The results of this study demonstrated that more than one-third of the general population in Serbia was classified as having multimorbidity. The study also identified six latent classes of multimorbidity through LCA: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory and Musculoskeletal. Furthermore, the presence of various air pollutants, along with chemical and microbial water contamination indicators, provides compelling evidence of their substantial influence on the odds of having multimorbidity.
The appropriate method of identifying and analyzing clusters of diseases represents an on-going debate among the scientific community. This has led to an incomplete and fragmented insight into the nature of multimorbidity and its impact on individuals, communities, and health care services [10]. Additionally, issues arise when comparing the methodologies used in different research, where some rely on clinical records and others on self-report; some mention only a small number of conditions, while others provide extensive lists of diseases. Several multi-country systematic reviews were conducted in order to gather information on the prevailing clusters of conditions, where a significant amount of research has focused on clusters consisting of just two conditions. These studies have identified depression, cardiometabolic, and musculoskeletal disorders as the most prevalent components of multimorbidity clusters worldwide. However, it is important to acknowledge that the available evidence suggests that the occurrence of certain combinations of conditions is probably greatly influenced by the specific environment and population in which the research is conducted [6, 37, 38]. However, very few of the published studies used different approaches to compare methods and identify differences in results, consequently increasing the reliability of their findings [10, 39]. Systematic reviews found that hierarchical clustering and factor analysis were the most common methods. LCA, an approach that classifies individuals using a probabilistic model based on the observed values of all included variables, on the other hand, was used only in a few studies [6,7,8, 40]. Olaya et al. conducted a study to describe the patterns of multimorbidity in a representative sample of Spanish adults using the LCA method [41]. Based on the presence or absence of 11 chronic conditions, this study found three clinically and statistically distinct latent classes of multimorbidity: the “cardiorespiratory/mental/arthritis” class, the “healthy” class, and the “metabolic/stroke” class. In a cross-sectional study on a sample of 4,574 senior Australians, Islam et al. [10] identified four clusters of diseases: “healthy” individuals, “asthma/bronchitis/arthritis/osteoporosis/depression and anxiety”, “high blood pressure (HBP)/diabetes”, and “cancer”, with stroke and heart disease either creating a separate group or “attaching” to other groups in different analyses. Moreover, the Australian study sample revealed that HBP and arthritis were the two predominant chronic diseases in both multimorbid triplets and comorbid pairs. Park et al. [42] found three groups of multimorbidity patterns in the general South Korean population. The first group was mostly healthy, the second group had heart and blood vessel diseases like dyslipidemia, hypertension, diabetes mellitus, and stroke, and the third group had asthma, arthritis, allergic rhinitis, thyroid disease, and depression. Whitson et al.’s study [43], which included Americans aged 65 + years, identified six latent classes, similar to our study results. Classes were determined based on 13 conditions, and participants were assigned to classes based on the highest calculated probability of membership to: “minimal disease”, “non-vascular”, “vascular”, “cardio-stroke-cancer”, “major neurologic disease”, and “very sick” class. Multimorbidity clusters in our study were based on 17 chronic conditions which were classified in following clusters: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory and Musculoskeletal.
Evidence indicates that a low level of education and living in a deprived area are associated with an increased probability of multimorbidity [44]. The systematic review and meta-analysis of 24 studies indicated that a low education level, compared to a high education level, was linked to a 64% higher likelihood of multimorbidity, with considerable heterogeneity among studies, partially attributable to the methods used for multimorbidity assessment. Rising deprivation was consistently linked to a increasing risk of multimorbidity, while the evidence regarding income was inconclusive [44]. In our study, secondary education mitigated most of the odds, indicating that early educational interventions might be particularly effective in reducing the likelihood of developing multiple chronic conditions. The income analysis reveals a pattern illustrating a consistent step-wise reduction in the odds of multimorbidity with each higher income quintile. Increases in income further lower multimorbidity odds for each level of attained education in our study.
Environmental pollutants, such as those affecting air and water quality, increase the risk of chronic conditions [45,46,47], and may contribute to the accumulation of chronic diseases, leading to multimorbidity. A new cross-sectional study looking at links between air pollution and multimorbidity in the UK Biobank found that long-term exposure to NO2 and PM2.5 pollutants was linked to higher rates of multimorbidity, the severity of multimorbid conditions, and the most of multimorbidity clusters [48]. Results on over 360,000 adults from the UK Biobank revealed that exposure to higher levels of NO2 and PM2.5 was associated with nine and ten patterns of multimorbidity, respectively, out of 11 identified patterns. The strongest associations were observed for respiratory, cardiovascular, and neurological multimorbidity and both NO2 and PM2.5 pollutants. Ronaldson et al. [48] used fully adjusted logistic regressions to assess the relationship between air pollution and multimorbidity patterns, finding a link between higher exposures to PM10 pollutants and multimorbidity pattern consisting of painful conditions. These results indicate that the chance of having a multimorbid condition increases with higher exposure to air pollution, therefore implying that air pollution could affect multiple body systems. In our study, higher levels of PM10 were significantly associated with higher multimorbidity odds in a representative sample of the Serbian population (OR: 1.02 (95% CI [1.02–1.03]). In particular, the increase of PM10 concentrations significantly increased the odds of having Multicondition and Musculoskeletal clusters (OR: 1.02 and 1.02, respectively).
