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

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.

A model-based approach to estimating the prevalence of disease combinations in South Africa

“…Many studies of multimorbidity are based on medical records [2][3][4] and miss most of the burden of undiagnosed and untreated disease. Many studies are conducted in specific locations, [5][6][7] which are not nationally representative, or are conducted only in older adults. [7][8][9][10] National household surveys typically rely on self-reported measures of past diagnosis or symptoms 8 9 11 and are often underpowered to estimate the prevalence of less frequent condition combinations.…”

Environmental Change, Changing Biodiversity, and Infections–Lessons for Kidney Health Community

“…A recent study has shown a high prevalence of multimorbidity in a chronic kidney diseases of unknown origin endemic area. 142 …”

Burden, determinants, consequences and care of multimorbidity in rural and urbanising Telangana, India: protocol for a mixed-methods study within the APCAPS cohort

“…Current evidence on the burden of multimorbidity in rural and urbanising India is limited. Incidence data are lacking, while prevalence estimates have fluctuated widely (ranging from 9% to 59%) owing to a frequent reliance on self-reported disease, which is known to grossly underestimate true disease prevalence in these settings 6–9. There is some evidence that multimorbidity clusters in India mirror those reported in high-income countries, specifically, clustering of cardiorespiratory (angina, asthma, chronic obstructive pulmonary disease (COPD)), metabolic (diabetes, obesity, hypertension) and mental articular (arthritis and depression) conditions 10–14.…”

“…There is some evidence that multimorbidity clusters in India mirror those reported in high-income countries, specifically, clustering of cardiorespiratory (angina, asthma, chronic obstructive pulmonary disease (COPD)), metabolic (diabetes, obesity, hypertension) and mental articular (arthritis and depression) conditions 10–14. However, multimorbidity clusters specific to rural and urbanising India may have not been identified as several locally prevalent conditions (eg, anaemia) were not measured in prior studies 9–11 13 14…”

“… 10–14 However, multimorbidity clusters specific to rural and urbanising India may have not been identified as several locally prevalent conditions (eg, anaemia) were not measured in prior studies. 9–11 13 14 …”