American Journal of Epidemiology

Background: Mixture epidemiology deploys sophisticated estimators, Bayesian kernel machine regression with causal mediation analysis (BKMR-CMA), quantile G-computation (QGC), and parametric G-computation, alongside conventional regression. Comparative evaluations have assumed additive, non-mediated data-generating processes, leaving conditions under which estimator choice determines causal validity uncharacterized. Methods: We developed a simulation framework using military-relevant exposure distributions (metals, per- and polyfluoroalkyl substances [PFAS], polychlorinated biphenyls [PCBs]) and allostatic load (AL) across three deployment tiers, with parameters drawn from military occupational health and contamination literature. Four data-generating processes were specified as directed acyclic graphs: direct effects with confounding (M1), full mediation through AL (M2), synergistic AL-exposure interaction (M3), and collider structure (M4). We evaluated ordinary least squares (OLS), QGC, G-computation, and BKMR-CMA on bias, root mean squared error, and 95% confidence interval coverage across 500 Monte Carlo replications at n = 500 and n = 1,000. Results: No estimator dominated across all mechanisms. Under M1, OLS and G-computation produced near-identical modest positive bias; BKMR-CMA achieved lower root mean squared error through kernel shrinkage. Under M2, BKMR-CMA exhibited severe positive bias for AL (mean bias = +0.579 SD units; coverage = 32.8%). Under M3, BKMR-CMA was the only estimator achieving nominal 95% coverage for AL (95.2%), while regression-based approaches fell to 83.6%. Under M4, G-computation produced persistent bias and near-zero coverage for lead, reflecting structural non-identification. Conclusions: Estimator validity is fundamentally mechanism-dependent. Researchers should base estimator choice on explicit causal assumptions about whether AL functions as confounder, mediator, moderator, or collider, particularly in military and occupational cohorts. We provide a mechanism-to-estimator mapping for applied researchers.

Abstract Background. Area-level cancer disparities are routinely estimated from public county data in which rates based on small counts (fewer than 16 cases or deaths) are suppressed. Analysts typically drop suppressed counties (complete-case analysis). Because suppression depends on case counts tied to population size and demographic composition, this missingness may be informative, but its effect on the disparity estimate has not, to our knowledge, been quantified. Methods. In a cross-sectional ecological study of 3,143 U.S. counties (analytic sample 3,018 with computable exposure) using one frozen public release of NCI State Cancer Profiles incidence and mortality data and ACS 2018-2022 5-year data, we estimated the most- versus least-deprived ICE(race+income) quintile rate ratio (RR) and rate difference for female breast, stomach, and cervix cancers under four suppression-handling methods: complete-case, available-case, bounding, and model-based small-area estimation. We characterized which counties were erased, and, following the ADEMP framework, ran a Monte Carlo simulation (1,000 replicates per cell; Monte Carlo standard error of bias approximately 0.0025) calibrated to the release to measure bias against a known truth. Analyses were pre-registered. Results. The suppressed fraction rose with rarity: 7.4% of counties for breast, 61.3% for stomach, and 75.7% for cervix incidence. Suppression was concentrated in the most-deprived quintile (cervix, 81.8% suppressed vs 63.8% least-deprived) and overwhelmingly removed rural rather than minority residents (cervix: 81% of the rural but 9% of the minority population erased). For breast (little suppression) the RR was 0.87 (95% CI 0.85-0.89) and identical across methods; for cervix incidence the complete-case RR (1.56) exceeded the model-based estimate (1.50), and for cervix mortality (91% suppressed) complete-case (1.86) exceeded model-based (1.56) by 16% with a wide bounding interval (1.88-2.62). In calibrated simulation, population-weighted complete-case bias was small (less than 2%) at the observed deprivation-county-size correlation and grew with rarity, threshold, and unweighted aggregation; its direction was conditional, becoming positive (over-estimation) as deprived counties became smaller. Conclusions. Complete-case handling of suppressed counties over-estimates rare-cancer area disparities relative to methods that retain them, while silently erasing most of the rural and most-deprived communities the estimate is meant to represent. The effect is negligible for common cancers and grows with rarity. Public-data disparity analyses should report the suppressed fraction and use bounded or model-based estimates by default. Keywords: cancer disparities; small-count suppression; Index of Concentration at the Extremes; informative missingness; small-area estimation; rural health.

