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Adverse childhood experiences (ACEThis study used data on 14,484 participants of the 2015 China Health and Retirement Longitudinal Study (CHARLS) and 2014 CHARLS life history survey. Depression was assessed using the 10-item Center for Epidemiologic Studies Depression scale. Cognitive performance was evaluated via three composite measures: episodic memory, mental intactness, and global cognition. Twelve ACE indicators were measured using a validated questionnaire. Multiple regression models and stratified analyses explored the relationship between ACECompared with individuals without ACESelf-reported measures could favour recall bias.Our study suggests that ACE

BACKGROUND Evidence-based decision-making is essential to improve public health benefits and resources, especially in low-middle-income countries (LMICs), but the mechanisms of its implementation remain less straightforward. The availability of high-quality, reliable, and sufficient data in LMICs can be challenging due to issues such as lack of human resource capacity and weak digital infrastructure, among others. Health information systems (HIS) have been critical for aggregating and integrating health-related data from different sources to support evidence-based decision-making. Nutrition Information Systems (NIS), which are nutrition-focused HIS, collect and report on nutrition-related indicators to improve issues related to malnutrition and food security – and can assist in improving populations’ nutritional statuses and the integration of nutrition programming into routine health services. Data visualization tools (DVT) such as dashboards have been recommended to support such evidence-based decision-making, leveraging data from HIS/NIS. The use of such DVTs to support decision-making has largely been unexplored within LMIC contexts. In Bangladesh, the Mukto dashboard was developed to display and visualize nutrition-related performance indicators at the national and sub-national levels. However, despite this effort, the current use of nutrition data to guide priorities and decisions remains relatively nascent and under-utilized. OBJECTIVE The goal of the study is to better understand how Bangladesh’s NIS has been utilized and areas for improvement to facilitate its use for evidence-based decision-making towards ameliorating nutrition-related service delivery and health status of communities in Bangladesh. METHODS Primary data collection was conducted through qualitative semi-structured interviews with key policy-level stakeholders (n=24). Key informants were identified through purposive sampling and were asked questions around how the experiences and challenges with the NIS and related nutrition dashboards. RESULTS Main themes such as trust, data usability, person power, and data use for decision-making emerged from the data. Trust in both data collection and quality was lacking among many stakeholders. Poor data usability stemmed from unstandardized indicators, irregular data collection, and differences between rural and urban data. Insufficient person power and staff training coupled with infrastructural challenges can negatively affect data at the input stage. While stakeholders understood and expressed the importance of evidence-based decision-making, ultimately, they noted that the data was not being utilized to its maximum potential. CONCLUSIONS Leveraging DVTs can improve the use of data for evidence-based decision-making, but decision-makers must trust that the data is believable, credible, timely, and responsive. Results support the significance of a tailored data ecosystem, which has not reached its full potential in Bangladesh. Recommendations to reach this potential include ensuring a clear intended user base, and accountable stakeholders are present. Systems should also have the capacity to ensure data credibility and support ongoing person power requirements.

BackgroundAdverse childhood experiences (ACEs) are prevalent and have long lasting effects. This study aimed to explore the associations between ACEs exposure with subsequent depression and cognitive impairment and to assess whether sociodemographic characteristics modify these associations.MethodA total of 14,484 participants from the China Health and Retirement Longitudinal Study (CHARLS) 2015 and life history survey in 2014 were enrolled. Depression was assessed by the 10-item Center for Epidemiologic Studies Depression scale. Cognitive performance was evaluated by three composite measures: episodic memory, mental intactness and global cognition. A wide range of 12 ACE indicators were measured by a validated questionnaire. Multiple regression models and stratified analysis explore the relationship between accumulated ACEs with subsequent depression and cognitive impairment and potential modifiers.ResultsCompared with individuals without ACEs, those who experienced four or more ACEs have a higher risk of subsequent depression (adjusted odds ratio, aOR=2.65, 95% confidence intervals [CIs]: 2.21-3.16), poorer mental intactness (β= -0.317 [-0.508 to -0.125]) and worse global cognition (β= -0.437 [-0.693 to -0.181]). Trend analyses showed a dose-response association between accumulated ACEs with subsequent depression and cognitive impairment. The modifications of the association by age, sex, educational level and family’s financial status during childhood were not observed.ConclusionOur study suggests that higher ACEs exposure increases the risk of subsequent depression and cognitive impairment in Chinese adults regardless of sociodemographic characteristics. The findings provide important implications for mitigating the adverse effects of early-life stress and promoting health in adulthood.

