Dataset Information

Burden, patterns, and impact of multimorbidity in North India: findings from a rural population-based study.

ABSTRACT:

Aim

To estimate the prevalence, socio-demographic determinants, common disease combinations, and health impact of multimorbidity among a young rural population.

Methods

We conducted a cross-sectional survey among participants aged ≥30 years in rural Punjab, North India, from Jan 2019 to April 2019. Multimorbidity was defined as the coexistence of ≥two conditions using a 14-condition tool validated in India. We also calculated a multimorbidity-weighted index (MWI), which provides a weight to each disease based on its impact on physical functioning. Logistic regression was conducted to evaluate the association with sociodemographic variables, mental health (PHQ-9), physical functioning (ADL scale), and self-rated health (SRH).

Results

We analyzed data from 3213 adults [Mean age 51.5 (±13), 54% women]. Prevalence of single chronic condition, multimorbidity, and MWI was 28.6, 18% and - 1.9 respectively. Age, higher wealth index and ever use alcohol were significantly associated with multimorbidity. Overall, 2.8% of respondents had limited physical functioning, 2.1% had depression, and 61.5% reported low SRH. Poorer health outcomes were more prevalent among the elderly, women, less educated, and those having lower wealth index and multimorbidity, were found to be significantly associated with poor health outcomes.

Conclusions

The burden of multimorbidity was high in this young rural population, which portends significant adverse effects on their health and quality of life. The Indian health system should be reconfigured to address this emerging health priority holistically, by adopting a more integrated and sustainable model of care.

SUBMITTER: Gupta P

PROVIDER: S-EPMC9159928 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Burden, patterns, and impact of multimorbidity in North India: findings from a rural population-based study.

Gupta Priti P Patel Shivani A SA Sharma Hanspria H Jarhyan Prashant P Sharma Rakshit R Prabhakaran Dorairaj D Tandon Nikhil N Mohan Sailesh S

BMC public health 20220602 1

<h4>Aim</h4>To estimate the prevalence, socio-demographic determinants, common disease combinations, and health impact of multimorbidity among a young rural population.<h4>Methods</h4>We conducted a cross-sectional survey among participants aged ≥30 years in rural Punjab, North India, from Jan 2019 to April 2019. Multimorbidity was defined as the coexistence of ≥two conditions using a 14-condition tool validated in India. We also calculated a multimorbidity-weighted index (MWI), which provides a ...[more]

PMID: 35655207

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Project description:BackgroundMultimorbidity estimates are expected to increase in India primarily due to the population aging. However, there is a lack of research estimating the burden of multimorbidity in the Indian context using a validated tool. We estimated the prevalence and determinants of multimorbidity amongst the adult population of the rural Uddanam region, Andhra Pradesh.MethodsThis community-based cross-sectional study was conducted as a part of an ongoing research program. Multistage cluster sampling technique was used to select 2419 adult participants from 40 clusters. Multimorbidity was assessed using Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) tool, collecting information on 13 chronic diseases. Patient Health Questionnaire (PHQ-12) was used to screen for depression. Multiple logistic regression was conducted to identify the strongest determinants of multimorbidity.ResultsOf the 2419 participants, 2289 completed the MAQ-PC tool. Mean age (standard deviation) of participants was 48.1 (13.1) years. The overall prevalence of multimorbidity was 58.5% (95% CI 56.5-60.6); with 30.7%, 15.6%, and 12.2% reporting two, three, and four chronic conditions, respectively. Acid peptic disease-musculoskeletal disease (44%) and acid peptic disease-musculoskeletal disease-hypertension (14.9%) were the most common dyad and triad. Among metabolic diseases, diabetes-hypertension (28.3%) and diabetes-hypertension-chronic kidney disease (7.6%) were the most common dyad and triad, respectively. Advancing age, female gender, and being obese were the strongest determinates of the presence of multimorbidity. Depression was highly prevalent among the study population, and participants with higher PHQ-12 score had 3.7 (2.5-5.4) greater odds of having multimorbidity.ConclusionsOur findings suggest that six of 10 adults in rural India are affected with multimorbidity. We report a higher prevalence of multimorbidity as compared with other studies conducted in India. We also identified vulnerable groups which would guide policy makers in developing holistic care packages for individuals with multimorbidity.

