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

Open AccessPublished:September 10, 2022DOI:https://doi.org/10.1016/j.cegh.2022.101143

      Abstract

      Problem considered

      The health outcome of women is generally poor in India. Women are suffering from various chronic illness. So, this study seeks to assess the prevalence, pattern and correlates of multimorbidity among adult and old aged women in India.

      Methods

      The study used data from the second wave of the Study on Global AGEing and adult health (SAGE), conducted in 2015. The study employed cross-tabulation, Pearson’s chi-square test, and multivariate logistic regression analyses to assess the prevalence and correlates of multimorbidity among adult and old aged women in India.

      Results

      The prevalence of multimorbidity among the women was 47.69%. The prevalence of back pain was highest among adult (18-59 years) and old-aged (60+ years) women. The risk of having multimorbidity significantly increases with age. Our study confirmed that all the 16 isolated chronic morbidities have considerable coherence with the types of multimorbidity. Variables like age, education, work status, perceived loneliness, and self-rated health were associated with multimorbidity.

      Conclusions

      The study suggests that the concern of multimorbidity among women in India should be prioritized with an integrated co-management approach in all diseases-specific programs to reduce and prevent the health burden in the country. 

      Keywords

      1. Introduction

      Women in developing countries lack access to basic healthcare and suffer with serious life-debilitating illness and life-threatening health issues.
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      An introduction to global women's health.
      Similarly, in India, girls and women are generally disadvantaged in every aspect, including social, cultural, economic, and educational settings.
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      Gender discrimination and empowerment of women in India: a view.
      Women tend to experience inequity and discrimination throughout their lives, from childhood to old age in food intake, nutrition, education, access to a resource and health care, and other opportunities.
      • Jawaregowda S.K.
      • Angadi M.M.
      Gender differences in nutritional status among under five children in rural areas of Bijapur district, Karnataka, India -.
      This condition synergistically contributes to higher susceptibility to disease and poor physical and mental health across the life span.
      • Borooah V.K.
      Gender bias among children in India in their diet and immunisation against disease.
      Large-scale and regional studies revealed that adult women in India experienced more problems than men, such as chronic respiratory disease, asthma, interstitial lung disease, and pulmonary sarcoidosis, non-communicable diseases and depressive disorders.
      India State-Level Disease Burden Initiative CRD Collaborators
      The burden of chronic respiratory diseases and their heterogeneity across the states of India: the Global Burden of Disease Study 1990-2016.
      • Sharma S.K.
      • Vishwakarma D.
      • Puri P.
      Gender disparities in the burden of non-communicable diseases in India: evidence from the cross-sectional study.
      India State-Level Disease Burden Initiative Mental Disorders Collaborators
      The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990-2017.
      Arokiasamy et al. (2015) work on global aging and adult health for six countries (China, Ghana, India, Mexico, Russian Federation, and South Africa) highlighted that the prevalence rate of multimorbidity was 5% more among the women (24.8%) than the prevalence of men (19.00%).
      • Arokiasamy P.
      • Uttamacharya U.
      • Jain K.
      • et al.
      The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal?.
      Likewise, Kshatri et al. (2020) found that the prevalence of multimorbidity is relatively higher in females (50.4%) than males(47.4%).
      • Kshatri J.S.
      • Palo S.K.
      • Bhoi T.
      • Barik S.R.
      • Pati S.
      Prevalence and patterns of multimorbidity among rural elderly: findings of the AHSETS study.
      Thus, it is a matter of public health concern because multimorbidty may maximize the susceptibility of adverse impact on health outcomes and lower quality of life of women.
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      • Liew D.
      Recent patterns of multimorbidity among older adults in high-income countries.
      Indeed, it can add the over-consumption of medicine, complexity to disease management, burden of health care services, repetitive hospitalization, which may eventually increase the health expenditure of a country.
      • Bezerra de Souza D.L.
      • Oliveras-Fabregas A.
      • Espelt A.
      • et al.
      Multimorbidity and its associated factors among adults aged 50 and over: a cross-sectional study in 17 European countries.
      ,
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      • Lin E.H.
      • Von Korff M.
      • et al.
      Collaborative care for patients with depression and chronic illnesses.
      This multiple disease burden could be attributed to various reasons like women's biological characteristics, demographic, socio-economic, and behavioural factors.
      • Blümel J.E.
      • Carrillo-Larco R.M.
      • Vallejo M.S.
      • Chedraui P.
      Multimorbidity in a cohort of middle-aged women: risk factors and disease clustering.
      To the best of our knowledge, most studies on multimorbidity in India and other countries were carried out on particular age groups, such as young middle-aged, older age group, women in reproductive age and overall age group.
      • Blümel J.E.
      • Carrillo-Larco R.M.
      • Vallejo M.S.
      • Chedraui P.
      Multimorbidity in a cohort of middle-aged women: risk factors and disease clustering.
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      • Swain S.
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      • Knottnerus J.A.
      • van den Akker M.
      Pattern and severity of multimorbidity among patients attending primary care settings in Odisha, India.
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      Prevalence and patterns of multi-morbidity in the productive age group of 30-69 years: a cross-sectional study in Pathanamthitta District, Kerala.
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      • Singh F.
      Interplay of multimorbidity and polypharmacy on a community dwelling frail elderly cohort in the peri-urban slums of Delhi, India.
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      • Singh S.K.
      • Pati S.
      Burden and determinants of multimorbidity among women in reproductive age group: a cross-sectional study based in India.
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      • Mik Sk
      Persistence of multimorbidity among women aged 15-49 Years in India: an analysis of prevalence, patterns and correlation.
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      Prevalence and pattern of multimorbidity among adults in a primary care rural setting.
      Despite those research, the gender-based study on multimorbidity using large-scale data focusing on both adult and older women altogether is very limited in the Indian context and other countries. Against this backdrop, the study seeks to assess the prevalence and correlates of multimorbidity across women's adult and old age groups in India. Understanding the prevalence, pattern, and disease combination of multimorbidity can be helpful in the formulation and implementation of appropriate programs such as prevention, diagnosis, and treatment for women in India.