Background: In prospective cohort studies, where an exposure is collected repeatedly, interest often lies in determining whether the timing of that exposure has a differential effect on a later outcome. The Structured Life Course Modeling Approach (SLCMA), where users select between temporal hypotheses of exposure specified a priori, provides one way to analyse such longitudinal data. However, few studies using SLCMA consider the effect of time-varying covariates (TVC) which may impact associations. Methods: We present a modified version of the SLCMA - called direct and mediated effects (DME)-SLCMA - which corrects for TVC. We first develop the DME-SLCMA method, test it through simulation, and apply it to psychosocial data from the Drakenstein Child Health Study (DCHS, n=336) to investigate relationships between maternal psychopathology, TVC of socioeconomic status, and offspring depressive symptoms. Results: We found that, on average, offspring depressive symptoms score increased by 3.9% (95% CI: 1.0%-6.9%, p = 0.039) for each unit of maternal psychopathology (SRQ) at 48 months whilst adjusting for time-varying socioeconomic status (at 18, 30, 42 and 54 months). Our simulations identified several realistic scenarios where selections ignoring TVC - with TVC mediated exposure effects present - were prone to be incorrect, including our DCHS example. Conclusion: DME-SLCMA is a robust new approach for life course modelling in the presence of time-varying covariates. We recommend adjusting for TVC whenever possible, and, when not possible, our simulation study identified that scenarios where mediated effects are comparable, or greater, in magnitude to direct effects are most prone to confounding.

Seroprevalence surveys reveal the extent of humoral immunity against pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and under some circumstances represent cumulative incidence of prior infection. However, antibody waning - or seroreversion - biases these estimates by reducing assay sensitivity in a time-varying manner. Because assay sensitivity decays over time, naively using serosurveys can substantially bias estimates of SARS-CoV-2 cumulative incidence and fatality rates. The Bayesian assay-specific, time-varying sensitivity adjustment developed in this paper can reliably correct for this bias and account for the delay between infection and serosurvey. In seroprevalence studies conducted in the United States in 2020, adjusting for time-varying sensitivity increased cumulative incidence by up to 1.4-fold, with an adjustment of 1.08 for a national study. Our estimates contrast with a previously published 2-fold adjustment that did not account for assay design. This suggests that previous analyses overestimated cumulative incidence by applying seroreversion corrections that did not account for assay-specific effects, or underestimated cumulative incidence by not applying seroreversion corrections. These biases imply fatality rate underestimation and overestimation, respectively. Our model provides a framework for design-specific time-varying sensitivity corrections in seroprevalence surveys for other pathogens.

Background Differential contributions to transmission across age groups have been reported for many respiratory infections, including SARS-CoV-2. They are crucial for estimating the impact of age-specific interventions. Disentangling these age-dependent contributions remains challenging, as they may reflect differences in contact rates, biological susceptibility, or infectiousness. Aim We aim to jointly estimate age-specific per-contact infectiousness and susceptibility and their effect on the impact of age-specific interventions. Methods The age-specific infectiousness and susceptibility were jointly estimated in a Bayesian framework by combining contact data with transmission pair data (who-infected-whom). We applied this approach to 197,840 self-reported household transmission pairs collected in the Netherlands during the COVID-19 pandemic. Using these estimates, we projected the expected impact of school closure and work-from-home measures during the early stages of an epidemic in the absence of other interventions. Results Both infectiousness and susceptibility to SARS-CoV-2 infection were lowest in children aged 0-9 years and highest in adults over 30 years old, with 2- to 4.5-fold differences between these groups. Projected impacts of age-specific interventions indicated that school closures would reduce the reproduction number by 8% or 29% when age-specific susceptibility and infectiousness were or were not considered, respectively. Conversely, working-from-home policies would lead to reductions of 41% with and 20% without age-specific infectiousness and susceptibility. Conclusion Our method enables robust estimation of age-specific infectiousness and susceptibility. Accounting for these age heterogeneities is essential for projecting the impact of age-targeted interventions. Our approach is adaptable to other respiratory infections and can guide more tailored public health responses.

