The Lancet Public Health

Background: Multiple factors can drive heat susceptibility, but are typically analyzed individually. This study investigates high-dimensional heterogeneity across multiple domains in the effect of hot days on emergency hospitalizations across German districts. Methods: We obtained emergency hospitalization rates per 1,000 in 3-day windows for the summers of 2005-2023 from the German Diagnosis-Related Groups-Statistic, linked with spatio-temporal temperature information. A hot day was defined as at least one day above the district-level 98th percentile heat index. Multiple characteristics of the demographic, health/social, and environmental domains were selected as potential effect modifiers. We implemented a generalized random forest, where outcome and propensity scores were modeled with fixed effects estimators to control for seasonality and district-level confounding. We divided the conditional average treatment effects (CATE) into quartiles to map their spatial distribution and identify key effect modifiers via the variable importance and observed indicator profiles across quartiles. Results: We found substantial variation across districts regarding heat-related emergency hospitalizations. The top three indicators for predicting this effect heterogeneity were the percentage of people seeking protection, population below age 6, and air pollution levels. The observed indicator profile for the most susceptible districts was characterized by a larger percentage of vulnerable populations, higher car density, and fewer hospital beds. Conclusion: There is substantial variation in heat susceptibility across and within districts in Germany. We propose an approach that can help understanding such complex interplay of indicators from different domains driving heterogeneity.

Background: Non-optimal air temperature increases the risk of preterm birth, but how temperature-related risks may translate into future population burden remains uncertain. We aimed to estimate the association between temperature and preterm birth in Sweden and assess how preterm birth burden may change under plausible future temperature conditions. Methods: We conducted a nationwide population-based cohort study of singleton births in Sweden from 1973 to 2018. Weekly mean temperature was assigned at the parish level according to maternal residence. Cox proportional hazards models with a distributed lag nonlinear model were used to estimate nonlinear and delayed associations between temperature and preterm birth. The estimated exposure response relationship and baseline hazards were then incorporated into an individual-based microsimulation of gestational progression under projected temperatures for 2025 to 2055. Future projections were examined across multiple climate models under SSP1-2.6 and SSP3-7.0 scenarios. Outcomes included the annual number and cumulative incidence of preterm births and the temperature-attributable fraction. Results: Among 4,338,825 singleton births, 211,397 (4.9%) were preterm. Temperature preterm birth associations were modest overall. The clearest increase in hazard was observed following exposure to very low temperatures, particularly -10C at short lags. Higher temperatures showed no clear increase in risk. From 2025 to 2055, the annual cumulative incidence of preterm birth and the temperature-attributable fraction was expected to remain broadly stable, with limited differences between emissions scenarios. In contrast, the absolute number of preterm births was expected to over the projection period, primarily reflecting increases in the projected number of pregnancies rather than substantial changes in temperature-associated risk. Conclusions: Very low temperatures were associated with a modest short-term increase in preterm birth risk in Sweden, hot temperatures showed little evidence of increased risk. Applying these associations to future climate conditions suggested limited changes in preterm birth incidence or the proportion attributable to temperature through 2055. However, the absolute burden of preterm birth may increase as the population at risk grows. This highlights the importance of considering demographic change alongside climate projections when assessing future maternal and neonatal health burden.

