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Rising challenge of multiple morbidities among the rural poor in India--a case of the Sundarbans in West Bengal.

Introduction

By 2020, noncommunicable diseases (NCDs) in the context of India are estimated to contribute to almost 67% of the deaths and to majority of the disability adjusted life years lost. [1] Morbidity attributable to diseases such as ischemic heart disease, cancer, chronic obstructive pulmonary disorder, musculoskeletal problems, and stroke rose from 1990 to 2010 in India. [2] Risk factors such as smoking, household pollution, and lack of nutrition have been found to be the major contributors to these diseases. The economic implications of these diseases are complex through catastrophic expenditures for the cost of treatment and loss of output and quality of life, especially in the poor households. [3] Although known as the diseases of the wealthy, recent studies show that these NCDs are also prevalent among the poor and the vulnerable. NCDs among the rural and poor population in the country pose a complex public health challenge of bridging the widening inequities in health and health care. The complication is further worsened in the case of multiple chronic diseases, which is also known as multiple morbidities.

Multimorbidity or multiple chronic conditions are defined as two or more chronic diseases occurring in an individual at the same point in time without any reference to an index disease. [4] Two specific reasons mandate this discussion: (a) multiple chronic conditions are known to increase with age. They imply specialized clinical management and greater financial expenditure. [5] With the geriatric population set to account for more than 10% of the total population by 2026 in India, multimorbidities require serious consideration and [6] (b) evidence clearly points at the increasing prevalence of chronic diseases in rural areas. Studies have extensively researched the burden and implication of these diseases individually, while a few studies have focused on the graver issue of comorbidities--mostly in the area of psychiatry and mental illnesses. [7]

Although recent literature from across the globe points at the rising burden of multimorbidities and its implications on the geriatric population, yet very little evidence exists in the context of the rural poor in India. [8] In this article, we present evidence on the burden of multimorbidity in the remote rural islands of the Sundarbans of West Bengal in India. Sundarbans, a vast spread of forest islands, belong to the most backward regions in West Bengal. They are an epitome of poverty and vulnerability owing to climate and geographical challenges. [9] Similar to many rural regions of the country, quality health care is often inaccessible. This study focuses on the issue of multimorbidity or multiple chronic conditions in the rural population of West Bengal. We assessed the burden and determinants of multimorbidity in a rural setting such as the Sundarbans as a pretext to the wider debate on the issue of NCDs and the need for equitable solutions.

Materials and Methods

Study Area

The Sundarbans delta, cluster of more than 100 islands in the extreme south of the state of West Bengal in India, is one of the world heritage sites. [10] It comprises of a unique biosphere of reserve forests that are intersected by many tidal rivers, lakes, and estuaries and paint a very formidable terrain. The area outside this reserve forest is equally hostile, comprising of 54 islands spread across 19 administrative blocks--with a population of around 4.5 million, the Sundarbans is the epitome of poverty, deprivation, and an acute livelihood struggle against climatic and geographical challenges. [11]

In 2009, the Sundarbans witnessed the cyclone Aila that wiped out a large portion of river embankments, caused thousands of villages to disappear under water, killed hundreds of people, and rendered more than 400,000 homeless. [12] As a consequence of these frequent climatic shocks and the remote nature of these islands, the health status of the people in Sundarbans is in a deplorable state. Research in the Sundarbans has underlined the poor state of maternal and child health along with many additional emerging public health challenges. [13]

Study Design

The design of the study has been explained in another publication by the authors. [14] A household survey was conducted in 2009 in 19 blocks of the Sundarbans in West Bengal, India. A two-stage random sampling procedure was used with 57 primary sampling units (PSUs) selected in the first stage, that is, three PSUs being randomly selected from each block. The second stage included the selection of 1,141 households that included 10% oversampling to adjust for nonresponse. One respondent aged 40 years or older was randomly selected from the sampled households and interviewed for information on NCDs. The survey instrument was based on the instrument used in World Health Survey. [15] A total of 831 households that were interviewed had at least one member aged older than age of 40. After data cleaning, 815 cases were used in the analysis. The study has adhered to the ethical guidelines for biomedical research on human subjects and is approved by the institutional review board of the institute. A verbal consent was obtained from the respondents before administering the survey instrument. Every respondent was read their right to voluntary participation and details of the risks and benefits; the right to refusal was outlined.