      2. Materials and methods

      This study is based on the secondary data collected from the second wave of the Study on Global AGEing and adult health (SAGE). A nationally representative survey was conducted among adults in six countries: China, Ghana, India, Mexico, the Russian Federation, and South Africa. The survey was carried out in India across six states; Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh, and West Bengal. The SAGE (Wave-1) was initiated by the World Health Organization (WHO) in 2007, and its consecutive wave-2 was conducted in 2015. This longitudinal study aims to obtain a nationally representative sample of respondents aged 50+ years, with a comparatively smaller cohort of the adult population aged 18–49 years for comparison. The survey adopted multi-stage stratified cluster sampling. For the analysis, both households and individual weights were estimated based on six states, age groups, and sex. A detailed description of the sample design and the survey procedure is provided in the SAGE (Wave 2), India national report.
      • Arokiasamy P.
      • Sekher T.V.
      • Lhungdim H.
      • Dhar M.
      • Roy A.K.
      Study on Global AGEing and Adult Health (SAGE) Wave 2, India National Report.
      SAGE was approved by the World Health Organization's Ethical Review Committee. SAGE obtained all necessary ethical approval from their respective ethical review board before the collection of data. Ethical clearance was received by SAGE's partner organisations in each country through their respective institutional review bodies.
      The SAGE Wave-2 covered a total of 9116 adult population in India. Out of the total respondents, 4170 were male, and 4946 were female respondents. As our study focused on the women respondents, we excluded the male respondents and women whose data is incomplete in data set. So, 4898 female respondents were included in the final analysis.
      The primary outcome variable of this study is multimorbidity among the adult and old aged women. We define multimorbidity in this study as the presence of two or more chronic illnesses from a list of 16 diseases presented in the SAGE Wave 2 dataset. During the SAGE Wave 2 India survey, the respondents were asked, “Have you ever been told by a doctor/health care professional that you have (name of the disease)?". The response was then categorized as Yes (1) and No (0). The current study only analyzed the women from the individual data files. To construct the variable of multimorbidity, we coded '0' if the respondent has less than “one chronic disease” and coded ‘1′ if the respondent has more than “two chronic diseases” preceding the 12 months of the survey.
      We have considered socio-economic and demographic backgrounds characteristics in this study. The predictors that were considered in this analysis are age (18–34, 35–49, 50–59, 60–69, 70 years and above), marital status (never married, currently married/cohabiting, divorced/separated/widowed), level of education (no formal education/less than primary/primary/secondary/higher secondary and above), place of residence (rural/urban), caste (Scheduled Tribes-ST/Scheduled Castes-SC/Other Backward Classes-OBC/Others), religion (Hindu/Muslim/Others) of the respondents. Behavioral characteristics include alcohol consumption, physical activity, perceived loneliness, quality of life, and self-rated health.
      The descriptive statistics tool investigated the distribution of socio-demographic and economic characteristics in the study sample. Several explanatory variables were employed to calculate the prevalence of multimorbidity. Pearson's chi-square test was performed to show the association between outcome and independent variables. Bivariate and multivariate logistic regression models were used to examine the socio-demographic and economic factors of multimorbidity among the women. The regression results are shown as unadjusted and adjusted odds ratio (ORs) with 95% confidence intervals (CIs), with p < 0.05 regarded statistically significant. STATA version 14.0 (StataCorp, LP, College Station, TX, USA) was used to perform the statistical analyses. The proportion of respondents with different chronic diseases was classified into respective age groups. Additionally, disease-specific prevalence and distribution of several diseases across 16 conditions were investigated.