It remains unknown whether global warming may have impact on childhood anemia, especially in low-income and middle-income countries (LMIC). We projected childhood anemia cases attributable to climate change in sub-Saharan Africa using anthropometric data of children under 5 years from 26 countries and an ensemble of high-resolution climate change simulations. Compared with the baseline period, the childhood anemia cases attributable to climate change would increase 6,754 cases per 100,000 person-year under the SSP5-8.5 scenario in 2090s, which would be almost 2-fold and over 3-fold more than those in SSP2-4.5 and SSP1-2.6 scenarios. This multi-country study presented the first-hand evidence on significantly increased burden of childhood anemia attributable to global warming in sub-Saharan Africa. The result revealed the vulnerabilities and inequalities of children in climate-sensitive regions, and highlighted the importance of mitigation and adaptation strategies to reduce the impact of global warming, especially among vulnerable subgroups.

Ambient particulate matter (PM) pollution in China continues to be a major public health challenge. With the release of the new WHO air quality guidelines in 2021, there is an urgent need for China to contemplate a revision of air quality standards (AQS). In the recent decade, there has been an increase in epidemiological studies on PM in China. A comprehensive evaluation of such epidemiological evidence among the Chinese population is central for revision of the AQS in China and in other developing countries with similar air pollution problems. We thus conducted a systematic review on the epidemiological literature of PM published in the recent decade. In summary, we identified the following: (1) short-term and long-term PM exposure increase mortality and morbidity risk without a discernible threshold, suggesting the necessity for continuous improvement in air quality; (2) the magnitude of long-term associations with mortality observed in China are comparable with those in developed countries, whereas the magnitude of short-term associations are appreciably smaller; (3) governmental clean air policies and personalized mitigation measures are potentially effective in protecting public and individual health, but need to be validated using mortality or morbidity outcomes; (4) particles of smaller size range and those originating from fossil fuel combustion appear to show larger relative health risks; and (5) molecular epidemiological studies provide evidence for the biological plausibility and mechanisms underlying the hazardous effects of PM. This updated review may serve as an epidemiological basis for China's AQS revision and proposes several perspectives in designing future health studies.

BACKGROUND Highly effective COVID-19 vaccines are available and free of charge in the United States. With adequate coverage, their use may help return life back to normal and reduce COVID-19–related hospitalization and death. Many barriers to widespread inoculation have prevented herd immunity, including vaccine hesitancy, lack of vaccine knowledge, and misinformation. The Ad Council and COVID Collaborative have been conducting one of the largest nationwide targeted campaigns (“It’s Up to You”) to communicate vaccine information and encourage timely vaccination across the United States. More than 300 major brands, digital and print media companies, and community-based organizations support the campaigns to reach distinct audiences. OBJECTIVE The goal of this study was to use aggregated mobility data to assess the effectiveness of the campaign on COVID-19 vaccine uptake. METHODS Campaign exposure data were collected from the Cuebiq advertising impact measurement platform consisting of about 17 million opted-in and deidentified mobile devices across the country. A Bayesian spatiotemporal hierarchical model was developed to assess campaign effectiveness through estimating the association between county-level campaign exposure and vaccination rates reported by the Centers for Disease Control and Prevention. To minimize potential bias in exposure to the campaign, the model included several control variables (eg, age, race or ethnicity, income, and political affiliation). We also incorporated conditional autoregressive residual models to account for apparent spatiotemporal autocorrelation. RESULTS The data set covers a panel of 3104 counties from 48 states and the District of Columbia during a period of 22 weeks (March 29 to August 29, 2021). Officially launched in February 2021, the campaign reached about 3% of the anonymous devices on the Cuebiq platform by the end of March, which was the start of the study period. That exposure rate gradually declined to slightly above 1% in August 2021, effectively ending the study period. Results from the Bayesian hierarchical model indicate a statistically significant positive association between campaign exposure and vaccine uptake at the county level. A campaign that reaches everyone would boost the vaccination rate by 2.2% (95% uncertainty interval: 2.0%-2.4%) on a weekly basis, compared to the baseline case of no campaign. CONCLUSIONS The “It’s Up to You” campaign is effective in promoting COVID-19 vaccine uptake, suggesting that a nationwide targeted mass media campaign with multisectoral collaborations could be an impactful health communication strategy to improve progress against this and future pandemics. Methodologically, the results also show that location intelligence and mobile phone–based monitoring platforms can be effective in measuring impact of large-scale digital campaigns in near real time.

