An one-stop career platform detailing Schools & Universities offering English language, Bachelor, Master and PhD programs with course fee, living cost, scholarships, visa details, etc.
---------------------------
Cyprus College
cycollege.ac.cy
The college was founded in 1961 with the purpose to provide a well rounded education of high calibre where students can acquire the necessary academic knowledge.
---------------------------
Wroclaw University
international.uni.wroc.pl
Founded in 1702 by Leopold I Habsburg. Since the beginning of 20th century the university has produced 9 Nobel Prize winners.
---------------------------
Volyn University
vdu.edu.ua
The history dates back to 1940. At present, the university includes 4 institutes, 14 faculties and 73 departments.
---------------------------
Berkeley College
berkeleycollege.edu
Through the power of internet, Berkeley college online brings the classroom to you anywhere in the world with the same high level of support as On-Campus classes.
---------------------------
AIS
ais.ac.nz
New Zealand's largest international degree provider. The programmes are focused on the global marketplace.
---------------------------
WORLD UNIVERSITY DIRECTORY
Medical Education American Journal of Epidemiology - current issues
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Despite increasing prevalence of hypertension in youth and high adult cardiovascular mortality rates, the long-term consequences of youth-onset hypertension remain unknown. This is due to limitations of prior research, such as small sample sizes, reliance on manual record review, and limited analytic methods, that did not address major biases. The Study of the Epidemiology of Pediatric Hypertension (SUPERHERO) is a multisite, retrospective registry of youth evaluated by subspecialists for hypertension disorders. Sites obtain harmonized electronic health record data using standardized biomedical informatics scripts validated with randomized manual record review. Inclusion criteria are index visit for <span style="font-style:italic;">International Classification of Diseases, 10th Revision</span> (ICD-10) code–defined hypertension disorder on or after January 1, 2015, and age < 19 years. We exclude patients with ICD-10 code–defined pregnancy, kidney failure on dialysis, or kidney transplantation. Data include demographics, anthropomorphics, US Census Bureau tract, histories, blood pressure, ICD-10 codes, medications, laboratory and imaging results, and ambulatory blood pressure. SUPERHERO leverages expertise in epidemiology, statistics, clinical care, and biomedical informatics to create the largest and most diverse registry of youth with newly diagnosed hypertension disorders. SUPERHERO’s goals are to reduce CVD burden across the life course and establish gold-standard biomedical informatics methods for youth with hypertension disorders.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Accurately measuring gender and sex is crucial in public health and epidemiology. Iteratively reexamining how variables—including gender and sex—are conceptualized and operationalized is necessary to achieve impactful research. Reexamining gender and sex advances epidemiology toward its goals of health promotion and disease elimination. While we cannot reduce the complexities of sex and gender to simply an issue of measurement, striving to capture these concepts and experiences accurately must be an ongoing dialogue and practice—to the benefit of the field and population health. We assert that epidemiology must counteract misconceptions and accurately measure gender and sex in epidemiology. We aim to summarize existing critiques and guiding principles in measuring gender and sex that can be applied in practice.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>The objective of this study was to estimate the effect of geographic variation in historic slavery on perinatal outcomes (chronic hypertension, hypertensive disorders of pregnancy [HDP], very preterm birth [VPTB], or very low birth weight birth [VLBW]) among Black people living in states where slavery was legal in 1860; and to test mediation by Black homeownership. We linked data on the proportion of enslaved residents from the 1860 Census to natality data on outcomes (2013-2021) using resident county. The percentage of Black residents in a county who owned their home was a potential mediator. We fit log binomial models to estimate risk ratios (RRs) representing total and controlled direct effects (accounting for Black homeownership) of the proportion enslaved on outcomes, accounting for potential confounding, using marginal structural models. Among 2 443 198 included births, 8.8% (213 829) experienced HDP, 4.1% (100 549) chronic hypertension, 3.3% (81 072) VPTB, and 2.6% (62 538) VLBW. There was an increase in chronic hypertension and VPTB risk, but not HDP or VLBW, in counties with a 10% greater proportion of enslaved residents in 1860 (adjusted RR [95% CI], chronic hypertension: 1.06 [1.02-1.1]; VPTB: 1.02 [1.00-1.05]; HDP: 1.00 [0.98-1.02]; and VLBW: 1.01 [1.00-1.03]). There was not evidence of mediation by Black homeownership. We conclude that historic slavery remains relevant for perinatal health.