ABSTRACT
Context:
Multimorbidity (MM), defined as the presence of two or more chronic health conditions in an individual, can be more burdensome than single chronic disease.
Aims:
To estimate the prevalence of MM and determine its drivers among middle-aged and older adults in a city of North India.
Materials and Methods:
A community-based, cross-sectional study was conducted in semiurban and rural areas of Aligarh in Uttar Pradesh, India, among 420 adults aged ≥45 years using simple random sampling. Sociodemographic, dietary, anthropometry, clinical information, and diagnosed diseases were collected. Descriptive statistics, Chi-square test, and logistic regression analysis were done using IBM SPSS V.20.0.
Results:
We found the prevalence of MM was 40.2% (P = 0.40, 95% CI: 35.5%–45.1%), females > males (42.1% vs 37.5%). The odds of MM was almost 5 times higher in adults aged >75 years compared to 45–55 years old (AOR: 4.73; 95% CI: 1.40–16.05) and 2 times higher in urban areas (AOR: 2.10; 95% CI: 1.25–3.51). Physically inactive adults [AOR: 4.75 (95% CI: 2.19–10.28)], those who ever consumed tobacco or alcohol (AOR: 3.20; 95% CI: 1.85–5.54), those with lack of dietary diversity (AOR: 1.94; 95% CI: 1.04–3.63), and those morbidly obese (AOR: 10.17; 95% CI: 2.55–40.59) were at risk.
Conclusions:
With four out of ten adults having MM, its burden is high, especially in semiurban areas. Targeted interventions to reduce physical inactivity, obesity, and tobacco consumption and to increase dietary diversity are recommended.
Keywords: Adults, determinants, multimorbidity, prevalence
Introduction
An increasing life expectancy leads to accumulation of multiple chronic diseases,[1] which is termed as multimorbidity (MM), defined by WHO as “the presence of two or more chronic health conditions.[2]” Various studies have reported increasing age, female sex, higher socioeconomic class, physical inactivity, smoking and alcohol intake, and economic dependency as its sociodemographic determinants.[3,4]
This study was done to estimate the prevalence of MM among adults aged 45 years and above in a nonmetropolitan city of Uttar Pradesh, India, and to determine its sociodemographic and behavioral correlates in the same.
Materials and Methods
Study population, sample size, and sampling
This study was a community-based cross-sectional study conducted among adults aged ≥45 years in semiurban and rural field practice areas of the Department of Community Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, over a period of 1 year (August 2021 to July 2022). A sample size of 420 was required based on an estimated prevalence of 53.8%[5] for a study with 5% alpha error and 80% power, taking 5% allowable error and a 10% nonresponse rate.
The sample was drawn using the Probability Proportional to Size (PPS) technique. The three semiurban communities had a total population of 11,863 with 1628 households. Rural areas consisted of six villages with a population of 20,331 and 3490 households. The registered households formed the sampling frame. Households were chosen using simple random sampling. Only one individual satisfying the inclusion criteria was selected from each household using the lottery method. Adults aged ≥45 years and those permanently residing in the study area and giving written informed consent were included. Pregnant and puerperal females, mentally/physically challenged individuals, and nonconsenting adults were excluded from the study. In case no adult was found eligible in that household, the next household in the list was approached.
Study tools and variables
The study subjects were interviewed using a predesigned, structured questionnaire, which was tested using a pilot study involving 100 participants (excluded from the present study), and necessary changes were made accordingly.
Sociodemographic information obtained included age, sex, area of residence, religion, education, occupation, marital status, number of family members, and type of family. The per capita monthly income was calculated, and socioeconomic status (SES) was defined using Modified BG Prasad Classification, 2021.[6] Economic dependency was present if the participants were not earning/did not receive any pension, and were dependent monetarily on others for their daily expenditure.
Behavioral factors included dietary factors assessed by 24-hour dietary recall, any tobacco/alcohol consumption in the past 1 month, and physical inactivity (defined by WHO as <150 minutes of moderate intensity physical activity per week).[7] Activities like brisk walking, cycling, gardening, farming, exercise, and yoga performed for at least 10 continuous minutes per day during the last week were added up to make two categories (< or ≥150 min/week).