      3. Results

      3.1 Background characteristics of the respondents

      The majority of the respondents were 50–59 years (35.06%), and about 66.34% were currently married, 78.08% lived in rural areas, 45.13% belonged to OBC, 17.68% SC, and 84% and 12.24% belonged to the Hindu religion and Muslims respectively (Table 1). Additionally, 30.17% and 29.45% of women do not have any formal education, and less than primary respectively. About 21.55% of women belong to the highest wealth quintile, 58% were currently working, and 22% engaged in vigorous physical activity.
      Table 1Background characteristics of women aged 18–70+ years in India, SAGE Wave-2.
      VariablesFrequency (n) (Total sample = 4898)Percentage
      Age group
      18–344248.66
      35–4973314.97
      50–59171735.06
      60–69128226.17
      70plus74215.15
      Marital Status
      Never Married1773.61
      Currently Married/Cohabitating325566.46
      Widowed/Separated/Divorced146629.93
      Residence
      Urban107521.95
      Rural382378.05
      Caste
      Schedule tribe4038.23
      Schedule caste86317.62
      Other backward class (OBC)221145.14
      Others142129.01
      Religion
      Hindu411984.1
      Muslim59412.13
      Others1853.78
      Education
      No formal education59130.17
      Less than primary57729.45
      Primary39320.06
      Secondary24912.71
      Higher secondary1155.87
      College and above341.74
      Wealth quintile
      Lowest97719.75
      Second94319.07
      Middle95519.31
      Fourth100520.32
      Highest106621.55
      Currently working
      Yes86758
      No61841.62
      Vigorous-intensity activity
      Yes106822
      No385378.3
      Ever consumed alcohol
      No480097.3
      Yes1332.7
      Ever smoked
      No410983.3
      Yes82416.7
      Quality of life
      Very Good4148.41
      Good177836.14
      Moderate216243.94
      Bad56611.5
      Self-rated health
      Very Good2795.65
      Good160732.56
      Moderate225045.58
      Bad80016.21

      3.2 Prevalence of chronic morbidity

      Fig. 1 indicates the prevalence of individual chronic morbidity among women in India. It was revealed that the prevalence of back pain was highest among adult (18–59 years) and old-aged (60+ years) women. Next to back pain, adult women had hypertension (13.83%), arthritis (12.44%), tightness in the chest (11.71%) on the other hand, 30.24% of old aged women had cataracts (30.24%), hypertension (27.29%) and arthritis (24.22%).
      Fig. 1
      Fig. 1Prevalence of chronic morbidity among adult and old aged women.