Project description:ObjectiveThe aim of this study was to assess the impact of the burden and patterns of multimorbidity on disability domains.DesignIn a cross-sectional study of 425 older adults from the Boston Rehabilitative Impairment Study of the Elderly, participants self-reported 13 chronic conditions and underwent assessment of body function (leg strength, velocity, and power, trunk extensor endurance, leg range of motion, foot sensation), activities (400-m walk test, Short Physical Performance Battery, Late Life Function and Disability Instrument function scores) and participation (Late Life Function and Disability Instrument participation scores). The association between multimorbidity patterns (identified by latent class analysis) and disablement measures, as well as multimorbidity burden (captured by a multimorbidity score) and disablement measures, was tested.ResultsLatent class analysis identified three classes-low multimorbidity, high multimorbidity, and predominantly musculoskeletal conditions. Class membership (multimorbidity pattern) was not associated with disablement measures, but multimorbidity score was associated with poor performance in all domains. A 1-point higher multimorbidity score was associated with lower scores in body functions (by 0.06 SD unit), activities (0.07-0.10 SD units), as well as participation (0.07-0.09 units).ConclusionMultimorbidity counts may be an excellent tool for risk stratification and identification of persons in need of rehabilitation.To claim cme creditsComplete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME CME OBJECTIVES: Upon completion of this article, the reader should be able to (1) describe and distinguish the effect of multimorbidity burden and multimorbidity patterns on three disability domains in older adults; (2) identify and discuss possible reasons why high multimorbidity burden may result in a restriction among social participation in older adults; and (3) detect disability risk among older patients during clinical assessment by using a simple count of common chronic conditions.LevelAdvanced ACCREDITATION: The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Project description:BackgroundThe burden of gestational diabetes mellitus (GDM) appears to be increasing in India and may be related to the double burden of malnutrition. The population-based incidence and risk factors of GDM, particularly in lower socio-economic populations, are not known. We conducted analyses on data from a population-based cohort of pregnant women in South Delhi, India, to determine the incidence of GDM, its risk factors and association with adverse pregnancy outcomes (stillbirth, preterm birth, large for gestational age babies) and need for caesarean section.MethodsWe analyzed data from the intervention group of the Women and Infants Integrated Interventions for Growth Study (WINGS), an individually randomized factorial design trial. An oral glucose tolerance test (OGTT) was performed at the time of confirmation of pregnancy, and for those who had a normal test (≤140 mg), it was repeated at 24-28 and at 34-36 weeks. Logistic regression was performed to ascertain risk factors associated with GDM. Risk ratios (RR) were calculated to find association between GDM and adverse pregnancy outcomes and need for caesarean section.Results19.2% (95% CI: 17.6 to 20.9) pregnant women who had at least one OGTT were diagnosed to have GDM. Women who had prediabetes at the time of confirmation of pregnancy had a significantly higher risk of developing GDM (RR 2.08, 95%CI 1.45 to 2.97). Other risk factors independently associated with GDM were woman's age (adjusted OR (AOR) 1.10, 95% CI 1.06 to 1.15) and BMI (AOR 1.04, 95% CI 1.01 to 1.07). Higher maternal height was found to be protective factor for GDM (AOR 0.98, 95% CI 0.96 to 1.00). Women with GDM, received appropriate treatment did not have an increase in adverse outcomes and no increased need for caesarean section CONCLUSIONS: A substantial proportion of pregnant women from a low to mid socio-economic population in Delhi had GDM, with older age, higher BMI and pre-diabetes as important risk factors. These findings highlight the need for interventions for prevention and provision of appropriate management of GDM in antenatal programmes.Clinical trial registrationClinical Trial Registry - India, #CTRI/2017/06/008908 ( http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=19339&EncHid=&userName=society%20for%20applied%20studies ).