Excess salt intake was taken as ≥5 g/day.[8] Dietary diversity was defined according to Food and Agriculture Organization (FAO) guidelines[9] as the number of food groups that a person had consumed within the last 24 hours out of a total of nine food groups, namely, 1) starchy staples, 2) dark-green leafy vegetables, 3) other vitamin A-rich fruits and vegetables, 4) other fruits and vegetables, 5) organ meat, 6) meat and fish, 7) eggs, 8) legumes, nuts, and seeds, and 9) milk and milk products. If an individual consumed >15 g of a particular food group per day, 1 point was given; ≥5 points fulfilled the criteria of adequate dietary diversity.[9] Calorie consumption was assessed using the 24-hour recall method,[10] and the caloric adequacy (adequate, deficient, or excess), based on the caloric requirements for Indians, was given by the National Institute of Nutrition (NIN)-ICMR.[11]
MM was defined as the presence of two or more chronic health conditions (WHO).[2] A list of 20 diseases commonly found in the Indian experience[12] was taken: hypertension, diabetes mellitus, stroke, chronic obstructive pulmonary disease, asthma, arthritis, heart disease, impaired vision, tuberculosis, depression, cancer, human immunodeficiency virus (HIV), acid peptic disease, chronic kidney disease, chronic liver disease, deafness, chronic backache, hypothyroidism, dementia, and epilepsy.
Depression was assessed using the validated Hindi version of 9-item Patient Health Questionnaire (PHQ-9) (scored from 0 to 27).[13] A cutoff score of ≥10 was considered as depression. All other diseases were either self-reported as diagnosed or considered if the individual was taking allopathic medications. The responses were recorded as “0” for “no disease”, “1” for “one” of the above diseases, and “2” for having “2 or more diseases”. For logistic regression analysis, the dependent variable was dichotomized as the absence (no or one disease) or presence (≥ two diseases) of MM.
Anthropometric measurements included weight (in kg), height (in cm), and waist circumference (WC) (in cm). Participants’ weights were recorded using a digital weighing scale to the nearest 0.5 kg, without shoes and in light clothing.[14] Height (measured to the nearest 0.1 cm) and WC were recorded using a nonstretchable inch tape. WW was recorded with the inch-tape midway between the inferior margin of the last rib and the crest of the ilium.[15] The nutritional status using body mass index (BMI, in kg/m2) for the Asian population (underweight = <18.9 kg/m2, normal weight =18.9 to 22.9 kg/m2, overweight =23.0 to 24.9 kg/m2, obese =25.0 to 29.9 kg/m2, morbidly obese = >30.0 kg/m2) [16] was derived. Central obesity was defined as WC >80 cm in women and >90 cm in men.[17]
Ethical considerations
Prior to the start of the study, ethical clearance was obtained from the Institutional Ethical Committee (IECJNMC/516). All participants gave informed consent. Confidentiality was maintained, and appropriate referral was done when required.
Statistical analysis
IBM SPSS 20.0 software was used to tabulate and analyze the collected and cleaned data. Descriptive statistics were carried out with mean and standard deviation (SD) for continuous variables and percentages for categorical data. Chi-square test was used to study the association of categorical variables with MM. Logistic regression analysis was carried out to determine independent determinants of MM. The adjusted odds ratio with 95% confidence interval was calculated. A P value less than 0.05 was considered statistically significant.
Results
Sociodemographic, behavioral, dietary, and clinical characteristics of study participants
Out of 420 participants, 45.7% (192) were males and 63.1% (265) resided in rural areas. Other sociodemographic factors are shown in Table 1. The majority of the respondents (43.3%) were in the age group of 45–55 years, and most were married (74.5%), while more than half (53.1%) were illiterate. The distribution of diseases in the participants is given in Figure 1. Hypertension had the highest prevalence (39.5%), followed by arthritis (23.1%). Behavioral factors, central obesity, and physical inactivity are given in Table 2. One-third of the participants (31.2%) had any tobacco or alcohol consumption in the past 1 month.
Table 1.
