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 Table of Contents 
ORIGINAL ARTICLE
Year : 2021  |  Volume : 10  |  Issue : 8  |  Page : 2900-2914  

Profile of multimorbidity in outpatients attending public healthcare settings: A descriptive cross-sectional study from Odisha, India


1 ICMR Regional Medical Research Centre, Bhubaneswar, Odisha, India
2 Independent Researcher, New Delhi, India
3 All India Institute of Medical Sciences, Bibinagar, Telangana, India
4 International Institute for Population Sciences, Mumbai, Maharashtra, India
5 Centre for Chronic Diseases and Injuries and Indian Institute of Public Health Bhubaneswar, Public Health Foundation of India, Bhubaneswar, Odisha, India

Date of Submission10-Dec-2020
Date of Decision18-Feb-2021
Date of Acceptance24-Feb-2021
Date of Web Publication27-Aug-2021

Correspondence Address:
Dr. Sanghamitra Pati
Director and Scientist G, ICMR-Regional Medical Research Centre, Department of Health Research, Bhubaneswar -751 023, Odisha
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jfmpc.jfmpc_2436_20

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  Abstract 


Background: Multimorbidity, the co-occurrence of two or more long-term conditions (LTC) in individuals, is associated with greater healthcare utilization, expenditure, and premature mortality, thus positing a challenge for patients and healthcare providers. Given its sparsely available epidemiological evidence, we aimed to describe the profile of multimorbidity in a representative sample of public healthcare outpatients in India. Methods: A facility-based cross-sectional study was conducted from 1st July to 31st December 2015 in Odisha, India. Fifteen public healthcare facilities were selected by stratified random sampling. Data was collected from 1,870 adult outpatients attending these settings using Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) tool. Result: Nearly 3/4th of both women and men outpatients were either obese or overweight. >1/2 had multimorbidity (≥2 LTC) while 1/3rd had ≥3 LTC. Most prevalent condition was hypertension (63%), followed by chronic backache and arthritis. Cancer and psychiatric illness were least reported. Multimorbidity increased with age group, socioeconomic status, and education level. Females across all age groups had higher reported multimorbidity than males. Diabetes--hypertension was frequently occurring dyad. Both physical and mental component of quality of life was reduced in multimorbidity. Conclusion: Multimorbidity is becoming a norm in healthcare practice with high prevalence in females and older adults. Health services for non-communicable diseases need to include commonly occurring dyads along with health promotion. Higher prevalence in females reinforces the need to incorporate gender differences while studying multimorbidity. Analysis of multimorbidity epidemiology through an equity lens could illuminate the underpinning complexities and heterogeneities of this phenomenon.

Keywords: Comorbidities, cross-sectional, gender, MAQ-PC, multimorbidity, social determinants


How to cite this article:
Pati S, Sinha R, Panda M, Puri P, Pati S. Profile of multimorbidity in outpatients attending public healthcare settings: A descriptive cross-sectional study from Odisha, India. J Family Med Prim Care 2021;10:2900-14

How to cite this URL:
Pati S, Sinha R, Panda M, Puri P, Pati S. Profile of multimorbidity in outpatients attending public healthcare settings: A descriptive cross-sectional study from Odisha, India. J Family Med Prim Care [serial online] 2021 [cited 2021 Sep 24];10:2900-14. Available from: https://www.jfmpc.com/text.asp?2021/10/8/2900/324702




  Introduction Top


During the past few decades, in parallel with improvements in living conditions, changing lifestyle, increased healthcare efficacy and longevity, low- and middle-income countries (LMICs) are witnessing a steep rise in non-communicable diseases (NCDs). Similarly, owing to the availability of definitive therapeutics, few infectious diseases are becoming chronic in nature.[1] Consequentially, many LMICs are experiencing colliding epidemics of chronic infectious, tropical and NCDs, thus leading to growing number of population with multimorbidity.[2] In fact, multimorbidity is becoming a “norm than exception” in healthcare practice across LMICs and no longer limited to older adults or affluent societies.[3] India, the largest demography, is challenged by dual burdens of escalating prevalence NCDs and sustained presence of chronic infectious diseases.[4]