Background: Previous studies indicated that regional fluctuations in COVID-19 impact were associated with socioeconomic, demographic and environmental factors early in the pandemic. It is unclear whether factors associated with regional COVID-19 mortality and SARS-CoV-2 infections changed over the course of the pandemic. Methods: We evaluated the association of ecological factors with municipality-specific COVID-19 mortality in the national population of Austria between January 2020 and June 2023. Analyses were conducted separately per calendar year and pooled across years via random-effects meta-analysis. Outcomes were analysed using univariable and multivariable zero-inflated negative binomial (ZINB) regression, including annually averaged environmental exposome exposure (including air pollution and UV radiation), socioeconomic, demographic and infection dissemination predictors with significance threshold set to p<0.005. Secondary analysis modelled SARS-CoV-2 infections using of negative-binomial model (NB). Two additional approaches for smaller predictor set selection were applied as sensitivity analyses. Results: Within the entire observation period 18,430 COVID-19 deaths were reported. In univariable analyses (pooled RR (99.5% CI)), COVID-19 mortality was significantly associated with the proportion of commuters (0.79 (0.73-0.86)), residents aged 75-84 (1.18 (1.01-1.37)) and [&ge;]85 years (1.29 (1.04-1.60)), residents with at most compulsory education (1.15 (1.02-1.29)), employed residents aged 15-64 (0.89 (0.85-0.93)), and homeowners (0.91 (0.83-0.99)), as well as with precipitation (0.94 (0.89-0.99)) and the average time with a mean temperature of 5-15 C (0.95 (0.90-0.99)). In multivariable analyses the share of population aged 85 or older (1.30 (1.09-1.54)) and home ownership (0.92 (0.87-0.98)) were independently significant. Mortality predictor estimates were relatively stable across pandemic years. Across 5,963,882 documented SARS-CoV-2 infections share of employed in 15- to 64-year-olds (1.01 (1.01-1.02)), part-time employment rate (1.01 (1.00-1.02)) and district level nursing care allowance recipiency (0.97 (0.95-0.98)) were associated with detected infections. Overnight stays, population 85 or older, income, compulsory education or less and air pollution all had multiple significant estimates with opposite directionality, leading to non-significant overall associations. Sensitivity analyses outcomes mirrored main analyses outcomes. Conclusions: Throughout the pandemic in Austria, demographic and socioeconomic factors were associated with COVID-19 mortality and infections, but many associations, especially for infections, were unstable over time. These findings underscore the importance of structural vulnerabilities in epidemic impact.

Background: Risk of recurrent overdose is elevated following non-fatal overdose (NFOD) survival; timely interventions may reduce this risk. The specific role of mental health care in reducing recurrence is relatively underexplored, and post-overdose care has seldom been examined for sex differences. This study examined sex differences in the association between mental health-related primary care contacts following NFOD and overdose recurrence risk among people in British Columbia (BC) with a documented depression diagnosis history. Methods: This was a nested case-control study using linked administrative health data from the Provincial Health Services Authority Platform for Analytics and Data. The source cohort (n=7,022) comprised BC residents with a history of a documented depression diagnosis between 2010-2014 and at least one NFOD between 2015-2024. Cases experienced a recurrent overdose within one year of their index overdose; controls were selected using incidence density sampling. Exposures were the presence and number of mental health related primary care contacts in the 30- or 90-day window following the index overdose. Conditional logistic regression models were used to estimate adjusted incidence rate ratios (aIRRs) and confidence intervals (CI), overall and by sex. Secondary analyses were conducted using psychiatrist contacts. Results: Mental-health related primary care contact rates were low, with only 16.3% and 28.1% of the cohort with any contact within 30 and 90 days, respectively. Mental health-related primary care contacts were more strongly associated with reduced recurrence risk in the 30-day window (any contact aIRR: 0.57 [CI: 0.46-0.71]) than in the 90-day window (aIRR: 0.78 [CI: 0.65-0.94]). Any contact within 30 days was more strongly associated with lower recurrence risk among males (aIRR: 0.53 [CI: 0.39-0.72]) than among females (aIRR: 0.67 [CI: 0.50-0.91]), whereas associations between the number of contacts and recurrence risk were similar for males and females. Any psychiatrist contact within 30 days was more strongly associated with lower recurrence risk among females (aIRR: 0.50 [CI: 0.34-0.73]) than males (aIRR: 0.70 [0.52-0.95]). Discussion: Mental health-related care contacts shortly following NFOD were associated with lower overdose recurrence risk among people with a documented depression diagnosis history, with modest sex differences that varied by provider type. Low rates of contacts overall suggest gaps in access to care following NFOD, particularly among males. Further research on sex differences in post-overdose care are warranted.