Sociodemographic factors and their association with MM among middle-aged and older adults (n=420)
| Sociodemographic factors | n=420 n (%) | Multimorbidity absent (n=251) n (%) | Multimorbidity present (n=169) n (%) | Test of Significance |
|---|---|---|---|---|
| Sex | ||||
| Male | 192 (45.7) | 119 (62.0) | 73 (38.0) | χ2=0.723, df=1, P=0.395 |
| Female | 228 (54.3) | 132 (57.9) | 96 (42.1) | |
| Place of residence | ||||
| Rural | 265 (63.1) | 169 (63.8) | 96 (36.2) | χ2=4.806, df=1, P=0.028 |
| Semiurban | 155 (36.9) | 82 (52.9) | 73 (47.1) | |
| Age group (years) | ||||
| 45-55 | 182 (43.3) | 128 (70.3) | 54 (29.7) | χ2=20.052, df=3, P=0.000 |
| 56-65 | 137 (32.6) | 79 (57.7) | 58 (42.3) | |
| 66-75 | 80 (19.0) | 36 (45.0) | 44 (55.0) | |
| >75 | 21 (5.0) | 8 (38.1) | 13 (61.9) | |
| RELIGION | ||||
| Hindu | 231 (55.0) | 147 (63.6) | 84 (36.4) | χ2=2.871, df=1, P=0.090 |
| Muslim | 184 (43.8) | 102 (55.4) | 82 (44.6) | |
| Others* | 05 (1.2) | - | - | |
| MARITAL STATUS | ||||
| Married | 313 (74.5) | 196 (62.6) | 37.4% (117) | χ2=4.173, df=1, P=0.041 |
| Widowed/Unmarried/Divorced | 107 (25.5) | 55 (51.4) | 52 (48.6) | |
| TYPE OF FAMILY | ||||
| Nuclear | 123 (29.3) | 78 (63.4) | 45 (36.9) | χ2=0.965, df=1, P=0.326 |
| Joint | 297 (70.7) | 173 (58.2) | 124 (41.8) | |
| EDUCATION | ||||
| Illiterate | 223 (53.1) | 133 (59.6) | 90 (40.4) | χ2=3.290, df=5, P=0.655 |
| Primary School | 62 (14.8) | 36 (58.1) | 26 (41.9) | |
| Upper Primary School | 53 (12.6) | 29 (54.7) | 24 (45.3) | |
| Secondary School | 36 (8.6) | 25 (69.4) | 11 (30.6) | |
| Senior Secondary School | 20 (4.8) | 14 (70.0) | 6 (30.0) | |
| Graduate | 26 (6.2) | 14 (53.8) | 12 (46.2) | |
| OCCUPATION | ||||
| Unemployed/Retired | 120 (28.6) | 59 (49.2) | 61 (50.8) | χ2=26.267, df=6, P=0.000 |
| Laborer | 37 (8.8) | 27 (89.2) | 3 (10.8) | |
| Semiskilled | 23 (5.5) | 20 (87.0) | 3 (13.0) | |
| Skilled | 27 (6.4) | 16 (59.3) | 11 (40.7) | |
| Clerk/Shop/Farm | 76 (18.1) | 44 (57.9) | 32 (42.1) | |
| Professional | 10 (2.4) | 5 (50.0) | 5 (50.0) | |
| Homemaker | 127 (30.2) | 74 (58.3) | 53 (41.7) | |
| Socioeconomic status (Rs.)** | ||||
| Upper Class (pper C | 24 (5.7) | 14 (58.3) | 10 (41.7) | χ2=3.306, df=4, P=0.508 |
| Upper Middle Class (3931-7862) | 54 (12.9) | 31 (57.4) | 23 (42.6) | |
| Middle Class (2359-3930) | 88 (21.0) | 46 (52.3) | 42 (47.7) | |
| Lower Middle Class (1179-2385) | 171 (40.7) | 108 (63.2) | 63 (36.8) | |
| Lower Class (<1179) | 83 (19.8) | 52 (62.7) | 31 (37.3) | |
| Economic dependency | ||||
| Dependent | 181 (43.1) | 129 (54.0) | 110 (46.0) | χ2=7.724, df=1, P=0.005 |
| Independent | 239 (56.9) | 122 (67.4) | 59 (32.6) | |
*“Others” (Christians/Sikhs/Buddhists) being very few in frequency were excluded from analysis for ease of statistical test application. **Modified B.G. Prasad Classification, 2021
Figure 1.
Distribution of various chronic diseases among the study participants (n = 420). *Since an individual has a chance of having more than one disease, the total percentage does not add up to 100.0
Table 2.