Project description:BackgroundPopulation ageing challenges health care systems due to the high prevalence and impact of multimorbidity in older adults. However, little is known about how chronic conditions present in certain multimorbidity patterns, which could have great impact on public health at several levels. The aim of our study was to identify and describe multimorbidity patterns in low-, middle-, and high-income countries.MethodsWe analyzed data from the Collaborative Research on Ageing in Europe project (Finland, Poland, and Spain) and the World Health Organization's Study on Global Ageing and Adult Health (China, Ghana, India, Mexico, Russia, and South Africa). These cross-sectional studies obtained data from 41,909 noninstitutionalized adults older than 50 years. Exploratory factor analysis was performed to detect multimorbidity patterns. Additional adjusted binary logistic regressions were performed to identify associations between sociodemographic factors and multimorbidity.ResultsOverall multimorbidity prevalence was high across countries. Hypertension, cataract, and arthritis were the most prevalent comorbid conditions. Two or three multimorbidity patterns were found per country. Several patterns were identified across several countries: "cardio-respiratory" (angina, asthma, and chronic obstructive pulmonary disease), "metabolic" (diabetes, obesity, and hypertension), and "mental-articular" (arthritis and depression).ConclusionsA high prevalence of multimorbidity occurs in older adults across countries, with low- and middle-income countries gradually approaching the figures of richer countries. Certain multimorbidity patterns are present in several countries, which suggest that common underlying etiopathogenic factors may play a role. Deeper understanding of these patterns may lead to the development of preventive actions to diminish their prevalence and also give rise to new, comprehensive approaches for the management of these co-occurring conditions.

Project description:ObjectivePatients with multimorbidity may carry a large symptom burden. Symptoms are often what drive patients to seek healthcare and they also assist doctors with diagnosis. We examined whether symptom burden is additive in people with multimorbidity compared with people with a single morbidity.DesignThis is a longitudinal cohort study drawing on questionnaire and Danish national registry data. Multimorbidity was defined as having diagnoses from at least two out of ten morbidity groups. Associations between morbidity groups and symptom burden were estimated with multivariable models.ParticipantsIn 2012, 47 452 participants from the Danish Symptom Cohort answered a questionnaire about symptoms (36 symptoms in total), including whether symptoms were affecting their daily activities (impairment score) and their worries about present symptoms (worry score) (the highest score among the 36 symptoms on a 0-4 scale).Main outcome measureThe primary outcome was symptom burden.ResultsParticipants without morbidity reported 4.77 symptoms (out of 36 possible). Participants with one, two or three morbidities reported more symptoms than patients without morbidity (0.95 (CI 0.86 to 1.03), 1.87 (CI 1.73 to 2.01) and 2.89 (CI 2.66 to 3.12), respectively). Furthermore, they reported a higher impairment score (0.36 (0.32 to 0.39), 0.65 (0.60 to 0.70) and 1.06 (0.98 to 1.14)) and a higher worry score (0.34 (0.31 to 0.37), 0.62 (0.57 to 0.66) and 1.02 (0.94 to 1.10)) than participants without morbidity. In 45 possible combinations of multimorbidity (participants with two morbidities), interaction effects were additive in 37, 41 and 36 combinations for the number of symptoms, impairment score and worry score, respectively.ConclusionParticipants without morbidity reported a substantial number of symptoms. Having a single morbidity or multimorbidity resulted in approximately one extra symptom for each extra morbidity. In most combinations of multimorbidity, symptom burden was additive.

Dataset Information

Burden, patterns, and impact of multimorbidity in North India: findings from a rural population-based study.

Aim

Methods

Results

Conclusions

Publications

Burden, patterns, and impact of multimorbidity in North India: findings from a rural population-based study.

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