      3.3 Prevalence of multimorbidity by background characteristics

      Table 2 shows the level of multimorbidity by the demographic, socio-economic characteristics of the women. The study observed 26.30% and 47.69% of women had single morbidity and multimorbidity respectively. The prevalence of multimorbidity significantly increased with age. It sharply increased at the age group 30–39 years (25.45%) of women, and reached at peak above 69 years. Women predominantly residing in the urban areas (52%) have a comparatively higher prevalence of multimorbidity than their rural (46.43%) counterparts. The women belonging to OBC (48.98%) had highest prevalence of multimorbidity than ST (43.18%).
      Table 2Prevalence of multimorbidity among women by background characteristics in India. (SAGE Wave-2).
      VariablesNo morbidityOne morbidityTwo or more morbidityP-value
      Frequency (n) (Total sample = 1274)PercentageFrequency (n) (Total sample = 1288)PercentageFrequency (n) (Total sample = 2336)Percentage
      Age group
      18–3424257.0812028.006214.620.000
      35–4926035.0021830.0025534.79
      50–5944726.0048128.0178945.95
      60–6923017.9430924.1074357.96
      70plus9512.8016021.5648765.63
      Marital Status
      Never Married10760.454123.162916.380.000
      Currently Married/Cohabiting91428.0590527.78143944.00
      Widowed/Separated/Divorced25317.2934223.3886859.00
      Residence
      Urban25023.2626425.0056152.000.003
      Rural102427102426.79177546.43
      Caste
      Schedule tribe130329925.0017443.180.092
      Schedule caste2272622826.0040847.28
      Other backward class (OBC)5442558426.41108348.98
      Others37326.2537726.5367147.00
      Religion
      Hindu108426109627.00193947.070.186
      Muslim15225.5914123.7430150.67
      Others38215128.009652.00
      Education
      No formal education14124.0615526.4529049.490.000
      Less than primary15727.41492626746.60
      Primary117301032617044.00
      Secondary11345.566024.197530.24
      Higher secondary and above5436.243020.136543.62
      Wealth quintile
      Poor513275002688147.000.111
      Middle25727.225927.4142945.40
      Rich50424.4852925.69102649.83
      Currently working
      Yes24628.627431.8634039.530.000
      No10817.6515124.6735357.68
      Vigorous-intensity activity
      Yes2001926124.660056.550.000
      No107428.05102526.77173045.18
      Ever consumed alcohol
      No125926.42124826225947.000.000
      Yes1511.3640307758.00
      Ever smoked
      No113728.00108326.55185945.570.000
      Yes13717.002052547758.00
      Quality of life
      Very Good14735.6813131.813433.000.000
      Good49628.0249127.7478344.24
      Moderate52424.3453124.66109851.00
      Bad10519.001352432157.00
      Self-rated health
      Very Good12946.578329.966523.000.000
      Good57135.7145428.3957435.90
      Moderate48821.8758726.31115651.82
      Bad8611.0016321.0054168.00
      More than half of the women suffered from multimorbidity belonging to other religions (52%), highest wealth quintile (52.41%), currently not working (57.68%), engaged in vigorous-intensity activity (56.55%), consumed alcohol (58%), separated/divorced/cohabitating (59.57%).

      3.4 The number of chronic morbidities across age groups

      Fig. 2 illustrates the contribution of morbidity and multimorbidity across age groups. Our study identified an explicit chronic morbidities pattern which rises with the increase in age of the women. About 57.08% of women aged 18–34 years had no morbidity, while only 12.8% of women aged 70+ years reported having no morbidity. The prevalence of two morbidities gradually increases from 18–34 years to 70+ years, but it peaks at aged 35–49 years (18.83%). We found sharp rise in the prevalence of 3+ chronic conditions with each consecutive age group. The burden of 4+ chronic morbidities was found to be higher among women aged 70 years (25.34%). However, the study also observed that a considerable share (2.12%) of adult women aged 18–34 years was suffering from 4+ chronic morbidities (2.12%).
      Fig. 2
      Fig. 2Number of chronic morbidities among women by age groups. (SAGE Wave-2).

