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Research Medical American Journal of Epidemiology - current issues
<span class="paragraphSection">In the article by VanderWeele and Li,<a href="#ref1" class="reflinks"><sup>1</sup></a> although the technical algebraic results given in theorem 2 are correct, there were some typographic errors in the surrounding text. In the text of theorem 2 itself, the word “outcome” should have been “exposure,” per the text preceding it, though the algebraic results that follow are correct as written. In the text following theorem 2, “association between exposure A and measurement Y*” should have read “association between measurement A* and outcome Y.” The authors apologize for any confusion this may have caused.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Over the past 35 years, the term “leaky vaccine” has gained widespread use in both mathematical modeling and epidemiologic methods for evaluating vaccines. Here, we present a short history, as we recall it, of how the term was coined in the context of the history of sporozoite malaria vaccines that were thought in the 1980s to be possibly leaky. We draw a contrast with the all-or-none vaccine mechanism and review a few consequences for study design and population-level effects. We invite readers to contribute information covering the period preceding our memories in the 1980s, because we may have overlooked something.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Meta-analysis is a powerful analytic method for summarizing effect estimates across studies. However, conventional meta-analysis often assumes a linear exposure-outcome relationship and does not account for variability over the exposure ranges. In this work, we first used simulation techniques to illustrate that the linear-based meta-analytical approach may result in oversimplistic effect estimation based on 3 plausible nonlinear exposure-outcome curves (S-shape, inverted U-shape, and M-shape). We showed that subgroup meta-analysis that stratifies on exposure levels can investigate nonlinearity and identify the consistency of effect magnitudes in these simulated examples. Next, we examined the heterogeneity of effect estimates across exposure ranges in 2 published linear-based meta-analyses of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) on changes in mean birth weight or risk of preterm birth. The reanalysis found some varying effect sizes and potential heterogeneity when restricting to different PFAS exposure ranges, but findings were sensitive to the cut-off choices used to rank the exposure levels. Finally, we discussed methodological challenges and recommendations for detecting and interpreting potential nonlinear associations in meta-analysis. Using meta-analysis without accounting for exposure range could contribute to literature inconsistency for exposure-induced health effects and impede evidence-based policymaking. Therefore, investigating result heterogeneity by exposure range is recommended.<strong>This article is part of a Special Collection on Environmental Epidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>With the emergence of Omicron during the pandemic and the establishment of antibody waning over time, vaccine effectiveness, especially against infection, declined sharply from the original levels seen after the initial rollout. However, studies have demonstrated that they still provided substantial protection vs severe/fatal disease even with Omicron and after waning. Social media has been rife with reports claiming vaccines provided no benefit and some even claiming they made things worse, often driven by simple presentations of raw observational data using erroneous arguments involving epidemiologic fallacies including the <span style="font-style:italic;">base rate fallacy</span>, <span style="font-style:italic;">Simpson’s paradox</span>, and the <span style="font-style:italic;">ecological fallacy</span> and ignoring the extensive bias especially from confounding that is an inherent feature of these data. Similar fallacious arguments have been made by some in promoting vaccination policies, as well. Generally, vaccine effectiveness cannot be accurately estimated from raw population summaries but instead require rigorous, careful studies using epidemiologic designs and statistical analysis tools attempting to adjust for key confounders and sources of bias. This article summarizes what aggregated evidence across studies reveals about effectiveness of the mRNA vaccines as the pandemic has evolved, chronologically summarized with emerging variants and highlighting some of the fallacies and flawed arguments feeding social media-based claims that have obscured society’s collective understanding.