Multiple Morbidities

We defined multiple morbidities as the presence of two or more chronic conditions in a person at the time of survey. We have defined the presence of a chronic condition as "self-reported diagnosis or from symptoms highly suggestive of such a condition." The respondents were asked if they were diagnosed with angina, arthritis, asthma, hypertension/high blood pressure, diabetes/high blood sugar, and cataract specifically, from any health provider. Only these diseases were considered given their burden in the Indian context. A reference period of 3 months was taken for the abovementioned diseases.

We used a clinical algorithm for categorizing respondents as highly probable cases based on the criteria outlined by Levesque et al. [16] using symptoms for chronic conditions [Table 1]. There are no typical symptoms that characterize hypertension and diabetes; hence self-reported diagnosis was used as a criterion. We used a clinical algorithm to improve the identification of cases with chronic conditions in the rural setting. Self-reported diagnosis alone can in a rural context risks under estimation of the actual burden of disease owing to illiteracy, lack of common knowledge and understanding of health and diseases, and lack of access to quality care to appropriately lead all symptomatic cases to treatment.

Sociodemographic Variables

Information on background characteristics including age, location, education, caste, and perceived poverty was gathered. Education was classified into three categories--"Illiterate/literate without formal education," "primary education," and "secondary or higher education." The respondents were classified into general, other backward castes (OBC), and scheduled caste (SC)/scheduled tribes (STs) castes. The study area was classified into remote and nonremote zones based on the proximity of the blocks with the city. [13] We classified the respondents into three categories for perceived poverty--"always had deficit of income," "had occasional deficit," and "rare or no deficit."

Risk Factors

Smoking exposure was defined as the minimum pack years of exposure to smoking based on the definition provided by Lee et al. [17] We calculated the minimum pack years of exposure based on the minimum number of cigarettes that the respondent smoked in a day and the number of years since he/she has been smoking tobacco, to approximate the pack years of exposure to smoking. The respondents were classified as consuming sufficient amount of vegetables if they consumed more than five servings a day. [18] "Servings" was defined as the number of items with vegetables multiplied by number of meals per day. Body Mass Index (BMI) was calculated using weight and height measurements taken from the respondents using a standard tape and weighing machine.

Statistical Analysis

The outcome variable under consideration is multiple morbidities among the respondents. We first did an exploratory analysis of the prevalence of multiple morbidities in the study area against background characteristics using STATA Version 11.0 software. The outcome variables of interest being ordinal in nature, we chose a Partial Proportional Odds (PPO) Model for the above-mentioned set of predictors. Gender segregated analysis was done to control for the influence of gender on the outcome variables. For the model, initially, only those predictors that were significant in the univariate model were entered, followed by all hypothesized predictors. The goodness of fit of these two nested models was compared using log likelihood ratio test. We checked if the predictors in the final model met the assumption that the odds are identical for the predictors through the various cut points (ordinal categories in the outcome variable-1) of the outcome variable through the Wald statistic. A final PPO model was built for multimorbidity, constraining all predictors that met the proportional odds assumption, while the predictors that did not meet the assumption were left unconstrained.

Result

A total of 815 respondents were included in the analysis after data cleaning, of which there were 397 female and 418 male respondents. The average age of the respondents was 54.90 years. Table 2 shows the demographic composition of the study sample.

The prevalence of multiple morbidities was found to be 44.05% (CI 95%: 40.60-47.53). The crude overall prevalence of individual chronic conditions was 21.96% (CI 95%: 19.1624.96) for angina, 46.38% (CI 95%: 42.91-49.87 for arthritis, 22.94% (CI 95%: 20.09-25.98) for asthma, 39.38% (CI 95%: 36.01-42.83) for cataract, 8.95% (CI 95%: 7.08-11.13 for diabetes, and 8.71% (CI 95%: 6.86-10.86) for hypertension. The prevalence of multiple morbidities by age and gender are shown in Figure 1. The prevalence rates are found to increase with age in both male and female respondents of the study population with a greater increase among the female respondents.

