For the present study, the source of the data utilized was the NFHS-5, conducted between 2019 and 2021. The Ministry of Health and Family Welfare (MoHFW), Government of India, was responsible for both funding and conducting each NFHS with support from the International Institute for Population Sciences (IIPS), Mumbai, and ICF, United States [25]. The ICF contributed technical assistance through the Demographic and Health Survey (DHS) program.

The NFHS-5 followed a stratified two-stage sampling method. All 707 districts recognized in India at the time were considered for sampling with each district being stratified into urban and rural areas. For the first stage of sampling, urban and rural samples were obtained. To obtain rural samples, villages in rural areas were selected as primary sampling units (PSUs) utilizing a probability proportional to size (PPS) method and similarly for urban areas, Census Enumeration Blocks (CEBs) were selected as PSUs [25]. Each PSU was treated as a rural or urban cluster and from these clusters, the second stage of sampling was conducted in which 22 random households were selected from each cluster using an equal probability systematic selection [25]. Collected data pertained to 101,839 men aged 15–54 and 724,115 women aged 15–49 across 636,399 households [24]. These data included demographics, socioeconomic characteristics, health-related data, and various anthropometric/biomarker data (height, weight, blood pressure, blood glucose, etc.). To obtain data relevant to the men sampled, the NFHS-5 “Male Recode” and “household recode” datasets were merged [26]. All 101,839 men sampled were eligible to be included in our analyses (with exclusions arising from subsequent missing data/modeling constraints).

STATA 17, edition BE (Basic), was used to conduct all relevant tasks such as merging datasets, defining/creating variables, and conducting analyses. For univariate analysis, first descriptive statistics were explored. For the continuous/discrete variables (age and media exposure), mean, standard deviation, median, and interquartile range (IQR) values were determined. For all other variables (categorical/binary), frequency distribution of observations was reported. To test if differences in univariate findings across urban and rural areas were statistically significant, chi-square tests ( χ 2 ) and Mann-Whitney tests were conducted respectively for categorical/binary and continuous/discrete variables. To avoid type 1 error inflation for these multiple comparisons, the Bonferroni correction was applied.

For bivariate analysis, an unadjusted binary logistic regression was run for each independent variable with multimorbidity to determine unadjusted odds ratios (OR). For multivariable analysis, multi-level mixed-effect binary logistic regression models were employed. Two separate multivariable models were conducted to determine adjusted associations between predictors of interest and multimorbidity separately for urban area and rural area respondents. Both models took into consideration all sociodemographic and lifestyle factors previously discussed. Furthermore, both models accounted for districts as the cluster variable (random intercept) to adjust for any possible clustering effect. To test the goodness-of-fit of each multivariable model, likelihood ratio tests (LRT) were conducted against a regular non-multilevel binary logistic regression [35]. All analyses were conducted using unweighted data due to the unavailability of NFHS-5 sampling weights.

Within this study, various population characteristics and their associations to multimorbidity amongst men aged 15–54 was explored. Prevalence of multimorbidity was found to be greater in urban areas as compared to rural. Additionally urban and rural areas were found to have statistically significant differences in regard to sociodemographic and lifestyle characteristics. However, as previously mentioned, sampling weights were unavailable for the NFHS-5. As such, data could not be weighted prior to analysis making all univariate findings unweighted. With existing literature commonly presenting national prevalence estimates based on weighted data, our univariate findings are incomparable.

With various discrepancies being found across urban and rural areas regarding predictors, it becomes of interest how the causal relationship between predictors and multimorbidity is affected by area. It is possible that area specific characteristics act as effect modifiers and alter the found effect of predictors across the strata of urban and rural areas. As such, the urban/rural perspective becomes ever important for public health interest. When further interpreting the significant findings of this study and difference across urban and rural areas, we interpret the findings as follows.

Consistent with literature, age was found to have a positive association to multimorbidity [8, 13, 21, 29–32]. As individuals age, it is thought that susceptibility to the accumulation of chronic health conditions is accelerated due to the progressive dysregulation of organ systems [36]. Similarly, positive association findings regarding the wealth-multimorbidity association were also consistent with the literature. However, it is of interest that in rural areas, all quintiles were statistically significant, but in urban areas, this was true only for the two richest (fourth and fifth) quintiles. It is possible that due to urban areas having improved health systems, the progression of NCDs is better controlled, and that it is not until much greater levels of wealth are considered that negative implications of wealth become unmanageable [37]. Previous studies have reported, for the occupation-multimorbidity association, that physically intensive jobs reduce odds of multimorbidity [12]. This study expands on such findings as specific physically intensive occupations were found to have protective effects in both urban and rural areas. Regions of residence had no clear trend in findings, but variation in odds across regions can likely be associated to highly diverse characteristics such as nutrition, rate of urbanization, environment, healthcare, etc. Furthermore, increasing BMI, increasing media exposure, and consumption of alcohol each resulted in increased odds of multimorbidity, thus having findings consistent with the literature [8, 21, 29–32]. However, the latter two predictors were solely significant in rural areas. Surprising was the finding that tobacco consumption had a protective effect in rural areas. Such findings are not expected as tobacco has commonly been accepted as a risk factor for many health conditions. However, social desirability bias must be considered due to the self-reported nature of this variable. If respondents mispresented their risky lifestyle behaviors, such erroneous findings may be explained. Another predictor found to have an unclear association with multimorbidity was education. However, education has frequently been reported to have a varying association with multimorbidity based on operational definition [38].

