Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach

Abstract

ObjectiveIn the absence of adequate nationally-representative empirical evidence on multimorbidity, the existing healthcare delivery system is not adequately oriented to cater to the growing needs of the older adult population. Therefore, the present study identifies frequently occurring multimorbidity patterns among older adults in India. Further, the study examines the linkages between the identified patterns and socioeconomic, demographic, lifestyle and anthropometric correlates.DesignThe present findings rest on a large nationally-representative sample from a cross-sectional study.Setting and participantsThe study used data on 58 975 older adults (45 years and older) from the Longitudinal Ageing Study in India, 2017–2018.Primary and secondary outcome measuresThe study incorporated a list of 16 non-communicable diseases to identify commonly occurring patterns using latent class analysis. The study employed multinomial logistic regression models to assess the association between identified disease patterns with unit-level socioeconomic, demographic, lifestyle and anthropometric characteristics.ResultsThe present study demonstrates that older adults in the country can be segmented into six patterns: ‘relatively healthy’, ‘hypertension’, ‘gastrointestinal disorders–hypertension–musculoskeletal disorders’, ‘musculoskeletal disorders–hypertension–asthma’, ‘metabolic disorders’ and ‘complex cardiometabolic disorders’. Additionally, socioeconomic, demographic, lifestyle and anthropometric factors are significantly associated with one or more identified disease patterns.ConclusionsThe identified classes ‘hypertension’, ‘metabolic disorders’ and ‘complex cardiometabolic disorders’ reflect three stages of cardiometabolic morbidity with hypertension as the first and ‘complex cardiometabolic disorders’ as the last stage of disease progression. This underscores the need for effective prevention strategies for high-risk hypertension group. Also, targeted interventions are essential to reduce the burden on the high-risk population and provide equitable health services at the community level.

Exploring multimorbidity clusters in relation to healthcare use and its impact on self-rated health among older people in India

“…Among the identified clusters, the relatively healthy cluster is consistent with the previous studies reporting a similar latent class representing the majority of the study sample, ranging from 50 to 70 percent [ 17 , 34 – 37 ]. The second most prevalent cluster, the metabolic disorders cluster, comprising comorbidity of hypertension and diabetes, is also supported by previous studies [ 17 , 37 ].…”

“…These methods are often regarded as capable of uncovering complex and multivariate multimorbidity patterns. To our knowledge, no similar study has been conducted in India, with the exception of study by Puri et al that was limited to examining patterns and associated factors among people aged 45 years or above [ 17 ]. From an etiological and clinical perspective, there is still limited knowledge to understand what makes health conditions tend to co-occur among older population, particularly in countries experiencing a growing aging population like India.…”

Risk factors of multimorbidity among older adults in India: A systematic review and meta‐analysis

“…7,11 Their capacity to perform activities of daily living may progressively deteriorate 12,13 hence, leading to poor quality of life and eventually leading to premature deaths. [14][15][16][17] In India, several primary studies have been carried out to identify risk factors of multimorbidity among older adults, 1,[18][19][20][21][22][23][24][25][26][27][28] but to date, no systematic review has been conducted on this topic that brings together all the available evidence. The findings could be used when developing health interventions for addressing multimorbidity among older adults in India.…”

“…In India, several primary studies have been carried out to identify risk factors of multimorbidity among older adults, 1 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 but to date, no systematic review has been conducted on this topic that brings together all the available evidence. The findings could be used when developing health interventions for addressing multimorbidity among older adults in India.…”

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

“…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. 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…”