The results of a recently conducted study by Luo et al. [49] showed a relationship between cardiometabolic multimorbidity and air pollution. This large prospective cohort study, which included 410,494 middle- and old-age participants from the UK Biobank, showed that exposure to air pollution was associated with a higher risk of almost all phases of cardiometabolic multimorbidity progression, which included the development of first cardiometabolic disease, transition from first cardiometabolic disease to cardiometabolic multimorbidity, and death from baseline and first cardiometabolic disease [49]. Our study results demonstrated that higher concentrations of PM10 particles and O3 increased the odds of having Metabolic syndrome cluster of multimorbidity (OR: 1.01 and 1.02, respectively).
Recently, Su et al. [50] used a nationally representative sample of all Chinese people over 60 years old to study how long-term exposure to PM2.5 and O3 affects cardiovascular and metabolic diseases. According to Su et al. [50], being exposed to PM2.5 steadily raised the risk of cardiometabolic diseases and various cardiometabolic multimorbidity clusters. They also found that the O3 air pollutant became a major risk factor for cardiometabolic multimorbidity after a certain dose. Additionally, the study found a significant positive correlation between the prevalence of diabetes, hypertension, and cardio-cerebrovascular diseases, as well as the prevalence of different subtypes of cardiometabolic multimorbidity, and PM2.5 concentrations. It was found that for every 10 units increase in PM2.5 levels, there was a 2.2% higher risk of diabetes and high blood pressure, a 5.4% higher risk of cardiovascular diseases and high blood pressure, a 5.6% higher risk of cardiovascular diseases and diabetes, and a 7.6% higher risk of diabetes, hypertension, and cerebrovascular diseases. Although there wasn’t a strong overall link between long-term exposure to the O3 pollutant and different groups of cardiometabolic multimorbidity. Su et al. [50] found that the risk of cardiovascular diseases, diabetes, and all types of cardiometabolic multimorbidity was significantly higher in the group that was exposed to high levels of O3 (88–99.5 µg/m3). In addition to Su et al.’s [50] findings, our study revealed a statistically significant relationship between O3 air pollutants and multimorbidity, with higher O3 concentrations significantly associated with higher multimorbidity odds in a representative sample of the Serbian population (OR: 1.03 (95% CI [1.02–1.03]). The studies conducted by Luo et al. [49] and Su et al. [50] did not consider the effects of other pollutants, such as carbon monoxide and sulfur dioxide, despite long follow-up periods and large sample sizes that allowed the establishment of a causal association between multimorbidity and air pollution exposure. Our study measured the concentrations of both carbon monoxide and sulfur dioxide, and confirmed the relationship between sulfur dioxide and multimorbidity (OR: 1.01 (95% CI [1.00–1.02]). In particular, exposure to the increased levels of SO2 significantly rises the odds of having Multicondition (OR: 1.02), Musculoskeletal (OR: 1.01) and Respiratory cluster (OR: 1.02).
A special emphasis should be placed on the results of our study providing evidence of association between ground level ozone and multimorbidity. Ozone pollution is a global health hazard with increasing concentrations and a growing disease burden. The disease burden is expected to continue due to the rising concentrations and improved understanding of ozone effects beyond the lung [51]. However, ascertaining chronic ozone exposure remains inconclusive, and co-exposure to other air pollutants like PM2.5 and PM10 makes it challenging to examine chronic effects. Future studies should incorporate advanced technologies to monitor and compute ozone exposures [51]. Control policies are needed, especially in developing countries where PM2.5 and PM10 reductions are primarily focused. In our study, a clear relationship between O3 exposure and multimorbidity was found (OR: 1.03 (95% CI [1.02–1.03]), particularly with Multicondition (OR:1.08), Musculoskeletal (OR:1.08) and Metabolic syndrome cluster (OR:1.02). A significant negative correlation that was found between O3 and NO2 concentrations (p < 0.001) is consistent with previous literature findings [52].