Behavioral factors and central obesity and their association with MM among middle-aged and older adults (n=420)
| Behavioral and clinical factors | n=420 n (%) | Multimorbidity absent (n=251) n (%) | Multimorbidity present (n=169) n (%) | Test of Significance |
|---|---|---|---|---|
| SALT INTAKE | ||||
| <5 g/day | 153 (36.5) | 101 (66.0) | 52 (34.0) | χ2=3.911, df=1, P=0.048 |
| ≥5 g/day | 267 (63.5) | 150 (56.2) | 117 (43.8) | |
| Dietary diversity | ||||
| Adequate (≥5 food groups) | 93 (22.1) | 65 (69.9) | 28 (30.1) | χ2=5.098, df=1, P=0.024 |
| Inadequate (<5) | 327 (77.9) | 186 (56.9) | 141 (43.1) | |
| Calorie intake per day | ||||
| Inadequate | 277 (65.9) | 174 (62.8) | 103 (37.2) | χ2=3.546, df=2, P=0.170 |
| Adequate | 99 (23.6) | 55 (55.6) | 44 (44.4) | |
| Excess | 44 (10.5) | 22 (50.0) | 22 (50.0) | |
| ANY tobacco/alcohol consumption in the past 1 month | ||||
| Yes | 131 (31.2) | 66 (50.4) | 65 (49.6) | χ2=6.966, df=1, P=0.008 |
| No | 289 (68.6) | 185 (64.0) | 104 (36.0) | |
| Physical inactivity | ||||
| Present | 316 | 159 (50.3) | 157 (49.7) | χ2=47.346; df=1, P=0.000 |
| Absent | 104 | 92 (88.5) | 12 (11.5) | |
| Central obesity | ||||
| Present | 211 (50.2) | 103 (48.8) | 108 (51.2) | χ2=6.966, df=1, P=0.008 |
| Not present | 209 (49.8) | 148 (70.8) | 61 (29.2) |
More than three-fourths (77.9%) were consuming ≥5 g of salt per day, and 22.1% were consuming a diverse diet. Calorie intake was adequate in 23.6%, inadequate in 65.9%, and excess in 10.5% of respondents. Out of 420 participants, three-fourths of the study participants were physically inactive (75.7%) and one-third were physically active (24.3%). Nearly half of the participants (49.8%) had central obesity [Table 2].
More than one-third of participants (37.4%) were normal weight, 8.3% were underweight, and more than one-fourth (28.6%) were overweight. Around one-fifth (19.8%) of the participants were obese, and 6.0% were morbidly obese.
Prevalence of multimorbidity
It was found to be 40.2% (169 / 420) (95% C.I. =35.5%–45.1%). Females had a higher prevalence (42.1%) than males (37.5%), but this difference was not statistically significant (χ2 = 0.723, df =1, P = 0.395). The prevalence of MM was significantly higher among those residing in semiurban areas than in the rural areas (47.1% vs 36.2%) (χ2 = 4.806, df =1, P = 0.028) [Table 1].
Determinants of multimorbidity
As shown in Table 1, MM was found to increase with increasing age group. Almost two-thirds (61.9%) of the participants >75 years of age had MM. Also, almost half [48.2% (105/218)] of the participants ≥60 years of age had MM as compared to 31.7% (66/208) in those <60 years, which was statistically significant (χ2 = 11.844; df =1; P = 0.001). Marital status was also significantly associated with MM (χ2 = 4.173; df =1; P = 0.041), with widowed/unmarried/divorced participants having a higher proportion of MM (48.6%).
A significant association was found between the occupation of the participants and MM.
Those with sedentary occupations (professionals or unemployed/retired individuals) had a higher proportion of MM as compared to those involved in manual work (semiskilled workers/laborers) (χ2 = 26.267; df =6; P = 0.000). MM was also significantly higher in those who were economically dependent (46.0%). There was no significant association between MM and the type of family, religion, education status, and socioeconomic status of the participants [Table 1].
MM was also significantly associated with any tobacco or alcohol consumption in the past 1 month. Around half (49.6%) of those who smoked/chewed any tobacco or drank alcohol in the past 1 month had MM, as compared to 36.0% who did not have any such history (χ2 = 6.966; df =1; P = 0.008) [Table 2].
Association between dietary diversity and MM was found to be statistically significant (χ2 = 5.098; df =1; P = 0.024). The prevalence of MM was higher among those with inadequate dietary diversity (43.1%), as compared to those with adequate dietary diversity (30.1%). Increased salt intake was significantly associated with MM (χ2 = 3.911; df =1; P = 0.048). Those respondents who consumed excess salt per day had a higher prevalence of MM (43.8%) as compared to those who did not (34.0%). There was no significant association between calorie intake and MM (χ2 = 3.546; df =2; P = 0.170). Half (50.0%) of those who consumed excess calories, 44.4% of those who consumed adequate calories, and 37.2% of those with inadequate calories had MM.
Physical inactivity (PI) also had a significant direct association with MM. It was found in almost half of physically inactive adults (49.7%) (χ2 = 47.346; df =1; P = 0.000) [Table 2].
