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Research ArticleOriginal Research

Prevalence, Correlates, and Outcomes of Multimorbidity Among Patients Attending Primary Care in Odisha, India

Sanghamitra Pati, Subhashisa Swain, Mohammad Akhtar Hussain, Shridhar Kadam and Chris Salisbury
The Annals of Family Medicine September 2015, 13 (5) 446-450; DOI: https://doi.org/10.1370/afm.1843
Sanghamitra Pati
1Public Health Foundation of India, Indian Institute of Public Health, Bhubaneswar, Odisha, India
MBBS, MD, MPH
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  • For correspondence: sanghamitra.pati@iiphb.org
Subhashisa Swain
1Public Health Foundation of India, Indian Institute of Public Health, Bhubaneswar, Odisha, India
MPH
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Mohammad Akhtar Hussain
2Division of Epidemiology and Biostatistics, School of Public Health, The University of Queensland, Brisbane, Australia
MBBS, MD
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Shridhar Kadam
1Public Health Foundation of India, Indian Institute of Public Health, Bhubaneswar, Odisha, India
MBBS, MD, MPH
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Chris Salisbury
3Centre for Academic Primary Care, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
MB, ChB(Bristol), MSc(Lond), DRCOG, FRCGP, MD
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  • Figure 1
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    Figure 1

    Percentage of population with more than 1 chronic condition, by age and sex.

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    Table 1

    Multimorbidity Prevalence and Its Association With Different Sociodemographic Characteristics: Results From Univariate and Multivariate Analyses (N = 1,649)

    CharacteristicsNo. (Weighted %)Percentage With Multimorbiditya Weighted % (95% CI)Crude Odds Ratio (95% CI)Adjusted Odds Ratio (95% CI)
    Age-groupb[Reference][Reference]
     18–29 y373 (22.6)5.8 (1.99–9.6)
     30–39 y297 (18.1)22.2 (15.1–29.4)4.79 (2.73–8.39)6.10 (3.19–11.65)c
     40–49 y346 (20.5)24.3 (17.7–30.9)6.14 (3.58–10.54)8.49 (4.45–16.117)c
     50–59 y266 (16.7)36.2 (27.9–44.5)11.24 (6.54–19.29)16.41 (8.55–31.48)c
     60–69 y236 (14.6)36.9 (28.1–45.8)11.09 (6.41–19.19)16.14 (8.38–31.10)c
     ≥70 y131 (07.5)44.4 (33.0–55.8)15.01 (8.25–12.30)22.35 (11.15–44.71)c
    Sexb
     Male921 (55.8)25.1 (22.1–28.0)[Reference][Reference]
     Female728 (44.2)32.5 (29.0–35.9)1.37 (1.08–1.73)1.61 (1.23–2.12)c
    Place of living
     Rural1,493 (90.4)25.5 (23.2–27.8)[Reference][Reference]
     Urban156 (9.6)28.5 (27.8–29.3)1.36 (0.88–2.11)1.39 (0.86–2.27)
     Ethnicity
     Aboriginal471 (28.0)27.7 (26.3–29.2)[Reference][Reference]
     Nonaboriginal1,178 (71.4)28.5 (27.6–29.4)1.68 (1.28–2.21)1.57 (1.16–2.13)c
    Socioeconomic status
     Below poverty line1,035 (61.6)28.8 (27.8–29.7)[Reference][Reference]
     Above poverty line601 (38.4)27.5 (26.2–28.8)1.34 (1.05–1.70)1.35 (1.03–1.78)c
    Schooling
     No School642 (38.1)35.0 (33.7–36.3)[Reference]
     Primary completed514 (30.7)28.3 (27.1–29.5)1.22 (0.93–1.61)1.61 (1.17–2.22)c
     Secondary and above493 (31.1)20.1 (19.6–21.1)0.68 (0.50–0.91)1.53 (1.03–2.25)c
    Marital status
     Currently married1,321 (79.8)29.3 (28.5–30.1)[Reference][Reference]
     Currently not married328 (20.2)24.3 (22.0–26.6)0.74 (0.55–1.01)1.45 (0.97–2.15)
    Facility
     Public849 (61.0)28.1 (27.1–29.1)[Reference][Reference]
     Private800 (39.0)28.6 (27.5–29.7)1.35 (1.06–1.71)1.40 (1.08–1.81)c
     Total1,649 (100.0)28.3 (25.9–30.7)
    • ↵a Two or more conditions.

    • ↵b The prevalence of multimorbidity across age-group was adjusted for sex, and across sex was adjusted for age; for others prevalence was adjusted for age and sex.