INTRODUCTION: Alzheimers disease and related dementias (AD/ADRD) arise from cumulative environmental, social, behavioral, and biological influences across the life course. The neural exposome framework conceptualizes how exogenous, behavioral, and endogenous factors interact to shape brain health; however, its application to preclinical AD/ADRD research, particularly in rural populations, remains limited. METHODS: We developed and piloted a community-embedded, decentralized research model to operationalize the neural exposome framework among cognitively unimpaired adults aged 45+ in two rural Midwestern U.S. communities, integrating environmental, social, behavioral, geospatial, and biological measures to evaluate exposure-related neurobiological and cognitive vulnerability. RESULTS: This approach demonstrated high feasibility and acceptability, achieving strong recruitment, retention, data completeness, and multidomain biomarker collection in rural community-based settings DISCUSSION: Pilot findings support the feasibility of neural exposome-informed research in rural U.S. communities and highlight its potential to advance prevention-oriented research on brain health and AD/ADRD.

Background: In the absence of high-resolution response data, exposure-response modelling often relies on aggregated low-frequency exposure data, leading to loss of high-resolution information. Mixed Data Sampling (MIDAS) from econometrics offers an alternative but is limited due to its inability to make high-resolution predictions, inflexible likelihoods and penalised nonlinear functions, and limited visualization options. We propose a mixed-frequency Distributed Lag Non-linear Model (mf-DLNM) which can eliminate the need to aggregate exposure data in environmental epidemiology and provide high resolution predictions for time series studies. Methods: We evaluated the inference and predictive performance of the mf-DLNM. To evaluate its ability to estimate exposure-response relationships, we applied mf-DLNM and same-frequency (sf)-DLNM using data from the West Midlands, UK. Additionally, we compared the predictive performance of mf-DLNM with sf-DLNM and MIDAS across nine regions of England. As MIDAS cannot predict at the resolution of the predictor (daily), we compared the predictive performance of mf-DLNM and MIDAS at weekly resolution. To test the model's ability to predict high temporal resolution risk (daily), we compared sf-DLNM (with access to daily mortality counts) with mf-DLNM (with access only to weekly mortality counts). Results: In the West Midlands example, mf-DLNM performed comparably to sf-DLNM in estimating daily risk of temperature on respiratory mortality. Furthermore, mf-DLNM and MIDAS exhibited similar performance for weekly predictions. For high-resolution predictions, mf-DLNM and sf-DLNM showed nearly similar performance, despite mf-DLNM having access only to low-resolution response data. Conclusion: This mixed-frequency approach in environmental epidemiology overcomes the limitations of predicting health risks using aggregated exposure data and provides estimates of high-resolution outcomes in the absence of high-frequency health outcome datasets.

Lower cognitive ability measured in childhood or late adolescence has been consistently associated with higher mortality risk across adulthood. However, this evidence largely relies on single assessments, leaving it unclear to what extent mortality risk reflects cognitive differences established early in life versus developmental divergence during adolescence - a period of substantial neurocognitive plasticity. Using two nationally representative Swedish cohorts comprising 9,412 males born in 1948 and 1953, we linked cognitive ability assessed in primary school at age 13 years and military conscription at age 18 years to all-cause and cause-specific mortality recorded in nationwide registers through 2025. We decomposed late-adolescent cognitive ability into childhood cognitive level and adolescent cognitive change and evaluated their independent associations with mortality. Childhood cognitive level (HR = 0.81; 95% CI, 0.78-0.85) and adolescent cognitive change (HR = 0.84; 95% CI, 0.79-0.89) independently predicted lower mortality risk, also after adjustment for parental education. Childhood cognitive level and adolescent cognitive change showed partially distinct cause-specific patterns. Childhood cognitive level was most strongly associated with mortality from intrinsic causes, whereas adolescent cognitive change showed relatively stronger associations with external causes, particularly accidental deaths. Although adolescent cognitive change was associated with psychosocial factors including education and psychiatric diagnosis at conscription, its association with mortality persisted after adjustment for these factors. These findings suggest that cognitive development during adolescence carries independent prognostic information regarding long-term survival beyond cognitive level established by late childhood, highlighting adolescence as a consequential period for lifelong health.