The disease-wise segregation of multimorbidity shows that at least 70% of all respondents presenting any chronic conditions such as angina, hypertension, diabetes, cataract, and asthma showed multiple chronic conditions [Figure 2].

The odds ratios of the predictors in the PPO model are illustrated in Table 3. The results show that age, education, employment status, BMI, vegetable intake, and smoking were significant predictors of the multimorbidity among men. Age was also a significant predictor of multiple chronic conditions among women. The odds of multiple morbidities when compared with single or no condition was 4.64 times (CI 95%: 1.74-12.33) among respondents older than 70 years, while the odds of one or more conditions was significant for respondents aged 60-69 years. The odds of single or multiple chronic conditions decreased with increasing education among both men and women. Men with primary or secondary education and employed men were found to be less likely to report chronic multiple conditions. The odds of single or multiple chronic conditions is found to increase with smoking exposure. However, the odds are found to be higher among heavy/ moderate smokers when compared with light smokers. A unit increase in BMI contributed to a marginal increase in the likelihood of the outcome. BMI showed a marginal impact on the outcome variable of interest with a unit increase in BMI among women. Men with sufficient vegetable consumption were half as likely to report multiple morbidities when compared with the reference group, while women were more likely to report multiple chronic conditions.

Discussion

Our study shows that 44.05% of the population older than 40 years of age has multiple morbidities. Estimates from the cross-sectional study by Banjare & Pradhan et.al based on self report for adults older than 60 years of age also showed an overall prevalence of 56.80% in Odisha in India. [19] But, our estimates are very high when compared with the multimorbidity prevalence reported from the Lasi-Pilot Study in 4 states in India. [20] These differences in the estimates are probably due to differences in the operational definitions of multimorbidity and the age groups considered. Our estimates were also lower than prevalence figures from other cross-sectional studies that were facility based or drew data from health and patient records, probably, because reports from patients at hospitals are more likely to capture more chronic conditions. [21]

Age and gender have been found to be strong predictors of multimorbidity. We found a steady increase in the prevalence of multimorbidity with age among both men and women. This trend of increasing prevalence of multimorbidity with age is in consonance with morbidity trends shown in other studies.

Evidences from cross-sectional studies in the general population have shown a higher odds of multiple chronic conditions among the older age groups. [22] Our study findings show that the prevalence of multiple chronic conditions were visibly higher among women and is confirmed by evidence from both India and other countries on gender differences in individual chronic conditions. [23,24] The odds of multiple chronic conditions were slightly higher for female respondents aged older than 70 years when compared with male respondents. Our study shows that both men and women exhibit a greater chance of multiple chronic conditions with age even though the burden is higher among women. This differs from the results reported by Rizza et al. [25] that showed that the burden of multiple morbidities was slightly lower among women with increase in age. [25]

Education has been found to have a negative association with multiple chronic conditions. This is supported by evidence from studies that have also shown a similar negative association between multimorbidity and educational status. [26] The probable explanation can be that education improves an individual's ability to access health care and awareness that contributes to better health outcomes. It is interesting to note that education shows no significant association with the outcome among women, probably owing to their backward social and familial position that affects their health seeking behavior and awareness. Risk factors such as BMI were found to be positively associated with multimorbidity prevalence among both the genders. This confirms the association of high BMI with multimorbidity reported in a cross-sectional study of the general population in Quebec. [27]

We found a positive association for smoking with multiple morbidities among men. This corresponds to results from a study by Fortin et al [27] on lifestyle behaviors and multimorbidity where smoking was associated with the outcome among men. However, studies on multimorbidity in Sweden and South Africa have shown no significant impact of smoking behaviors on the prevalence of multimorbidity in the study population. [26,28] The odds for multimorbidity was positive for moderate smoking. The decrease in odds among heavy smokers was probably owing to under perception of disease risks and severity among heavy smokers. [29] Vegetable consumption showed a negative association with multimorbidity among men and positive association among women. [30] Our results are in line with the study done by Fortin et al. [27] among men and women in Quebec, Canada, wherein the odds of multimorbidity were higher among women (when compared with men) who had sufficient fruit and vegetable intake. [27]