      3.5 Relationship between types of multimorbidity and specific disease

      Fig. 3 denotes the share of occurrence of the specific chronic morbidity with the types of multimorbidity among women. For example, the share of respondents suffering from backpain alone is 24.56%, whereas respondents suffering from backpain along with one other chronic morbidity are 26.62%, following two other chronic morbidities are 20.42% and three or more other chronic morbidities are 28.4%. Similarly, the share of respondents suffering from lung disease alone is 1.52%, whereas share of respondents suffering from lung disease along with one other chronic morbidity are 9.09%, following two other chronic morbidities are 15.5% and three or more other chronic morbidities are 74.24%.
      Fig. 3
      Fig. 3Relationship between types of multimorbidity and specific disease.

      3.6 Factors associated with multimorbidity among women

      In multivariate logistic regression analysis, the factors associated with multimorbidity were presented (Table 3). While controlling the cofounders in the model, it was found that the age of the women has a strong association with multimorbidity. Women aged 50–59 years (AOR: 6.87; 95% CI: 2.76, 17.13), 60–69 years (AOR: 8.90; 95% CI: 3.33, 23.79), 70 years and above (AOR: 30.55; 95% CI: 7.66, 121.77) were more likely to have multimorbidity compared to those of women aged 18–34 years.
      Table 3Bivariate and multivariate logistic regression analysis of socio-demographic and economic factors associated with multimorbidity among women in India, SAGE Wave 2.
      VariablesCrude OR95% CIAdjusted OR95% CI
      Age group
      18–3411
      35–493.11
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (2.29–4.24)4.32
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.74–10.75)
      50–594.96
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (3.73–6.61)6.87
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (2.76–17.13)
      60–698.05
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (6.01–10.77)8.90
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (3.33–23.79)
      70plus11.15
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (8.19–15.19)30.55
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (7.66–121.77)
      Marital Status
      Single11
      Currently Married4.04
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (2.70–6.05)0.9(0.28–2.91)
      Widowed/Others7.43
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (4.92–11.21)0.7(0.19–2.51)
      Place of residence
      Rural11
      Urban1.26
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.10–1.44)0.93(0.57–1.51)
      Caste
      Schedule tribe11
      Schedule caste1.18(0.93–1.50)0.46(0.18–1.21)
      Other backward class (OBC)1.26
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.02–1.57)0.62(0.266–1.49)
      Others1.18(0.94–1.47)0.55(0.22–1.35)
      Religion
      Hindu11
      Muslim1.15(0.97–1.37)0.73(0.28–1.87)
      Others1.21(0.90–1.63)1.14(0.45–2.90)
      Education
      No education11
      Less than primary0.89().71–1.12)0.48
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (0.28–0.82)
      Primary0.79(0.61–1.02)1.22(0.64–2.34)
      Secondary0.44
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (0.32–0.61)1.04(0.50–2.19)
      Higher Secondary and above0.79(0.55–1.13)1.42(0.63–3.21)
      Wealth Index
      Poor11
      Middle0.96(0.82–1.12)0.55(0.30–1.01)
      Rich1.14
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.01–1.29)0.64(0.37–1.12)
      Currently Working
      No11
      Yes0.48
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (0.39–0.59)0.52
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (0.32–0.83)
      Feel lonely
      No11
      Yes1.80
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.54–2.09)2.30
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.22–4.34)
      Self-rated health
      Very Good11
      Good1.83
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.36–2.46)4.86
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.55–15.19)
      Moderate3.51
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (2.62–4.69)6.37
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (2.02–20.09)
      Bad7.09
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (5.17–9.72)17.96
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (4.63–69.71)
      Quality of life
      Very Good11
      Good1.65
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.31–2.06)1.51(0.69–3.31)
      Moderate2.16
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.73–2.70)2.01(0.90–4.49)
      Bad2.77
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (2.13–3.62)0.67(0.19–2.31)
      Engaged in vigorous-intensity activity
      No11
      Yes1.58
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.38–1.81)1.54(0.95–2.48)
      Have you ever consumed alcohol
      No11
      Yes1.55
      p < 0.05; OR Odds ratio, CI Confidence interval.
      (1.09–2.21)0.58(0.16–2.16)
      a p < 0.05; OR Odds ratio, CI Confidence interval.
      The women with less than primary education were less likely to have multimorbidity (AOR: 0.48: 95% CI: 0.28, 0.82) than the uneducated women. The odds of multimorbidity were substantially two times higher among women who perceived loneliness (AOR 2.30; 95% CI: 1.22, 4.34) than those who did not sense loneliness. The women not engaged in work were less likely to have multimorbidity (AOR 0.52; 95% CI: 0.32, 0.83) compared to those working women. The self-rated health condition of the women was found to be strongly associated with multimorbidity. The odds of multimorbidity increased by almost 6.37 and 17 times among women who reported their self-rated health as moderate and poor, respectively than the women noting their self-health as excellent counterparts.