Background Pediatric immunizations for Respiratory Syncytial Virus (RSV), including monoclonal antibodies for infants and vaccines for pregnant people, have become broadly available and can prevent severe RSV outcomes in infants. However, quantifying the impact of RSV immunization in prevention of severe pediatric illness at the population-level is limited by lack of RSV case surveillance data. The Massachusetts Department of Public Health (DPH) conducted a modeling analysis using routine public health surveillance data to estimate the state-level impact of new RSV immunization products on Emergency Department (ED) visits and hospitalizations in Massachusetts for highest risk pediatric groups. Methods A scenario projection tool, called R.Scenario.Vax, was utilized to simulate RSV-associated ED hospital encounters by age group in the context of newly available immunizations. ED visit and hospitalization data from the National Syndromic Surveillance Program (NSSP) during the time period 10/08/2017--10/19/2024 were analyzed, scaled to account for changes in RSV testing practices over time and missing encounter volume in historic data, and utilized to inform model fit of a "typical" RSV season. RSV immunization data from the Massachusetts Immunization Information System (MIIS) for the 2023--2024 and 2024--2025 RSV seasons informed high and moderate pediatric RSV immunization coverage scenarios and their impact was compared to a counterfactual reference scenario of no new immunizations. Median projections were quantitatively and qualitatively compared to observed 2024--2025 season data. Percent reduction in hospital encounters and encounters averted per 10,000 population were calculated for each scenario as compared to the reference. Results Projections for the youngest at-risk age groups showed significantly lower RSV-associated ED visits and hospitalizations during the 2024--2025 season for both high and moderate immunization coverage scenarios. Median projections for infants under 6 months old in the highest coverage scenario, wherein nearly all infants were immunized, showed 72.6% lower ED visits and 73.4% lower hospitalizations when compared to the reference scenario, equating to 262 ED visits and 85 hospitalizations averted per 10,000 population. Conclusions Our results support the use of modeling methods for public health insights and suggest that RSV immunizations for infant populations result in significantly lower RSV-related ED encounters in Massachusetts.

BackgroundHIV-related stigma remains a primary barrier to the elimination of the HIV epidemic worldwide. No studies have examined long-term changes in the distribution of stigmatizing attitudes within populations. MethodsWe conducted a whole-population, open cohort study of adults in 8 villages in rural southwestern Uganda, with 5 biennial surveys spanning 2014-2024 (N=1,776 at baseline; 869 participated in all waves). We measured individual negative attitudes toward people with HIV ("public stigma") and perceptions of negative attitudes among others ("perceived stigma") using parallel 15-item scales. We estimated mean stigma scores, computed inequality measures at each wave, and decomposed inequality by sociodemographic characteristics. Leveraging the cohort design, we estimated intraclass correlation coefficients and rank-order stability over time. ResultsBoth public and perceived stigma declined substantially from baseline to endline and became concentrated in an increasingly smaller subgroup of the population. Theil decomposition failed to identify any stratifying variables that explained more than 3% of this variation: nearly all the inequality in HIV stigma occurred within population subgroups rather than between them. In longitudinal analyses, public stigma showed trait-like properties (intraclass correlation coefficient=0.35; 95% CI, 0.31-0.38) and meaningful rank-order stability (baseline-to-endline r=0.41). Perceived stigma showed no rank-order stability, no appreciable between-person variance, and universal convergence to low levels regardless of baseline. ConclusionsBoth public and perceived HIV stigma declined substantially in this rural Ugandan population, but remaining public stigma has become concentrated within a persistent minority. Sociodemographic profiling to target individuals who carry persistently negative attitudes toward people with HIV is unlikely to succeed.

Background. Tobacco smoke exposure, quantified by serum cotinine, is associated with cardiovascular, metabolic, and sleep-related health risks. The relationship between biomarker-verified tobacco smoke exposure and objectively measured, free-living wrist-worn ambient light patterns has not been examined in a nationally representative U.S. adult sample. Methods. We analyzed NHANES 2011-2014 cross-sectional data from 6,937 adults aged >20 years with valid serum cotinine and wrist-worn Physical Activity Monitor (PAM) ambient light data. Seven light outcomes were modeled using survey-weighted linear regression with log2(cotinine+1) as the continuous exposure across four covariate adjustment levels. Benjamini-Hochberg false discovery rate (FDR) correction was applied across the 7 outcomes within each model. Results. In Model 2 (adjusted for age, sex, race/ethnicity, education, poverty-income ratio, BMI, and survey cycle; N = 6,350), higher serum cotinine was associated with significantly higher nighttime light (beta = +0.024, 95% CI: 0.010, 0.038; p-FDR = 0.014) and lower evening light (beta = -0.031, 95% CI: -0.055, -0.008; p-FDR = 0.042). In exploratory behavioral models without alcohol (Model 3a; N = 5,766), both nighttime and evening associations remained FDR-significant. After additional adjustment for alcohol, which substantially reduced the sample due to 37.6% missingness (Model 3b; N = 3,866), the nighttime association attenuated below the FDR threshold, while the evening association remained FDR-significant. Categorical analyses showed progressively higher nighttime light across cotinine groups, and a hypothesis-generating sex interaction was identified (p-interaction = 0.001). Conclusions. Higher serum cotinine concentrations were associated with higher nighttime and lower evening ambient light after sociodemographic adjustment. Attenuation after behavioral adjustment and the cross-sectional design preclude causal inference. Longitudinal studies with formal mediation analyses are needed to clarify the temporal ordering and mechanisms linking tobacco smoke exposure, smoking-related behaviors, and personal light-dark cycle patterns.

