Iodine deficiency (ID) poses a significant global public health challenge. This study aimed to analyze trends from 1990 to 2021 and project future patterns ≤2050 using the extreme gradient boosting (XGBoost) model, with Shapley additive explanations (SHAP), to identify key factors and inform public health strategies. Data on ID from the Global Burden of Disease (GBD) 2021 study were used to model and predict its burden ≤2050 using XGBoost, with SHAP enhancing model interpretability. In 1990, global incident cases of ID were 7.51 million (age-standardized incidence rate [ASIR]: 126.11/100,000), rising to 8.08 million by 2021 (ASIR: 105.99/100,000, a 15.96% decrease), and projected to reach 8.48 million by 2050 (ASIR: 108.20/100,000). Prevalent cases increased from 146.42 million in 1990 (age-standardized prevalence rate [ASPR]: 2801.80/100,000) to 180.81 million in 2021 (ASPR: 2213.98/100,000, a 20.98% decrease), with 194.51 million expected by 2050 (ASPR: 1900.01/100,000). Disability-adjusted life years (DALYs) dropped from 2.46 million in 1990 (age-standardized disability-adjusted life year rate [ASDR]: 46.19/100,000) to 2.25 million in 2021 (ASDR: 27.67/100,000, a 40.10% decrease) but are projected to rise slightly to 2.51 million by 2050 (ASDR: 25.51/100,000). SHAP analysis identified iodized salt coverage as a key factor, with higher coverage levels associated with reduced ID burden in most countries. Women and people aged 10-30 y had higher incidence rates, although prevalence and DALYs peaked among those aged 20-45 y. Central and Eastern Sub-Saharan Africa and South Asia will continue to bear the highest burden through 2050. The XGBoost+SHAP model identified age, sex, and iodized salt coverage as key factors, with women and younger populations being high-risk groups. Strengthening iodization programs, improving health care access, targeted education, and consistent monitoring of vulnerable populations are essential to mitigate future risks and improve health outcomes.

Introduction: Geographical accessibility is important against health equity, particularly for less developed countries as Nepal. It is important to identify the disparities in geographical accessibility to the three levels of public health facilities across Nepal, which has not been available. Methods: Based on the up-to-date dataset of Nepal formal public health facilities in 2021, we measured the geographical accessibility by calculating the travel time to the nearest public health facility of three levels (ie, primary, secondary and tertiary) across Nepal at 1×1 km² resolution under two travel modes: walking and motorised. Gini and Theil L index were used to assess the inequality. Potential locations of new facilities were identified for best improvement of geographical efficiency or equality. Results: Both geographical accessibility and its equality were better under the motorised mode compared with the walking mode. If motorised transportation is available to everyone, the population coverage within 5 min to any public health facilities would be improved by 62.13%. The population-weighted average travel time was 17.91 min, 39.88 min and 69.23 min and the Gini coefficients 0.03, 0.18 and 0.42 to the nearest primary, secondary and tertiary facilities, respectively, under motorised mode. For primary facilities, low accessibility was found in the northern mountain belt; for secondary facilities, the accessibility decreased with increased distance from the district centres; and for tertiary facilities, low accessibility was found in most areas except the developed areas like zonal centres. The potential locations of new facilities differed for the three levels of facilities. Besides, the majority of inequalities of geographical accessibility were from within-province. Conclusion: The high-resolution geographical accessibility maps and the assessment of inequality provide valuable information for health resource allocation and health-related planning in Nepal.

Soil-transmitted helminth infections affect tens of millions of individuals in the People’s Republic of China (P.R. China). There is a need for high-resolution estimates of at-risk areas and number of people infected to enhance spatial targeting of control interventions. However, such information is not yet available for P.R. China.

Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10–15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions.

To develop and validate an index to quantify the multimorbidity burden in Chinese middle-aged and older community-dwelling individuals. We included 20,035 individuals aged 45 and older from the China Health and Retirement Longitudinal Study (CHARLS) and 19,297 individuals aged 65 and older from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Health outcomes of physical functioning (PF), basic and instrumental activities of daily living (ADL and IADL) and mortality were obtained. Based on self-reported disease status, we calculated five commonly used western multimorbidity indexes for CHARLS baseline participants. The one that predicted the health outcomes the best was selected and then modified through a linear mixed model using the repeated individual data in CHARLS. The performance of the modified index was internally and externally evaluated with CHARLS and CLHLS data. The multimorbidity-weighted index (MWI) performed the best among the five indexes. In the modified Chinese multimorbidity-weighted index (CMWI), the weights of the diseases varied greatly (range 0.2–5.1). The top three diseases with the highest impact were stroke, memory-related diseases and cancer, corresponding to weights of 5.1, 4.3 and 3.4, respectively. Compared with the MWI, the CMWI showed better model fits for PF and IADL with larger R² and smaller Akaike information criterion, and comparable prediction performances for ADL, IADL and mortality (e.g. the same predictive accuracy of 0.80 for ADL disability). The CMWI is an adequate index to quantify the multimorbidity burden for Chinese middle-aged and older community-dwelling individuals. It can be directly computed via disease status examined in regular community health check-ups to facilitate health management.