Multimorbidity is a novel concept encompassing all the medical conditions of an individual; defined as the co-occurrence of two or more long-term conditions (LTCs), each one of which is either: (1) A somatic NCD of long duration, such as a hypertension or diabetes; (2) A mental illness of chronic nature, like mood disorder or dementia; (3) An infectious disease of long duration, for example, HIV.[5] Individuals with multimorbidity experience inferior quality of life (QoL), perceive poorer physical and mental health, and have a lower functional capacity. They incur greater healthcare use and cost with heightened risk of hospital admissions and premature mortality, thus positing a daunting challenge for patients, physicians, and healthcare system alike.[6] In fact, evidence reveals that it is not chronic conditions by themselves that increase the resource cost, rather, it is the number of types of conditions, that is, multimorbidity which is responsible.[7]

Our systematic review on epidemiology of multimorbidity in South Asia had revealed that majority of studies have focussed on geriatric population; and few have investigated multimorbidity in all age groups; considered a selected panel of NCDs and did not include long-term infectious diseases.[8] To fill this knowledge gap, we had conducted first ever study that estimated multimorbidity in primary care (public and private) settings and found the prevalence to be one third.[9] This study had a limitation in being confined to primary healthcare population only.

Public healthcare system in India has a three tier structure encompassing primary (primary health centres and community health centres), secondary (district hospital and sub-divisional hospitals), and tertiary levels (medical college or referral hospitals).[10] As there is no stringent gate-keeping system, many individuals could directly be attending the outpatient department of higher facility bypassing the primary healthcare, thus necessitating inclusion of three levels for a comprehensive assessment of multimorbidity.[11]

Such profile could provide significant insights toward developing contextual patient-centric care, targeted population-level prevention services, and improved outcome strategies. With the newly formed “health and wellness centres” (HWCs) under the National Health Policy poised to deliver integrated chronic care at the community level, an understanding of the distribution of multimorbidity with respect to social determinants such as gender, economic status, literacy and could be more illuminating for equitable care planning.[12],[13]

In view of the above, the present study aimed to landscape multimorbidity among outpatients attending public healthcare settings. Our key objectives were: (a) estimation of self-reported multimorbidity among outpatients attending public healthcare facilities and its association with age, gender, education, socioeconomic status, (b) identification of commonly occurring combinations of multimorbidity, (c) relationship between multimorbidity and QoL.


  Methods Top


Study design and study setting

This facility-based cross-sectional study was conducted from 1st July to 31st December 2015 in Khurda district, Odisha, an eastern state of India. A total of 15 public healthcare facilities were selected by stratified random sampling. This included one referral hospital, two secondary level hospitals, four community health centres (two each from urban and rural), and eight primary healthcare centres (four each from urban and rural).

Sampling and sample size

Based on 31% prevalence of multimorbidity in our previous primary care study, 95% confidence interval, 80% power, and 1.5 design effect, and adjusting for non-response (10%), the final sample size calculated was 1,870.[14] This was proportionately divided across study facilities based on the daily patient attendance and sampling fraction for each facility. Adult individuals (≥18 years) attending the outpatient's department (OPD) of the selected health facilities were recruited through systematic random sampling. Those who provided the consent were interviewed after the consultation to avoid any disruption in the OPD management. Also, the exit interviews helped us to record the diagnosis in detail by going through the prescriptions. To avoid duplication, each patient was given unique identification number. For patients too ill to participate, or having debilitating physical or mental disabilities, data was collected from their attendants. Pregnant women and those who had already been interviewed previously under the present study were excluded.

Data collection

We used MAQ-PC, our previously developed and validated multimorbidity assessment questionnaire to collect data [Appendix I].[15] The MAQ-PC comprised: sociodemographic data, multimorbidity assessment, and outcomes. The multimorbidity assessment elicited the presence of any of the 18 listed chronic diseases. These conditions were self-reported doctor diagnosed and cross-validated from prescription checks and medications. Open options for “any other conditions” were added to capture unlisted conditions if any. SF12 and PHQ 9 were used to assess Health Related Quality of Life (HRQoL) and depression, respectively.[16] All interviews were conducted by two trained nurses well versed with local language and patient history taking. Each interview spanned 20-- 30 min. The bodyweight and height of every respondent was measured at enrolment.