While SARS-CoV-2 and influenza continue to place a significant burden on population health, within-household differences in decisions towards vaccination and seeking care across these two pathogens, and across sociodemographic groups, remain largely unexplored. By conducting a household-level survey in Illinois, we found that many individuals made inconsistent decisions about vaccination: among all adults, 29% were vaccinated for only one of COVID-19 or influenza, and among those with children in the home, 39% lived with a child whose influenza or COVID-19 vaccination status differed from their own. A higher proportion of adults were vaccinated against COVID-19 compared to influenza, while the opposite was true for those younger than 18 years old. These differences hold even when accounting for disparities in coverage by age, race/ethnicity, political affiliation, and socioeconomic status. While vaccinated individuals consistently reported wanting to protect themselves or others, those who declined vaccination reported highly heterogeneous reasons ranging from resource constraints to distrust or misconceptions about vaccination. These differences are even more pronounced for COVID-19, with larger partisan gaps and higher refusal driven by safety concerns, lack of trust, or religious reasons than those who decide not to get the influenza vaccine. In contrast to vaccination, the decision to seek medical care when sick showed opposite sociodemographic trends, that are likely attributable to illness severity. Our findings highlight that closing gaps in COVID-19 and influenza vaccination coverage will require an integrative strategy that accounts for diverse motivations, fears, and barriers to access, while addressing social inequalities common to both diseases.

Background: Prostate cancer survival varies by stage at diagnosis, and Black men experience a disproportionate burden of advanced disease. We examined whether neighborhood deprivation, measured by Area Deprivation Index (ADI), contributes to racial differences in metastatic presentation. Methods: We conducted a population-based study of men diagnosed with prostate cancer in the Ohio Cancer Incidence Surveillance System from 1996 to 2016. The primary endpoint was distant-stage disease at diagnosis. Generalized additive models assessed nonlinear associations of ADI and diagnosis year with metastatic risk. Inverse probability of treatment weighting (IPTW) models estimated odds ratios comparing Black with White men after sequential adjustment for diagnosis year, age, insurance, and ADI. Results: Among 135,095 men, 18,690 were Black and 116,405 were White. Distant-stage disease occurred in 7.0% of Black men and 5.0% of White men. Black men had higher median ADI (60.9 vs. 47.3). Medicaid-insured men had the highest unadjusted odds of metastatic presentation (OR, 4.68; 95% CI, 4.13-5.31), exceeding uninsured men (OR, 2.91; 95% CI, 2.54-3.34). In IPTW models without age adjustment, the odds ratio decreased from 1.54 to 1.24 after adding insurance and ADI. In age-adjusted IPTW models, the odds ratio decreased from 1.79 to 1.41 after adding insurance and ADI. Generalized additive models showed increasing metastatic risk at higher ADI values and after 2008. Conclusions: Neighborhood deprivation and insurance-related access explained part, but not all, of the excess odds of metastatic diagnosis among Black men. Impact: Integrating ADI into cancer surveillance may improve identification of populations at risk for late-stage diagnosis.

Background and Objectives: SARS-CoV-2 (COVID-19) continues to mutate, circulate, and adversely impact health and quality of life. While COVID-19 vaccines remain safe and effective, uptake remains low, especially among children, the youngest of whom were not vaccine-eligible until after Omicron and are underrepresented in published research. This study estimated vaccine effectiveness (VE) among under-5-year-olds. Methods: We used Virginia Department of Health surveillance data from June 2022 through October 2022 to conduct a test negative case-control study. We estimated VE derived from odds ratios (ORs) of reported infections using logistic regression among children aged 6-months to 5-years. Results: Using the earliest positive (cases) or negative (controls) post-vaccine-eligible test results, the VE associated with two doses of a COVID-19 vaccine was 78% (95% CI=45%, 93%; p=0.004) in unadjusted analyses and 70% (95% CI=25%, 91%, p=0.023) when adjusting for age, sex, prior testing behavior, and prior reported infections. The adjusted VE was 74% (95% CI=28%, 94%; p=0.025) among those with no prior positives reported and 45% (95% CI=-302%, 97%; p=0.569) among those with a prior positive reported. Conclusions: These results show that even though the vaccine was not closely matched to the dominant variants circulating during the time period analyzed, it was effective at reducing the risk of reported infections. This study adds to the body of knowledge on pediatric COVID-19 VE in an underrepresented age-group and in a rural region, illustrates the utility of surveillance data for evaluation, and can inform vaccine decisions to improve vaccine uptake for young children.

