How Can Community Data Be Leveraged to Advance Primary Health Care? A Scoping Review of Community-Based Health Information Systems.

BACKGROUND: Community-based health information systems (CBISs) can provide critical insights into how community health systems function, and digitized CBISs may improve the quality of community-level data and facilitate integration and use of CBISs within the broader health system. This scoping review aims to understand how CBISs have been implemented, integrated, and used to support community health outcomes in low- and middle-income countries (LMICs). METHODS: Both peer-reviewed and gray literature were included; relevant articles were identified using key terms and controlled vocabulary related to community/primary health care, health information systems, digital health, and LMICs. A total of 11,611 total records were identified from 5 databases and the gray literature. After deduplication, 6,985 peer-reviewed/gray literature were screened, and 95 articles/reports were included, reporting on 105 CBIS implementations across 38 countries. RESULTS: Findings show that 55% of CBISs included some level of digitization, with just 28% being fully digitized (for data collection and reporting). Data flow from the community level into the health system varied, with digitized CBISs more likely to reach national-level integration. National-level integration was primarily seen among vertical CBISs. Data quality challenges were present in both paper-based and digitized CBISs, exacerbated by fragmentation of the community health landscape with often parallel reporting systems. CBIS data use was constrained to mostly vertical and digitized (partially or fully) CBISs at national/subnational levels. CONCLUSION: Digitization can play a pivotal role in strengthening CBIS use, but findings demonstrate that CBISs are only as effective as the community health systems they are embedded within. Community-level data are often not being integrated into national/subnational health information systems, undermining the ability to understand what the community health needs are. Furthermore, stronger investments within community health systems need to be in place broadly to reduce fragmentation and provide stronger infrastructural and systemic support to the community health workforce.

Global warming may significantly increase childhood anemia burden in sub-Saharan Africa.

Childhood anemia constitutes a global public health problem, especially in low- and middle-income countries (LMICs). However, it remains unknown whether global warming has an impact on childhood anemia. Here, we examined the association between annual temperatures and childhood anemia prevalence in sub-Saharan Africa and then projected childhood anemia burden attributable to climate change. Each 1°C increment in annual temperature was associated with increased odds of childhood anemia (odd ratio = 1.138, 95% confidence interval: 1.134-1.142). Compared with the baseline period (1985-2014), the attributable childhood anemia cases would increase by 7,597 per 100,000 person-years under a high-emission scenario in the 2090s, which would be almost 2-fold and over 3-fold more than those projected in moderate- and low-emission scenarios. Our results reveal the vulnerabilities and inequalities of children for the excess burden of anemia due to climate warming and highlight the importance of climate mitigation and adaptation strategies in LMICs.

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: Spatiotemporal Big Data Analysis.

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.

Adverse childhood experiences and their impacts on subsequent depression and cognitive impairment in Chinese adults: A nationwide multi-center study.

BACKGROUND: Adverse childhood experiences (ACES) are prevalent and have long-lasting effects. This study explored how ACE exposure is associated with subsequent depression and cognitive impairment and whether sociodemographic characteristics modify that association. METHOD: This 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 ACES and subsequent depression and cognitive impairment, as well as potential modifiers. RESULTS: Compared with individuals without ACES, those who experienced four or more ACES had a higher risk of subsequent depression (adjusted odds ratio, aOR = 2.65, 95 % confidence intervals [CIs]: 2.21 to 3.16), poorer mental intactness (ß = -0.317 [-0.508 to -0.125]), and worse global cognition (-0.437 [-0.693 to -0.181]). Trend analyses showed a dose-response association between accumulated ACES and subsequent depression and cognitive impairment. No modifications of those associations by age, sex, educational level, or family's financial status during childhood were observed. LIMITATIONS: Self-reported measures could favour recall bias. CONCLUSION: Our study suggests that ACES increase the risk of subsequent depression and cognitive impairment in Chinese adults regardless of sociodemographic characteristics. These findings provide important implications for mitigating the adverse effects of early-life stress and promoting health in adulthood.

Overview of particulate air pollution and human health in China: Evidence, challenges, and opportunities.

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.

Adaptation and psychometric evaluation of a scale to measure oral pre-exposure prophylaxis-related stigma among key and vulnerable populations in Kenya.

INTRODUCTION: As oral pre-exposure prophylaxis (PrEP) services scale up throughout sub-Saharan Africa (SSA), clients continue to face challenges with sustained PrEP use. PrEP-related stigma has been shown to influence engagement throughout the HIV PrEP care continuum throughout SSA. Validated quantitative measures of PrEP-related stigma in SSA are of critical importance to better understand its impacts at each stage of the HIV PrEP care continuum. This study aimed to psychometrically evaluate a PrEP-related stigma scale for use among key and vulnerable populations in the context of a Kenya national PrEP programme. METHODS: As part of a larger prospective cohort study nested within Kenya's Jilinde programme, this study used baseline data collected from 1135 participants between September 2018 and April 2020. We used exploratory factor analysis to evaluate the factor structure of a PrEP-related stigma scale. We also assessed convergent construct validity of the PrEP-Related Stigma Scale by testing for expected correlations with depression and uptake of HIV services. Finally, we examined the relationship between PrEP-related stigma and key demographic, psychosocial and behavioural characteristics. RESULTS: We identified four dimensions of PrEP-related stigma: (1) interpersonal stigma, (2) PrEP norms, (3) negative self-image and (4) disclosure concerns. The scale demonstrated strong internal consistency (α = 0.84), was positively correlated with depressive symptoms and negatively correlated with uptake of HIV services. Multivariable regression analysis demonstrated associations between PrEP-related stigma and sex worker identity. CONCLUSIONS: The adapted and validated PrEP-Related Stigma Scale can enable programmes to quantify how PrEP-related stigma and its dimensions may differentially impact outcomes on the HIV PrEP care continuum, evaluate stigma interventions and tailor programmes accordingly. Opportunities exist to validate the scale in other populations and explore further dimensions of PrEP-related stigma.

Association Between Ambient Air Pollutants Exposure and Preterm Birth in Women Who Underwent in vitro Fertilization: A Retrospective Cohort Study From Hangzhou, China.

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 <35 years and those who underwent two embryos transferred. 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.

Application of land use regression to assess exposure and identify potential sources in PM2.5, BC, NO2 concentrations.

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.

Digital Technology and the Future of Health Systems.

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.

Nitrogen dioxide air pollution and preterm birth in Shanghai, China.

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.

Association between ambient particulate matter air pollution and ST-elevation myocardial infarction: A case-crossover study in a Chinese city.

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.