We prospectively examined whether community-level social capital plays a significant role in developing Posttraumatic Growth (PTG) among older survivors of the 2011 Japan Earthquake and Tsunami. The baseline survey was conducted 7 months before the disaster among residents of a city located 80km west of the earthquake epicenter. The survey inquired about participants' health status and social capital (informal socializing and social participation, and social cohesion). Approximately 2.5 years after the disaster, we surveyed older survivors to assess their disaster experiences. A follow-up survey in 2022 inquired about PTG in the 11 years following experiences of the disaster (n = 1,819). Multilevel linear regression analysis showed that pre-disaster community-level informal socializing and social participation was associated with higher PTG scores (coefficient = 0.25, 95% CI: 0.02, 0.47). In cross-classified multilevel regression, maintenance of higher community-level informal socializing and social participation during the post-disaster period was associated with higher scores of PTG (coefficient = 0.22, 95% CI: 0.07, 0.37). Pre-disaster community-level informal socializing and social participation was associated with higher PTG scores among older survivors. Interventions encouraging social interactions among neighbors may be effective in promoting PTG of survivors after natural disasters.

Racial inequities in postpartum weight have been documented with limited studies on the influences of racism and other forms of discrimination. In a prospective longitudinal study applying ecological momentary assessment (EMA) and ambulatory assessment of weight, we measured the association between discrimination, stress and postpartum weight change. The Postpartum Mothers Mobile Study (PMOMS) is a cohort of 313 pregnant and birthing individuals who were followed during their second and third trimester through 1 year postpartum. They were recruited in clinical settings between 2017-2020 in a major city center in Pennsylvania. Measures of racism and gender-based discrimination were collected via random EMA surveys throughout pregnancy and postpartum; and weight was collected via blue-tooth enabled scales at least weekly. Among Black participants, a 10% increase in the number of days a participant experienced racial discrimination in the past month was associated with retaining 0.3 more pounds; 10% increase in EMA gender discrimination was associated with retaining 0.4 more pounds; and EMA stress reported in the past month was associated with decreased weight retention. Chronic experiences of racial and gender discrimination may contribute to weight retention immediately after pregnancy and beyond.

We investigated California mortality and social determinants of health, as measured by the Healthy Places Index (HPI), which is a composite measure of 23 indicators of neighborhood (census tract) economic conditions, education, transportation, housing, social capital, environmental pollution, built-environment, and access to health care. We aggregated deaths to 2010 census tract boundaries for leading causes, 2015 to 2019, and COVID-19, 2020-2021, from death certificates and populations from the American Community Survey, 2015 to 2019. We age-adjusted and stratified death rates by HPI deciles, age, gender, and race/ethnicity, and examined HPI dose-response with segmental regression. For all causes, cancer, cardiovascular disease, COVID-19, diabetes, cirrhosis of the liver, injuries, and Alzheimer's disease (ages 65-74 years), mortality rates were inversely related to HPI decile. For all causes mortality, the rate ratio between the 1st and 10th decile (reference) was 1.63 (CI95%: 1.60-1.66), and, for COVID-19, the rate ratio was 7.61 (CI95%: 7.14-8.12). The population attributable fraction was 24% for all causes and 72% for COVID-19. Age, gender, race/ethnicity modified the steepness and shape of dose-response curves. The findings illustrate opportunities to incorporate area-based socioeconomic measures in routine public health surveillance, and to reinforce policies and programs that reduce health inequities.

A few studies investigated critical periods of temperature and the risks of stillbirth and preterm birth. This study aimed to identify critical periods of composite biothermal stress (Universal Thermal Climate Index, UTCI) for stillbirth and spontaneous preterm birth (sPTB). From the Midwives Notification System, 415,271 singleton births between 1st January 2000 and 31st December 2015 were linked to spatiotemporal UTCI in Western Australia. Covariate-adjusted weekly and monthly distributed lag non-linear Cox regression from twelve weeks before conception to birth were performed. Relative to median exposure (14.2 °C), extreme UTCI levels (1st-10th and 90th-99th centiles) were associated with higher hazards of stillbirth and sPTB, especially stronger at lower than higher exposures. Critical susceptible periods at 1st centile (10.2°C) exposure were found during gestational weeks 21-42 with the strongest hazard of 1.14 (95% CI 1.03, 1.27) in the 42nd week for stillbirth and during gestational weeks 26-36 with the strongest hazard of 1.09 (95% CI 1.06, 1.12) in the 36th week for sPTB. Monthly exposure showed a similar pattern but with greater magnitude. Mid to late gestation showed critical susceptible periods of biothermal stress on the birth outcomes, suggesting further studies and timely climate-related healthcare interventions.