    • ↵c We used a binary logistic regression model, with P <.05 considered to be significant.

    • View popup
    Table 2

    Negative Binomial Regression Showing Factors Influencing Health Care Use Within Patients With Multiple Morbidities

    CharacteristicsIRR for Medicines Taken No. (95% CI)IRR for Visits to Hospital No. (95% CI)
    Age
     18–29 y[Reference][Reference]
     30–39 y1.17 (0.64–2.14)1.94 (0.91–4.12)
     40–49 y1.25 (0.70–2.23)2.15 (1.07–4.34)a
     50–59 y1.20 (0.68–2.12)1.86 (1.01–3.77)a
     60–69 y1.13 (0.63–2.02)2.02 (1.02–2.99)a
     ≥70 y1.28 (0.69–2.34)1.85 (0.90–3.81)
    Sex
     Male[Reference][Reference]
     Female1.00 (0.82–1.21)1.07 (0.77–1.49)
    Place of living
     Rural[Reference][Reference]
     Urban0.83 (0.68–1.00)1.09 (0.64–1.88)
    Ethnicity
     Aboriginal[Reference][Reference]
     Nonaboriginal1.21 (0.90–1.63)1.08 (0.80–1.46)
    Schooling
     No schooling[Reference][Reference]
     Primary completed0.73 (0.59–1.01)1.43 (1.08–1.89)a
     Secondary and above0.89 (0.71–1.10)1.49 (1.03–2.24)a
    Socioeconomic Status
     Below poverty line[Reference][Reference]
     Above poverty line1.13 (0.93–1.36)1.03 (0.77–1.36)
    Marital status
     Currently married[Reference][Reference]
     Currently not married1.11 (0.84–1.47)1.17 (0.85–1.61)
    Facility
     Public[Reference][Reference]
     Private1.39 (1.17–1.66)a1.20 (0.91–1.58)
    Number of morbidities1.30 (1.20–1.40)a1.05 (0.91–1.21)
    • IRR = incidence rate ratio.

    • ↵a Significant at P <.05, adjusted for age-group, sex, ethnicity, education, socioeconomic status, marital status, place of living, number of morbidities as continuous and types of facility.

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  • The Article in Brief

    Prevalence, Correlates, and Outcomes of Multimorbidity Among Patients Attending Primary Care in Odisha, India

    Sanghamitra Pati , and colleagues

    Background Multimorbidity (having two or more chronic conditions) is linked to higher health care utilization and expenditures and worse quality of life. Information on multimorbidity in developing countries is limited. This is the first study to estimate the prevalence of multimorbidity among adult primary care patients in India, specifically in the state of Odisha.

    What This Study Found Almost 30 percent of primary care patients in Odisha, India have multiple physical and mental health problems, a finding that is consistent with prevalence studies around the world. Twenty-eight percent of patients had two or more chronic conditions. Patients who were female, older, of higher socioeconomic status, and more educated had higher odds of having multimorbidities. In addition, consultation in private versus public hospitals and more chronic conditions were associated with a significant increase in the number of medicines prescribed. India?s higher prevalence of multimorbidity and hospitalization among those with higher socioeconomic status contrasts with Western countries where lower socioeconomic status is associated with greater morbidity burden. This might be attributed to contrasting socioeconomic patterns of risk factors for noncommunicable diseases in India and lower health care seeking and higher probability of underdiagnosis in low-income populations.

    Implications

    • The authors call for India to redesign national health programs, shifting the focus from single diseases to managing the complexity of multimorbidity.
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The Annals of Family Medicine: 13 (5)
The Annals of Family Medicine: 13 (5)
Vol. 13, Issue 5
September/October 2015
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Prevalence, Correlates, and Outcomes of Multimorbidity Among Patients Attending Primary Care in Odisha, India
Sanghamitra Pati, Subhashisa Swain, Mohammad Akhtar Hussain, Shridhar Kadam, Chris Salisbury
The Annals of Family Medicine Sep 2015, 13 (5) 446-450; DOI: 10.1370/afm.1843

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Prevalence, Correlates, and Outcomes of Multimorbidity Among Patients Attending Primary Care in Odisha, India
Sanghamitra Pati, Subhashisa Swain, Mohammad Akhtar Hussain, Shridhar Kadam, Chris Salisbury
The Annals of Family Medicine Sep 2015, 13 (5) 446-450; DOI: 10.1370/afm.1843
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Subjects

  • Domains of illness & health:
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  • Person groups:
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  • Methods:
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  • Other topics:
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  • India
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