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Common genetic variation throughout the genome and rare coding variants identified to date explain about half of the inherited genetic component of epithelial ovarian cancer risk. It is likely that rare variation in the noncoding genome will explain some of the unexplained heritability, but identifying such variants is challenging. The primary problem is a lack of statistical power to identify individual risk variants by association, as power is a function of sample size, effect size, and allele frequency. Power can be increased by using burden tests, which test for the association of carriers of any variant in a specified genomic region. This has the effect of increasing the putative effect allele frequency. PAX8 is a transcription factor that plays a critical role in tumor progression, migration, and invasion. Furthermore, regulatory elements proximal to target genes of PAX8 are enriched for common ovarian cancer risk variants. We hypothesized that rare variation in PAX8 binding sites is also associated with ovarian cancer risk but unlikely to be associated with risk of breast, colorectal, or endometrial cancer. We have used publicly available, whole-genome sequencing data from the UK 100,000 Genomes Project to evaluate the burden of rare variation in PAX8 binding sites across the genome. Data were available for 522 ovarian cancers, 2984 breast cancers, 2696 colorectal cancers, 836 endometrial cancers, and 2253 noncancer controls. Active binding sites were defined using data from multiple PAX8 and H3K27 chromatin immunoprecipitation sequencing experiments. We found no association between the burden of rare variation in PAX8 binding sites (defined in several ways) and risk of ovarian, breast, or endometrial cancer. An apparent association with colorectal cancer was likely to be a technical artifact as a similar association was also detected for rare variation in random regions of the genome. Despite the null result, this study provides a proof-of-principle for using burden testing to identify rare, noncoding germline genetic variation associated with disease. Larger sample sizes available from large-scale sequencing projects, together with improved understanding of the function of the noncoding genome, will increase the potential of similar studies in the future.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Depression is a major contributor to the global burden of disease. There is limited understanding of how environmental exposures may contribute to depression etiology. We used wave 4 of the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine associations between low-level ambient air pollution exposure and depressed mood in a generally healthy population of over 10 000 24-32 year olds. Annual mean PM<sub>2.5</sub> levels in the 2008-2009 study were close to the current US standard. In fully adjusted quasibinomial logistic regression models, there were no meaningful associations between IQR increases in air pollutant and change in depressed mood status regardless of specific pollutant or moving average lags. In interaction effects models, an IQR increase in lag day 0-30 PM<sub>2.5</sub> resulted in 1.20 (95% CI, 1.02-1.41) times higher likelihood of having depressed mood but only for persons with chronic lung disease (interaction <span style="font-style:italic;">P = .</span>04); the association was null for participants without chronic lung disease (OR, 0.98; 95% CI, 0.91-1.05). Our findings suggest that among persons with a lifetime history of chronic lung disease, greater exposure to even low-level PM<sub>2.5</sub>, PM<sub>10</sub>, and sulfate may be associated with modest increases in the likelihood of having depressed mood.<strong>This article is part of a Special Collection on Environmental Epidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>We conducted this systematic review and meta-analysis to clarify the trend of precocious puberty (PP) incidence after the COVID-19 outbreak and explore potential contributing factors, such as age at presentation and body mass index (BMI) SD score (SDS). Children visiting pediatric endocrinology clinics for the first time for suspected PP were included. We searched databases until February 28, 2023, for studies reporting various indicators of PP incidence before and during the pandemic. Total numbers of events and observations were recorded. A meta-analysis was performed to compare the odds of PP, BMI SDS, and age at presentation between the 2 periods. The dose-response relationships between time points (by number of years away from the pandemic) and PP risk were explored. In summary, a total of 32 studies including 24 200 participants were recruited. The COVID-19 pandemic was associated with the increasing odds of PP among children referred for a suspicious condition (odds ratio = 1.96; 95% CI, 1.56-2.47; <span style="font-style:italic;">I</span><sup>2</sup> = 54%; <span style="font-style:italic;">P</span> < .001). Sensitivity analysis confirmed the robustness of the findings. The BMI SDS did not vary between the 2 periods, whereas age at presentation was lower after the pandemic. Precocious puberty incidence increased more rapidly during the pandemic period than during the prepandemic period. <strong>Trial registration:</strong> International Prospective Register of Systematic Reviews (PROSPERO; identifier: CRD42023402212)</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>It is unclear whether the large secular