      4. Discussion

      The present study explored the pattern and combination of chronic diseases and the factors associated with multimorbidity among women in India. In this study, the women had 47.69% prevalence rate of multimorbidity, which is higher than other studies in India, Iran (19.4%) and European countries (range 19.7%–47.30%)
      • Bezerra de Souza D.L.
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      Multimorbidity and its associated factors among adults aged 50 and over: a cross-sectional study in 17 European countries.
      ,
      • Puri P.
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      Burden and determinants of multimorbidity among women in reproductive age group: a cross-sectional study based in India.
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      Our study explored that the shared number of chronic conditions significantly varied across the age-groups of women, and the prevalence of multimorbidity rises with increases in aging women. This could be possibly attributed to biological factors because aging leads to molecular and cellular damage over a period of time which eventually increases the risk of diseases. Our findings corroborates with other studies which indicated that biological aging influences the development of chronic diseases among women.
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      Our study confirmed that all isolated morbidities have considerable coherence with the types of multimorbidity. For example, back pain, hypertension, arthritis, cataract, teeth problem etc were dominated with two, three, and four chronic morbidities. Similarly other studies also highlighted the relationship between back pain, arthritis, hypertension, depression and the development of multimorbidity.
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      • Belvederi Murri M.
      • et al.
      The complex interplay between depression and multimorbidity in late life: risks and pathways.
      Therefore, the current study suggests to add multimorbidity as the core component in the specific disease related policy to reduce the health burden in the country. But there is no explanation about the multimorbidity in most of the diseases control programmes in India. Like for instance hypertension control intervention has never addressed multimorbidity in the related health policy in the country.
      This study found that age was significantly associated with multimorbidity condition among women. Our finding is consistent with several other studies.
      • Puri P.
      • Kothavale A.
      • Singh S.K.
      • Pati S.
      Burden and determinants of multimorbidity among women in reproductive age group: a cross-sectional study based in India.
      ,
      • Vargese S.S.
      • Mathew E.
      • Johny V.
      • Kurian N.
      • Raju A.S.
      Prevalence and pattern of multimorbidity among adults in a primary care rural setting.
      ,
      • Alimohammadian M.
      • Majidi A.
      • Yaseri M.
      • et al.
      Multimorbidity as an important issue among women: results of a gender difference investigation in a large population-based cross-sectional study in West Asia.
      ,
      • de S Santos Machado V.
      • Valadares A.L.
      • Costa-Paiva L.H.
      • Osis M.J.
      • Sousa M.H.
      • Pinto-Neto A.M.
      Aging, obesity, and multimorbidity in women 50 years or older: a population-based study.
      This could be explained by the fact that women's estrogen levels decrease with aging, which elevates the chance of morbidity.
      • Puri P.
      • Kothavale A.
      • Singh S.K.
      • Pati S.
      Burden and determinants of multimorbidity among women in reproductive age group: a cross-sectional study based in India.
      Findings from our present study reveal that education is a significant predictor of multimorbidity. Women's education attainment less than primary were found to be at higher risk of multimorbidity than women who had no education. Other studies have already substantiated the inverse relation between education and multimorbidity
      • Blümel J.E.
      • Carrillo-Larco R.M.
      • Vallejo M.S.
      • Chedraui P.
      Multimorbidity in a cohort of middle-aged women: risk factors and disease clustering.
      ,
      • Alimohammadian M.
      • Majidi A.
      • Yaseri M.
      • et al.
      Multimorbidity as an important issue among women: results of a gender difference investigation in a large population-based cross-sectional study in West Asia.
      ,
      • Nagel G.
      • Peter R.
      • Braig S.
      • et al.
      The impact of education on risk factors and the occurrence of multimorbidity in the EPIC-Heidelberg cohort.
      ,
      • Marengoni A.
      • Winblad B.
      • Karp A.
