Prevalence, pattern, and correlates of multimorbidity among adult and old aged women in India

Debsarma, Dhiman; Saha, Jayanti; Choudhary, Bikramaditya K.

doi:10.1016/j.cegh.2022.101143

Cited by 2 publications

(3 citation statements)

References 41 publications

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“…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][30][31][32]. However, the latter two predictors were solely significant in rural areas.…”

Section: Interpretation and Implicationssupporting

confidence: 82%

“…Consistent with literature, age was found to have a positive association to multimorbidity [8,13,21,[29][30][31][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].…”

Section: Interpretation and Implicationssupporting

confidence: 70%

“…Based on an extensive literature review conducted regarding predictors of multimorbidity across India [8,13,17,21,22,[29][30][31][32], a variety of individual-level characteristics were considered for this study which could best be grouped as 1) sociodemographic factors and 2) lifestyle factors. Sociodemographic factors included: age, education, region of residence, wealth, religion, current marital status, occupation, and caste.…”

Section: Independent Variablesmentioning

confidence: 99%

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Predictors of Multimorbidity (Defined as Diabetes + Hypertension) Amongst Males Aged 15–54 in India: An Urban/Rural Split Analysis

Brar,

Kandala,

Terry

et al. 2024

Int J Public Health

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Objectives: This study aimed to determine which sociodemographic and lifestyle factors may act as predictors of multimorbidity (defined as diabetes + hypertension) amongst men aged 15–54 within urban and rural areas of India.Methods: Data from the latest 2019–2021 India NFHS-5 survey were utilized. Presumed cases of multimorbidity were defined as men who had DM + HTN. A total of 22,411 men in urban areas and 66,768 rural men were analyzed using mixed-effect multi-level binary logistic regression models.Results: Various predictors were found to have a statistically significant association to multimorbidity. Urban areas: Age, region of residence, wealth, religion, occupation, and BMI. Rural areas: Age, education, region of residence, wealth, occupation, caste, BMI, alcohol consumption, media exposure, and tobacco consumption.Conclusion: Departing from the broad operational definitions often studied within literature, this study provided insight into one of the most prevalent specific multimorbidities across India. The urban/rural split analyses revealed substantial differences in high-risk characteristics across both areas, which have commonly been overlooked. These findings may better inform policymakers and assist in effectively reducing multimorbidity-related burden through area-specific preventative programs.

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Section: Interpretation and Implicationssupporting

confidence: 82%

Section: Interpretation and Implicationssupporting

confidence: 70%

Section: Independent Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Predictors of Multimorbidity (Defined as Diabetes + Hypertension) Amongst Males Aged 15–54 in India: An Urban/Rural Split Analysis

Brar,

Kandala,

Terry

et al. 2024

Int J Public Health

View full text Add to dashboard Cite

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Assessing the modifiable and non-modifiable risk factors associated with multimorbidity in reproductive aged women in India

Das,

Saha,

Das

et al. 2024

BMC Public Health

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Background Reproductive span is the foundation of every woman’s health in later life. India is currently facing a growing burden of multiple morbidities among the women in their reproductive age group which may further increase over the coming decades. The purpose of the present study aimed to identify different modifiable and non-modifiable risk factors affecting multimorbidity among the women in reproductive age group in Indian context. Methods Secondary data were obtained from the Demography and Health Survey (DHS), conducted in India during 2019–2021. A total of 671,967 women aged 15–49 years were selected for this present study. Descriptive, association studies and multinominal logistic regression analyses were performed to accomplish the objectives. Results Currently, 6.3% of total study participant’s reproductive age group women suffered from multimorbidity in India. Never consuming protein, fruits, vegetables and milk increase the chances of developing multimorbidity. Consumption of fried foods, aerated drinks and addiction towards tobacco and alcohol also has a greater influence on the prevalence of multimorbidity. The prevalence of multimorbidity is sharply increased with increasing age and Body Mass Index (BMI). Regionally, the prevalence of multimorbidity was found more among the women hailed from eastern and north-eastern India. Conclusion To reduce the risk of developing multimorbidity, targeted interventions are needed in the form of educating every woman concerning the importance of having minimum health-related knowledge, maintaining healthy lifestyle, weight management and having proper and balanced diet.

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Prevalence, pattern, and correlates of multimorbidity among adult and old aged women in India

Cited by 2 publications

References 41 publications

Predictors of Multimorbidity (Defined as Diabetes + Hypertension) Amongst Males Aged 15–54 in India: An Urban/Rural Split Analysis

Predictors of Multimorbidity (Defined as Diabetes + Hypertension) Amongst Males Aged 15–54 in India: An Urban/Rural Split Analysis

Assessing the modifiable and non-modifiable risk factors associated with multimorbidity in reproductive aged women in India

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