decline in religiosity has contributed to the dramatic rise in the “deaths of despair.” We contribute to the recent epidemiologic literature estimating more rigorous effects of religiosity on health by examining the association between religiosity and the diseases of despair via regression, sibling fixed effects (SFE) analyses, instrumental variable (IV), and cross-lag analyses. We used the US Add Health sample when respondents were in Waves (W) 3-5 (ages: 18-43). We measured religious service attendance and a composite outcome consisting of painkiller abuse, past-year suicidal ideation, and weekly binge drinking. We estimated linear probability models, SFE, IV, and cross-lag models. Confounders included parental socio-demographics, community/school characteristics, and individual socio-demographics. Greater religious service attendance was negatively associated with the composite outcome in the pooled sample (β =-0.031; p < .5) and at each wave (W3 β=-0.025; W4 β=-0.040; W5 β=-0.028; all p < .5). Conclusions were similar in SFE models (W3-5 pooled β=-0.013), IV models (W4 β=-0.081; W3-5 pooled β=-0.064, all p < .5, F>100, and overidentification p > .10) and cross-lag models (W3-5 pooled β=-0.023, p < .5). The consistent results across models suggests that the large decline in religious service attendance likely contributed to the rise in the deaths of despair.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Birth rates in Canada and the United States declined sharply in March 2020 and deviated from historical trends. This decline was absent in similarly developed European countries. We argue that the selective decline was driven by incoming individuals, who would have traveled from abroad and given birth in Canada and the United States had there been no travel restrictions during the COVID-19 pandemic. Furthermore, by leveraging data from periods before and during the COVID-19 travel restrictions, we quantified the extent of births by incoming individuals. In an interrupted time series analysis, the expected number of such births in Canada was 970 per month (95% CI, 710-1200), which is 3.2% of all births in the country. The corresponding estimate for the United States was 6700 per month (95% CI, 3400-10 000), which is 2.2% of all births. A secondary difference-in-differences analysis gave similar estimates, at 2.8% and 3.4% for Canada and the United States, respectively. Our study reveals the extent of births by recent international arrivals, which hitherto has been unknown and infeasible to study.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>We investigated the association between outdoor artificial light-at-night (ALAN) exposure and cardiometabolic risk in the GCAT study. We included 9752 participants from Barcelona (59% women) and used satellite images (30 m resolution) and estimated photopic illuminance and the circadian regulation–relevant melanopic equivalent daylight illuminance (melanopic EDI). We explored the association between ALAN exposure and prevalent obesity, hypertension, and diabetes with logistic regressions and assessed the relationship with incident cardiometabolic diseases ascertained through electronic health records (mean follow-up 6.5 years) with Cox proportional hazards regressions. We observed an association between photopic illuminance and melanopic EDI and prevalent hypertension, odds ratio (OR) = 1.09 (95% CI, 1.01-1.16) and 1.08 (1.01-1.14) per interquartile range increase (0.59 and 0.16 lux, respectively). Both ALAN indicators were linked to incident obesity (hazard ratio [HR] = 1.29, 1.11-1.48 and 1.19, 1.05-1.34) and hemorrhagic stroke (HR = 1.73, 1.00-3.02 and 1.51, 0.99-2.29). Photopic illuminance was associated with incident hypercholesterolemia in all participants (HR = 1.17, 1.05-1.31) and with angina pectoris only in women (HR = 1.55, 1.03-2.33). Further research in this area and increased awareness on the health impacts of light pollution are needed. Results should be interpreted carefully since satellite-based ALAN data do not estimate total individual exposure.<strong>This article is part of a Special Collection on Environmental Epidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as in usual causal “per-protocol” estimands. However, when sustained use is challenging to satisfy in practice, the usefulness of these estimands can be limited. Here we propose a different class of estimands: <span style="font-style:italic;">separable effects for adherence</span>. These estimands compare modified medications, holding fixed a component responsible for nonadherence. Under assumptions about treatment components’ mechanisms of effect, a separable effects estimand can quantify the effectiveness of medication initiation strategies on an outcome of interest under the adherence mechanism of one of the medications. These assumptions are amenable to interrogation by subject-matter experts and can be evaluated using causal graphs. We describe an algorithm for constructing causal graphs for separable effects, illustrate how these graphs can be used to reason about assumptions required for identification, and provide semi-parametric weighted estimators.