      • Fratiglioni L.
      Prevalence of chronic diseases and multimorbidity among the elderly population in Sweden.
      The possible explanation is that education improves health-related knowledge and decision-making power about their health, influences their lifestyle behaviour, and, most importantly, determines their socio-economic status.
      • Marengoni A.
      • Winblad B.
      • Karp A.
      • Fratiglioni L.
      Prevalence of chronic diseases and multimorbidity among the elderly population in Sweden.
      Similarly, women engaged in vigorous intense activity appeared to be strongly associated with an increased likelihood of multimorbidity than those not involved in intense activity. This finding is consistent with a previous study conducted in England.
      • Singer L.
      • Green M.
      • Rowe F.
      • Ben-Shlomo Y.
      • Morrissey K.
      Social determinants of multimorbidity and multiple functional limitations among the ageing population of England.
      In contrast, a survey conducted in Quebec exhibits no association between multimorbidity and physical activity.
      • Hudon C.
      • Soubhi H.
      • Fortin M.
      Relationship between multimorbidity and physical activity: secondary analysis from the Quebec health survey.
      This could be because physical activity lessens the immune system, improve psychological outcome, and lower the risk of mental problems like anxiety, stress, depression, and quality of life.
      • Silva L.
      • Seguro C.S.
      • de Oliveira C.
      • et al.
      Physical inactivity is associated with increased levels of anxiety, depression, and stress in Brazilians during the COVID-19 pandemic: a cross-sectional study.
      ,
      The Lancet Public Health
      Time to tackle the physical activity gender gap.
      Along with these, physical activity helps prevent the occurrence of non-communicable diseases.
      • Reiner M.
      • Niermann C.
      • Jekauc D.
      • Woll A.
      Long-term health benefits of physical activity--a systematic review of longitudinal studies.
      In India, most women are engaged in household activities that are insufficient for their health.
      • Mathews E.
      • Lakshmi J.K.
      • Ravindran T.K.
      • Pratt M.
      • Thankappan K.R.
      Perceptions of barriers and facilitators in physical activity participation among women in Thiruvananthapuram City, India.
      Additionally, to undertake physical activity, women have to face several barriers such as personal barriers (lack of time and motivation), environmental barriers (fear of injury, non-availability of safe space), and cultural barriers (fear, shame, family obligations) preventing them from achieving the recommended levels of physical activity.
      • Mathews E.
      • Lakshmi J.K.
      • Ravindran T.K.
      • Pratt M.
      • Thankappan K.R.
      Perceptions of barriers and facilitators in physical activity participation among women in Thiruvananthapuram City, India.
      • Lawton J.
      • Ahmad N.
      • Hanna L.
      • Douglas M.
      • Hallowell N.
      'I can't do any serious exercise': barriers to physical activity amongst people of Pakistani and Indian origin with Type 2 diabetes.
      • Eyler A.A.
      • Baker E.
      • Cromer L.
      • King A.C.
      • Brownson R.C.
      • Donatelle R.J.
      Physical activity and minority women: a qualitative study.
      Our study found no significant association between wealth quintile and multimorbidity. But a study carried out in Bangladesh indicates that the wealth quintile is associated with a higher risk of multimorbidity.
      • Khanam M.A.
      • Streatfield P.K.
      • Kabir Z.N.
      • Qiu C.
      • Cornelius C.
      • Å Wahlin
      Prevalence and patterns of multimorbidity among elderly people in rural Bangladesh: a cross-sectional study.
      We found that marital status is not associated with multimorbidity. This finding is consistent with other study.
      • Schäfer I.
      • Hansen H.
      • Schön G.
      • et al.
      The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study.
      Moreover, our findings confirmed that working women are at higher risk of multimorbidity than those not working. The result corroborates with the findings of another study.
      • Seo S.
      Multimorbidity development in working people.
      The possible explanation could be women might face enormous barriers to managing their health-related issues as they are already overburdened with responsibilities at home and work. Seo (2019) study revealed that child lifting, holding on chest, and carrying are the significant cause of multimorbidity among younger women working population.
      • Seo S.
      Multimorbidity development in working people.