Background Human challenge trials provide a unique opportunity to quantify pathogen infectivity in terms of the probability of infection given an inoculated dose. However, between-pathogen comparisons are often distorted by individual heterogeneity in host susceptibility and by differences in background immunity across trial populations. We examined how dose-dependent infection risks differ across common respiratory viruses when such heterogeneity is explicitly incorporated. Methods We conducted a systematic review of human challenge trials for four respiratory viruses: respiratory syncytial virus (RSV), influenza virus, rhinovirus, and adenovirus. Using the extracted data, we fitted dose-response models under different distributional assumptions, allowing both continuous susceptibility variation and discrete immune fractions. We compared alternative heterogeneity models and evaluated pathogen-specific dose-response patterns using original and scaled dose metrics. Results All four viruses showed substantial heterogeneity in host susceptibility, and models assuming homogeneous susceptibility were unsupported. RSV and influenza were best described by models with a distinct immune or effectively non-susceptible subgroup, and the estimated immune proportions were approximately 40% and 25%, respectively. In contrast, rhinovirus and adenovirus were better explained by continuously distributed susceptibility, with little evidence of a fully immune subgroup. On a scaled dose axis, rhinovirus and adenovirus showed steeper increases in infection risk with dose than RSV and influenza. Conclusions The structure of susceptibility heterogeneity differs across common respiratory viruses, which in turn shapes dose-dependent infection risks. Incorporating this heterogeneity is essential for valid cross-pathogen comparison and for interpreting human challenge data in epidemiologic and public health contexts.

We examined associations between a 15-component urinary biomarker mixture related to consumer product chemical exposure and wearable-derived circadian light exposure patterns in U.S. adults. Using National Health and Nutrition Examination Survey (NHANES) 2011-2014, we studied adults aged 20 years or older with valid wrist-worn ambient light data and urinary chemical biomarkers (N = 1,666). Eight circadian light metrics were derived from hour-level ActiGraph GT3X+ data. A standardized chemical burden index and quantile g-computation were used in survey-weighted linear regression adjusted for age, sex, race/ethnicity, poverty-income ratio, education, body mass index, cotinine, sleep duration, and season. Higher chemical burden was associated with greater morning light ({beta} = 0.54; 95% confidence interval [CI]: 0.14, 0.94), greater nighttime light ({beta} = 0.55; 95% CI: 0.21, 0.89), and earlier light centroid timing ({beta} = -1.37 hours; 95% CI: -2.14, -0.59) after false discovery rate (FDR) correction. Quantile g-computation confirmed these three outcomes. No sex modification was observed (all interaction P > .23). Higher consumer product chemical mixture burden co-occurred with an early-shifted circadian light exposure profile, consistent with shared behavioral, occupational, and environmental determinants.

Background: Vaccination coverage estimates and case-based surveillance have limitations in evaluating immunization programs. Serosurveillance offers a complementary approach by directly measuring population immunity. We assessed whether serologic analyses across multiple antigens (i.e., measles, diphtheria, tetanus) could provide additional insights into vaccination program performance. Methods: We conducted a matched case-control study among children aged 2- to 10-years-old (n=1286) in Zambia using specimens from the 2016 ZAMPHIA survey. Using previously generated data on measles serostatus, measles seronegative children (i.e., cases) were matched to measles seropositive children (i.e., controls) on sex, age, HIV infection status, and province. Samples were tested for tetanus and diphtheria antitoxin IgG antibodies using commercial enzyme immunoassays. We estimated the odds of tetanus and diphtheria seropositivity by measles serostatus using conditional logistic regression and examined age-specific antibody dynamics. Results: Measles seronegative children had 1.7-fold increased odds (95% credible interval [CrI]: 1.3-2.1) of being tetanus seronegative compared to measles seropositive children. Diphtheria serostatus had no significant association with measles serostatus (odds ratio: 1.3; 95% CrI: 0.9-1.7). Tetanus seroprevalence declined monotonically with age. However, diphtheria seroprevalence initially declined through 5 years of age, then increased again beginning at 6 years of age despite the lack of vaccine booster doses given after the primary series in infancy, potentially from asymptomatic or subclinical infections. Conclusions: Serologic analyses revealed measles serostatus was positively associated with tetanus serostatus (where seropositivity arises only via vaccination and not infection), suggesting children who are measles seronegative are more likely to have missed DTP vaccination. We additionally found that measles serostatus was not associated with diphtheria serostatus, suggesting that antibody responses to diphtheria continue to boost beyond infancy when DTP vaccination is given. Our findings support consideration of DTP booster doses in Zambia to address waning tetanus immunity and further investigation of potential diphtheria carriage and transmission.