We analyse the effect of waiting time for elective musculoskeletal treatment on labour market outcomes in England using hospital records for working-age adults who received treatment between April 2015 and March 2023 to Census, tax and benefit records. To address clinical prioritisation and unobserved severity, we leveraged provider-level waiting time as an instrument for individual waiting time and estimated two-stage least squares models adjusted for baseline labour market outcomes, demographic and socioeconomic characteristics, and provider, referral-time and procedure fixed effects. We find that longer waits reduce annual employee earnings, with effects persisting for up to five years. For an additional four weeks of waiting, the largest estimated annual losses range from {pound}301 for osteoarthritis to {pound}487 for other joint disorders. There was no evidence of effects on employment status or benefit receipt, suggesting that delays primarily operate through reduced productivity or hours worked among those who remain employed. Reducing waiting times may not only improve health outcomes but help patients maintain earnings and support economic

Background Under-five mortality remains a major public health challenge in Zambia despite substantial reductions over recent decades. Demographic and Health Survey (DHS) data are commonly analysed using Cox proportional hazards models; however, violations of the proportional hazards assumption and unobserved cluster-level heterogeneity may compromise inference. This study compared advanced survival modelling approaches to determine the most appropriate framework for analysing under-five mortality using pooled Zambia DHS data. Methods We conducted a pooled secondary analysis of the 2018 and 2024 Zambia Demographic and Health Survey child recode datasets. The analysis included 9,982 children, among whom 266 died before their fifth birthday. A survey-weighted Cox proportional hazards model was fitted as the reference model and assessed using Schoenfeld residual diagnostics. To address identified methodological limitations, an extended Cox model, a shared-frailty model, and a Bayesian survival model were subsequently fitted. Model performance, assumptions, and the consistency of adjusted hazard ratio estimates were compared across modelling approaches. Results The global Schoenfeld test indicated violation of the proportional hazards assumption ({chi}2=51.26, p=0.002). In the adjusted extended Cox model, male children had higher mortality risk than females (aHR 1.95, 95% CI 1.40-2.73), while children with gestation [&ge;]9 months had lower mortality risk than those with shorter gestation (aHR 0.21, 95% CI 0.14-0.32). Average and large birth size were associated with lower mortality compared with small birth size (aHR 0.47, 95% CI 0.34-0.66 and aHR 0.57, 95% CI 0.39-0.84, respectively). Newborn health checks before discharge were associated with lower mortality (aHR 0.50, 95% CI 0.38-0.66), as was any antenatal care (aHR 0.25, 95% CI 0.09-0.75). A high-risk fertility pattern involving fourth or higher births with an interval [&ge;]24 months was also associated with lower mortality (aHR 0.57, 95% CI 0.41-0.80). The shared-frailty model produced similar estimates, including increased mortality among male children (aHR 1.39, 95% CI 1.08-1.78) and twins or multiple births (aHR 2.74, 95% CI 1.36-5.53). Bayesian estimates were broadly consistent with the extended Cox model. Conclusions Advanced survival models provided a more robust analytical framework than the conventional Cox model for evaluating under-five mortality in nationally representative DHS data. While the extended Cox model accommodated time-varying effects, the shared-frailty model captured unobserved cluster-level heterogeneity, and the Bayesian model confirmed the stability of parameter estimates. Incorporating these complementary approaches can improve the validity of survival analyses and strengthen evidence for child survival research and policy in low- and middle-income settings.