Data analysis

During analysis five more LTC was included from the additional list totalling to 23. A score of ≥10 in PHQ-9 was taken as the cutoff value for depression. Obesity was defined as a body mass index ≥30.0 kg/m2. Age was divided into six (18--29, 30--39, 40--49, 50--59, 60--69, ≥70 years) categories. Educational status was classified as illiterate, primary, high school, higher secondary, graduation, and above. Economic status was categorized to those above (APL) and below poverty line (BPL). Multimorbidity was defined as two or more long-term conditions (≥2 LTC). The presence of LTC categories (none, single, multimorbidity) across these categorical variables was expressed as percentages. Data was analyzed using SPSS 20.

Ethical approval and participant consent

The ethical approval was accorded by Odisha State Research and Ethics committee (vide Letter No. 101/SHRMU dated 05/05/2015). Approval of the selected health facility and OPD in charge was obtained in prior. Each participant was briefed on the study objective and information being assessed. Written informed consent was obtained from all participants, and necessary steps were taken to ensure confidentiality and anonymity of patients at every step.


  Results Top


Participant characteristics

Out of 1,870 participants, nearly 3/5th (63%) were males while 2/5th (37%) comprised females. Majority belonged to the age group of 50--59 years (34.4%), followed by 60--69 years (27.3%), and 40--49 years (21.7%). Nearly a quarter of the participants were BPL. Maximum number of study participants (~75%) had higher secondary education while 2/5th were graduate and above. 45.3% participants were employed, rest comprised homemakers (27.6%), retired (18.2%), and unemployed (8.9%).

Profile of chronic conditions and multimorbidity

More than half of the study outpatients were obese and one-fifth were overweight. Nearly 75% of participants (male and female) had overweight or obesity. Contrarily, underweight was 1.6% and 4.2% among males and females, respectively. Around 84% respondents had one or more LTC while 16% did not report any morbidity. One third of participants had single morbidity, 25% reported two, while 30% were having three or more LTCs. Overall, 55% outpatients were having multimorbidity.

Hypertension was the most leading LTC (62.3%) bearing similar proportion across male and females. Next ranking was acid-peptic disease (APD) (25%). Chronic back ache (CBA) and arthritis followed the list, affecting 1/5th participants while visual impairment ranked fourth (15%). Diabetes (12%), thyroid disease (10%), and chronic lungs disease (8.3%) followed, respectively. Heart disease (6%) and chronic kidney disease (CKD) (5%), hearing impairment (4%) were present in <10% of outpatients. Stroke, epilepsy, depression was reported among 2% respondents in each category while that being 1% for cancer and tuberculosis.

Multimorbidity and deprivation

Individuals under APL category had slightly higher prevalence of multimorbidity than their BPL counterparts. The prevalence of single and three LTCs was similar in both groups [Figure 1]. Chronic lung disease, stroke, hearing difficulty, and epilepsy were reported higher in BPL group while APL had higher reporting of diabetes, hypertension, heart disease, CKD, and depression.
Figure 1: Multimorbidity by deprivation status among respondents

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With respect to education, diabetes, hypertension, heart and thyroid disease, CKD was seen to be increasing with education level while the proportion of respondents with other chronic conditions were almost similar across different education levels. There was no noticeable difference in rural--urban distribution of LTCs.

Gender and multimorbidity

At the lower age group (18--29 years), multimorbidity was around 5% in both genders. With increasing age, the proportion of multimorbidity increases steeply in both males and females. However, this rise in proportion of multimorbidity with increase in age was higher for females than males. For example, multimorbidity among females in the age group 60--69 years was 45% which rose to 55% in the age group ≥70 years [Figure 2]. The same for males in the age group 60--69 and 70+ years, was 35% and 45%, respectively, both being lower than that observed in females. The leading chronic condition among male and female was almost the same. Proportions of females reporting APD, CBA, arthritis, thyroid disease were higher than males.
Figure 2: Distribution of multimorbidity by age and gender among respondents