<strong>This article is part of a Special Collection on Pharmacoepidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>This commentary responds to the article by Qureishi et al (<span style="font-style:italic;">Am J Epidemol</span>. 2024;193(10):1313–1317) that criticizes a new proposal for “positive epidemiology.” They argue that positive epidemiology, as it is being proposed and conducted, ignores supraindividual social contextual factors that constrain the well-being of some individuals more than others, and it could exacerbate inequalities if applied at a population level, among other harms. They offer an alternative approach to defining causal factors that are helpful for well-being and seek to ground their view in human rights and economic justice frameworks. This commentary considers their criticisms of positive epidemiology and suggests that their alternative, as well as all research into positive health and well-being, would benefit from drawing on the ongoing debates and literature in health equity and justice philosophy. A coherent conception of health and well-being, the link between health/well-being and theories of justice, and the capabilities approach are discussed. The efforts at conducting epidemiology for the causes and distribution of good health and well-being grounded in justice are welcomed.<strong>This article is part of a Special Collection on Mental Health</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>The test-negative design (TND) has been commonly used to study vaccine effectiveness (notably regarding COVID-19 and influenza vaccines) and recently has been proposed as a valid design to study causal risk factors of diseases during an outbreak. In April 2022, there was a worldwide outbreak of mpox (formerly called monkeypox) that resulted in an international public health emergency. The TND has been used in a few studies to investigate vaccine effectiveness and risk factors of mpox, using epidemiologic databases. However, several issues prevent such a study design from being valid for this purpose. Problems stem from stigma surrounding mpox, which affects a person’s decision to seek health care. This poses a challenge to the similar health care–seeking behavior assumption that is central for test-negative studies. Further limitations include the differential diagnoses of mpox, which have notable differences from mpox that may be easily detected by clinicians or patients but are unlikely to be included in epidemiologic databases or electronic health records. Herein, the caveats regarding the use of the TND are discussed in the context of the mpox outbreak, as well as potential steps that may allow it to be used effectively.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Deimplementation is the discontinuation or abandonment of medical practices that are ineffective or of unclear effectiveness, ranging from simply unhelpful to harmful. With epidemiology expanding to include more translational sciences, epidemiologists can contribute to deimplementation by defining evidence, establishing causality, and advising on study design. An estimated 10%-30% of health care practices have minimal to no benefit to patients and should be targeted for deimplementation. The steps in deimplementation are (1) identify low-value clinical practices, (2) facilitate the deimplementation process, (3) evaluate deimplementation outcomes, and (4) sustain deimplementation, each of which is a complex project. Deimplementation science involves researchers, health care and clinical stakeholders, and patient and community partners affected by the medical practice. Increasing collaboration between epidemiologists and implementation scientists is important to optimizing health care delivery.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Seroprevalence studies of SARS-CoV-2 infections often have been based on study populations with nonrandom and nonrepresentative samples, limiting the generalizability of their results. In this study, the representativity and the generalizability of the baseline estimate (data collected from October 16, 2020, to April 18, 2021) of a pediatric seroprevalence study based in Montréal were investigated. The change in the estimates of seroprevalence were compared between 2 different weighting methods: marginal standardization and raking. The target population was the general pediatric population of Montréal, based on 2016 Canadian census data. Study results show variation across the multiple weighting scenarios. Although both weighting methods performed similarly, each possesses its own strengths and weaknesses. However, raking was preferred for its capacity to simultaneously weight for multiple underrepresented study population characteristics.