Researchers must often rely on observational data to draw conclusions about the causal relationship between an exposure and an outcome. This is common in environmental epidemiology research, where the assignment to an exposure can be unethical or impossible. Consider a setting in which we seek to estimate excess mortality during heat waves, but are unable to randomize individuals to moderate or extreme heat. We propose to follow a causal framework for non-randomized time series that uses what we call historical time series matching to construct hypothetical randomized experiments, in which background covariates are balanced. We applied this framework and assessed the following causal question: Are daily mortality counts among elderly Americans higher during hot versus warm summer time periods? We addressed this causal question by drawing frequentist, Fisherian, and Bayesian inferences for five American cities. We estimated the average treatment effect of heat on three-day mortality with 95% Fisherian intervals to be 0.5 [-3.6, 4.4] deaths in Pittsburgh, 3.7 [0.7, 6.6] deaths in Seattle, 12.5 [6.1, 18.9] deaths in Chicago, 8.5 [1.4, 15.6] deaths in Los Angeles, and 20.3 [11.2, 29.5] deaths in New York.

We develop an age- and immunity-structured COVID-19 model with setting-specific contacts, calibrated to Ontario incidence and hospital admissions before Omicron. We simulate counterfactual vaccination strategies and contact restrictions in schools, workplaces, and other community settings, evaluating projected severe-case demand against an illustrative healthcare-capacity benchmark. Vaccination combined with workplace and community contact restrictions keeps simulated demand within capacity. In contrast, relaxing restrictions in either setting produces large surges and repeated capacity threats, even when school restrictions remain. School-only changes produce smaller effects on severe disease. These findings highlight the value of vaccination and setting-specific contact restrictions that prioritize workplace and community settings for pathogens with adult-concentrated severe outcomes.

Introduction Medicines account for 20% of the National Health Service (NHS) carbon footprint, but it is unclear how consistently they are measured by individual NHS trusts. This study assessed medicine carbon footprint (MCF) practices across NHS trusts in England and whether these changed between 2021 and 2024. Methods Freedom of Information requests were sent to NHS trusts about MCF practices in 2021 and 2024. Responses were analysed to determine the proportion of trusts calculating MCFs, the medicines included and the emission factors used for spend-based calculations. Reported spend-based factors were compared with the median factor for each year to assess the effect of factor choice on MCFs. Results Of 196 trusts included in analysis, 178 (91%) responded for 2021 and 182 (93%) for 2024. Among responding trusts, 30 of 178 calculated MCFs in 2021 compared with 46 of 182 in 2024. Spend-based medicine footprinting increased from 19 trusts in 2021 to 31 in 2024. Ten different factors were used by 15 trusts in 2021 and 13 by 21 trusts in 2024. Reported factors ranged from 31% below to 258% above the median in 2024. Conclusions Medicine carbon footprinting remained uncommon, with only one-quarter of responding NHS trusts calculating a footprint in 2024. Among trusts using spend-based methods, wide variation in emission factors produced substantially different estimated MCFs. Clear national guidance on spend-based methodology could improve consistency in the short term, while greater use of standardised product-level approaches may better support medicine decarbonisation over time.

BackgroundSocial isolation (SI) is an established risk factor for chronic disease and mortality. How people travel, including the modes of transportation they use, may shape opportunities for social interaction and influence the impacts of SI. This study assessed heterogeneity of the association between SI and mortality across primary modes of non-commute transportation. MethodsWe analyzed cohort data from the UK Biobank, following participants aged 40-69 years from 2006 to 2022. SI at baseline was derived from three items and dichotomized. Cox models were used to estimate the association between SI and mortality, adjusting for socioeconomic factors, health behaviours, and comorbidities. Effect modification by participants primary mode of non-commute transportation was assessed using interaction terms with SI. ResultsThere were 490,114 participants with complete baseline exposure data. SI was associated with increased hazard of all-cause (Hazard Ratio [HR] = 1.34, 95% Confidence Interval [CI]: 1.31-1.38) and cardiovascular mortality (HR = 1.49, CI: 1.41-1.57). The association with cardiovascular mortality was stronger among participants who only used public transport compared with those who only used a car (interaction HR = 1.17, CI: 1.00-1.37). In contrast, using a car alongside active or public transport, compared to using a car alone, attenuated the association between SI and all-cause mortality (interaction HR = 0.93, CI: 0.87-0.99). ConclusionMixed transport, including a car, showed the greatest reduction in the association between SI and all-cause mortality. Our findings motivate further exploration of the role of transportation modes in enhancing social connection, mobility, and health.

