Background Managing noncommunicable diseases through primary healthcare has been identified as the key strategy to achieve universal health coverage but is challenging in most low- and middle-income countries. Stroke is the leading cause of death and disability in rural China. This study aims to determine whether a primary care-based integrated mobile health intervention (SINEMA intervention) could improve stroke management in rural China. Methods and findings Based on extensive barrier analyses, contextual research, and feasibility studies, we conducted a community-based, two-arm cluster-randomized controlled trial with blinded outcome assessment in Hebei Province, rural Northern China including 1,299 stroke patients (mean age: 65.7 [SD:8.2], 42.6% females, 71.2% received education below primary school) recruited from 50 villages between June 23 and July 21, 2017. Villages were randomly assigned (1:1) to either the intervention or control arm (usual care). In the intervention arm, village doctors who were government-sponsored primary healthcare providers received training, conducted monthly follow-up visits supported by an Android-based mobile application, and received performance-based payments. Participants received monthly doctor visits and automatically dispatched daily voice messages. The primary outcome was the 12-month change in systolic blood pressure (BP). Secondary outcomes were predefined, including diastolic BP, health-related quality of life, physical activity level, self-reported medication adherence (antiplatelet, statin, and antihypertensive), and performance in “timed up and go” test. Analyses were conducted in the intention-to-treat framework at the individual level with clusters and stratified design accounted for by following the prepublished statistical analysis plan. All villages completed the 12-month follow-up, and 611 (intervention) and 615 (control) patients were successfully followed (3.4% lost to follow-up among survivors). The program was implemented with high fidelity, and the annual program delivery cost per capita was US$24.3. There was a significant reduction in systolic BP in the intervention as compared with the control group with an adjusted mean difference: −2.8 mm Hg (95% CI −4.8, −0.9; p = 0.005). The intervention was significantly associated with improvements in 6 out of 7 secondary outcomes in diastolic BP reduction (p < 0.001), health-related quality of life (p = 0.008), physical activity level (p < 0.001), adherence in statin (p = 0.003) and antihypertensive medicines (p = 0.039), and performance in “timed up and go” test (p = 0.022). We observed reductions in all exploratory outcomes, including stroke recurrence (4.4% versus 9.3%; risk ratio [RR] = 0.46, 95% CI 0.32, 0.66; risk difference [RD] = 4.9 percentage points [pp]), hospitalization (4.4% versus 9.3%; RR = 0.45, 95% CI 0.32, 0.62; RD = 4.9 pp), disability (20.9% versus 30.2%; RR = 0.65, 95% CI 0.53, 0.79; RD = 9.3 pp), and death (1.8% versus 3.1%; RR = 0.52, 95% CI 0.28, 0.96; RD = 1.3 pp). Limitations include the relatively short study duration of only 1 year and the generalizability of our findings beyond the study setting. Conclusions In this study, a primary care-based mobile health intervention integrating provider-centered and patient-facing technology was effective in reducing BP and improving stroke secondary prevention in a resource-limited rural setting in China. Trial registration The SINEMA trial is registered at ClinicalTrials.gov NCT03185858.
Background The system-integrated technology-enabled model of care (SINEMA) trial aimed to evaluate the effectiveness of a community-based multi-component intervention for secondary prevention of stroke in rural China. Objective To present the detailed statistical analysis plan for the trial prior to database locking and data analysis. Methods The detailed analysis plan outlines primary and secondary outcome measures, describes the over-arching data analysis principles to be adopted as well as more detailed descriptions of specific analytical approaches for effectiveness analyses, as well strategies to handle missing outcome data. Discussion Publication of the statistical analysis plan increases the transparency of the data analysis procedure and reduces potential bias in trial reporting. Trial registration The trial was registered with clinicaltrials.gov (NCT03185858).
Background: Work is needed to better understand how joint exposure to environmental and economic factors influence cancer. We hypothesize that environmental exposures vary with socioeconomic status (SES) and urban/rural locations, and areas with minority populations coincide with high economic disadvantage and pollution. Methods: To model joint exposure to pollution and SES, we develop a latent class mixture model (LCMM) with three latent variables (SES Advantage, SES Disadvantage, and Air Pollution) and compare the LCMM fit with K-means clustering. We ran an ANOVA to test for high exposure levels in non-Hispanic black populations. The analysis is at the census tract level for the state of North Carolina. Results: The LCMM was a better and more nuanced fit to the data than K-means clustering. Our LCMM had two sublevels (low, high) within each latent class. The worst levels of exposure (high SES disadvantage, low SES advantage, high pollution) are found in 22% of census tracts, while the best levels (low SES disadvantage, high SES advantage, low pollution) are found in 5.7%. Overall, 34.1% of the census tracts exhibit high disadvantage, 66.3% have low advantage, and 59.2% have high mixtures of toxic pollutants. Areas with higher SES disadvantage had significantly higher non-Hispanic black population density (NHBPD; P < 0.001), and NHBPD was higher in areas with higher pollution (P < 0.001). Conclusions: Joint exposure to air toxins and SES varies with rural/urban location and coincides with minority populations. Impact: Our model can be extended to provide a holistic modeling framework for estimating disparities in cancer survival. See all articles in this CEBP Focus section, “Environmental Carcinogenesis: Pathways to Prevention.”
Recent studies have found that both food deserts (FD) and lower socio-economic status (SES) are individually associated with increased breast cancer mortality in the US. However, further work is needed to investigate their combined contribution to breast cancer mortality. Furthermore, valid inference for area-level disease mapping requires careful consideration of spatial clustering. In our study, we utilize data from the USDA Food Access Research Atlas and the American Community Survey. Breast cancer mortality data come from the National Center of Health Statistics 2014 report. We consider a latent class mixture model to determine deprivation categories which incorporate six SES proportion variables (no car, poverty, no HS graduation, crowded housing, unemployment, crowded housing), two FD (Low income and > 1 mile from supermarket and receiving snap benefits and > 1 mile from supermarket) variables. Our latent class model has three levels: Low, Moderate, and High, making up 36.6%, 45.6%, and 17.8% of US counties, respectively. We then incorporated these levels as a fixed effect in a Bayesian hierarchical spatial negative binomial model using R-INLA. In this model, we account for both spatially structured and unstructured effects. Counties classified as “High” on our deprivation categories were associated with a 50% increase in breast cancer mortality rates (95% CrI: [1.12, 2.02]). Also, the county proportion of women >65 was significantly associated with 1.42 times higher breast cancer mortality (95% CrI [1.37, 1.42]). Policies that allow for access in the face of deprivation may contribute to lower overall breast cancer mortality. Citation Format: Kara McCormack. Social determinants, food deserts, and their combined contribution to breast cancer mortality [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-03-02.
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