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Age and multimorbidity

As depicted in [Figure 3], the percentage of people with multimorbidity increased with age. Around 20% of 18--29 years' age group had at least one LTC which steadily rose to 80% (four fold) in the age group ≥70 years. Similarly, 2% individual in the age group 18--29 had multimorbidity (≥2 LTC) while that reached up to 20% (10-fold) in the age group 70+ years. Similarly, 1% 18--29 years' age group had 3 or more LTC which became around 15% (15 times) in the 70+ age group.
Figure 3: Distribution of multimorbidity in various age groups

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Hypertension was 44.3% in 30--39 years of age while 74.7% in the 60--69 years. Diabetes, another common chronic condition, exhibited a sharp increase in proportion from 6.1% in 18--29 years' age group to 39.8% in the age group of >70 years. Conditions like stroke and cancer were present among 1--3% of patients of all the age groups from 40 to 70+ years while epilepsy, depression, and tuberculosis were found more among the younger age group of 30--39 years. Hearing impairment was found significantly in the age group 60--69 (6%) and 70+ (10.3%).

Pattern of multimorbidity (dyads)

[Table 1] presents the matrix of pair-wise occurrence (dyads) of chronic condition among the outpatients. Diabetes was the most common co-occurring condition, primarily with hypertension (11.8%), arthritis (9.4%), heart disease (6.5%), vision impairment (5.6%), and chronic lung disease (CLD) (5.2%). After diabetes, hypertension co-occurred with different LTCs, notably with arthritis (17.7%), APD (17.7%), CBA (16.3%), and vision impairment (10.9%). Arthritis mostly co-occurred with CBA (9.2%), APD (6.5%), and vision impairment (5.4%). APD + CBA was seen among 6.8%, while CKD and hypertension among 4.7% cases. Thyroid + diabetes, thyroid + hypertension were in 4.6% and 6.5% respondents, respectively. The least was cancer being less than 0.5% co-occurring with other LTC.
Table 1: Dyads of multimorbidity among study participants

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Multimorbidity and health related quality of life

The physical and mental component scores for HRQoL was computed for patients with none, single, and ≥2 chronic conditions [Figure 4]. The mean QoL score for physical component was 43.56 for patients with no LTC and it reduced to 42.48 for patients with single chronic condition while it was 41.07 for multimorbidity. Similarly, mean mental QoL score considerably declined from 44.14 for patients with nil LTC to 40.79 for patients with multimorbidity.
Figure 4: Quality of Life index (a) Physical Component (b) Mental Component score

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  Discussion Top


Multimorbidity is a pugnacious challenge for public healthcare systems in LMICs, yet very few studies have assessed its burden in these settings.[17] Despite the substantial magnitude of NCDs, multimorbidity remains under-explored in healthcare settings in India.[8] Our study aimed to describe the first ever profile of multimorbidity among adult out patients attending public healthcare settings (primary, secondary, and tertiary level) in Odisha. We broadly described the proportion of out patients having multimorbidity and how it varied with sociodemographic characteristics and QoL.

More than half outpatients were found to have multimorbidity; one third (30%) were with ≥3 LTCs, while one quarter (25%) had two long standing conditions. Around 80% had at least one chronic condition. Our previous studies on primary care settings had found similar prevalence but of lower magnitude (31%) thus establishing multimorbidity as a frequently encountered phenomenon across all levels of healthcare practice.[9]

Multimorbidity was more common in those above poverty line compared to BPL. One of the reasons could be the increased access to healthcare among APC outpatients. However, as most of the previous studies have adjusted for the effects of socioeconomic status while investigating outcomes of multimorbidity, further studies examining multimorbidity across socio-economic gradient could provide a clearer picture.[18]

Among individual LTCs, hypertension led the chart being prevalent in 2/3rd outpatients (64%) in congruence with previous studies. The increased proportion could be for improved health literacy, better symptom appraisal by physicians and routine screening under the NCD programme. At the same time, such observed high prevalence is worrisome since hypertension is an established risk factor for cardiovascular disease and mortality.[19]

We found more than half outpatients as obese and one-fifth overweight with 3/4th of both male and female having either of the two. Both increased blood pressure, and obesity as individual metabolic risks have potential for consequent morbidities. The coinciding of hypertension and overweight particularly in young age group outpatients is concerning. HWCs may consider increasing interventions to recognize, manage and prevent obesity and nutritional advice with health promotion in routine healthcare practice.[20],[21],[22] Interestingly, we found the prevalence of thyroid diseases to be 10% across all age groups (30--70+), whereas the prevalence of psychiatric illness and cancer were the least.