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a potentially useful approach. One method, the Poisson tree-based scan statistic (TBSS), assumes that the expected outcome counts, based on incidence of outcomes in the control group, are estimated without error. This assumption may be difficult to satisfy with a small control group. Our simulation study evaluated the effect of imprecise incidence proportions from the control group on TBSS’ ability to identify maternal outcomes in pregnancy research. We simulated base case analyses with “true” expected incidence proportions and compared these with imprecise incidence proportions derived from sparse control samples. We varied parameters that have an impact on type I error and statistical power (exposure group size, outcome’s incidence proportion, and effect size). We found that imprecise incidence proportions generated by a small control group resulted in inaccurate alerting, inflation of type I error, and removal of very rare outcomes for TBSS analysis due to “zero” background counts. Ideally, the control size should be at least several times larger than the exposure size to limit the number of false positive alerts and retain statistical power for true alerts.This article is part of a Special Collection on Pharmacoepidemiology.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Development of new therapeutics for a rare disease such as cystic fibrosis is hindered by challenges in accruing enough patients for clinical trials. Use of external controls from well-matched historical trials can reduce prospective trial sizes, and this approach has supported regulatory approval of new interventions for other rare diseases. Here we consider 3 statistical methods that incorporate external controls into a hypothetical clinical trial of a new treatment to reduce pulmonary exacerbations in cystic fibrosis patients: (1) inverse probability weighting, (2) bayesian modeling with propensity-score–based power priors, and (3) hierarchical bayesian modeling with commensurate priors. We compare the methods via simulation study and in a real clinical-trial data setting. Simulations showed that bias in the treatment effect was less than 4% using any of the methods, with type I error (or in the bayesian cases, posterior probability of the null hypothesis) usually less than 5%. Inverse probability weighting was sensitive to similarity in prevalence of the covariates between historical and prospective trial populations. The commensurate prior method performed best with real clinical trial data. Using external controls to reduce trial size in future clinical trials holds promise and can advance the therapeutic pipeline for rare diseases.<strong>This article is part of a Special Collection on Pharmacoepidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>We examined associations between modifiable and nonmodifiable cancer-related risk factors measured at endometrial cancer diagnosis and during early survivorship (~3 years postdiagnosis) with second primary cancer (SPC) risk among 533 endometrial cancer survivors in the Alberta Endometrial Cancer Cohort using Fine and Gray subdistribution hazard models. During a median follow-up of 16.7 years (IQR, 12.2-17.9), 89 (17%) participants developed an SPC; breast (29%), colorectal (13%), and lung (12%) cancers were the most common. Dietary glycemic load before endometrial cancer diagnosis (≥90.4 vs < 90.4 g/day: subhazard ratio [sHR] = 1.71; 95% CI, 1.09-2.69), as well as older age (≥60 vs < 60 years: sHR = 2.48; 95% CI, 1.34-4.62) and alcohol intake (≥2 drinks/week vs none: sHR = 3.81; 95% CI, 1.55-9.31) during early survivorship, were associated with increased SPC risk. Additionally, reductions in alcohol consumption from prediagnosis to early survivorship significantly reduced SPC risk (sHR = 0.34; 95% CI, 0.14-0.82). With 1 in 6 survivors developing an SPC, further investigation of SPC risk factors and targeted surveillance options for high-risk survivors could improve long-term health outcomes in this population. Reductions in dietary glycemic load and alcohol intake from prediagnosis to early survivorship showed promising risk reductions for SPCs and could be important modifiable risk factors to target among endometrial cancer survivors.<strong>This article is part of a Special Collection on Gynecological Cancer</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Mendelian randomization is an epidemiologic technique that can explore the potential effect of perturbing a pharmacological target. Plasma caffeine levels can be used as a biomarker to measure the pharmacological effects of caffeine. Alternatively, this can be assessed using a behavioral proxy, such as average number of caffeinated drinks consumed per day. Either variable can be used as the exposure in a Mendelian randomization investigation, and to select which genetic variants to use as instrumental variables. Another possibility is to choose variants in gene regions with known biological relevance to caffeine level regulation. These choices affect the causal question that is being addressed by the analysis, and the validity of the analysis assumptions. Further, even when using the same genetic variants, the sign of Mendelian randomization estimates (positive or negative) can change depending on the choice of exposure. Some genetic variants that decrease caffeine metabolism associate with higher levels of plasma caffeine, but lower levels of caffeine consumption, as individuals with these variants require less caffeine consumption for the same physiological effect. We explore Mendelian randomization estimates for the effect of caffeine on body mass index, and discuss implications for variant and exposure choice in drug target Mendelian randomization investigations.