Objectives: Exposure to air pollutants has been linked to preterm birth (PTB) after natural conception. However, few studies have explored the effects of air pollution on PTB in patients who underwent in vitro fertilization (IVF). We aimed to investigate the association between ambient air pollutants exposure and PTB risk in IVF patients.Methods: This retrospective cohort study included 2,195 infertile women who underwent IVF treatment from January 2017 and September 2020 in Hangzhou Women's Hospital. Totally 1,005 subjects who underwent a first fresh embryo(s) transfer cycle were analyzed in this study. Residential exposure to ambient six air pollutants (PM2.5, PM10, SO2, NO2, CO, O3) during various periods of the IVF timeline were estimated by satellite remote-sensing and ground measurement. Cox proportional hazards models for discrete time were used to explore the association between pollutants exposure and incident PTB, with adjustment for confounders. Stratified analyses were employed to explore the effect modifiers.Results: The clinical pregnancy and PTB rates were 61.2 and 9.3%, respectively. We found that PM2.5 exposure was significantly associated with an increased risk of PTB during 85 days before oocyte retrieval [period A, adjusted hazard ratio, HR=1.09, 95%CI: 1.02–1.21], gonadotropin start to oocyte retrieval [period B, 1.07 (1.01–1.19)], first trimester of pregnancy [period F, 1.06 (1.01–1.14)], and the entire IVF pregnancy [period I, 1.07 (1.01–1.14)], respectively. An interquartile range increment in PM10 during periods A and B was significantly associated with PTB at 1.15 (1.04–1.36), 1.12 (1.03–1.28), and 1.14 (1.01–1.32) for NO2 during period A. The stratified analysis showed that the associations were stronger for women aged Conclusions: Our study suggests ambient PM2.5, PM10, and NO2 exposure were significantly associated with elevated PTB risk in IVF patients, especially at early stages of IVF cycle and during pregnancy.

Background Highly effective COVID-19 vaccines are available and free of charge in the United States. With adequate coverage, their use may help return life back to normal and reduce COVID-19–related hospitalization and death. Many barriers to widespread inoculation have prevented herd immunity, including vaccine hesitancy, lack of vaccine knowledge, and misinformation. The Ad Council and COVID Collaborative have been conducting one of the largest nationwide targeted campaigns (“It’s Up to You”) to communicate vaccine information and encourage timely vaccination across the United States. More than 300 major brands, digital and print media companies, and community-based organizations support the campaigns to reach distinct audiences. Objective The goal of this study was to use aggregated mobility data to assess the effectiveness of the campaign on COVID-19 vaccine uptake. Methods Campaign exposure data were collected from the Cuebiq advertising impact measurement platform consisting of about 17 million opted-in and deidentified mobile devices across the country. A Bayesian spatiotemporal hierarchical model was developed to assess campaign effectiveness through estimating the association between county-level campaign exposure and vaccination rates reported by the Centers for Disease Control and Prevention. To minimize potential bias in exposure to the campaign, the model included several control variables (eg, age, race or ethnicity, income, and political affiliation). We also incorporated conditional autoregressive residual models to account for apparent spatiotemporal autocorrelation. Results The data set covers a panel of 3104 counties from 48 states and the District of Columbia during a period of 22 weeks (March 29 to August 29, 2021). Officially launched in February 2021, the campaign reached about 3% of the anonymous devices on the Cuebiq platform by the end of March, which was the start of the study period. That exposure rate gradually declined to slightly above 1% in August 2021, effectively ending the study period. Results from the Bayesian hierarchical model indicate a statistically significant positive association between campaign exposure and vaccine uptake at the county level. A campaign that reaches everyone would boost the vaccination rate by 2.2% (95% uncertainty interval: 2.0%-2.4%) on a weekly basis, compared to the baseline case of no campaign. Conclusions The “It’s Up to You” campaign is effective in promoting COVID-19 vaccine uptake, suggesting that a nationwide targeted mass media campaign with multisectoral collaborations could be an impactful health communication strategy to improve progress against this and future pandemics. Methodologically, the results also show that location intelligence and mobile phone–based monitoring platforms can be effective in measuring impact of large-scale digital campaigns in near real time.

Digital health is having a profound effect on health systems, changing the balance of power between provider and patient, enabling new models of care, and shifting the focus of health systems toward client-centered health care within low- and middle-income countries. Though many of these changes are just being felt due to resistance by organizations and individuals reluctant to change the status quo, the explosive growth of digital technology globally means that these changes are inevitable. We can expect to see increasing use of telemedicine for remote diagnostics and treatment, protocol-driven health care to improve quality of care, and better access to goods and services through changes in the organization of transportation and delivery services. Data will become central to health systems, whether big data and artificial intelligence tools for surveillance, planning, and management or "personalized data" in the form of universal electronic record systems and customized treatment protocols. As with any disruptive innovation, the growth of digital health will also bring challenges, including who owns, controls, and manages the data being collected and how to maintain privacy and confidentiality in this data-rich world.