Strengths and Limitations

The study has tried to estimate the burden of multiple chronic conditions by using an operational definition to circumvent the limitations of self-reported disease by including highly symptomatic cases. It differs from popular literature in the area of NCDs by using a symptomatic assessment of burden in rural areas such as the Sundarbans.

Conclusion

The study provides evidence on an often ignored aspect of NCDs in India. Multimorbidity is an emerging public health challenge even in the rural regions of India. The increasing burden of multiple chronic conditions with age implies reduced quality of life, disability, and vulnerability. The results show that the prevalence of multimorbidity is significant even in the remote rural regions such as the Sundarbans that fare poorly on many socioeconomic and development indicators. The evidence serves as a case, to bring to light the immediate need for attention to the issue with responsive systems to monitor and cater to the rising burden among the fast aging population of the country's rural poor.

DOI: 10.5455/ijmsph.2016.25082015129

Acknowledgments

We would like to extend our thanks to Professor Arindam Das for his support in finalizing the manuscript. We acknowledge the inputs and effort by all current and former members of the team in data collection and support for this study. We also acknowledge the scientific support extended by "Future Health Systems: Innovations for equity".

References

[1.] Srinath Reddy K, Shah B, Varghese C, Ramadoss A. Responding to the threat of chronic diseases in India. Lancet 2005; 366(9498):1744-9.

[2.] Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJL. Global Burden of Disease and Risk Factors. Washington DC: Oxford University Press and World Bank, 2006. pp. 1-468.

[3.] Bloom DE, Cafiero E, Jane-Llopis E, Abrahams-Gessel S, Reddy Bloom L, Fathima S, et al. The Global Economic Burden of Noncommunicable Diseases. Switzerland: World Economic Forum, 2011.

[4.] Dodel R. [Multimorbidity: concept, epidemiology and treatment]. Nervenarzt 2014; 85(4):401-8.

[5.] Salisbury C. Multimorbidity: redesigning health care for people who use it. Lancet 2012; 380(9836):7-9.

[6.] Ministry of Social Justice and Empowerment. Implementation of Schemes for Welfare of Senior Citizens- Thirty-Ninth Report. New Delhi: Ministry of Social Justice and Empowerment, 2014.

[7.] Srivastava A, Sreejayan K, Joseph AM, Sharma PSVN. Indian research on comorbidities. Indian J Psychiatry 2010; 52(Suppl 1): S246-9.

[8.] Prados-Torres A, Calderon-Larranaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. J Clin Epidemiol 2014; 67(3):254-66.

[9.] WWF. Sundarbans : Future Imperfect Climate Adaptation Report, 2010. Available at: http://assets.wwfindia.org/downloads/sund arbans_future_imperfect_climate_adaptation_report.pdf (last accessed 23 July 2015).

[10.] UNESCO. World Heritage List, 1987. Available at: http://whc. unesco.org/en/list/452 (last accessed 23 July 2015).

[11.] Government of West Bengal. District Human Development Report - South 24 Paraganas. West Bengal: Government of West Bengal, 2009.

[12.] Mukhopadhyay A. Cyclone Aila and the Sundarbans : An Enquiry into the Disaster and Politics of Aid and Relief. Kolkata: Mahanirban Calcutta Research Group, 2009.

[13.] Kanjilal B, Mazumdar PG, Mukherjee M, Mondal S, Barman D, Singh S, et al. Health care in the Sundarbans (India): Challenges and Plan for a Better Future. Kolkata: Institute of Health Managment Research, 2010.

[14.] Vadrevu L, Kumar V, Kanjilal B, Professor A. Gender differentials in the impact of multi-morbidity on self rated health in rural West Bengal in India. IOSR J Nurs Heal Sci 2015; 4:16-21.

