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. 2023 Jan 2:13:26335565221149623.
doi: 10.1177/26335565221149623. eCollection 2023 Jan-Dec.

Patterns of multimorbidity among a community-based cohort in rural India

Balaji Gummidi  1 , Vaishali Gautam  1 , Oommen John  1   2 , Arpita Ghosh  1   2 , Vivekanand Jha  1   2   3   4
Affiliations

Patterns of multimorbidity among a community-based cohort in rural India

Balaji Gummidi et al. J Multimorb Comorb. .

Abstract

Background: Multimorbidity estimates are expected to increase in India primarily due to the population aging. However, there is a lack of research estimating the burden of multimorbidity in the Indian context using a validated tool. We estimated the prevalence and determinants of multimorbidity amongst the adult population of the rural Uddanam region, Andhra Pradesh.

Methods: This community-based cross-sectional study was conducted as a part of an ongoing research program. Multistage cluster sampling technique was used to select 2419 adult participants from 40 clusters. Multimorbidity was assessed using Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) tool, collecting information on 13 chronic diseases. Patient Health Questionnaire (PHQ-12) was used to screen for depression. Multiple logistic regression was conducted to identify the strongest determinants of multimorbidity.

Results: Of the 2419 participants, 2289 completed the MAQ-PC tool. Mean age (standard deviation) of participants was 48.1 (13.1) years. The overall prevalence of multimorbidity was 58.5% (95% CI 56.5-60.6); with 30.7%, 15.6%, and 12.2% reporting two, three, and four chronic conditions, respectively. Acid peptic disease-musculoskeletal disease (44%) and acid peptic disease-musculoskeletal disease-hypertension (14.9%) were the most common dyad and triad. Among metabolic diseases, diabetes-hypertension (28.3%) and diabetes-hypertension-chronic kidney disease (7.6%) were the most common dyad and triad, respectively. Advancing age, female gender, and being obese were the strongest determinates of the presence of multimorbidity. Depression was highly prevalent among the study population, and participants with higher PHQ-12 score had 3.7 (2.5-5.4) greater odds of having multimorbidity.

Conclusions: Our findings suggest that six of 10 adults in rural India are affected with multimorbidity. We report a higher prevalence of multimorbidity as compared with other studies conducted in India. We also identified vulnerable groups which would guide policy makers in developing holistic care packages for individuals with multimorbidity.

Keywords: India; Multimorbidity; chronic kidney disease; multimorbidity assessment questionnaire for primary care tool; rural.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Proportion of participants with and without multimorbidity conditions among study population (n = 2289).
Figure 2.
Figure 2.
(a): Tree map depicting burden of morbidity combinations among common dyads. (b): Tree map depicting burden of morbidity combinations among common triads.
Figure 3.
Figure 3.
Age wise distribution of the morbidities among study cohort.
See this image and copyright information in PMC

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