A statistically significant association between MM and central obesity was found. MM was present in more than half (51.2%) of the participants with central obesity as compared to about a quarter (29.2%) in those who had no central obesity (χ2 = 21.130; df =1; P = 0.000) [Table 2].
With regard to generalized obesity, the proportion of MM was found to increase with increasing BMI categories, and this association was found to be statistically significant. Around one-third (26.8%) of the respondents in the “normal” BMI category had MM, increasing to 80.0% in the “morbidly obese” category. However, 42.9% of underweight participants also had MM [Figure 2].
Figure 2.
Association between body mass index categories and multimorbidity (n = 420).
All sociodemographic, behavioral, and clinical factors found to have significant association with MM were entered into a multivariate logistic regression model to assess independent determinants for MM, which are shown in Table 3. The odds of having MM was almost two and a half times high [AOR: 2.43 (95% CI: 1.14–5.19)] in participants belonging to the age group of 65–75 years and almost 5 times high in the participants belonging to the age group of >75 years [AOR: 4.73 (95% CI: 1.40–16.05)]. Study participants residing in semiurban areas were twice as likely to have MM compared to their rural counterparts [AOR: 2.10 (95% CI: 1.25–3.51)], and semiskilled workers had lower odds of having MM [AOR: 0.14 (95% CI: 0.02–0.93)]. The odds of having MM was almost 2 times higher in those who had inadequate dietary diversity [AOR: 1.94 (95% CI: 1.04–3.63)], 5 times higher [AOR: 4.75 (95% CI: 2.19–10.28)] in those with physical inactivity, and 10 times higher in the morbidly obese [AOR: 10.17 (95% CI: 2.55–40.59)]. Those who consumed tobacco or alcohol were three times more likely to have MM [AOR: 3.20 (95% CI: 1.85–5.54)].
Table 3.
Independent determinants of MM using logistic regression analysis (n=420)
| Variables | Adjusted Odds Ratio* | 95% Confidence Interval* | P |
|---|---|---|---|
| Being 55-65 years old | 1.65 | 0.95-3.00 | 0.102 |
| Being 65-75 years old | 2.43 | 1.14-5.19 | 0.022 |
| Being >75 years old | 4.73 | 1.40-16.05 | 0.013 |
| Semi urban area of residence | 2.10 | 1.25-3.51 | 0.005 |
| Laborer | 0.28 | 0.04-1.85 | 0.180 |
| Semiskilled worker | 0.14 | 0.02-0.93 | 0.042 |
| Skilled worker | 0.72 | 0.13-3.89 | 0.706 |
| Clerk/Shop/Farm | 0.68 | 0.16-2.86 | 0.600 |
| Professional | 0.89 | 0.12-6.42 | 0.910 |
| Homemaker | 1.46 | 0.74-2.91 | 0.277 |
| Being Widowed/Unmarried/Divorced | 0.61 | 0.34-1.11 | 0.107 |
| Economic Dependency | 2.07 | 0.51-8.41 | 0.312 |
| Physical Inactivity | 4.75 | 2.19-10.28 | 0.000 |
| Any Tobacco/alcohol consumption in past month | 3.20 | 1.85-5.54 | 0.000 |
| Absence of Dietary Diversity | 1.94 | 1.04-3.63 | 0.037 |
| Excess calorie intake | 1.73 | 0.98-3.03 | 0.056 |
| Salt intake ≥5 g | 1.48 | 0.94-2.33 | 0.091 |
| Presence of Central Obesity | 1.29 | 0.74-2.24 | 0.374 |
| Being normal weight | 0.56 | 0.23-1.34 | 0.193 |
| Being Overweight | 1.01 | 0.39-2.59 | 0.988 |
| Being Obese | 2.14 | 0.79-5.80 | 0.137 |
| Being Morbidly Obese | 10.17 | 2.55-40.59 | 0.001 |
*Adjusted Odds Ratio=AOR; 95% Confidence Interval=95% C.I.hepa
Discussion
Our study reports a significant burden of MM among middle-aged and older adults of a nonmetropolitan city of the most populous state of India. With four out of ten adults having MM, we have found its prevalence to be high. India, belonging to the class of low- and middle-income countries (LMICs), adds chronic infectious diseases like tuberculosis and HIV into the mix of noncommunicable diseases which have already started on an exponential growth trajectory in the country.[18] Our study showed that the odds of MM increased with the study participants’ ages. This was also seen in studies involving participants in the 45–60 year age group, like the Longitudinal Ageing Study in India (45.26%)[19] and another conducted in China (45.5%).[20] Studies which were only conducted among the geriatric group have reported a higher prevalence of MM.[21,22] This could be due to the increased clustering of diseases with increasing age.