[15.] World Health Organization. World Health Survey--Guide to Administration and Question by Question Specifications. Geneva: WHO, 2002.

[16.] Levesque JF, Mukherjee S, Grimard D, Boivin A, Mishra S. Measuring the prevalence of chronic diseases using population surveys by pooling self-reported symptoms, diagnosis and treatments: results from the World Health Survey of 2003 for South Asia. Int J Public Health. 2013; 58(3):435-47.

[17.] Lee YH, Shin MH, Kweon SS, Choi JS, Rhee JA, Ahn HR, et al. Cumulative smoking exposure, duration of smoking cessation, and peripheral arterial disease in middle-aged and older Korean men. BMC Public Health 2011; 11:94.

[18.] World Health Organization. WHO STEPS Manual Section 4: Reporting and Disseminating Results Overview. Geneva: WHO, 2008. Available at: http://www.who.int/chp/steps/manual/en/ (last accessed 23 July 2015).

[19.] Banjare P, Pradhan J. Socioeconomic inequalities in the prevalence of multi-morbidity among the rural elderly in Bargarh District of Odisha (India). PLoS One 2014; 9(6):e97832.

[20.] Fortin M, Hudon C, Haggerty J, van den Akker M, Almirall J. Prevalence estimates of multimorbidity: a comparative study of two sources. BMC Health Serv Res 2010; 10:111.

[21.] Violan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M, et al. Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies. PLoS One 2014; 9(7):e102149.

[22.] Aggarwal AN, Chaudhry K, Chhabra SK, D'Souza GA, Gupta D, Jindal SK, et al. Prevalence and risk factors for bronchial asthma in Indian adults: a multicentre study. Indian J Chest Dis Allied Sci 2006; 48:13-22.

[23.] Yiengprugsawan V, Lim LLY, Carmichael GA, Sidorenko A, Sleigh AC. Measuring and decomposing inequity in self-reported morbidity and self-assessed health in Thailand. Int J Equity Health 2007; 6:23.

[24.] Rizza A, Kaplan V, Senn O, Rosemann T, Bhend H, Tandjung R et al. Age- and gender-related prevalence of multimorbidity in primary care: the swiss fire project. BMC Fam Pract 2012; 13:113.

[25.] Alaba O, Chola L. The social determinants of multimorbidity in South Africa. Int J Equity Health 2013; 12:63.

[26.] Fortin M, Haggerty J, Almirall J, Bouhali T, Sasseville M, Lemieux M. Lifestyle factors and multimorbidity: a cross sectional study. BMC Public Health 2014; 14:686.

[27.] Melis R, Marengoni A, Angleman S, Fratiglioni L. Incidence and predictors of multimorbidity in the elderly: a population-based longitudinal study. PLoS One 2014; 9(7):e103120.

[28.] Lee C. Perceptions of immunity to disease in adult smokers. J Behav Med 1989; 12(3):267-77.

[29.] Ruel G, Levesque JF, Stocks N, Sirois C, Kroger E, Adams RJ, et al. Understanding the evolution of multimorbidity: evidences from the North West Adelaide Health Longitudinal Study (NWAHS). PLoS One 2014; 9(5):e96291.

Lalitha Vadrevu, Vinod Kumar, Barun Kanjilal

Department of Hospital and Health Management, IIHMR University, Jaipur, Rajasthan, India.

Correspondence to: Lalitha Vadrevu, E-mail: vadrevul@gmail.com

Received August 25, 2015. Accepted October 24, 2015

Table 1: Criteria for the assessment of NCDs Conditions (reference
period is last 3 months) Angina

[Have been diagnosed with angina by any provider in the past] OR

[Have pain or discomfort in the chest when you walk uphill or hurry
in the last 3 months? AND Pain or discomfort in your chest when you
walk at an ordinary pace on level ground in the last 3 months?]
Arthritis

[Have been diagnosed with arthritis in the past] OR

[Has pain, aching, stiffness, or swelling in or around the joint
[like arms, hands, legs, or feet], which were not related to an
injury and lasted for more than a month? AND A feeling of stiffness
in the joint in the morning after getting up from bed, or after a
long rest of the joint without movement that occurred regularly?
AND Stiffness for more than 30 min.