The findings of this study have implications for not only men across India but also relevant policymakers who act to protect the health of the population. Currently, across India, health policies such as preventative plans have been quite basic. One such example has been the use of the World Health Organization (WHO) “Action Plan of Global Strategy for the Prevention and Control of Noncommunicable Diseases” to control the progression of NCDs, and therefore subsequently multimorbidity. Within this action plan, the WHO highlights basic guidelines that may assist in improving population health [39, 40]. India, being a WHO member state, has committed to incorporating the objectives and goals of this plan into various national public health programs [39]. Some basic examples from this plan are the focus on reducing alcohol consumption, improving diets, and increased physical activity [40]. However, since this action plan was made for many nations ranging from high-income to LMICs, it fails to acknowledge that common associations between risk factors and health outcomes may not be consistent across all nations. In LMICs such as India, the drastic differences across urban and rural areas may reduce the efficacy of national health programs, with more areas specific programs being required. One such example from this study is that while certain factors such as BMI may be a statistically significant predictor of multimorbidity across India, alcohol is only significant within rural areas. Policymakers can benefit from such findings as they can be better informed on risk factors in each area allowing for more efficient and effective resource allocation. By focusing efforts such as screening and health promotion amongst those who possess high-risk characteristics in each area, there may be earlier diagnosis of multimorbidity, possibly resulting in reduced complications and associated burdens.

Furthermore, the implications on the health systems of India must be considered. Currently, there exists a mixed framework for healthcare in which there are both public and private (for-profit and not-for-private) providers [41]. Funding many of the public facilities is publicly funded health insurance (PFHI) [42]. However, compared to other nations, India has minimal allocation to their healthcare when considered as a percentage of their GDP [43]. As such, public services have often been associated with reduced quality and inadequacies in resources [41]. Many individuals have opted for private care which is usually an out-of-pocket expense (OOPE) [41]. However, with rural areas being associated with lesser wealth (when compared to urban), this creates further disparity in access to healthcare. As a result of these OOPE inequities, universal health coverage (UHC) has become a goal for India with programs such as the “Ayushman Bharat” program and “Pradhan Mantri Jan Arogya Yojana” being established [42]. These programs aim to create more health wellness centers and provide additional coverage to those families that may need it [42]. The findings of this study can be used to inform relevant parties regarding the extensive list of predictors of multimorbidity across both areas. With a greater number of sociodemographic and lifestyle factors being found to be associated to multimorbidity in rural areas (as compared to urban), this may incentivize governments to allocate further funding to rural areas where OOPEs may not be feasible for most residents. This would allow policymakers to create programs that better support those who otherwise may receive little to no support. Additionally, as we determined which population characteristics have increased odds of multimorbidity across both areas, new UHC programs such as those previously discussed can use such information to better design screening and intervention protocols.

This study is not without its limitations, specifically with the data and methodology. Firstly, due to the cross-sectional nature of the NFHS data analyzed, causality between the dependent (multimorbidity) and independent variables cannot be established. Furthermore, since the majority of the data collected in the NFHS was self-reported, it is possible that respondents may introduce response bias if they answer questions inaccurately, regardless of whether or not it is intentional. One specific example may be social desirability bias, in which respondents may provide inaccurate information regarding sensitive topics such as tobacco consumption, alcohol consumption, or caste.

Due to the previously mentioned lack of availability of sampling weights, all analyses in this study were unweighted. Therefore, the univariate findings of this study could not be compared to the literature. Lastly, the limitation in the multimorbidity outcome variable must be acknowledged. To define multimorbidity, various data elements were utilized from the NFHS-5. However, under such a definition, any case of multimorbidity found is presumed multimorbidity. Found cases are not clinically confirmed cases that are firm in diagnosis but rather diagnoses based on the information we had available.

Being one of the first studies to focus on diabetes and hypertension multimorbidity in India, this study provides an in-depth understanding of one of the most prevalent specific multimorbidities. This study identified high-risk characteristics in both urban and rural areas through its split analysis and contributes a better understanding of predictors to the literature. Furthermore, this was done using the latest data available, making findings reflective of the current male population of India. This study sets a precedent for further focused research into specific multimorbidities. Findings can be used by relevant policymakers and health systems across India so they can better support those affected by multimorbidity. As India continues to face public health issues, such studies may be crucial in controlling the associated burdens.