BACKGROUND: Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data. OBJECTIVE: Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM(2.5)), black carbon (BC) and nitrogen dioxide (NO(2)) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study. METHOD: Two one-week integrated samples were collected at 30 PM(2.5) (BC) sites and 45 NO(2) sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model. RESULTS: LUR explained 65% of the spatial variability in PM(2.5), 78% in BC and 73% in NO(2). Mean (±Standard Deviation) of predicted PM(2.5), BC and NO(2) exposure levels were 48.3 (±6.3) μg/m(3), 7.5 (±1.4) μg/m(3) and 27.3 (±8.2) μg/m(3), respectively. Weak spatial corrections (Pearson r = 0.05–0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM(2.5), BC and NO(2) levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO(2) levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100–700m) were found for BC and NO(2). CONCLUSION: We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM(2.5), NO(2) and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM(2.5), NO(2) and BC concentrations.

Background Abundant epidemiological studies have revealed that short-term exposure to ambient air pollution increased the incidence of ischemic heart diseases. However, few investigations have explored the association between air pollution and ST-elevation myocardial infarction (STEMI), one major subtype of such events. Methods We conducted a time-stratified case-crossover study in two major hospitals of Yancheng, a city in East China, from January 2015 to February 2018. We used conditional logistic regression models to explore the association between hourly concentrations of air pollutants and STEMI hospitalizations. We explored potential effect modification in susceptible subgroups by age, gender, smoking status, and comorbidities. Two-pollutant models were fitted to test the robustness of the association. Results We identified a total of 347 STEMI patients. In single-pollutant models, each 10 μg/m3 increase in concentrations of fine and inhalable particulate matter (PM) (lag 13–24 h) was associated with increments of 5.27% [95% confidence interval (CI): 1.09%, 9.46%] and 3.86% (95%CI: 0.83%, 6.88%) in STEMI hospitalizations, respectively. We observed slightly larger associations of STEMI hospitalization with PM in patients who were older than 65, female, non-smoker, and with comorbidities (hypertension, diabetes or hyperlipidemia). The associations were generally robust to adjustment of criteria gaseous pollutants except for carbon monoxide. Conclusion This is the first study in China that suggested acute exposure to elevated PM concentrations may trigger STEMI. Patients with cardiometabolic comorbidities were slightly more susceptible to air pollution.

Background Nitrogen dioxide (NO2) is a typical indicator of traffic-related air pollution, and few studies with exposure assessment of high resolution have been conducted to explore its association with preterm birth in China. Objectives To investigate the association between NO2 exposure based on a land use regression (LUR) model and preterm birth in Shanghai, China. Methods A retrospective cohort study was performed among 25,493 singleton pregnancies in a major maternity hospital in Shanghai, China, from 2014 to 2015. A temporally adjusted LUR model was used to predict the prenatal exposure to NO2 based on residence address of each gravida. Logistic regression was performed to evaluate the associations of ambient NO2 exposure with preterm birth during six exposure periods, including the entire pregnancy, the first trimester, the second trimester, the third trimester, the last month, and the last week before delivery. Sensitivity analysis with a matched case-control design was conducted to test the robustness of the association between NO2 exposure and preterm birth. Results The average NO2 concentrations during the entire pregnancy was 48.23 µg/m3 among all participants. A 10 µg/m3 increase in NO2 concentrations was associated with preterm birth, with an adjusted odds ratio of 1.03 (95% confidence interval [CI]: 0.96,1.10) for exposures during the entire pregnancy, 1.00 (95%CI: 0.95,1.06) in the first trimester, 1.01 (95%CI: 0.96,1.07) in the second trimester, 1.07 (95%CI: 1.02,1.13) in the third trimester, 1.10 (95%CI: 1.04,1.15) and 1.05 (95%CI: 1.00,1.09) in the month and week before delivery, respectively. The results of the matched case–control analysis were generally consistent with those of main analyses. Conclusion NO2 may increase the risk of preterm birth, especially for exposures during the third trimester, the month and the week before delivery in Shanghai, China.

Background Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data. Objective Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM2.5), black carbon (BC) and nitrogen dioxide (NO2) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study. Method Two one-week integrated samples were collected at 30 PM2.5 (BC) sites and 45 NO2 sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model. Results LUR explained 65% of the spatial variability in PM2.5, 78% in BC and 73% in NO2. Mean (±Standard Deviation) of predicted PM2.5, BC and NO2 exposure levels were 48.3 (±6.3) μg/m3, 7.5 (±1.4) μg/m3 and 27.3 (±8.2) μg/m3, respectively. Weak spatial corrections (Pearson r = 0.05–0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM2.5, BC and NO2 levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO2 levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100–700m) were found for BC and NO2. Conclusion We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM2.5, NO2 and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM2.5, NO2 and BC concentrations.