Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Hand, foot, and mouth disease (HFMD) has remained a serious public health threat since its first outbreak in China. Analyzing the province-level spatiotemporal distribution of HFMD and mapping the relative risk in mainland China will help determine high-risk provinces and periods of infection outbreaks for use in formulating new priority areas for prevention and control of this disease. Furthermore, our study examined the effect of air pollution on HFMD nationwide, which few studies have done thus far.
Data were collected on the number of provincial monthly HFMD infections, air pollution, meteorological variables, and socioeconomic variables from 2014 to 2017 in mainland China. We used spatial autocorrelation to determine the aggregate distribution of HFMD incidence. Spatiotemporal patterns of HFMD were analyzed, risk maps were developed using the Bayesian spatiotemporal model, and the impact of potential influencing factors on HFMD was assessed.
In our study, from 2014 to 2017, the HFMD annual incidence rate in all provinces of mainland China ranged from 138.80 to 203.15 per 100,000 people, with an average annual incidence rate of 165.86. The temporal risk of HFMD for 31 Chinese provinces exhibited cyclical and seasonal characteristics. The southern and eastern provinces had the highest spatial relative risk (RR > 3) from 2014 to 2017. The HFMD incidence risk in provinces (Hunan, Hubei, and Chongqing) located in central China increased over time. Among the meteorological variables, except for the mean two-minute wind speed (RR 0.6878; 95% CI 0.5841, 0.8042), all other variables were risk factors for HFMD. High GDP per capita (RR 0.9922; 95% CI 0.9841, 0.9999) was a protective factor against HFMD. The higher the birth rate was (RR 1.0657; 95% CI 1.0185, 1.1150), the higher the risk of HFMD. Health workers per 1,000 people (RR 1.2010; 95% CI 1.0443, 1.3771) was positively correlated with HFMD.
From 2014 to 2017, the central provinces (Hunan, Hubei, and Chongqing) gradually became high-risk regions for HFMD. The spatiotemporal pattern of HFMD risk may be partially attributed to meteorological and socioeconomic factors. The prevalence of HFMD in the central provinces requires attention, as prevention control efforts should be strengthened there.

Brucellosis is a highly contagious zoonotic and systemic infectious disease caused by Brucella, which seriously affects public health and socioeconomic development worldwide. Particularly, in China accumulating eco-environmental changes and agricultural intensification have increased the expansion of human brucellosis (HB) infection. As a traditional animal husbandry area adjacent to Inner Mongolia, Datong City in northwestern China is characterized by a high HB incidence, demonstrating obvious variations in the risk pattern of HB infection in recent years. In this study, we built Bayesian spatiotemporal models to detect the transfer of high-risk clusters of HB occurrence in Datong from 2005 to 2020. Geographically and Temporally Weighted Regression and GeoDetector were employed to investigate the synergistic driving effects of multiple potential risk factors. Results confirmed an evident dynamic expansion of HB from the east to the west and south in Datong. The distribution of HB showed a negative correlation with urbanization level, economic development, population density, temperature, precipitation, and wind speed, while a positive correlation with the normalized difference vegetation index, and grassland/cropland cover areas. Especially, the local animal husbandry and related industries imposed a large influence on the spatiotemporal distribution of HB. This work strengthens the understanding of how HB spatial heterogeneity is driven by environmental factors, through which helpful insights can be provided for decision-makers to formulate and implement disease control strategies and policies for preventing the further spread of HB.

