Search

Using a prospective, multicenter cohort (the Hispanic Community Health Study/Study of Latinos), the authors describe all-cause mortality across different U.S. Hispanic/Latino groups and how it is affected when sociodemographic, acculturation-related, lifestyle, and clinical factors are accounted for. Visual Abstract. Cumulative All-Cause Mortality in Diverse Hispanic/Latino Adults: Using a prospective, multicenter cohort (the Hispanic Community Health Study/Study of Latinos), the authors describe all-cause mortality across different U.S. Hispanic/Latino groups and how it is affected when sociodemographic, acculturation-related, lifestyle, and clinical factors are accounted for. Background: All-cause mortality among diverse Hispanic/Latino groups in the United States and factors underlying mortality differences have not been examined prospectively. Objective: To describe cumulative all-cause mortality (and factors underlying differences) by Hispanic/Latino background, before and during the COVID-19 pandemic. Design: Prospective, multicenter cohort study. Setting: Hispanic Community Health Study/Study of Latinos. Participants: 15 568 adults aged 18 to 74 years at baseline (2008 to 2011) of Central American, Cuban, Dominican, Mexican, Puerto Rican, South American, and other backgrounds from the Bronx, New York; Chicago, Illinois; Miami, Florida; and San Diego, California. Measurements: Sociodemographic, acculturation-related, lifestyle, and clinical factors were assessed at baseline, and vital status was ascertained through December 2021 (969 deaths; 173 444 person-years of follow-up). Marginally adjusted cumulative all-cause mortality risks (11-year before the pandemic and 2-year during the pandemic) were examined using progressively adjusted Cox regression. Results: Before the pandemic, 11-year cumulative mortality risks adjusted for age and sex were higher in the Puerto Rican and Cuban groups (6.3% [95% CI, 5.2% to 7.6%] and 5.7% [CI, 5.0% to 6.6%], respectively) and lowest in the South American group (2.4% [CI, 1.7% to 3.5%]). Differences were attenuated with adjustment for lifestyle and clinical factors. During the pandemic, 2-year cumulative mortality risks adjusted for age and sex ranged from 1.1% (CI, 0.6% to 2.0%; South American) to 2.0% (CI, 1.4% to 3.0%; Central American); CIs overlapped across groups. With adjustment for lifestyle factors, 2-year cumulative mortality risks were highest in persons of Central American and Mexican backgrounds and lowest among those of Puerto Rican and Cuban backgrounds. Limitation: Lack of data on race and baseline citizenship status; correlation between Hispanic/Latino background and site. Conclusion: Differences in prepandemic mortality risks across Hispanic/Latino groups were explained by lifestyle and clinical factors. Mortality patterns changed during the pandemic, with higher risks in persons of Central American and Mexican backgrounds than in those of Puerto Rican and Cuban backgrounds. Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

Design‐based analysis, which accounts for the design features of the study, is commonly used to conduct data analysis in studies with complex survey sampling, such as the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). In this type of longitudinal study, attrition has often been a problem. Although there have been various statistical approaches proposed to handle attrition, such as inverse probability weighting (IPW), non‐response cell weighting (NRCW), multiple imputation (MI), and full information maximum likelihood (FIML) approach, there has not been a systematic assessment of these methods to compare their performance in design‐based analyses. In this article, we perform extensive simulation studies and compare the performance of different missing data methods in linear and generalized linear population models, and under different missing data mechanism. We find that the design‐based analysis is able to produce valid estimation and statistical inference when the missing data are handled appropriately using IPW, NRCW, MI, or FIML approach under missing‐completely‐at‐random or missing‐at‐random missing mechanism and when the missingness model is correctly specified or over‐specified. We also illustrate the use of these methods using data from HCHS/SOL. [ABSTRACT FROM AUTHOR]