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Prenatal organophosphate (OP) pesticide exposure may be associated with reduced fetal growth, although studies are limited and have mixed results. We investigated associations between prenatal OP pesticide exposure and fetal size and modification by fetal sex. Maternal urinary concentrations of dialkyl phosphate (DAP) metabolites were measured at 3 time points. Fetal biometrics were obtained from ultrasounds in the second (<span style="font-style:italic;">n</span> = 773) and third (<span style="font-style:italic;">n</span> = 535) trimesters. Associations between pregnancy-averaged ΣDAP and fetal biometry <span style="font-style:italic;">z</span> scores were determined through multiple linear regression. Modification by sex was investigated through stratification and interaction. In the second trimester, one ln-unit increase in ΣDAP was associated with lower estimated fetal weight (–0.15 SD; 95% CI, –0.29 to –0.01), head circumference (–0.11 SD; CI, –0.22 to 0.01), biparietal diameter (–0.14 SD; CI, –0.27 to –0.01), and abdominal circumference (–0.12 SD; CI, –0.26 to 0.01) in females. In the third trimester, one ln-unit increase in ΣDAP was associated with lower head circumference (–0.14 SD; CI, –0.28 to 0.00) and biparietal diameter (–0.12 SD; CI, –0.26 to 0.03) in males. Our results suggest that prenatal OP pesticide exposure is negatively associated with fetal growth in a sex-specific manner, with associations present for females in mid-gestation and males in late gestation.<strong>This article is part of a Special Collection on Environmental Epidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>CpG site methylation patterns have potential to improve differentiation of high-grade screening-detected cervical abnormalities. We assessed CpG differential methylation (DM) and differential variability (DV) in high-grade (CIN2+) vs low-grade (≤ CIN1) lesions. In ≤ CIN1 (<span style="font-style:italic;">n</span> = 117) and CIN2+ (<span style="font-style:italic;">n</span> = 31) samples, cervical sample DNA underwent testing with Illumina HumanMethylation arrays. We assessed DM and DV of CpG methylation M-values among 9 cervical cancer–associated genes. We fit CpG-specific linear models and estimated empirical Bayes standard errors and false discovery rates (FDRs). An exploratory epigenome-wide association study (EWAS) aimed to detect novel DM and DV CpGs (FDR < 0.05) and Gene Ontology (GO) term enrichment. Compared to ≤ CIN1, CIN2+ exhibited greater methylation at <span style="font-style:italic;">CCNA1</span> cluster 1 (M-value difference 0.24; 95% CI, 0.04-0.43) and <span style="font-style:italic;">RARB</span> cluster 2 (0.16; 95% CI, 0.05-0.28), and lower methylation at <span style="font-style:italic;">CDH1</span> cluster 1 (–0.15; 95% CI, –0.26 to –0.04). CIN2+ exhibited lower variability at <span style="font-style:italic;">CDH1</span> cluster 2 (variation difference –0.24; 95% CI, –0.41 to –0.05) and <span style="font-style:italic;">FHIT</span> cluster 1 (–0.30; 95% CI, –0.50 to –0.09). EWAS detected 3534 DM and 270 DV CpGs. Forty-four GO terms were enriched with DM CpGs related to transcriptional, structural, developmental, and neuronal processes. Methylation patterns may help triage screening-detected cervical abnormalities and inform US screening algorithms.<strong>This article is part of a Special Collection on Gynecological Cancer</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Recently, a bespoke instrumental variable method was proposed, which, under certain assumptions, can eliminate bias due to unmeasured confounding when estimating the causal exposure effect among the exposed. This method uses data from both the study population of interest and a reference population in which the exposure is completely absent. In this article, we extend the bespoke instrumental variable method to allow for a nonideal reference population that may include exposed individuals. Such an extension is particularly important in randomized trials with nonadherence, where even individuals in the control arm may have access to the treatment under investigation. We further scrutinize the assumptions underlying the bespoke instrumental method and caution the reader about the potential nonrobustness of the method to these assumptions.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated natural language processing (NLP) models to address this issue. We utilized all available clinical notes from the Mass General Brigham health-care system for 2100 older adults, identifying 154 949 paragraphs of interest through automatic scanning for FRI-related keywords. Two clinical experts directly labeled 5000 paragraphs to generate benchmark-standard labels, while 3689 validated patterns were annotated, indirectly labeling 93 157 paragraphs as validated-standard labels. Five NLP models, including vanilla bidirectional encoder representations from transformers (BERT), the robustly optimized BERT approach (RoBERTa), ClinicalBERT, DistilBERT, and support vector machine (SVM), were trained using 2000 benchmark paragraphs and all validated paragraphs. BERT-based models were trained in 3 stages: masked language modeling, general boolean question-answering, and question-answering for FRIs. For validation, 500 benchmark paragraphs were used, and the remaining 2500 were used for testing. Performance metrics (precision, recall, F1 scores, area under the receiver operating characteristic curve [AUROC], and area under the precision-recall [AUPR] curve) were employed by comparison, with RoBERTa showing the best performance. Precision was 0.90 (95% CI, 0.88-0.91), recall was 0.91 (95% CI, 0.90-0.93), the F1 score was 0.91 (95% CI, 0.89-0.92), and the AUROC and AUPR curves were [both??] 0.96 (95% CI, 0.95-0.97). These NLP models accurately identify FRIs from unstructured clinical notes, potentially enhancing clinical-notes–based research efficiency.