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Prior infection with SARS-CoV-2 can provide protection against infection and severe COVID-19. We aimed to determine the impact of preexisting immunity on vaccine effectiveness (VE) estimates. We systematically reviewed and meta-analyzed 66 test-negative design studies that examined VE against infection or severe disease (hospitalization, intensive care unit admission, or death) for primary vaccination series. Pooled VE among studies that included people with prior COVID-19 infection was lower against infection (77%; 95% CI, 72-81) and severe disease (86%; 95% CI, 83-89) compared with studies that excluded people with prior COVID-19 infection (pooled VE against infection: 87% [95% CI, 85-89]; pooled VE against severe disease: 93% [95% CI, 91-95]). There was a negative correlation between VE estimates against infection and severe disease, and the cumulative incidence of cases before the start of the study or incidence rates during the study period. We found clear empirical evidence that higher levels of preexisting immunity were associated with lower VE estimates. Prior infections should be treated as both a confounder and effect modificatory when the policies target the whole population or are stratified by infection history, respectively.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Stroke is a leading cause of death in the United States across all race/ethnicity and sex groups, though disparities exist. We investigated the potential for primary prevention of total first stroke for Americans aged 20 years or older, stratified by sex and race/ethnicity. Specifically, we calculated population attributable fractions (PAFs) of first stroke for 7 potentially modifiable risk factors: smoking, physical inactivity, poor diet, obesity, hypertension, diabetes, and atrial fibrillation. Population attributable fractions are a function of (1) the relative risk of first stroke for people with the exposure and (2) the prevalence of the risk factor in the population. Relative risks came from recent meta-analyses, and sex- and race/ethnicity–specific prevalence estimates came from the 2015-2018 National Health and Nutrition Examination Survey or Multi-Ethnic Study of Atherosclerosis (for atrial fibrillation only). Approximately one-third of strokes (35.7% [95% CI, 21.6-49.0] for women; 32.7% [95% CI, 19.2-45.1] for men) were attributable to the 7 risk factors we considered. A 20% proportional reduction in stroke risk factors would result in approximately 37 000 fewer strokes annually in the United States. The estimated PAF was highest for non-Hispanic Black women (39.3%; 95% CI, 24.8-52.3) and lowest for non-Hispanic Asian men (25.5%; 95% CI, 14.6-36.2). For most groups, obesity and hypertension were the largest contributors to stroke rates.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Prebiotics may influence the risk of hormone-related female cancers by modulating the gut microbiota involved in estrogen metabolism. We evaluated the association of fiber-type prebiotic intake with breast, endometrial, and ovarian cancers. Data derived from a network of Italian hospital-based case-control studies (1991-2006), including 2560 cases of cancer of the breast (<span style="font-style:italic;">n</span> = 2588 control participants), 454 of the endometrium (<span style="font-style:italic;">n</span> = 908 control participants), and 1031 of the ovary (<span style="font-style:italic;">n</span> = 2411 control participants). Inulin-type fructans and selected fructo-oligosaccharides (namely, nystose, kestose, and 1F-β-fructofuranosylnystose) and galacto-oligosaccharides (namely, raffinose and stachyose) were quantified in food products <span style="font-style:italic;">via </span>laboratory analyses. Prebiotic intake was estimated by multiplying intake according to food frequency questionnaire responses by the foods’ prebiotic content. Odds ratios (ORs) and the corresponding 95% CIs were derived by multiple logistic regression models. Nystose intake was marginally directly associated with breast (for quartile 4 vs quartile 1: OR = 1.20; 95% CI, 1.00-1.45), ovarian (OR = 1.39; 95% CI, 1.04-1.84), and endometrial (OR = 1.32; 95% CI, 0.85-2.03) cancer risk. High amounts of 1F-β-fructofuranosylnystose intake were inversely associated with ovarian cancer (OR = 0.67; 95% CI, 0.52-0.85). Inulin-type fructans, kestose, raffinose, and stachyose were not associated with the 3 cancers. The intake of most fiber-type prebiotics was not appreciably and consistently associated with breast, endometrial, and ovarian cancer risks.<strong>This article is part of a Special Collection on Gynecological Cancer.