Multi-Morbidity, Poverty, and Community Context: An Analysis of Factors Related to Medical Complexity At Midlife
Methods: We analyzed data from ten years of the National Longitudinal Survey of Youth. Upon turning 40, female respondents (n=4,296) reported chronic health conditions ever experienced. A latent class analysis classified respondents based on these health conditions. We determined number of latent classes using Bayseian Information Criterion (BIC) and model interpretability. Respondent class membership became the dependent variable in a multinomial regression. Model predictors were indicators of adverse economic and social conditions. These predictors included measures of the number times in the ten previous annual waves of data collection that respondents lived in poverty or reported adverse community conditions. Adverse community conditions were measured with a series of questions about how frequently respondents felt crime, abandoned buildings, unemployment, police protection, public transit, poor parental supervision, and disrespect for laws were problems in the community. Additional covariates included race/ethnicity, education, weight, and substance use.
Results: BIC statistics identified 4-classes. A low medical condition class (LMC) contained 62% of respondents. LMC members reported few MCs (M=0.79, SD=0.021). Two moderate medical condition classes, MMC1 and MMC2, contained 29% and 4% of respondents, respectively. These individuals reported between 3 and 5 MCs. Compared to MMC1, MMC2 members were more likely to report ulcers and stomach problems. A high medical condition class (HMC) included the 4% of respondents who reported a high number of MCs (M=10.27, SD=0.25). HMC members reported more severe headaches, musculoskeletal pain, respiratory problems, and cancer than other classes. After covariate adjustment, the multinomial regression revealed economic and social conditions as significant predictors of class membership. Each additional year spent below poverty was associated with 1.75 higher log odds of membership in HMC versus LMC. Each additional year respondents reported inadequate public transit and parental supervision of children as serious problems was associated with 1.51 and 1.60 higher log odds of membership in HMC. Problems with public transit were associated with 1.01 and 0.96 higher log odds of membership in MMC1 and MMC2 versus LMC, respectively.
Implications: These findings support a link between poverty and adverse social conditions and the accrual of multiple medical conditions. Understanding the nature of these relationships is relevant for micro and macro-practice. Greater attunement to the link between health issues and economic and social adversity becomes critical to assessment and service coordination. Further, macro-practitioners could sharpen community level needs assessment and target macro-level interventions to achieve broader community health benefits.