Insights into relationship of environmental inequalities and multimorbidity: a population-based study

Abstract

Background: Substantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators.

Methods: This ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency.

Results: The overall prevalence of multimorbidity was 33.4% [32.6%-34.2%]. Six latent classes of multimorbidity were identified: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory, and Musculoskeletal. Annual increases in PM10 and SO2 concentrations, as well as daily increases in O3 concentrations, significantly raised the odds of having multimorbidity (OR = 1.02, 95% CI 1.02-1.03; OR = 1.01, 95% CI 1.00-1.02 and OR = 1.03, 95% CI 1.02-1.03, respectively). A pattern of increased risk was observed with rising levels of water contamination. Exposure to physico-chemical, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability, respectively, of having multiple chronic conditions. There was strong evidence that air pollutants, as well as chemical and microbial water contamination, were significantly associated with higher odds of the most common clusters of multimorbidity identified by LCA.

Conclusion: There is compelling evidence of an association between multimorbidity and environmental pollution, suggesting that exposure to air pollutants and water contaminants may contribute to disease accumulation and help explain geographically and socioeconomically patterned inequalities. These findings underscore the need for extensive studies that simultaneously measure both multimorbidity and pollution to explore their complex interrelationships.

Keywords: Air pollution; Disease clusters; Inequalities; Latent class analysis; Multimorbidity; Water pollution.

Fig. 1

The distribution of the relative multimorbidity burden among the sampled statistical regions of the Republic of Serbia. Shaded regions represent differences from national average given in percent

Fig. 2

The distribution of relative disease prevalence among the sampled statistical regions of the Republic of Serbia. Shaded regions represent differences from national average given in percent

Fig. 3

Heatmap of latent class models. A clustered heatmap displays the posterior probabilities from the latent class model. Columns represent individual disease observations, and rows correspond to the latent classes. The heatmap colors reflect the probability that a subject belongs to a given latent class, with warmer colors indicating higher probabilities. Appended dendrograms to the left and top of the heatmap illustrate the hierarchical clustering of categories based on their class membership probabilities. The dendrogram branches indicate the relative similarity between subjects, with shorter branch lengths representing closer groupings

Fig. 4

Association between ambient air pollutants (ozone, sulfur dioxide, nitrogen dioxide and PM10) and increasing probability of multimorbidity. All marginal estimates are covariate-adjusted (age, sex, income, education)

Fig. 5

Average adjusted posterior distributions of multimorbidity probability by chemical and microbial contamination. Water contamination (chemical and microbial) increases the expected probability of multimorbidity. Shaded areas represent conventional credible intervals. All marginal estimates are covariate-adjusted (age, sex, income, education)