Structural cisheterosexism is a root cause of LGBTQ health inequities. Amidst ongoing legal attacks on LGBTQ populations' rights, research is needed to examine changes in policy contexts over time and associated implications for population health and inequities. To address this gap, we constructed state-level structural cisheterosexism trajectories for each U.S. state/D.C. from 1996-2016. We used sequence analysis to quantify differences between trajectories and cluster analysis to group similar trajectories. We evaluated associations between trajectory clusters and individual-level health(care) outcomes (self-rated health, frequent mental distress, lacking insurance, lacking a doctor, avoiding care due to cost) from the 2017 Behavioral Risk Factor Surveillance System, in the overall sample and by LGBTQ status (LGBTQ vs. cisheterosexual), using multilevel logistic models. From 38 unique trajectories, we identified five trajectory clusters: "consistently-predominantly-discriminatory", "consistently-fairly-discriminatory", "moderate-with-increasing-protection", "discriminatory-change-to-fairly-protective", and "fairly-discriminatory-change-to-predominantly-protective." Overall, health(care) was worse in states characterized by consistently discriminatory laws compared to states with increasingly protective laws and disproportionately so for LGBTQ people. Findings underscore the need to abolish harmful, cisheterosexist state laws and enact protective laws to advance LGBTQ health equity. More broadly, this study demonstrates the utility of sequence and cluster analysis for assessing long-term population health impacts of structural-level determinants.

Extant research shows that race adjustment in epidemiologic analyses could lead to masking of systemic racism. In this study, we compare race-adjusted and -unadjusted years of life lost (YLL), a measure of societal burden, to understand the impact of race adjustment in YLL estimation. We used North Carolina (NC) Violent Death Reporting System data from 2006-2019 linked to 2006-2019 race-adjusted and -unadjusted life tables from the Centers for Disease Control and Prevention by calendar year and age at death. We calculated total YLL and YLL per death from suicide and homicide deaths for non-Hispanic black and non-Hispanic white NC residents using both the race-adjusted and -unadjusted life tables. We found that YLL and YLL/death from suicide and homicide deaths for non-Hispanic white individuals were almost identical regardless of race adjustment. However, race-adjusted life tables vastly underestimated total YLL and YLL per death for non-Hispanic black NC residents. Overall, race adjustment resulted in an underestimation of 14,907 YLL from homicide deaths (3.1 fewer YLL/death) and 4,414 YLL from suicide deaths (2.8 YLL/death) for black NC residents. Our study shows that the baked-in underestimation of YLL for non-Hispanic Black Americans when using race-adjusted life tables hides racialized health disparity and perpetuates inequity.

Findings for greenspace's impacts on birth outcomes are largely dependent on vegetation indexes. Examinations are needed for various greenspace indicators given varying pathways for fetal development. This prospective cohort study assessed the impacts of prenatal greenspace exposure on preterm birth (PTB), term low birthweight (TLBW), birthweight, and estimated fetal weight (EFW) for pregnant women in the New York City area, 2016-2023 (n=2765). Longitudinal greenspace exposure was measured for residential histories during pregnancy using the Enhanced Vegetation Index (EVI) for 1000m buffers and four park metrics, namely, the total number, sum of area, and the accessibility of parks within residential buffers (500 m) and the distance to the closest park. Multivariable regression models were used to estimate the associations for quartiles of exposure (with the first quartile [Q1] as reference). Greenspace exposure was not associated with TLBW, birthweight, or EFW. Odds ratios of PTB for the Q2, Q3, and Q4 EVI exposure groups compared to the Q1 group were 0.65 (95% CI: 0.43-0.98), 0.51 (0.32-0.80), and 0.56 (0.35-0.90), respectively. PTB risks decreased in higher exposure groups (Q2-Q4) of the total park number. Results indicate the benefits of prenatal greenspace exposure for fetal maturity and neonatal outcomes.