</strong></span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Dating abuse research on lesbian, gay, and bisexual (LGB) populations tends to aggregate LGB participants for comparisons with heterosexuals and often excludes nonassaultive dating abuse and abuse that takes place on online dating applications. In the present study, we used the Pew Research Center’s 2019 American Trends Panel Wave 56 data set (<span style="font-style:italic;">n</span> = 4712) to compare ever experiencing several types of nonassaultive on- and offline dating abuse between bisexual women (<span style="font-style:italic;">n</span> = 402), lesbian women (<span style="font-style:italic;">n</span> = 207), heterosexual women (<span style="font-style:italic;">n</span> = 1802), bisexual men (<span style="font-style:italic;">n</span> = 225), gay men (<span style="font-style:italic;">n</span> = 575), and heterosexual men (<span style="font-style:italic;">n</span> = 1501). We found that gay men and bisexual women generally had the greatest odds of experiencing online dating abuse. Bisexual and heterosexual women had the greatest odds of experiencing some offline abuse (eg, being touched in an uncomfortable way), but gay men, bisexual women, and bisexual men had the greatest odds of experiencing other offline abuse (eg, having their contact information or a sexual image of them shared nonconsensually). Findings highlight how assessments of nonassaultive dating abuse in on- and offline contexts via analyses of more specified gender/sex and sexual identity groups can broaden understandings of dating abuse victimization, especially among sexual minority populations.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Cardiovascular disease is a leading cause of death globally. Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB), compared in the ONTARGET trial (Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial), each prevent cardiovascular disease. However, trial results may not be generalizable, and their effectiveness in underrepresented groups is unclear. Using trial emulation methods within routine-care data to validate findings, we explored the generalizability of ONTARGET results. For people prescribed an ACEi/ARB in the UK Clinical Practice Research Datalink GOLD dataset from January 1, 2001, to July 31, 2019, we applied trial criteria and propensity-score methods to create an ONTARGET trial-eligible cohort. Comparing ARB with ACEi, we estimated hazard ratios for the primary composite trial outcome (cardiovascular death, myocardial infarction, stroke, or hospitalization for heart failure) and secondary outcomes. Because the prespecified criteria were met, confirming trial emulation, we then explored treatment heterogeneity among 3 trial-underrepresented subgroups: females, persons aged ≥75 years, and those with chronic kidney disease. In the trial-eligible population (<span style="font-style:italic;">n</span> = 137 155), results for the primary outcome demonstrated similar effects of ARB and ACEi (hazard ratio = 0.97; 95% CI, 0.93-1.01), meeting the prespecified validation criteria. When extending this outcome to trial-underrepresented groups, similar treatment effects were observed by sex, age, and chronic kidney disease. This suggests that ONTARGET trial findings are generalizable to trial-underrepresented subgroups.This article is part of a Special Collection on Pharmacoepidemiology.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Older individuals residing in long-term care facilities (LTCFs) are often living with multimorbidity and exposed to polypharmacy, and many experience medication-related problems. Because randomized controlled trials seldom include individuals in LTCFs, pharmacoepidemiological studies using real-world data are essential sources of new knowledge on the utilization, safety, and effectiveness of pharmacotherapies and related health outcomes in this population. In this commentary, we discuss recent pharmacoepidemiological research undertaken to support the investigations and recommendations of a landmark public inquiry into the quality and safety of care provided in the approximately 3000 Australian LTCFs that house more than 240 000 residents annually, which informed subsequent national medication-related policy reforms. Suitable sources of real-world data for pharmacoepidemiological studies in long-term care cohorts and methodological considerations are also discussed.<strong>This article is part of a Special Collection on Pharmacoepidemiology.</strong></span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Epidemiologists have long argued that side effects of the stress response include preterm birth. Research reports that fear of lethal infection stressed pregnant persons at the outset of the coronavirus disease (COVID-19) pandemic and that “shutdowns” and “social distancing” impeded access to social support and prenatal care. The decline in preterm births in high-income countries, including the United States, during the early months of the pandemic therefore poses a paradox for science. Explanations of this “pandemic preterm paradox” remain untested. We applied time-series modeling to data describing 80 monthly conception cohorts begun in the United States from July 2013 through February 2020 to determine which of 3 explanations most parsimoniously explained the paradox. We infer that “prior loss,” or the argument that an increase in spontaneous abortions and stillbirths depleted the population of fetuses at risk of preterm birth, best explains data currently available. We describe the implications of these results for public health practice.