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Social connections may impact the dynamic trajectory of frailty. Using data from the British Regional Heart Study (BRHS) in the UK (<span style="font-style:italic;">n</span> = 715) and the US Health, Aging and Body Composition (Health ABC) Study (<span style="font-style:italic;">n</span> = 1256), we conducted multinominal regression analyses to examine the association of baseline and change in social engagement and loneliness with progression to prefrailty and frailty, as well as their association with reversal to prefrailty and robust status among older adults. A higher level of social engagement at baseline (BRHS: relative risk ratio [RRR] 0.69 [95% CI, 0.55–0.85]; Health ABC: 0.56 [0.45-0.70]) and an increase in social engagement (BRHS: 0.73 [0.59-0.90]; Health ABC: 0.51 [0.41-0.63]) were associated with a lower risk of developing frailty. In BRHS, a higher level of loneliness at baseline (1.42 [1.10-1.83]) and an increase in loneliness (1.50 [1.18-1.90]) raised the risk of developing frailty. For reversal of frailty, higher social engagement at baseline (Health ABC: 1.63 [1.08-2.47]) and an increase in social engagement (BRHS: 1.74 [1.18-2.50]; Health ABC: 1.79 [1.17-.274]) were beneficial. Social connections may be potentially important and modifiable factors in both preventing and reversing progression of frailty in older adults.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Objective outcomes for pediatric community-acquired pneumonia (CAP) are lacking. The desirability of outcome ranking (DOOR) and response adjusted for duration of antibiotic risk (RADAR) outcome encompass clinical benefit and adverse effects, while also accounting for antibiotic exposure. We evaluated DOOR and RADAR (DOOR/RADAR) through simulations and compared sample-size considerations to noninferiority designs in a hypothetical trial comparing antibiotics and no antibiotics (ie, placebo) for children with mild CAP. We also evaluated a trial comparing different durations of antibiotic therapy. Three scenarios were considered: 1 with no difference in DOOR between the 2 groups, 1 in which placebo is more efficacious, and another in which amoxicillin is more efficacious than placebo. The power to detect a difference between arms was greater using DOOR/RADAR compared with DOOR alone. Assuming a sample size of 200, DOOR had 2.5%, 50%, and 65% power to detect a statistical difference between arms for scenarios 1-3, respectively, significantly less than DOOR/RADAR. Importantly, DOOR/RADAR incorrectly identified placebo as superior in scenario 3, where amoxicillin was truly efficacious. Sample size requirements for noninferiority designs were larger to achieve similar levels of power as DOOR and DOOR/RADAR. DOOR/RADAR has the potential to lead to an incorrect conclusion declaring placebo superior when amoxicillin is efficacious.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>The serial interval (SI) distribution of an epidemic is used to approximate the generation time distribution, an essential parameter for inferring the transmissibility (${R}_t$) of an infectious disease. However, SI distributions may change as an epidemic progresses. We examined detailed contact tracing data on laboratory-confirmed cases of COVID-19 in Hong Kong, China, during the 5 COVID-19 waves from January 2020 to July 2022. We reconstructed the transmission pairs and estimated time-varying effective SI distributions and factors associated with longer or shorter intervals. Finally, we assessed the biases in estimating transmissibility using constant SI distributions. We found clear temporal changes in mean SI estimates within each epidemic wave studied and across waves, with mean SIs ranging from 5.5 days (95% credible interval, 4.4-6.6) to 2.7 days (95% credible interval, 2.2-3.2). The mean SIs shortened or lengthened over time, which was found to be closely associated with the temporal variation in COVID-19 case profiles and public health and social measures and could lead to biases in predicting ${R}_t$. Accounting for the impact of these factors, the time-varying quantification of SI distributions could lead to improved estimation of ${R}_t$, and could provide additional insights into the impact of public health measures on transmission.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Etiological heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily adjust appropriately for these sources of bias, or allow for formal comparisons of effects across different subtypes. We show that using inverse probability weighting (IPW) to fit a multinomial model to yield valid inference with this sampling design for subtype-specific exposure effects, and contrasts thereof. We compare the IPW approach to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study (1993-2001).