A multicomponent index method to evaluate the relationship between urban environment and CHD prevalence.

Cardiovascular disease (CVD) is the leading cause of death globally, coronary heart disease (CHD) is the main category of it. It has been shown that the urban built environment affects the occurrence of CHD, but most focus on single environmental factors. This study developed two multicomponent Urban Heart Health Environment (UHHE) Indexes (unweighted index and weighted index), which were based on the four main behavioral risk factors for CHD (unhealthy diet, lack of physical activity, smoking, and drinking). And we examined the relationship between the indexes and the prevalence of CHD. The prevalence calculation is based on the database of F Hospital patients, who have had coronary stent implantation (CSI). Furthermore, these single-center data were corrected to reduce underestimation of prevalence. We performed global (Ordinal Least Square) and local (Geographically Weighed Regression) regression analyses to assess the relationship between the two UHHE indexes and CHD prevalence. Both indexes showed a significant negative relationship with CHD prevalence. In its spatial relationship, a non-stationary was discovered. The UHHE indexes may help identify and prioritize geographical areas for CHD prevention and may be beneficial to urban design in China.

Cross-sectional human immunodeficiency virus incidence estimation accounting for heterogeneity across communities.

Accurate estimation of human immunodeficiency virus (HIV) incidence rates is crucial for the monitoring of HIV epidemics, the evaluation of prevention programs, and the design of prevention studies. Traditional cohort approaches to measure HIV incidence require repeatedly testing large cohorts of HIV-uninfected individuals with an HIV diagnostic test (eg, enzyme-linked immunosorbent assay) for long periods of time to identify new infections, which can be prohibitively costly, time-consuming, and subject to loss to follow-up. Cross-sectional approaches based on the usual HIV diagnostic test and biomarkers of recent infection offer important advantages over standard cohort approaches, in terms of time, cost, and attrition. Cross-sectional samples usually consist of individuals from different communities. However, small sample sizes limit the ability to estimate community-specific incidence and existing methods typically ignore heterogeneity in incidence across communities. We propose a permutation test for the null hypothesis of no heterogeneity in incidence rates across communities, develop a random-effects model to account for this heterogeneity and to estimate community-specific incidence, and provide one way to estimate the coefficient of variation. We evaluate the performance of the proposed methods through simulation studies and apply them to the data from the National Institute of Mental Health Project ACCEPT, a phase 3 randomized controlled HIV prevention trial in Sub-Saharan Africa, to estimate the overall and community-specific HIV incidence rates.