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Police violence is a pervasive issue that may have adverse implications for severe maternal morbidity (SMM). We assessed how the occurrence of fatal police violence (FPV) in one’s neighborhood before or during pregnancy may influence SMM risk. Hospital discharge records from California between 2002 and 2018 were linked with the Fatal Encounters database (<span style="font-style:italic;">n</span> = 2 608 682). We identified 2184 neighborhoods (census tracts) with at least 1 FPV incident during the study period and used neighborhood fixed-effects models adjusting for individual sociodemographic characteristics to estimate odds of SMM associated with experiencing FPV in one’s neighborhood anytime within the 24 months before childbirth. We did not find conclusive evidence on the link between FPV occurrence before delivery and SMM. However, estimates show that birthing people residing in neighborhoods where 1 or more FPV events had occurred within the preceding 24 months of giving birth may have mildly elevated odds of SMM than those residing in the same neighborhoods with no FPV occurrence during the 24 months preceding childbirth (odds ratio [OR] = 1.02; 95% confidence interval [CI], 0.99-1.05), particularly among those living in neighborhoods with fewer FPV incidents (1-2) throughout the study period (OR = 1.03; 95% CI, 1.00-1.06). Our findings provide evidence for the need to continue to examine the population health consequences of police violence.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Investigators often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual’s information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments’ benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments’ benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly robust unbiased transformation of the conditional ATE. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone dose to minimize a return to regular opioid use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision-making: The learned ODTR outperforms a clinically defined strategy.<strong>This article is part of a Special Collection on Pharmacoepidemiology.</strong></span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>This study examined how race/ethnicity, sex/gender, and sexual orientation intersect under interlocking systems of oppression to socially pattern depression among US adults. With cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (<span style="font-style:italic;">n</span> = 234 722), we conducted a design-weighted, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) under an intersectional framework to predict past-year and lifetime major depressive episodes (MDEs). With 42 intersectional groups constructed from 7 race/ethnicity, 2 sex/gender, and 3 sexual orientation categories, we estimated age-standardized prevalence and excess or reduced prevalence attributable to 2-way or higher interaction effects. Models revealed heterogeneity across groups, with prevalence ranging from 1.9% to 19.7% (past-year) and 4.5% to 36.5% (lifetime). Approximately 12.7% (past year) and 12.5% (lifetime) of total individual variance was attributable to between-group differences, indicating key relevance of intersectional groups in describing the population distribution of depression. Main effects indicated, on average, that people who were White, women, gay/lesbian, or bisexual had greater odds of MDE. Main effects explained most between-group variance. Interaction effects (past year: 10.1%; lifetime: 16.5%) indicated another source of heterogeneity around main effects average values, with some groups experiencing excess or reduced prevalence compared with main effects expectations. We extend the MAIHDA framework to calculate nationally representative estimates from complex sample survey data using design-weighted, Bayesian methods.<strong>This article is part of a Special Collection on Mental Health</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Traffic-related air pollution is a major concern for perinatal health. Determining causal associations, however, is difficult because high-traffic areas tend to correspond with lower socioeconomic neighborhoods and other environmental exposures. To overcome confounding, we compared pregnant individuals living downwind and upwind of the same high-traffic road. We leveraged vital statistics data for Texas from 2007 to 2016 (<span style="font-style:italic;">n</span> = 3 570 272 births) and computed hourly wind estimates for residential addresses within 500 m of high-traffic roads (ie, annual average daily traffic >25 000 vehicles) (10.9% of births). We matched pregnant individuals predominantly upwind with pregnant neighbors downwind of the same road segment (<span style="font-style:italic;">n</span> = 37 631 pairs). Living downwind was associated with a decrease of 11.6 g (95% CI, -18.01 to -5.21) in term birth weight. No associations were observed with low term birth weight, preterm birth, or very preterm birth. In distance-stratified models, living downwind within 50 m was associated with a decrease of 36.3 g (95% CI, -67.74 to -4.93) in term birth weight and living 51-100 m downwind was associated with an odds ratio of 3.68 (95% CI, 1.71-7.90) for very preterm birth. These results suggest traffic air pollution is associated with adverse birth outcomes, with steep distance decay gradients around major roads.