This study explores how discrimination experiences during the COVID-19 pandemic relate to anxiety and depressive symptoms in U.S. adults. Using a national representative intensive longitudinal survey, the study evaluates rapid subsequent changes in anxiety and depression when individuals undergo heightened discrimination beyond their usual experiences. The study used 23 survey timepoints, primarily with 2-week intervals, from the Understanding America Study (n=8,198). Time-varying and time-lagged associations between discrimination experiences and anxiety and depression were modeled using multi-level logistic random-effect repeated-measures regression models. The results showed that discrimination experiences were associated with moderate-to-severe anxiety and depressive symptoms, as well as more than one comorbid psychological distress symptom (adjusted Odds Ratios [AORs]=1.10 to 1.13). The association remained significant regardless of inter-individual differences in exposure to discrimination. Non-Hispanic Blacks, Asians, and other race/ethnicities exhibited stronger associations between discrimination and psychological distress (AORs=1.63 to 1.93) compared to Hispanic and White respondents (AORs=1.13 to 1.25). Our findings suggest that individuals experience a rapid deterioration in their emotional well-being when subjected to heightened levels of discrimination beyond their typical experiences.

Gaps in life expectancy between Americans with and without a college degree have widened markedly over the past three decades. One explanation points to increasing educational attainment changing the type of people with and without a degree. If pre-existing health in the two education groups changes as the fraction with a degree changes, health selection might explain the widening mortality gap.

In conducting a scoping review on the health effects of high-concentration cannabis products, we have uncovered pervasive methodological shortcomings within the cannabis literature. This paper begins by defining the 'causal effect' of interest for public health and delineating the desirable features of study design that can address crucial questions pertaining to public health and policy. We further delve into the methodological complexities inherent in studying the health effects of high-concentration cannabis products, describing challenges associated with the measurement of exposures and outcomes, confounding, selection bias, and the generalizability of findings. We introduce causal inference methods to mitigate potential biases in observational cannabis use studies. We identify specific areas that necessitate further development and investigation to deepen our understanding of this topic. Finally, this paper extends actionable recommendations, serving as a roadmap for upcoming research initiatives in this domain.

In epidemiological database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximised, at the expense of sensitivity. However, if the indicator has low sensitivity, measures of occurrence are underestimated. In association studies, risk difference is biased, and risk ratio may be biased as well, in either direction, if the sensitivity is differential across exposure groups. In this work, we show that if an auxiliary screening indicator can be defined to complement the main indicator, estimates of the positive predictive value of both indicators provide tools to estimate the sensitivity of the primary indicator, or a lower bound thereof. This mitigates bias in estimating the number of cases, prevalence, cumulative incidence, rate (particularly when the event is rare), and in association studies, risk ratio and risk difference. They also allow testing for non-differential sensitivity. While direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from re-use of existing databases, which may prove critical for regulatory and public health decisions.

Racial differences in healthcare access (HCA) may contribute to disparities in ovarian cancer (OC) survival. We used structural equation models (SEM) to examine associations between race and HCA domains (affordability, availability, accessibility) in relation to overall and OC-specific mortality. Non-Hispanic (NH)-Black and non-Black (Hispanic, NH-White) women diagnosed with OC in 2008-2015 were identified from SEER-Medicare. Cox proportional hazards regression was used to conduct mediation analysis for associations between race and HCA domains with overall and OC-specific mortality. SEM models adjusting for demographic and clinical covariates were used to estimate hazard ratios (HR) and 95% confidence intervals (CI). A total of 4,629 eligible OC patients were identified, including 255 (5.5%) patients who were NH-Black. In SEM adjusting for demographic, clinical, and HCA latent variables, there was a total effect of NH-Black race on overall (HR: 1.11, 95% CI: 1.03,1.19) and OC-specific mortality (HR: 1.16, 95% CI: 1.08, 1.24), which was primarily driven by a direct effect. There was a modest indirect association between NH-Black race and mortality through decreased treatment receipt, though not through HCA. There is a need for studies investigating additional social and biological mechanisms that contribute to worse cancer survival among NH-Black patients.

CP*Trends is a widely used SEER website used to explore temporal effects of period and cohort on cancer incidence and mortality. It provides a graphical display of smoothed rates, and a C-P Score that helps to assess the magnitude of the effect of cohort and period. This update provides results for African Americans and Whites. The C-P Score has an intrinsic bias favoring cohort because there are many more cohorts than periods. An adjusted C-P Score removes some of this advantage. Bootstrap confidence intervals are given, which allow one to see the effects of different sample sizes on the model results. Finally, users may control window size used in the smoothing algorithm, which helps to avoid over smoothing or masking of trends. The method is illustrated using data on cervical cancer incidence trends for African Americans and Whites, 1975-2018. Rates are higher for African Americans, and both races have contributions for cohort. However, the period effect is only strongly evident in Whites. Visual inspection of White trends suggests possible differences for those older and younger than age 50. These methods are applied in an interactive website displaying incidence and mortality trends for over 20 cancer sites in the US.

