male, smoking, and having career-building opportunities positively influence sleep quality. Interventions aimed at reducing stress and promoting healthy sleep practices are urgently needed within medical institutions.
{"title":"Sleep quality and psychological distress among Bangladeshi medical students: Prevalence, predictors, and sex-institutional differences","authors":"Abdul Muyeed , Ratul Rahman , Sumaiya Islam Suchi , Kawsar Ahmed , Tahmina Akter Tithi","doi":"10.1016/j.gloepi.2026.100243","DOIUrl":"10.1016/j.gloepi.2026.100243","url":null,"abstract":"<div><h3>Background</h3><div>Poor <strong>s</strong>leep quality and psychological distress are common in medical students worldwide. Understanding the relationship between sleep quality and psychological distress is crucial for enhancing student well-being and academic achievement. This study aimed to assess the prevalence and influencing factors of poor sleep quality and psychological distress among Bangladeshi medical students, and to explore sex and institutional differences.</div></div><div><h3>Methods</h3><div>A cross-sectional study was conducted among 378 medical students using a structured questionnaire. Data were collected using the Depression, Anxiety, and Stress Scale (DASS-21) and the Pittsburgh Sleep Quality Index (PSQI). Statistical analyses including confirmatory factor analysis (CFA), independent samples <em>t</em>-tests, and a bivariate test of association were conducted.</div></div><div><h3>Results</h3><div>The prevalence rates of poor sleep quality (67.2 %), depression (55.8 %), anxiety (58.7 %), and stress (38.6 %) were significantly high among medical students in Bangladesh. The CFA test recommended a three-factor model for DASS-21 and a two-factor model for PSQI. A moderately positive association was found between sleep quality and depression, anxiety, and stress. Independent samples <em>t</em>-tests showed that male students reported lower PSQI and DASS-21 scores. Additionally, depression (AOR = 2.61, 95 % CI: 1.37–4.99) and stress (AOR = 2.77, 95 % CI: 1.25–6.14) were found as the most significant predictors of sleep quality.</div></div><div><h3>Conclusions</h3><div>Psychological distress, excessive time spent on social media, and online games negatively influence sleep quality, while being a male, smoking, and having career-building opportunities positively influence sleep quality. Interventions aimed at reducing stress and promoting healthy sleep practices are urgently needed within medical institutions.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"11 ","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926532","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}
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