<strong>This article is part of a Special Collection on Environmental Epidemiology.</strong></span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Rural environments in the United States present challenges to wellness, but there is a lack of tools with which to categorize rurality at the subcounty level. The most common tool, the Food and Drug Administration’s 2010 Rural–Urban Commuting Area (RUCA) codes, uses data that are over a decade old and cannot accommodate regional differences in rurality. The purpose of this study was to develop a census-tract classification system of rurality and demonstrate its use in describing HIV outcomes. We transformed census-tract measures (population density, natural resource workforce, walkability index, household type, and air quality) into local scales of rurality using factor analysis. We surveyed public health practitioners to determine cutpoints and compared the resulting categorization with RUCA codes. We characterized the incidence of HIV in Washington State according to rurality category. Our classification system categorized 25% of census tracts as rural, 19% as periurban, and 56% as urban. Our survey yielded cutoffs that were more conservative in categorizing urban areas than RUCA codes. The rate of HIV diagnosis was substantially higher in urban areas. Our rural–urban classification system offers an alternative to RUCA codes that is more responsive to regional differences.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Modeling studies of household transmission data have helped characterize the role of children in influenza and coronavirus disease 2019 (COVID-19) epidemics. However, estimates from these studies may be biased since they do not account for the heterogeneous nature of household contacts. Here, we quantified the impact of contact heterogeneity between household members on the estimation of child relative susceptibility and infectivity. We simulated epidemics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like and influenza virus-like infections in a synthetic population of 1000 households, assuming heterogeneous contact levels. Relative contact frequencies were derived from a household contact study according to which contacts are more frequent in the father–mother pair, followed by the child–mother, child–child, and finally child–father pairs. Child susceptibility and infectivity were then estimated while accounting for heterogeneous contacts or not. When ignoring contact heterogeneity, child relative susceptibility was underestimated by approximately 20% in the two disease scenarios. Child relative infectivity was underestimated by 20% when children and adults had different infectivity levels. These results are sensitive to our assumptions of European-style household contact patterns; but they highlight that household studies collecting both disease and contact data are needed to assess the role of complex household contact behavior on disease transmission and improve estimation of key biological parameters.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>The prevalence and relative disparities of mental health outcomes and well-being indicators are often inconsistent across studies of sexual minority men (SMM) due to selection biases in community-based surveys (nonprobability sample), as well as misclassification biases in population-based surveys where some SMM often conceal their sexual orientation identities. The present study estimated the prevalence of mental health related outcomes (depressive symptoms, mental health service use, anxiety) and well-being indicators (loneliness and self-rated mental health) among SMM, broken down by sexual orientation using the adjusted logistic propensity score (ALP) weighting. We applied the ALP to correct for selection biases in the 2019 Sex Now data (a community-based survey of SMMs in Canada) by reweighting it to the 2015-2018 Canadian Community Health Survey (a population survey from Statistics Canada). For all SMMs, the ALP-weighted prevalence of depressive symptoms was 15.96% (95% CI, 11.36%-23.83%), while for mental health service use, it was 32.13% (95% CI, 26.09%-41.20%). The ALP estimates lie in between the crude estimates from the two surveys. This method was successful in providing a more accurate estimate than relying on results from one survey alone. We recommend to the use of ALP on other minority populations under certain assumptions.<strong>This article is part of a Special Collection on Mental Health.