Asthma

[Have been diagnosed with asthma in the past] OR

[Has attacks of wheezing or whistling breathing in the last 3
months? AND [Has had an attack of shortness of breath that came on
without obvious cause when you were not exercising or doing some
physical activity in the last 3 months? OR A feeling of tightness
in the chest OR Attacks of wheezing after exertion /exercise OR
Waking up with a feeling of tightness in the chest]

Cataract

[Have been diagnosed with cataract in the past] OR

[Has blurry and cloudy vision AND experiences vision problems with
light such as glare and brightness]

Hypertension

[Have got their blood pressure measured using an instrument in the
past 3 months] AND [Have been told that they have got "High" blood
pressure]

Diabetes

[Have been diagnosed with "Sugar" or "Diabetes" by any provider in
the past]

Table 2: Sociodemographic composition of the respondents

Sociodemographic           Males, (N = 418)      Females, (N = 397)
factors

                           Mean [+ or -] SD       Mean [+ or -] SD

Age (years)              54.90 [+ or -] 10.77   54.85 [+ or -] 10.66
BMI (kg/[m.sup.2])       19.92 [+ or -] 3.02    20.37 [+ or -] 3.75
Caste (%)
  General                       49.52                  45.09
  OBC                            4.78                   4.28
  Scheduled caste               45.69                  50.63
Perceived poverty (%)
  Always deficit                41.11                  39.69
  Occasional deficit            42.31                  39.44
  Rare or no deficit            16.59                  20.87
Education (%)
  Illiterate or                 25.12                  62.72
    literate without
    formal education
  Primary                       60.29                  34.51
  Secondary or higher           14.59                   2.77
Marital status (%)
  Married                       91.15                  64.48
  Widowed/separated/             8.85                  35.52
    unmarried
Location (%)
  Remote Sundarbans             41.15                  47.10
  Nonremote Sundarbans          58.85                  52.90
Occupational groups
(%)
  Employed                      79.19                  35.01
  Unemployed                    20.81                  64.99
Smoking (%)
  Nonsmokers                    39.90                  74.68
  Light smokers                 47.47                  24.55
  Moderate to heavy             12.63                   0.78
    smokers
Vegetable intake (%)
  Insufficient                  84.84                  89.75
  Sufficient                    15.16                  10.25

Sociodemographic           Total, (N = 815)
factors

                           Mean [+ or -] SD

Age (years)              54.87 [+ or -] 10.71
BMI (kg/[m.sup.2])       20.14 [+ or -] 3.41
Caste (%)
  General                       47.36
  OBC                            4.54
  Scheduled caste               48.10
Perceived poverty (%)
  Always deficit                40.42
  Occasional deficit            40.91
  Rare or no deficit            18.67
Education (%)
  Illiterate or                 43.44
    literate without
    formal education
  Primary                       47.73
  Secondary or higher            8.33
Marital status (%)
  Married                       78.17
  Widowed/separated/            21.84
    unmarried
Location (%)
  Remote Sundarbans             44.05
  Nonremote Sundarbans          55.95
Occupational groups
(%)
  Employed                      42.33
  Unemployed                    57.67
Smoking (%)
  Nonsmokers                    57.09
  Light smokers                 36.14
  Moderate to heavy              6.77
    smokers
Vegetable intake (%)
  Insufficient                  87.25
  Sufficient                    12.75

SD, standard deviation.