The COVID-19 pandemic's global impact has been devastating, causing millions of deaths. Our study investigates excess sepsis-related mortality trends over three years during the pandemic. Using CDC's National Vital Statistics System data from January 2018 to March 2023, we projected sepsis-related deaths during the pandemic using a Poisson log-linear regression model. We compared observed versus predicted deaths and analyzed temporal trends by demographics and regions. Among the 753,160 deaths documented between March 2020 and March 2023, a significant downward trend was noted in sepsis-related mortality rates from March 2022 to March 2023, coinciding with the surge of the Omicron variant. The excess mortality rates were 170.6 per million persons (95% CI: 168.2-172.6), 167.5 per million persons (95% CI: 163.6-170.9), and 73.3 per million persons (95% CI: 69.4-76.6) in the first, second, and third years, respectively. Increased sepsis-related mortality was observed across all age subgroups, with the greatest increase noted in those aged 85 years and above compared to middle- and young-aged decedents. Disparities were also observed across racial/ethnic, sex/gender subgroups, and geographic regions. This study highlights the effectiveness of current policies and prevention measures in response to the long-term circulating of SARS-CoV-2 in the community.

Age at menopause varies considerably among women and is linked to health after menopause. Body mass index is associated with age at natural menopause, but the influence of weight change remains unclear. Thus, we studied associations of adolescent to midlife weight change with age at natural menopause. We performed a retrospective population-based cohort study of 263,586 women aged 50-69 years attending BreastScreen Norway (2006-2015). The associations were estimated as hazard ratios (HRs) for having reached menopause using Cox proportional hazard models. We included nine categories of weight change based on recalls of adolescent weight compared to peers and quartiles of midlife weight in kilograms. We adjusted for year and country of birth, education, number of childbirths, height, smoking, and exercise. Women with the largest estimated weight loss had highest hazard of reaching menopause (adjusted HR 1.11, 95% CI: 1.06-1.17) compared to women with estimated stable average weight. Conversely, women with the largest estimated weight gain had lower hazard (adjusted HR 0.96, 95% CI: 0.93-0.99). Women with estimated stable high weight had lowest hazard of reaching menopause (adjusted HR 0.93, 95% CI: 0.90-0.95). Our findings suggest that changes in body weight across the life course may influence the timing of menopause.

While inverse probability of treatment weighting (IPTW) is a commonly used approach for treatment comparisons in observational data, the resulting estimates may be subject to bias and excessively large variance under lack of overlap. By smoothly down-weighting units with extreme propensity scores, i.e., those that are close (or equal) to zero or one, overlap weighting (OW) can help mitigate the bias and variance issues associated with IPTW. Although theoretical and simulation results have supported the use of OW with continuous and binary outcomes, its performance with survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST). We combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and provide computationally-efficient variance estimators when the propensity scores are estimated by logistic regression and the censoring process is estimated by Cox regression. We conduct simulations to compare the performance of weighting methods in terms of bias, variance, and 95% interval coverage, under various degrees of overlap. Under moderate and weak overlap, we demonstrate the advantage of OW over IPTW, trimming and truncation, with respect to bias, variance, and coverage when estimating RMST.

In this study, we assessed the overall impact of the Covid-19 pandemic in the United States between 2020 and 2023 through estimates of excess all-cause mortality. Monthly mortality rates over a 19-year period, stratified by age, sex and state of residence were used to forecast expected mortality for the pandemic years. A combination of models - two timeseries, a spatial random effects and a generalized additive -- was used to better capture uncertainty. Results indicate that US national excess mortality decreased in 2023 to 157 thousand (95% prediction interval: 35K-282K) from 502K (436K-567K), 574K(484K-666K) and 377K (264K-484K) during the years 2020-2022, respectively. Unlike in previous years, deaths with Covid-19 as the underlying-cause-of-death possibly accounted for all excess deaths during 2023. While for the older age groups (75+ years) the year 2020, before vaccines were available, had the highest excess mortality rate, the two younger age groups had the highest excess mortality in 2021. In each age group, women were estimated to have consistently lower excess mortality than men. West Virginia had the highest age-standardized excess mortality among all states in 2021 and 2022. Our findings demonstrate the value of a multi-model approach in capturing heterogeneity in excess mortality.