</strong></span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Multiracial people report higher mean Adverse Childhood Experience (ACE) scores and prevalence of anxiety than other racial groups. Studies using statistical interactions to test if associations between ACEs and anxiety are greater for this group than others have shown mixed results. Using data from waves 1 (1995-1997) through 4 (2008-2009) of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we simulated a stochastic intervention over 1000 resampled datasets to estimate the race-specific cases averted per 1000 of anxiety if all racial groups had the same exposure distribution of ACEs as Whites. Simulated cases averted were greatest for the Multiracial group, (median = −4.17 cases per 1000; 95% CI; −7.42 to −1.86). The model also predicted smaller risk reductions for Black participants (−0.76; 95% CI, −1.53 to −0.19). CIs around estimates for other racial groups included the null. An intervention to reduce racial disparities in exposure to ACEs could help reduce the inequitable burden of anxiety on the Multiracial population. Stochastic methods support consequentialist approaches to racial health equity, and can encourage greater dialogue between public health researchers, policymakers, and practitioners.<strong>This article is part of a Special Collection on Mental Health.</strong></span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Persistent endocrine-disrupting chemicals (EDCs) can dysregulate the stress response. We evaluated associations between persistent EDCs and perceived stress among participants in the Study of Environment, Lifestyle, and Fibroids (<span style="font-style:italic;">n</span> = 1394), a prospective cohort study of Black women. Participants completed the Perceived Stress Scale 4 (PSS-4) at baseline and every 20 months through 60 months (score range: 0-16); higher scores indicate higher stress. Endocrine-disrupting chemicals, including per- and polyfluoroalkyl substances, polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and organochlorine pesticides, were quantified in plasma samples at baseline. We fit bayesian kernel machine regression and linear mixed-effects models to estimate associations of EDCs (as a mixture and individually) with PSS-4 scores at baseline and at each follow-up visit, respectively. Increasing percentiles of the mixture were not strongly associated with PSS-4 scores at baseline, and no interactions were observed among EDCs. Several individual EDCs (eg, perfluorodecanoic acid, PCB 118, PBDE 99) were associated with higher PSS-4 scores at baseline or follow-up, and other EDCs (eg PCB 138/158) were associated with lower PSS-4 scores at baseline or follow-up. The directionality of associations for individual EDCs was inconsistent across follow-up visits. In conclusion, specific EDCs may be associated with perceived stress in Black women.<strong>This article is part of a Special Collection on Environmental Epidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Defining the effect of an exposure of interest and selecting an appropriate estimation method are prerequisites for causal inference. Current understanding of the ways in which an association between heat waves (ie, consecutive days of extremely high temperature) and an outcome depends on whether adjustment was made for temperature and how such adjustment was conducted is limited. In this paper we aim to investigate this dependency, demonstrate that temperature is a confounder in heat-wave–outcome associations, and introduce a new modeling approach with which to estimate a new heat-wave–outcome relationship: E[<span style="font-style:italic;">R</span>(<span style="font-style:italic;">Y</span>)|<span style="font-style:italic;">HW</span> = 1, <span style="font-style:italic;">Z</span>]/E[<span style="font-style:italic;">R</span>(<span style="font-style:italic;">Y</span>)|<span style="font-style:italic;">T</span> = <span style="font-style:italic;">OT</span>, <span style="font-style:italic;">Z</span>], where <span style="font-style:italic;">HW</span> is a daily binary variable used to indicate the presence of a heat wave; <span style="font-style:italic;">R</span>(<span style="font-style:italic;">Y</span>) is the risk of an outcome, <span style="font-style:italic;">Y</span>; <span style="font-style:italic;">T</span> is a temperature variable; <span style="font-style:italic;">OT</span> is optimal temperature; and <span style="font-style:italic;">Z</span> is a set of confounders including typical confounders but also some types of <span style="font-style:italic;">T</span> as a confounder. We recommend characterization of heat-wave–outcome relationships and careful selection of modeling approaches to understand the impacts of heat waves under climate change. We demonstrate our approach using real-world data for Seoul, South Korea. Our demonstration suggests that the total effect of heat waves may be larger than what may be inferred from the extant literature. An <span style="font-style:italic;">R</span> package, <span style="font-style:italic;">HEAT</span>, has been developed and made publicly available.<strong>This article is part of a Special Collection on Environmental Epidemiology</strong>.</span>
Home
Contact Us
Back to "News Updates - Homepage"