In addition to clean air, access to safe drinking water is crucial for health and further society development, as inadequate water and sanitation services expose individuals to health risks. Poor water management in urban, industrial, and agricultural areas leads to contaminated drinking water, affecting millions of people. Poor drinking water quality is the leading cause of morbidity and mortality in developing countries, emphasizing the importance of clean drinking water [22]. Based on the survey conducted by the World Health Organization (WHO), 80% of global diseases and 50% of child mortality worldwide can be attributed to inadequate drinking water quality. Additionally, there are over 50 diseases that are directly caused by poor drinking water quality. Unfortunately, despite the fact that much of the literature focuses on water pollution and a specific disease, there is a lack of research findings that systematically examine the impact of water pollution on human health and disease heterogeneity. Our study findings that a pattern of increased risk correlates with escalating contamination of water further support these findings. Exposure to physico-chemical contamination, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability of having multimorbidity. Especially, physico-chemical contamination increased the odds of having Multicondition (OR: 1.81), Musculoskeletal (OR: 1.39) and Cardiovascular cluster (OR: 1.99), while exposure to the microbiological contamination increased the odds of having Multicondition (OR: 2.05), Musculoskeletal (OR: 1.57), Cardiovascular (OR: 1.78), Respiratory (OR: 1.54) and Metabolic syndrome cluster (OR: 1.23). Combined water contamination rose the odds of having Multicondition (OR: 1.84) and Cardiovascular (OR: 1.99) clusters.
Recommendations
To promote equity in health and healthcare, it is vital to ensure that both healthcare providers and the public are aware of the prevalence and interconnections of multimorbidity, including the impacts of various health conditions, demographic factors, and environmental influences. Effective strategies may vary by geographical region, as solutions that are successful in one area may not be suitable for another due to differing regional priorities and needs [53]. Therefore, countries should leverage localized data to pinpoint specific focus areas at national or regional levels. This can be accomplished by gathering input from patients and professionals, analyzing local health statistics, and reviewing relevant literature in relation to local conditions.
To improve healthcare management for individuals with multiple conditions, several key actions should be considered [53]: implementing policy changes, adopting a systematic approach, identifying individuals who need additional support, prioritizing care coordination and self-management support, and simplifying treatment regimes. In Serbia, for instance, enhancing primary care within the framework of national healthcare coverage is crucial. Primary care providers should be trained as “expert generalists” and adopt a patient-centered approach tailored to those with multiple conditions. This involves integrating postgraduate training that covers multimorbidity concepts into both undergraduate medical education and ongoing healthcare training programs.
A systematic approach should improve communication and coordination across different healthcare levels and sectors, including primary, secondary, and tertiary care, as well as integrating health and social care. Guidelines should focus on managing multiple rather than single conditions. Care coordination should employ integrated electronic medical systems to identify individuals requiring additional support, while also promoting self-management strategies to empower patients in taking charge of their health. Simplifying treatment regimes and ensuring patient understanding of their treatments will enhance management of chronic diseases. Accepting multimorbidity as a typical rather than exceptional condition will facilitate the development of effective healthcare delivery for patients with multiple chronic conditions.
Strengths and limitations
The strength of our study lies in its ecological design utilizing a large nationally representative sample employing a wide age range of study participants (15 years and older). Ecological studies are commonly used to measure prevalence of diseases; they are easy to conduct using a routinely collected data, and may generate hypothesis on the relationship between environmental factors and health outcomes. In addition, the inclusion of additional chronic conditions such as lower spine deformity and other chronic back problems (i.e., back pain), as well as cervical deformity and other chronic problems with the cervical spine, enabled us to identify patterns of multimorbidity beyond those previously published. Obtained model for multimorbidity probabilities is accurate and suitable for interpretation. The model’s ability to correctly assign risk probabilities was assessed using the CORP approach suggested by Dimitriadis et al. [36]. Model-assigned values are then compared to empirically observed rates at automatically calculated optimal intervals to ensure the developed model is reliably assigned multimorbidity risk probabilities. Consequently, both overestimation and underestimation were assessed. However, when interpreting our findings, it is important to take into account some limitations. The limitations of ecological study design include presence of a bias and the lack of adjustment for major confounders such as smoking habits and the inability to infer causality. In addition, the observed weak correlations between the cardiovascular and respiratory clusters and most air pollutants may be attributed to the limited number of individuals who have only one condition within these clusters, and a substantial number of participants with cardiovascular and respiratory diseases classified within the Multicondition and Metabolic syndrome clusters. Furthermore, the used EHIS survey protocol incorporates a restricted selection of chronic conditions, chosen for their significant prevalence and consequential influence on health outcomes. Therefore, our study did not assess certain diseases (e.g., infectious diseases, thyroid disease, cataracts), and the inclusion of supplementary chronic conditions could potentially lead to distinct patterns. Lastly, the identification of chronic diseases in our study relied on self-reported data, which introduces the possibility of inaccuracies. However, previous large-scale population-based studies have extensively employed self-reported measures of chronic diseases, demonstrating a satisfactory level of accuracy, as evidenced by existing literature [54, 55].
Future directions
The complex interaction between specific pollutants and multimorbidity requires large pools of heterogeneous data. Real-time data that combines air and water pollution-related figures and health records (admissions, exacerbations) can aid in identifying the public health impact of air pollution on a national, municipal, or local level. More importantly, real-time data would be critical for designing effective interventions that are specific to the population affected by the specific pollutants. In the absence of clear real-time evidence, comprehensive studies exploring the long-term effect of environmental pollutants on disease accumulation would be essential to reducing the burden of multimorbidity.
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