This paper examines the impact of Cancer Care Ontario's regionalization of lung cancer surgery on blood transfusion use for thoracic surgery at The Ottawa Hospital. Using aggregate data scraped from the paper ''Changing trends in blood transfusion: an analysis of 244,013 hospitalizations'' by Shehata et al. (2014) we estimated the effect regionalization on the log mean number of blood transfusions among thoracic surgeons (who treat lung cancer), utilizing other surgical specialties as controls. We found that the average number of blood transfusions increased significantly among thoracic surgeons following the opening of a new cancer care centre at the hospital in December 2009, while control specialties showed no parallel change. Contextualizing these results in a modified ''population level'' treatment threshold model, it is argued that the main driver of this increase in blood transfusions among thoracic surgeons was due to the influx of less skilled thoracic surgeons to The Ottawa Hospital, and the closing of thoracic surgery units at smaller hospitals which was part of the broader regionalization process.

Background. Skilled healthcare workers are essential for neonatal survival; however, the Kenyan public health system has historically faced strikes compounded by a human resource crisis. Evidence on how strikes affect neonatal care and mortality remains limited. This study evaluated changes in neonatal admissions, clinical care processes, and inpatient mortality associated with 56 days of disruption during the 2024 Kenyan physicians' strike. Methods. We conducted a retrospective interrupted time-series analysis of 12,184 admissions to 11 Kenyan neonatal units implementing with the NEST360 Alliance, recorded in the Neonatal Inpatient Dataset from December 2023 to August 2024. Weekly admissions and inpatient mortality were modeled over a 72-week window (27,638 admissions) using hospital-clustered generalized estimating equations with seasonal terms. Care processes, case fatality, and birthweight-specific mortality were compared across study periods. Results. Weekly admissions fell from 413 to 189 during the strike (incidence rate ratio (IRR) 0.49, 95% confidence interval (CI) 0.31-0.75), approximately 1,700 fewer admissions than expected, and returned to baseline afterward (IRR 1.00, CI 0.81-1.23). Newborns admitted during the strike were of lower birthweight, more often premature, and more often outborn. Admissions fell across all birth-location categories, most steeply among inborn neonates. No measurable change was detected in documented inpatient coverage of care measures, in crude mortality (12.5% to 14.9%; risk ratio (RR) 1.07, CI 0.85-1.33), or in trend-adjusted mortality (IRR 0.88, CI 0.53-1.45). Case fatality among neonates with recorded infection doubled, from 4.7% to 9.6% (RR 2.06, CI 1.18-3.58; adjusted p = 0.047). Conclusions. The 2024 physicians' strike was associated with severe disruption to neonatal service utilization, with admissions returning to baseline when the strike ended. Among newborns who continued to reach care, inpatient mortality did not change measurably, but case fatality among those with recorded infection doubled. This analysis was confined to inpatient data; hence, the outcomes of newborns who did not reach the hospitals were not captured. Both inpatient and population-level data are needed to assess the full impact of the strike. Mitigating harm during future labor disruptions requires interventions such as resilient care models, formalized task-shifting protocols, and minimum service coverage agreements.

Background and objective. The WHO growth reference judges a child's height against the population at one age, without using the child's own earlier growth. This study built and validated a quantile regression based conditional growth reference predicting a child's future height for age distribution from early growth status. Methods. Using deidentified longitudinal annual school children health check data from three major cities in Vietnam, this study linked a baseline height and BMI for age Z score near age 6 to outcome visits near age 12 or 15 in 4,857 children. A linear quantile regression and a pooled quantile generalized additive model, fit by sex, were compared against a linear benchmark and a naive persistence model on a held out test set, using pinball loss, interval coverage, and width against the WHO 90% band. This study also tested adding growth phenotype and checked generalization across the three cities. Results. The pooled model outperformed the discrete quantile regression in every group. Every individualized 90% interval was narrower than the WHO band, by 34.6 to 54.5%. Growth phenotype improved accuracy in most groups. The model generalized well to a held out Hanoi or Haiphong but poorly to a held out Ho Chi Minh City, where coverage fell to between 0.35 and 0.52, though the all-city model stayed well calibrated for Ho Chi Minh City itself. Conclusion. Conditional quantile modeling gives individualized growth predictions markedly narrower than a fixed population reference and gains further from growth phenotype, but needs local validation before use at an unrepresented site.

