ABSTRACT Background Climate‐driven bushfires increasingly shape community health needs and challenge medical workforce capacity in Australia. Although population mobility can influence practitioner availability, it is unclear whether bushfires indirectly affect medical practitioner workforce distribution by prompting internal migration. Understanding these dynamics is critical for informing climate‐resilient health‐workforce policies. Methods We analysed a longitudinal panel of regional and remote Australian Local Government Areas (LGAs) from 2013 to 2021 using generalised estimating equations (GEEs) causal mediation analysis. Bushfire exposure was assessed using three complementary measures: (i) binary (any occurrence), (ii) cumulative (repeated events) and (iii) severity‐based (intensity of exposure). Outcomes were full‐time equivalent (FTE) medical practitioner, adjusted for remoteness and socio‐economic conditions. Internal migration was included as a potential mediator. Results Across all exposure metrics, bushfire‐affected LGAs exhibited significantly higher medical practitioner FTE rates. Cumulative exposure showed a positive association (0.29, p = 0.02), with severity‐weighted exposure yielding a similar directional effect (1.32, p = 0.04). Although internal migration was positively associated with FTE rates, it did not significantly mediate the relationship between bushfire exposure and workforce supply. Conclusions Bushfire exposure is associated with higher medical practitioner FTE rates in affected LGAs. However, internal migration does not appear to meaningfully mediate this relationship for our study region. For extreme and consecutive bushfires, defined as bushfire occurrence in at least five of the past 10 years, the evidence for mediation through population movement is limited. These findings suggest that changes in the medical workforce following bushfires are more likely driven by direct health system policies rather than by broader internal migration patterns. Planning for future climate‐related disasters should consider mechanisms that support responsive allocation and retention of medical practitioners, particularly in regional and remote areas with historically lower workforce levels.
ABSTRACT Background Poverty disproportionately affects women through labor inequities, unpaid care burdens, and limited resource control. In Somalia's fragile context, understanding subnational poverty disparities among reproductive‐age women is essential for targeted health interventions. Methods A cross‐sectional analysis was conducted using data from the 2020 Somalia Demographic and Health Survey (SDHS). The analytical sample included 13,123 birth records from women aged 15–49 years (unweighted n = 13,123; weighted N ≈ 12,774) with complete information on all variables included in the analysis. Poverty status was defined as poor versus nonpoor, using the DHS household wealth index (dichotomizing the poorest and poorer quintiles vs. middle, richer, and richest quintiles). Survey‐weighted descriptive analyses, bivariate associations using design‐based Rao−Scott F ‐tests, and multivariable survey logistic regression were performed in Stata version 18.0 to estimate the crude and adjusted odds ratios (aORs) with 95% confidence intervals (CIs) accounting for clustering, stratification, and sampling weights. Results The overall prevalence was 48.5% (95% CI: 42.4%–54.6%), ranging from 28.2% in Bay to 73.8% in Gedo. Primary schooling (aOR = 0.31, 95% CI: 0.23–0.44), secondary schooling (aOR = 0.08, 95% CI: 0.02–0.36), and higher education (aOR = 0.09, 95% CI: 0.01–0.92) were associated with lower odds of poverty. No media exposure (aOR = 4.47, 95% CI: 2.92–6.83), having an unemployed husband (aOR = 4.18, 95% CI: 3.28–5.31), home delivery (aOR = 3.90, 95% CI: 2.94–5.18), and barriers (aOR = 2.36, 95% CI: 1.84–3.02) were associated with higher odds of poverty. Compared with Awdal, the odds of poverty were lower in Bay, Sanaag, Sool, Bakool, Lower Juba, and Banadir. Conclusion Nearly half of women in the analytical sample were classified as poor on a relative wealth measure, with marked regional disparities and strong associations with education, information access, household dynamics, and maternal health use. Interventions could prioritize girls’ education, information access, employment, and maternal healthcare in high‐poverty areas. Clinical Trial Number Not applicable.