</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Conflicting results have appeared in the literature on whether the amount of nondense, adipose tissue in the breast is a risk factor or a protective factor for breast cancer (BC), and biological hypotheses supporting both have been proposed. We suggest here that limitations in study design and statistical methodology could potentially explain the inconsistent results. Specifically, we exploit recent advances in methodology and software developed for the joint analysis of multiple longitudinal outcomes and time-to-event data to jointly analyze dense and nondense tissue trajectories and the risk of BC in a large Swedish screening cohort. We also perform extensive sensitivity analyses by mimicking analyses/designs of previously published studies—for example, ignoring available longitudinal data. Overall, we do not find strong evidence supporting an association between nondense tissue and the risk of incident BC. We hypothesize that (1) previous studies have not been able to isolate the effect of nondense tissue from dense tissue or adipose tissue elsewhere in the body, that (2) estimates of the effect of nondense tissue on risk are strongly sensitive to modeling assumptions, or that (3) the effect size of nondense tissue on BC risk is likely to be small/not clinically relevant.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Postmenopausal women experience significant changes in body composition, particularly abdominal adipose tissue (AAT) deposition patterns, which influence cardiometabolic risk. Physical activity has demonstrable effects on body composition and overall health; however, there is little evidence for how physical activity influences AAT patterns and body composition in postmenopausal women. We emulated a target trial of physical activity interventions, including the 2018 Physical Activity Guidelines for Americans recommendations (≥150 minutes/week), on 3-year changes in AAT and body composition. We analyzed data from 4451 postmenopausal women aged 50-79 years in the Women’s Health Initiative (WHI) with repeated whole body Dual X-Ray Absorptiometry (DXA) scans with derived abdominal visceral (VAT) and subcutaneous adipose tissue (SAT). The mean AAT and body composition measures were estimated with the parametric-g formula. Over 3 years, interventions of increasing minutes of moderate activity would result in dose-dependent reductions in AAT, overall body fat and increases in lean soft tissue, with the greatest estimated benefit at the 2018 physical activity guideline recommendations. Compared to no intervention, if all participants had adhered to ≥150 mins/week of moderate physical activity, they would have 16.8 cm2 lower VAT (95% CI: −23.1, −10.4), 26.8 cm2 lower SAT (95% CI: −36.3, −17.3), 1.3% lower total body fat (95% CI: −1.8, −0.7), 1.2% higher total lean soft tissue (95% CI: 0.7-1.8), and 2.6 kg lower bodyweight (95% CI, −3.6, −1.5). We saw similar patterns in vigorous-intensity activity interventions. These results suggest that postmenopausal women who adhere to physical activity guideline recommendations would experience beneficial body composition changes over 3 years.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>We explored state-level indicators of structural racism on internalizing symptoms of depressive affect among US adolescents. We merged 16 indicators of state-level structural racism with 2015-19 Monitoring the Future surveys (<span style="font-style:italic;">n</span> = 41 258) examining associations with loneliness, self-esteem, self-derogation, and depressive symptoms using regression analyses. Students racialized as Black in states with bans on food stamp eligibility and temporary assistance for drug felony conviction had 1.37 times the odds of high depressive symptoms (95% confidence interval [CI], 1.01-1.89) compared to students in states without bans. In contrast, students racialized as White living in states with more severe disenfranchisement of people convicted of felonies had lower odds of high self-derogation (odds ratio [OR], 0.89; 95% CI, 0.78-1.02) and high depressive symptoms (OR, 0.83; 95% CI, 0.70-0.99) compared to states with less severe disenfranchisement. These findings demonstrate the need to address the legacy of structural racism at the state level to reduce mental distress for US youth.