Background/Aims This work aimed to estimate the annual tertiary healthcare and societal costs associated with paediatric congenital heart disease (CHD) in the United Kingdom using real-world data from a Level 1 cardiac surgical centre in the National Health Service (NHS). Methods A prevalence-based cost-of-illness analysis was conducted using patient-level inpatient and outpatient activity data from Evelina London Children's Hospital between 1 June 2023 and 31 May 2024. Costs were estimated from NHS and societal perspectives and extrapolated to national level using National Congenital Heart Disease Audit (NCHDA) procedural activity data. Healthcare costs included tertiary centre care costs. Societal costs included parental productivity losses and impacts on lifetime earnings for children. Deterministic sensitivity analysis explored parameter uncertainty. Results Approximately 9.6% of UK paediatric CHD procedures were undertaken at Evelina London Children's Hospital at a cost of {pound}15.0 million per annum, corresponding to a national estimate of {pound}156.4 million. Single-ventricle procedures accounted for 4.8% of inpatient activity but 19.4% of inpatient costs. Sensitivity analysis produced national healthcare cost estimates ranging from {pound}125.1 million to {pound}187.7 million. National productivity losses associated with parental absence from work were estimated at {pound}82.4 million annually. Individuals with CHD were estimated to experience a mean reduction in lifetime earnings of {pound}81,509. Conclusion Paediatric CHD imposes a substantial economic cost on the UK healthcare system and wider society. This study provides the first real-world national estimate of the economic impact of paediatric CHD in the UK and may inform future service planning, research investment, and innovation adoption strategies.

Background Timely overdose response depends not only on surveillance systems that detect overdose clusters, but also on communication pathways that deliver actionable information to the professionals positioned to respond. Understanding which overdose-related communications are received, perceived as useful, and translated into action is essential for improving overdose prevention and response infrastructure. Objective We assessed which overdose-related communication channels professionals reported receiving, which they perceived as most helpful, how organizations reported responding to overdoses or overdose clusters, and whether these patterns differed by professional role, county, and funding source. Methods We conducted an exploratory descriptive survey in 2024 using a convenience sample of professionals across Connecticut whose jobs involved overdose prevention or response (N=149). Using this survey data, we summarized reported receipt and perceived utility of overdose-related communication channels, organizational response actions, and internal receipt of Connecticut Department of Public Health overdose alerts. Results were stratified descriptively by professional role, county, and funding source. We interpreted findings using a public health communication-to-action cascade focused on signal detection, communication receipt, internal dissemination, perceived utility, and response action. Results Reported communication receipt and perceived utility differed most clearly by professional role. Peer information sharing was the most commonly reported communication source and was frequently perceived as helpful, but respondents who reported receiving formal channels such as state alerts, local alerts, or platform-based information often identified those channels as most helpful. Internal receipt of Connecticut Department of Public Health overdose alerts also varied by role. SSPs/substance use treatment programs and first responders more often reported outreach to people who use drugs, whereas government and health district respondents reported a more even mix of public communication and outreach activities. Conclusions Overdose-related communication in Connecticut appears role-dependent and unevenly distributed. Informal peer networks are widely used and perceived as helpful, while formal alert systems may require improvements to reach and actionability. Practical improvements include opt-in communication pathways tailored to professional roles, clearer access points for state and local alerts, internal dissemination protocols that specify who should receive which alerts, and alert-to-action playbooks that connect specific alert types to recommended response steps.