Table 3: Odds ratios for multiple morbidities by background
characteristics and risk factors-results from PPO model

                                          Males

                        Single or multimor-  Multimorbidity vs.
                           bidity vs. no     chronic condition
                         chronic condition   or single chronic
                                                condition

                        Odds     CI 95%     Odds      CI 95%
                        ratio               ratio

Age (years)
  40-49 (ref.)
  50-59                 1.29    0.73-2.28   1.29    0.73-2.28
  60-69                 3.51    1.49-8.29   1.53    0.72-3.25
  >70                   2.09    0.75-5.85   4.64    1.74-12.33
Education
  Illiterate (ref.)
  Primary               0.48    0.27-0.85   0.48    0.27-0.85
  >Secondary            0.40    0.17-0.93   0.87    0.37-2.01
Marital status
  Married (ref.)
  Unmarried/single/     0.54    0.23-1.25   0.54    0.23-1.25
    widowed
Perceived poverty
  No deficit (ref.)
  Always deficit        1.32    0.66-2.64   1.32    0.66-2.64
  Occasional deficit    1.08    0.54-2.12   1.08    0.54-2.12
Caste
  General (ref.)
  OBC                   1.17    0.40-3.42   1.17    0.40-3.42
  SC/ST                 1.23    0.75-2.00   1.23    0.75-2.00
Occupation
  Unemployed (ref.)
  Employed              0.33    0.16-0.67   0.33    0.16-0.67
Location
  Nonremote (ref.)
  Remote                0.75    0.46-1.22   0.75    0.46-1.22
  BMI continuous        1.14    1.05-1.23   1.14    1.05-1.23
Smoking
  Nonsmokers (ref.)
  Light smokers         1.68    1.00-2.81   1.68    1.00-2.81
  Moderate to heavy     1.38    0.66-2.86   1.38    0.66-2.86
    smokers
Vegetable intake
  Insufficient (ref.)
  Sufficient            0.53    0.28-1.00   0.53    0.28-1.00

                                         Females

                        Single or multimor-   Multimorbidity vs.
                           bidity vs. no     no chronic condition
                         chronic condition    or single chronic
                                                 condition

                        Odds      CI 95%     Odds      CI 95%
                        ratio                ratio

Age (years)
  40-49 (ref.)
  50-59                 2.56    1.39-4.72    2.56    1.39-4.72
  60-69                 2.78    1.44-5.37    2.78    1.44-5.37
  >70                   4.77    1.89-12.06   4.77    1.89-12.06
Education
  Illiterate (ref.)
  Primary               1.26    0.76-2.09    1.26    0.76-2.09
  >Secondary            0.84    0.24-2.90    0.84    0.24-2.90
Marital status
  Married (ref.)
  Unmarried/single/     1.38    0.79-2.42    1.38    0.79-2.42
    widowed
Perceived poverty
  No deficit (ref.)
  Always deficit        1.65    0.86-3.14    1.65    0.86-3.14
  Occasional deficit    1.16    0.61-2.22    1.16    0.61-2.22
Caste
  General (ref.)
  OBC                   0.56    0.19-1.64    0.56    0.19-1.64
  SC/ST                 0.87    0.54-1.42    0.87    0.54-1.42
Occupation
  Unemployed (ref.)
  Employed              1.47    0.89-2.42    1.47    0.89-2.42
Location
  Nonremote (ref.)
  Remote                1.32    0.81-2.15    1.32    0.81-2.15
  BMI continuous        1.07    1.00-1.15    1.07    1.00-1.15
Smoking
  Nonsmokers (ref.)
  Light smokers         0.92    0.53-1.60    0.92    0.53-1.60
  Moderate to heavy     1.06    0.08-13.31   1.06    0.08-13.31
    smokers
Vegetable intake
  Insufficient (ref.)
  Sufficient            1.92    0.87-4.24    1.92    0.87-4.24

CI, confidence interval.

Figure 2: Disease-wise segregation of single and multiple chronic
conditions (%).

               One condition (%)   Two or more conditions (%)

Diabetes       12.33               87.67
Hypertension   14.08               85.92
Cataract       26.17               73.83
Asthma         14.97               85.03
Arthritis      28.57               71.43
Angina         9.5                 90.5

Note: Table made from bar graph.
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Title Annotation:Research Article
Author:Vadrevu, Lalitha; Kumar, Vinod; Kanjilal, Barun
Publication:International Journal of Medical Science and Public Health
Article Type:Report
Geographic Code:9INDI
Date:Feb 1, 2016
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