Background: U.S. POINTER reported a modest structured-versus-self-guided difference in the annual rate of global cognitive change. However, the clinical and economic implications of this incremental standardized cognitive-slope benefit for delaying progression from cognitively normal status to mild cognitive impairment or dementia remain uncertain. Objectives: To translate the U.S. POINTER cognitive-slope benefit into clinically interpretable progression outcomes in ADNI, A4, and LEARN, and to summarize scenario-based economic implications in ADNI subgroups. Design: External-cohort translation analysis using two complementary analytic frameworks: an early-change landmark Cox model targeting Month 24 with a prespecified fallback window and a joint longitudinal-survival model. Setting: ADNI, A4, and LEARN. Participants: Cognitively normal participants. Landmark analytic samples included 399 ADNI participants with 61 events, 124 A4 participants with 37 events, and 394 LEARN participants with 45 events. Joint-model samples included 486 ADNI participants with 86 events, 1,064 A4 participants with 410 events, and 505 LEARN participants with 87 events. Intervention: No multidomain lifestyle intervention was administered in ADNI, A4, or LEARN. ADNI and LEARN were observational longitudinal cohorts, whereas A4 was a randomized solanezumab trial; the present analysis did not estimate solanezumab treatment effects. We evaluated a counterfactual +0.029 SD/year improvement in cohort-specific mPACC slope, corresponding to the structured-versus-self-guided cognitive-slope difference reported in U.S. POINTER. Measurements: The clinical outcome was sustained progression to mild cognitive impairment or dementia. Main translated measures were hazard ratios (HRs), 5-year risk differences (RDs), number needed to treat (NNT), and restricted mean survival time (RMST) differences. ADNI subgroup economic summaries used incremental 2-year delivery-cost scenarios and prespecified willingness-to-pay thresholds for prevented progression events and MCI-free years. Results: Landmark analyses yielded small translated effects. For the +0.029 SD/year slope shift, HRs were 0.972 (95% CI, 0.949-0.989) in ADNI, 0.998 (0.989-1.005) in A4, and 0.996 (0.990-1.003) in LEARN, with corresponding 5-year RDs of 0.31 percentage points (95% CI, 0.12-0.57), 0.06 (-0.13 to 0.27), and 0.08 (-0.05 to 0.20). Joint models produced larger effects, with HRs of 0.831 (95% CrI, 0.776-0.879), 0.917 (0.907-0.927), and 0.833 (0.746-0.907), and 5-year RDs of 1.26 percentage points (0.90-1.68), 3.04 (2.65-3.43), and 2.25 (1.24-3.45), respectively. Corresponding NNT values were 79.1, 32.9, and 44.5, and RMST gains were 0.297, 1.242, and 0.617 months. In exploratory ADNI subgroup analyses, the small joint-model APOE-{varepsilon}4+ & A{beta}+ subgroup (61 participants, 22 events) showed the largest translated clinical effect, with HR 0.775 (95% CrI, 0.597-0.919), RD 2.65 percentage points (0.93-4.82), NNT 37.7, and RMST gain 0.723 months. In an exploratory threshold exercise, assuming an incremental 2-year delivery cost of $400 per participant for a structured intervention relative to a self-guided/reference intervention and a willingness-to-pay threshold of $100,000 per prevented progression event, the largest threshold-based net monetary benefit was observed in the APOE-{varepsilon}4+ & A{beta}+ joint-model subgroup (+$2,250/person). On an MCI-free-year basis under the same incremental-cost assumption, this subgroup also had the largest threshold-based net monetary benefit (+$5,622/person). These values should be interpreted as scenario-dependent thresholds rather than empirical cost-effectiveness estimates. Conclusions: A U.S. POINTER-referenced structured-versus-self-guided cognitive-slope increment translated into directionally consistent reductions in predicted progression risk across ADNI, A4, and LEARN. The absolute clinical delay was generally modest and varied with cohort risk structure, biomarker/genotype enrichment, and analytic framework. Exploratory economic-threshold results suggested more favorable margins in higher-risk ADNI subgroups under low incremental-cost and high willingness-to-pay assumptions, but these findings should be interpreted as hypothesis-generating translation estimates rather than empirical cost-effectiveness evidence.