Abstract: Background: Opioid Use Disorder (OUD) imposes a rising mortality and health system burden, with opioid related deaths increasing from 8,400 in 2000 to 110,037 in 2023. Medications for opioid use disorder (MOUD) reduce mortality and morbidity, yet only 25.1% of people with OUD are treated with these medications, and inpatient MOUD initiation is inconsistent. Inpatient and Emergency Department (ED) treatment initiation and connection to outpatient care is suboptimal, missing critical opportunities to engage at-risk populations during acute care visits. Our goals were to determine the proportion of patients with OUD treated with MOUD during a hospital/ED stay, and the proportion successfully connected to outpatient care. Methods: This retrospective cohort study included patients 18 years or older with an OUD diagnosis at Duke University Hospital and Duke Regional Hospital (7/1/2023 to 12/31/2025) who had an inpatient or ED encounter with an associated OUD diagnosis code. Primary outcomes were buprenorphine at discharge, 30-day acute care return, and 30 day outpatient follow up. Adjusted modified Poisson regression models with cluster robust standard errors evaluated associations with age, sex, race/ethnicity, insurance, area deprivation index, encounter type, and comorbidity score. Results: Of 5,390 patients with OUD, 42.1% (2,267) had an acute care encounter, generating 3,893 encounters. Neither buprenorphine nor methadone was prescribed at discharge for 46.0% of encounters. Acute care return occurred in 33.6% and outpatient follow up in 32.2% of encounters within 30 days; only 7.4% of follow-ups included a buprenorphine prescription. ED encounters had lower discharge prescribing than inpatient encounters (RR 0.72, 95% CI 0.61,0.86). Black patients had higher 30 day acute care return than White patients (RR 1.16, 95% CI 1.02,1.30) Conclusions: Acute care encounters represent repeated, missed opportunities for MOUD initiation. Disparities were concentrated in post-discharge return and follow up rather than at initial prescribing, and attrition occurred at each transition along the screening-treatment-referral pathway.

As internet and mobile phone access increases, digital health interventions are increasingly used to support remote and low-resource populations. In many settings, the health impact of digital interventions is constrained by limited or short-lived engagement. We tested if conditional mobile data credit incentives increase digital intervention engagement within a digital intervention trial. This nested cluster randomized controlled trial was conducted with caregivers of children 3-9 months at baseline. Of 72 neighborhood clusters from the treatment arm of the parent trial (evaluating a digital early childhood development intervention), 20 were randomly assigned to treatment and 52 as controls. Eligible participants had a phone provider through which mobile data credits could be most affordably administered. Treated participants received around 250 MB mobile data credits every two weeks, conditional upon using the digital intervention at least once in the same period. Credits were digitally administered via a third-party service. Digital intervention engagement (primary outcome) was monitored continuously until endline data collection approximately 2 years after assignment. Secondary outcomes included caregivers views, behaviors, and early childhood development. We used intent-to-treat analyses to assess all outcomes. This trial was registered as NCT05202106 at clinicaltrials.gov and was completed in 2025. Participants were recruited between September 2021 and March 2023 (N Treatment = 147; N Control = 333). Conditional mobile data credits extended engagement with the digital early childhood development intervention by 6.50 weeks on average (treatment mean = 26.19 weeks, control mean = 19.69; p=0.011). No significant impacts were identified on secondary outcomes. As such, mobile data credits may be a cost-effective way for increasing caregiver engagement with digital health intervention in low resource settings.