      Our study also found that self-rated health is a strong predictor of multimorbidity. Several studies conducted in Japan, England, and Russia have also found the relationship between poor self rated health and mutimorbidity.
      • Ishizaki T.
      • Kobayashi E.
      • Fukaya T.
      • Takahashi Y.
      • Shinkai S.
      • Liang J.
      Association of physical performance and self-rated health with multimorbidity among older adults: results from a nationwide survey in Japan.
      • Mavaddat N.
      • Valderas J.M.
      • van der Linde R.
      • Khaw K.T.
      • Kinmonth A.L.
      Association of self-rated health with multimorbidity, chronic disease and psychosocial factors in a large middle-aged and older cohort from general practice: a cross-sectional study.
      • Kaneva M.
      • Gerry C.J.
      • Baidin V.
      The effect of chronic conditions and multi-morbidity on self-assessed health in Russia.
      This study also pointed out that religion and caste do not play any significant role in making a woman more vulnerable to multimorbidity. But a cross-sectional study conducted in India reiterated caste as another social background characteristic to be significantly associated with multimorbidity.
      • Hossain B.
      • Govil D.
      • Mik Sk
      Persistence of multimorbidity among women aged 15-49 Years in India: an analysis of prevalence, patterns and correlation.
      Likewise, Puri et al. (2021) study found that religion was related to multimorbidity.
      • Puri P.
      • Kothavale A.
      • Singh S.K.
      • Pati S.
      Burden and determinants of multimorbidity among women in reproductive age group: a cross-sectional study based in India.
      We further found that alcohol consumption is not associated with multimorbidity. Similarly, Kshatri, et al.(2020) and Fortin et al. (2014) studies found no association between alcohol consumption and multimorbidity.
      • Kshatri J.S.
      • Palo S.K.
      • Bhoi T.
      • Barik S.R.
      • Pati S.
      Prevalence and patterns of multimorbidity among rural elderly: findings of the AHSETS study.
      ,
      • Fortin M.
      • Haggerty J.
      • Almirall J.
      • Bouhali T.
      • Sasseville M.
      • Lemieux M.
      Lifestyle factors and multimorbidity: a cross sectional study.
      Arokiaswamy et al. (2015) study reveal that adults living in rural areas have a higher risk of multimorbidity than those living in urban areas.
      • Arokiasamy P.
      • Uttamacharya U.
      • Jain K.
      • et al.
      The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal?.
      However, in contrast, our results showed no association between residence and multimorbidity.
      The present study has a few limitations. The study investigated the effects of socio-demographic and behavioral factors without considering the severity of the disease. The cross-sectional nature of this study is a significant limitation of our study. Hence, this study cannot establish the causal relationship between the factors and multimorbidity. The multimorbidity analysis was limited to only 16 chronic morbidities; therefore, many other chronic diseases would impact the health condition. Despite these limitations, this study provides information using a large-scale nationwide survey. It helps to understand the socio-demographic and economic factors associated with multimorbidity. Hence, it will help policymakers prioritize and target all the factors associated with multimorbidity.

      5. Conclusion

      Our study identified that the prevalence of multimorbidity and its types increased with aging. The age, education, work status, perceived loneliness, self-rated health were associated with multimorbidity. Hence, we recommend that the socio-demographic and economic factors of the women should be taken into account while developing effective and appropriate program interventions. The multimorbidity pattern differs according to age; therefore, extensive research is required on the combination of chronic diseases across different age groups of women. Furthermore, the study suggests that the concern of multimorbidity among women in India should be prioritized with an integrated co-management approach in all diseases-specific programs to reduce and prevent the health burden in the country.

      Funding

      This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

      Declaration of competing interest

      The authors declare that they have no competing interests.

      Acknowledgments

      The authors cordially acknowledge Dr. K.S. James the Director & Sr. Professor of International Institute for Population Sciences (IIPS) for providing us the SAGE WAVE-2 dataset for conducting the study.

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