Background: The Universal Periodic Review (UPR) is a peer-review mechanism established to hold UN Member States accountable for human rights including the right to health, yet evidence on its impact on health outcomes is limited. We evaluated whether UPR engagement is associated with accelerated improvements in maternal health trajectories. Methods and Findings: We conducted a longitudinal ecological analysis of 89 countries with a baseline maternal mortality ratio (MMR) of 70 or greater per 100,000 live births in 2005. Outcomes were trajectories of annual MMR, skilled birth attendance (SBA), and contraceptive prevalence rate (CPR), from 2005 to 2023. The exposure was the volume of health-related UPR recommendations received across three cycles, thematically classified using a validated rule-based algorithm. Mixed-effects models adjusted for time-varying GDP per capita and historical fragility. The 89 countries received 41,733 UPR recommendations across three cycles, of which 405 (1%) were related to maternal health. Maternal health recommendations were preferentially directed at countries with higher baseline MMR and lower SBA. After adjustment, each additional maternal health recommendation was associated with a 0.24% [95% confidence interval (CI): 0.08, 0.40] faster annual reduction in MMR, a 0.52% [0.12, 0.91] faster annual gain in the odds of SBA, and a 0.21% [0.09, 0.34] faster annual gain in the odds of CPR. Broader recommendations on women's health and health systems and services were also associated with faster annual improvements in trajectories across all three outcomes; recommendations on abortion, family planning, sexual health and wellbeing, and sexual education tended to be directed towards lower-burden countries and were not associated with differences in any trajectories. It is important to note that the ecological design precludes causal inference. Conclusions: Receiving UPR recommendations on the themes of maternal health, womens health, and health systems and services are associated with accelerated improvements in maternal health trajectories among high-burden countries. These findings suggest that international human rights accountability mechanisms may have a role in supporting national progress on maternal health.

Background: Pregnancy loss has important implications for womens health. Although maternal age is a well-established risk factor, the contribution of routinely measured cardiometabolic and behavioral markers at population-scale remains incompletely characterized. Objective: To examine associations between cardiometabolic, nutritional, and behavioral risk markers and pregnancy loss among U.S. women of reproductive age. Methods: We conducted a cross-sectional analysis of 4,842 U.S. women aged 20-44 years with [≥]1 pregnancy using the National Health and Nutrition Examination Survey data (2013-2023). Pregnancy loss was defined as [≥]1 prior miscarriages. Exposures included body mass index, smoking exposure (cotinine), lipid biomarkers, vitamin D and folate, and a composite cardiometabolic-nutritional risk score. Survey-weighted logistic regression estimated adjusted odds ratios (aORs) and 95% confidence intervals, with bootstrap resampling for predictor robustness. Results: The weighted prevalence of pregnancy loss was 23%. Higher odds of pregnancy loss were associated with increasing age (aOR per year=1.02; 95% CI: 1.00-1.04), Non-Hispanic Black race (aOR=1.32; 95% CI: 1.00-1.74), overweight (aOR=1.56; 95% CI: 1.16-2.11), obesity (aOR=2.06; 95% CI: 1.39-3.05), and smoking (aOR=1.58; 95% CI: 1.19-2.10). Adverse lipid profiles, particularly elevated triglycerides (aOR=1.83; 95% CI: 1.16-2.90) and high low-density lipoprotein (aOR=2.97; 95% CI: 1.45-6.61), were independently associated with pregnancy loss. Vitamin D/folate were not stable predictors. Higher composite cardiometabolic-nutritional risk scores were observed among women with pregnancy loss (P=0.026). Conclusion: Pregnancy loss clustered with adverse cardiometabolic and behavioral risk markers in a nationally representative population. These findings highlight pregnancy loss as a marker of broader metabolic vulnerability supporting the need for longitudinal studies and cardiometabolic profiling to inform preconception care and risk stratification.