Background & Aims Personalized nutrition has the potential to improve nutritional intake and clinical outcomes in hospitalized patients by adapting to individual dietary needs and preferences. Despite its promise, no personalized nutrition product (PNP) for hospitalized patients is currently available. This study aimed to assess the acceptability of PNPs among hospitalized patients and hospital staff. Methods A prospective, ten-days, proof-of-concept study was conducted among in-hospital adult patients ([&ge;]18y). Study patients received a freshly 3D printed PNP, customized to match individual taste preferences and health profiles. Patients could reorder the product daily if they remained hospitalized >24h after consumption. Consumption was assessed by weighing the PNP before and after consumption. Acceptability, sensory feedback, and overall experience were evaluated using questionnaires. Hospital staff also evaluated sensory feedback and acceptability. Factors influencing consumption and liking among patients were evaluated by testing within-group differences. Correlations between consumption rates, liking and patient characteristics were assessed. Results Eighty-seven patients received 137 PNPs during the study period. The average overall liking score was 5.7 ({+/-}2.1) on a 1-9 scale, and median consumption rate was 51% [Interquartile Range 19-100]. Consumption rates positively correlated with overall liking (R2 = 0.50, p < 0.001). Patients' willingness to reorder was 3.5 ({+/-}2.3) on a 1-7 scale, and reorder rate 63%. The perceived value of a PNP service in a hospital setting (scale 1-7) was scored 5.3 ({+/-}1.7) by patients and 4.5 ({+/-}1.1) by staff. Conclusions PNPs were evaluated as moderately positive in terms of acceptability and value by both patients and staff. However, further optimization of the product is needed before evaluating clinical efficacy.

Health inequalities are often examined through single axes such as class or ethnicity, obscuring how these dimensions interlock. This study applies a longitudinal intersectional approach to examine physical and mental health trajectories across five generations in the UK. We analysed 14 waves of the UK Household Longitudinal Study (2009-2024) using longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). We modelled SF-12 physical and mental health trajectories for 398 intersectional strata defined by gender, ethnicity, occupational class, and generation. Physical health inequalities widened with age, consistent with cumulative disadvantage, while mental health inequalities were more evident among later-born cohorts. The 'Boomer' generation exhibited a relatively flat mental health trajectory across its observed age range. In contrast, 'Generation Z' had lower predicted mental health, particularly among White British women with no work history - a group with poorer predicted trajectories than their South Asian peers. Our study shows how health inequalities are structurally differentiated by cohort and intersectional position. Policy responses should support proportionate universalism, combining broad population-level support with intensified action for groups showing poorer health.

Background: COVID-19 movement restrictions reduced social contact worldwide, curbing viral spread but with unequal economic consequences where social safety nets are limited. Little evidence exists on how these impacts varied by occupation, wealth and age in resource-constrained settings. This paper examined pre-pandemic contact and lockdown-related change. Methods: The paper documents analysis of a population-representative survey (N=8,499) in Manicaland, Zimbabwe, during April-May 2021, capturing typical weekday contacts before and during the first COVID-19 lockdown. Negative binomial regression models assessed pre-lockdown contact counts by socio-economic characteristics, overall and by place (home, school, work, transport, entertainment); linear regression assessed predictors of lockdown declines. Results: Pre-COVID-19, respondents reported a median of 12 daily contacts (interquartile range [IQR] 4-47), with higher counts among manual labourers (incidence rate ratio [IRR] 2.21, 95% confidence interval [CI] 2.00, 2.44), retail and public sector workers (IRR 2.33, 95%CI 2.09, 2.60) and students (IRR 2.59, 95%CI 2.38, 2.82), males, wealthier respondents, under-30s, and urban and agricultural estate residents. Counts were highest in schools. During lockdown, contacts fell across all subgroups to a median of 8 (IQR 4-28). Declines were greatest among those with most pre-COVID-19 contacts: urban (mean -17.5, 95%CI -19.53, -15.37) and agricultural estate residents (-18.1, 95%CI -19.99, -16.13), students and teachers (-17.7, 95%CI -19.82, -15.59), males and 30-44 year olds. Drops were greatest in schools and on transport. Conclusion: Reductions in contact under blanket lockdown varied markedly between subgroups and places in Manicaland, Zimbabwe. Selectively targeting high-contact groups might have achieved similar transmission reductions while limiting economic damage, holding lessons for similar settings.