<strong>This article is part of a Special Collection on Mental Health</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Agreement to participate in case–control studies has become low. Healthy participant bias resulting from differential response proportions in cases and controls can distort results; however, the magnitude of bias is difficult to assess. We investigated the effect in a large population-based case–control study on breast cancer, with a participation rate of 43.4% and 64.1% for controls and cases, respectively. We performed a mortality follow-up in 2020 for 3813 cases and 7335 controls recruited during 2002-2005. Standardized mortality ratios (SMRs) for overall mortality and selected causes of death were estimated. The mean age at recruitment was 63.1 years. The overall mortality for controls was 0.66 times lower (95% CI, 0.62–0.69) than for the reference population. For causes of death other than breast cancer, SMRs were similar in cases and controls (0.70 and 0.64). Higher education was associated with lower SMRs in both cases and controls. Options for adjusting the healthy participant bias are limited if the true risk factor distribution in the underlying population is unknown. However, a relevant bias in this particular case–control study is considered unlikely since a similar healthy participant effect was observed for both controls and cases.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Nguyễn et al. (<span style="font-style:italic;">Am J Epidemiol.</span> 2024;193(10):1343-1351) analyzed data from the US National Survey of Drug Use and Health (NSDUH) to show that Asian American Native Hawaiian/Pacific Islander (AANHPI) adults with limited English proficiency have substantially lower levels of mental health services utilization compared with White adults without limited English proficiency. The findings add to the growing literature using an intersectionality framework to understand health and health care disparities. We comment on the authors’ notable examination of intersecting minoritized identities in mental health services utilization and the welcome emphasis on AANHPI health. We discuss the limitations of the NSDUH data, which are administered in English and Spanish only, and their limited ability to support analyses disaggregated by ethnoracial subgroups. We conclude by identifying gaps related to funding, training, and data disaggregation, and we highlight the role of mixed-methods approaches to advance our understanding of intersectionality and health disparities research.<strong>This article is part of a Special Collection on Mental Health</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>Despite the value of modern therapeutics, many obstacles prevent their optimal use. Overuse, underuse, and misuse are common, resulting in morbidity and mortality affecting billions of individuals across the world. Pharmacoepidemiology provides important insights into drug utilization, safety, and effectiveness in large populations, and it is an important method to identify opportunities to improve the value of therapeutics in clinical practice. However, for these opportunities to be realized, interventions to improve prescribing must be developed, evaluated, and implemented in the real world. We provide an overview of this process, focusing especially on how such interventions can be designed and deployed to maximize scalability, adoption, and impact. Prescribing represents a complex behavior with barriers and enablers, and interventions to improve prescribing will be most successful when developed, piloted and refined to maximize provider and patient acceptability. Carefully developed evaluations of interventions are also critical, and varied methods are available to empirically evaluate the intended and potential unintended consequences of interventions. With illustrative examples from the peer-reviewed literature, we provide readers with an overview of approaches to the essential and growing field of interventional pharmacoepidemiology.<strong>This article is part of a Special Collection on Pharmacoepidemiology</strong>.</span>
<span class="paragraphSection"><div class="boxTitle">Abstract</div>In 2023, Martinez et al (<span style="font-style:italic;">Am J Epidemiol</span>. 2023;192(3):483-496) examined trends in the inclusion, conceptualization, operationalization, and analysis of race and ethnicity among studies published in US epidemiology journals. Based on a random sample of articles (<span style="font-style:italic;">n</span> = 1050) published from 1995-2018, the authors describe the treatment of race, ethnicity, and ethnorace in the analytic sample (<span style="font-style:italic;">n</span> = 414, 39% of baseline sample) over time. The review supplies stark evidence of the routine omission and variability of measures of race and ethnicity in epidemiologic research. Between 32% and 19% of studies in each time stratum lacked race data; 61%-34% lacked ethnicity data. Informed by public health critical race praxis (PHCRP), this commentary discusses the implications of 4 problems the findings suggest pervade epidemiology: (1) a general lack of clarity about what race and ethnicity are; (2) the limited use of critical race or other theory; (3) an ironic lack of rigor in measuring race and ethnicity; and (4) the ordinariness of racism and White supremacy in epidemiology. The identified practices reflect neither current publication guidelines nor the state of the knowledge on race, ethnicity and racism; therefore, we conclude by offering recommendations to move epidemiology toward more rigorous research in an increasingly diverse society.</span>
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