Hand, foot and mouth disease (HFMD) caused by a variety of enteroviruses remains a major public health problem in China. Previous studies have found that social factors may contribute to the inconsistency of the relationship patterns between meteorological factors and HFMD, but the conclusions are inconsistent. The influence of social factors on the association between meteorology and HFMD is still less well understood. We aimed to analyze whether social factors affected the effect of meteorological factors on HFMD in Sichuan Province.
We collected daily data on HFMD, meteorological factors and social factors in Sichuan Province from 2011 to 2017. First, we used a Bayesian spatiotemporal model combined with a distributed lag nonlinear model to evaluate the exposure-lag-response association between meteorological factors and HFMD. Second, by constructing the interaction of meteorological factors and social factors in the above model, the changes in the relative risk (RR) under different levels of social factors were evaluated.
The cumulative exposure curves for average temperature, relative humidity, and HFMD were shaped like an inverted \"V\" and a \"U\" shape. As the average temperature increased, the RR increased and peaked at 19 °C (RR 1.020 [95% confidence interval CI 1.004-1.050]). The urbanization rate, per capita gross domestic product (GDP), population density, birth rate, number of beds in health care centers and number of kindergartens interacted with relative humidity. With the increase in social factors, the correlation curve between relative humidity and HFMD changed from an \"S\" shape to a \"U\" shape.
Relative humidity and average temperature increased the risk of HFMD within a certain range, and social factors enhanced the impact of high relative humidity. These results could provide insights into the combined role of environmental factors in HFMD and useful information for regional interventions.

Monitoring the spatial and temporal course of opioid-related drug overdose mortality is a key public health determinant. Despite previous studies exploring the evolution of drug-related fatalities following the stay-at-home mandates during the COVID-19 pandemic, little is known about the spatiotemporal dynamics that mitigation efforts had on overdose deaths. The purpose of this study was to describe the spatial and temporal dynamics of overdose death relative risk using a 4-week interval over a span of 5 months following the implementation of the COVID-19 lockdown in the city of Chicago, IL. A Bayesian space-time model was used to produce posterior risk estimates and exceedance probabilities of opioid-related overdose deaths controlling for measures of area-level deprivation and stay-at-home mandates. We found that area-level temporal risk and inequalities in drug overdose mortality increased significantly in the initial months of the pandemic. We further found that a change in the area-level deprivation from the first to the fourth quintile increased the relative risk of a drug overdose risk by 44.5%. The social distancing index measuring the proportion of persons who stayed at home in each census block group was not associated with drug overdose mortality. We conclude by highlighting the importance of contextualizing the spatial and temporal risk in overdose mortality for implementing effective and safe harm reduction strategies during a global pandemic.

Among high-income countries, Japan has a low prevalence of obesity, but little is understood about subnational trends and variations in body mass index (BMI), largely owing to the lack of data from representative samples of prefectures. We aimed to examine long-term trends and distributions of adult BMI at the prefecture level in Japan from the late 1970s using a spatiotemporal model.
We obtained cross-sectional data for 233,988 men and 261,086 women aged 20-79 years from the 44 annual National Health and Nutrition Surveys (NHNS) conducted during 1975-2018. We applied a Bayesian spatiotemporal model to estimate the annual time series of age-standardized and age-specific mean BMI by 20-year age group and sex for each of the 47 prefectures. We assessed socioeconomic inequalities in BMI across prefectures using the concentration index, according to population density.
In men, the age-standardized prefectural mean BMI ranged from 21.7 kg/m2 (95% credible interval, 21.6-21.9) to 23.1 kg/m2 (22.9-23.4) in 1975 and from 23.5 kg/m2 (23.3-23.7) to 24.8 kg/m2 (24.6-25.1) in 2018. In women, the age-standardized prefectural mean BMI ranged from 22.0 kg/m2 (21.9-22.2) to 23.4 kg/m2 (23.2-23.6) in 1975 and from 21.7 kg/m2 (21.6-22.0) to 23.5 kg/m2 (23.2-23.8) in 2018. Mean BMI was highest in the southernmost prefecture for most of the study period, followed by northeast prefectures. The increase in mean BMI was largest in southwest prefectures, which caught up with northeast prefectures over time. The concentration index was negative, indicating higher BMI in less-populated prefectures. Absolute values of the concentration index were greater in women than in men and increased over time.
There were variations in adult mean BMI across prefectures, and geographic distributions changed over time. Further national and local efforts are needed to address the rising trend in mean BMI, particularly among men in rural prefectures, and socioeconomic inequalities among women. Bayesian hierarchical modeling is useful for reconstructing long-term spatiotemporal trends of mean BMI by integrating small-sized survey samples at the prefecture level in the NHNS.