Distribution of multimorbidity across age and gender revealed some interesting pattern. Multimorbidity was found to increase with age group, ranging from 6% in youngest group of females (18--29 years) to 57% in oldest (>70 years). Prior studies have reported that the prevalence of multimorbidity increases with age and is associated with higher healthcare cost and household expenditure.[23],[24],[25] In our previous work describing the prevalence of multimorbidity among primary healthcare attendees, although the prevalence was not greater than in the age-matched general population, but the overall prevalence in outpatients was higher than general population.[26]

Female outpatients reported higher multimorbidity than males across all age groups which is a revelation. The true magnitude could even be greater given the lower healthcare-seeking by females in India.[27],[28] Few studies, similar to our results, have demonstrated increased risk of multimorbidity among females. Further, some of these child-bearing age group women may become pregnant.[29] Since mostly pregnant women have been excluded from multimorbidity studies, future research should specifically assess this special group. Moreover, we found a higher percentage of females without education than males (21.40% vs. 15.83%). This could further worsen the situation since multimorbidity is known to have negative relationship with literacy.[30]

HRQoL, a composite index of well-being, is widely acknowledged outcome in chronic disease management.[16] We observed considerable attrition in QoL (physical and mental) in the multimorbidity group vis-à-vis single LTC. Patients with multimorbidity undergo frequent physician consultations, longer hospital stays, higher healthcare costs; along with potential adverse effects of multiple drugs use and unrecognized treatment burden which substantially impairs QoL.[4] Our previous study assessing HRQoL in primary healthcare patients had found negative effects of multimorbidity on both physical and mental health. Further multimorbidity is found to accentuate frailty and reduces the QoL in the pre-frail spectrum.[31] Analyzing multimorbidity through an equity lens could illuminate the underpinning complexities and heterogeneities of this phenomenon, especially the embedded context in which illnesses are experienced, including individual circumstances.[32]

Analysis of pairwise occurrence or dyads of LTCs showed diabetes to be the most commonly co-existing condition with other LTCs, the highest being with hypertension. Global diabetes report has suggested integrated management for improving diabetes care outcomes, especially when it is co-occurring with hypertension.[33] Our findings reemphasize strategizing the present NCD program so as to identify and manage the most commonly occurring clusters effectively while improving QoL.[34] Moreover, the kaleidoscopic clustering of LTCs within the motif of multimorbidity requires adherence to clinical practice guidelines which have potential to improve outcomes for a range of LTCs, while developing new guidelines for certain groups of multimorbidity. With physical--mental multimorbidity becoming commonplace in healthcare practice, a shift in approach from conventional “one disease- one treatment” to “one patient- whole care” paradigm is needed.[35]

Strengths and limitations

As we did not include information on onset of the LTCs, it is challenging to elucidate the direction of the association between the clusters. While aiding in our general understanding of chronic diseases profile, our data, though 5 years old, still provides the first ever landscape of multimorbidity at public healthcare level. Further, the findings offer greater insights into future public health research, particularly towards vulnerability stratification within multimorbidity for patient management.[36]


  Conclusion Top


To summarize, our study presents the first ever landscape of multimorbidity patterns among out patients attending public healthcare settings from Odisha, India. We found that nearly 3/4th of outpatients, were either obese or overweight. More than half of the respondents had multimorbidity (≥2 LTC) while one-third of them had ≥3 LTC. Hypertension was the most prevalent LTC. Multimorbidity increased with age-group, socio-economic status and education level. Females across all age groups had higher reported multimorbidity than males. Diabetes was found to be the most common co-occurring condition with hypertension. Physical and mental component of QoL were reduced in multimorbidity. In view of the increasing number of patients with multimorbidity, public healthcare needs to be strategized to develop appropriate treatment guidelines and provide care for these populations. The higher reported prevalence of multimorbidity in females reinforces the need to incorporate gender differences while studying multimorbidity and designing models of care. Estimation of multimorbidity in women during pregnancy is a significant research need. The relationship between socioeconomic gradient and multimorbidity from equity perspective also needs to be looked at.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.


  Appendix 1 Top


















 
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