ABSTRACT Objective The objective of this research is to design and test a framework for secure monitoring of a vaccine cold‐chain with the help of Internet of Things (IoT) sensors, blockchain‐based provenance verification, and predictive analytics. Methods The architecture of PharmaChain IoT Vault is an integrated system comprising low‐power IoT telemetry acquisition, secure Message Queuing Telemetry Transport (MQTT)‐based communication, integrity verification using SHA‐256, a permissioned blockchain infrastructure, off‐chain telemetry storage, and a forecasting module for transformers. A real‐world vaccine cold‐chain logistics (VCL) dataset of 26,674 observations was used for experimental evaluation, spanning 30 vaccine batches, 12 monitoring sites, and multiple distribution steps. To evaluate the feasibility of secure predictive monitoring under heterogeneous operational conditions, descriptive analysis and baseline forecasting evaluation, along with blockchain‐enabled provenance verification, were performed. Results There was a significant temporal variability in the dataset due to the changes in storage, transportation, and distribution conditions. Forecasting experiments using a baseline autoregressive model showed that it outperformed a persistence baseline, with lower mean absolute error (12.58°C) and root mean square error (15.15°C), thereby validating the use of vaccine logistics telemetry for predictive monitoring. The blockchain layer preserved the provenance of telemetry records in a tamper‐evident manner by anchoring their SHA‐256 hashes in the ledger, enabling cryptographic integrity verification. Conclusions It is shown that secure telemetry acquisition, blockchain‐based provenance verification, and predictive analytics can be integrated into a single framework for vaccine cold‐chain monitoring. The proposed architecture can be used to develop a system for trustworthy, intelligent management of cold chains and to illustrate the challenges of forecasting in a heterogeneous logistics environment. Future research will involve testing more complex forecasting models and testing the framework in operational conditions. Clinical Trial Registration Not applicable. This study does not involve a clinical trial.
ABSTRACT Background Stigma toward persons with mental illness remains a major barrier to mental health care. No validated Bangla instrument is available to assess attitudes toward persons with mental illness in Bangladesh. Objective This study aimed to adapt and validate the culturally adapted Bangla version of the Attitudes Toward Persons with Mental Illness (APMI) scale and to assess attitudes among undergraduate medical students and Islamic religious leaders in Bangladesh. Methods A psychometric validation study was conducted from July 2025 to June 2026 among 489 participants (249 medical students and 240 Imams). The original 16‐item APMI scale was translated and culturally adapted following standard guidelines. The sample was independently divided into exploratory factor analysis (EFA; n = 245) and confirmatory factor analysis (CFA; n = 244) subsamples. EFA used polychoric correlations, minimum residual extraction, oblimin rotation, and parallel analysis. CFA compared one‐ and two‐factor models using the WLSMV estimator. Reliability, measurement invariance, group differences, and multivariable associations were also evaluated. Results EFA supported a two‐factor structure comprising Fear/Social Avoidance and Interpersonal Acceptance. The correlated two‐factor CFA model showed substantially better fit than the one‐factor model (scaled CFI = 0.967, TLI = 0.956, RMSEA = 0.071, 90% CI 0.049–0.092, SRMR = 0.046). Ordinal alpha was 0.83 and 0.78. Measurement invariance was supported at the configural, partial metric, and partial threshold/scalar levels. Medical students had lower scores than Imams. After adjustment, the difference remained significant ( β = 5.02, 95% CI 2.89–7.15; p < 0.001). Conclusion The 10‐item culturally adapted Bangla APMI scale demonstrates acceptable structural validity and internal consistency for assessing attitudes toward persons with mental illness for Bangla‐speaking populations.
ABSTRACT In January 2025, a US executive order initiated a significant restructuring of USAID, halting nearly all international spending for 90 days and terminating a substantial portion of its foreign aid contracts. This policy shift threatens to unravel decades of development progress, particularly in sub‐Saharan Africa, where USAID has been integral to health, food security, governance, and economic development. This commentary develops a multi‐phase transition framework to address the dissolution's impact on five key beneficiary countries: Tanzania, Kenya, Rwanda, the Democratic Republic of the Congo (DRC), and Ethiopia. These nations face setbacks in maternal and child health, disease prevention, agricultural productivity, and governance. We propose a structured approach to resilience, emphasizing diversified funding sources, progressive tax reforms, enhanced domestic resource mobilization, public‐private partnerships, and regional cooperation. By adopting these strategies, sub‐Saharan African nations can mitigate the effects of USAID's absence, safeguard vital development progress, and transition toward greater self‐reliance amid shifting global aid dynamics.