Facing severe PM2.5 pollution, China has adopted a series of clean air policies since 2013, but PM2.5 concentrations in China remain serious. Weighing both sustainable development and environmental protection, the Environmental Protection Tax Law was implemented in 2018 in China. This research employed a Bayesian space-time model to identify the impacts of the environmental protection tax on PM2.5 pollution (IEPTPM2.5P) at the provincial level in 2018 in China, combining remotely sensed and in-situ monitoring data. Then the influence factors of the IEPTPM2.5P was investigated using a Bayesian LASSO regression model. Results indicate that the IEPTPM2.5P resulted in a decreasing trend of annual PM2.5 concentrations in 31 provinces. The spatial pattern of the IEPTPM2.5P presented a distinct geographical feature. The highest five IEPTPM2.5P occurred in Beijing, Tianjin, Shanghai, Shandong, and Hebei, and the corresponding values were -1.81, -1.79, -1.52, -1.51, and -1.47 μg/m3 per year, respectively. Tourism output value associated negatively with the IEPTPM2.5P, and the other five variables associated positively with the IEPTPM2.5P. The urbanisation rate and relief amplitude were the top two influencing factors, with contributions of 36.3% and 19.3%, respectively. The IEPTPM2.5P increased 0.0141 μg/m3 per year (95% credibility interval (CI): 0.0013, 0.0259) if the urbanisation rate increased one percentage point. The influencing contributions and magnitudes of the tax rate for air pollutants and the environmental tax revenue are 9.6% and 12.1%, 0.0016 (95% CI:-0.0038, 0.0076) and 0.0108 (95% CI:-0.0188, 0.0412) μg/m3 per year, respectively.

Child maltreatment is a serious public health problem. Previous research demonstrates that child maltreatment clusters in low-income, racially homogenous neighborhoods. Little is known, however, about the structural correlates of spatial risk in small areas such as census tracts. Here we present additional information regarding the data and methods used in the recent article published in Child Abuse & Neglect entitled \"Variability and stability in child maltreatment risk across time and space and its association with neighborhood social & housing vulnerability in New Mexico: A Bayesian space-time model\" [1]. The present dataset merges child maltreatment data from the New Mexico Department of Public Health with multiple sources of publicly available data to create a novel public health analysis. Bayesian spatio-temporal modeling techniques were used to map the relative risk of substantiated child maltreatment across census tracts in the state, and to elucidate spatial and temporal heterogeneity in risk. The data was initially collected by the New Mexico Children, Youth and Families Department, the state organization that suspected child abuse and neglect cases are reported to and the organization that then substantiates these cases. The data were then sent to the New Mexico Community Data Collaborative, a data analytic organization under the umbrella of the New Mexico Department of Health. The point file consisting of home addresses of substantiated cases of child abuse was then aggregated by census tract, mapped for the entire state of New Mexico and made available to the public for research and analysis by different public health organizations and researchers (including the present researchers). The very purpose of making the data available to the public was to allow deeper investigations into trends and associations with other social determinants of health. This analysis demonstrates the public health importance of data sharing and accessibility.

OBJECTIVE: To explore the risk factors of pulmonary tuberculosis incidence in Wenchuan earthquake-stricken area during a nine-year period of time.
METHODS: The incidence and socioeconomic indicators in the 181 counties of Sichuan province from 2004 to 2012 were collected, multilevel extra-Poisson model was performed for variable screening, and Bayesian spatiotemporal models were fitted.
RESULTS: The morbidity showed a downward time trend from 2004 to 2012. The spatiotemporal interaction model was applied with the smallest deviance information criterion. The risk factors included: county latitude (0.949, 95%CI 0.765 to 1.098), float proportion (0.368, 95%CI 0.354 to 0.380), per capita GDP (-0.225, 95%CI -0.235 to -0.216), population density (0.072, 95%CI 0.041 to 0.105), and minority inhabited area (0.302, 95%CI 0.241 to 0.372). The variation of posterior median and Bayesian credibility interval was small, and the spatiotemporal patterns were similar among different periods.
CONCLUSIONS: The incidence presented a smooth curve without bursting after the earthquake during 2004 to 2012, and with a tendency of decreasing from north to south in Sichuan province; PTB countermeasures should be focused on the migrating population and in the minority inhabited and economic underdeveloped regions. Geographical adjacent structure was an important factor and regional collaborative prevention and control should be strengthened.