This is especially concerning as the diseases are chronic and tend to accumulate as individuals age, thus showing their combined effects in senescence. MM was found to be more prevalent in the semiurban areas in our study as compared to rural areas, a finding akin to other studies conducted in India[23,24] and Myanmar.[25] This could be due to a greater sedentary lifestyle found in semiurban areas.
Occupation was also an important risk factor for MM in our study, with relatively active workers (semiskilled workers like potters and weavers), having a lower risk. The lowest prevalence of MM was seen in laborers, and the highest in those who were unemployed or retired, similar to many such studies conducted in other parts of India[23,26,27,28] and Brazil,[29] where the prevalence of MM was higher in those who were retired (P < 0.001), followed by housewives/students.
Behavioral factors in this study reflected heavily on the prevalence of MM, similar to previous studies.[30] Physical inactivity was an important independent risk factor, with the odds of MM being 5 times higher in physically inactive individuals, a finding congruent with a previous study conducted in India, where the odds were almost one and a half times higher in those who were physically inactive.[23] Similar findings were also seen in the WHO Study on Global AGEing and Adult Health (SAGE) data.[31] This is reiterated by the fact that physical inactivity is one of the top four risk factors for noncommunicable diseases as reported by WHO.[7]
Tobacco/alcohol consumption was also an important independent contributor, with the odds of having MM almost three and a half times higher in those who had such history. Many other studies have also shown that consuming tobacco/alcohol is an independent risk factor of MM.[2,32,33,34,35]
The most important independent risk factor in our study was found to be morbid obesity, increasing the odds of having MM by ten times. Many studies conducted in India have shown a similar pattern, with significant association of obesity or morbid obesity with MM.[23,26,28,36] Interestingly, our study showed a higher prevalence of MM in underweight individuals as well, which could be explained by the inclusion of tuberculosis, HIV, and COPD, which are generally associated with a low BMI.[37,38,39] This finding was similar to that of a study conducted in India where the proportion of MM was higher in the underweight category (25.1%) as compared to those in the normal category (21.8%).[40]
Dietary diversity and salt intake were also found to be independent risk factors in our study, echoing studies by other researchers.[35]
The odds of having MM decreased with sufficient intake of vegetables in the diet in males in a study conducted in West Bengal[34] and in the overall participants in a WHO STEPS 2014 survey conducted in Botswana.[35] A Chinese study found that greater consumption of vegetables, fruits, and grains leads to a lower risk of MM,[41] and a sub-Saharan study found that greater salt intake was associated with a higher risk of cardiometabolic MM.[42] A UK women’s cohort study found that increased dietary intake of calories upped the risk of MM by 8%.[43]
Limitations of the study
Our study has a few limitations. Since this was a cross-sectional study, causality cannot be established. Physical inactivity was self-reported and not objectively measured. Dietary factors like consumption of fast foods, saturated, and transfat intake were not included in this study, which could be important determinants.
Conclusion
MM is especially concerning in today’s context as public health systems are adapting to address the burden of chronic diseases in the country. The present study concludes that the prevalence of MM is quite high in semiurban and rural areas of Aligarh, with four out of ten middle-aged and older adults suffering from the problem. It was found to increase with increasing age and was also higher in semiurban areas and in those with behavioral risk factors such as tobacco/alcohol consumption and physical inactivity. A high prevalence of MM in middle-aged and older adults can therefore be an impetus to plan health services catering to a complex of chronic diseases in these age groups specifically.
Behavior change communication (BCC) should be carried out in the community to spread awareness about the harmful effects of physical inactivity and substance addiction and to reiterate their role as risk factors for a host of diseases. Policies to improve physical activity, such as Fit India Movement,[44] and inculcation of good food habits should be promoted with a view to make physical activity integral in the lives of people. Existing policies to curb smoking should be implemented with more stringent efforts. Finally, healthcare should focus on the person as a whole and not as different organ systems by taking into account the socioeconomic and cultural environment, which can help in providing comprehensive health care.
Key Messages
The burden of MM among adults is high. Physical inactivity, any tobacco/alcohol consumption, and morbid obesity are significant modifiable determinants. Lifestyle modification is necessary for prevention.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
We thank all the participants of the study for their co-operation.
Funding Statement
Nil.
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