Health care utilization and out-of-pocket expenditure of type 2 diabetic patients: A study in primary care in Bhubaneswar, India

ABSTRACT

Background:

Globally, noncommunicable diseases (NCD) demand a higher healthcare expenditure. Among NCDs, diabetes mellitus is often associated with multiple, co-existing chronic conditions. In low- and middle-income countries where most of the healthcare expenditure is borne out of pocket, diabetes management may pose a significant financial stress.

Methods:

A cross-sectional study was conducted in 17 urban primary healthcare facilities of Bhubaneswar to assess the healthcare utilization and out-of-pocket expenditure among type 2 diabetes patients attending these facilities. Healthcare utilization was determined by the number of visits to healthcare facilities in the last 6 months, and out-of-pocket expenditure was assessed by outpatient consultation fees, medicines, travels to health care facilities, and diagnostic tests. Total out-of-pocket expenditure was defined as the sum of these costs.

Results:

The median number of visits in 6 months for diabetes patients with any comorbidity was 4 and 5 for diabetes patients with more than 4 comorbidities. Among the comorbid conditions, depression, stroke, auditory impairment, and acid peptic disease were associated with higher healthcare utilization. The total out-of-pocket expense was 2.3 times higher among diabetes patients with any comorbid condition compared to patients with diabetes only. The total median expenditure was higher for diabetes patients having stroke, heart diseases, kidney diseases, and cancer compared with other comorbid conditions. The association of comorbidity in diabetes patients with health care utilization and out-of-pocket expenditure is statistically significant after adjustment for sociodemographic characteristics and diabetes duration.

Conclusion:

Considerable expenditure is incurred by diabetes patients attending primary healthcare facilities for the management of diabetes and other chronic conditions. This is a significant burden for diabetes patients below the poverty line and with limited or no insurance cover. There is a need to increase the coverage of insurance schemes to address the chronic conditions management expenditure of outpatients.

Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with morbidity, disability, and premature mortality. Diabetes mellitus (DM) patients frequently suffer from complications and related or unrelated comorbidities.[1,2] The debilitating nature of DM is associated with significant direct and indirect costs for treatment, managing complications and comorbidities. The increased use of healthcare resources with the presence of comorbidities is well established.[3] Struijs et al.,[4] for example, have inferred that different comorbid conditions have different effects on healthcare utilization and that nonvascular comorbidities are as important utilization drivers as vascular comorbidity for patients with diabetes, while DM patients without comorbidity use less care compared to those with comorbidity.

Among low- and middle-income countries like India, where almost two-thirds of healthcare financing is out-of-pocket, DM patients face an enormous cost burden. The absence of any cover or minimal insurance policies further amplify their costs and jeopardize their access to the necessary healthcare.[5] Bhojani et al.[6] concluded in their study in a poor urban neighborhood in South India that the out-of-pocket spending on chronic conditions doubled the number of people living below the poverty line in one month, with further deepening of their poverty. Attaei et al. observed a decline in adherence to medicines with an increase in out-of-pocket expenses, and improved adherence with low out-of-pocket expenditures and enhanced insurance coverage including medicine costs.[7] With a rapidly increasing number of DM patients in India, the burden of DM on total healthcare expenditure is likely to increase and, potentially, will have important consequences for the sustainability of healthcare.[8] This poses a challenge to the strengthening of the Indian healthcare system and the government’s plan to achieve universal health coverage by 2022.[9]

According to the latest National Family Health Survey report (NFHS 5), almost 50% of Indian households are not covered by any health insurance. The survey also reported nonuniform and unequal insurance coverage among the various regions. States with government-funded insurance schemes like Telangana, Andhra Pradesh, and Kerala had more than 60% of households with insurance but states like Bihar, Manipur, and Jammu Kashmir reported less than 20% of household with any type of health insurance.[10] Presently, some of the government-funded healthcare schemes in India are Pradhanmantri Jan Arogya Yojna (PMJAY) under Ayushman Bharat, Awaz Health Insurance Scheme, Aam Admi Bima Yojna, Bhamashah Swasthya Bima Yojana, Biju Swasthya Kalyan Yojana, Central Government Health Scheme (CGHS), Employees’ State Insurance Scheme, Dr YSR Aarogyasri Health Care Trust Andhra Pradesh State Government, Telangana State Government–Employees and Journalists Health Scheme, Rashtriya Swasthya Bima Yojana, West Bengal Health Scheme, and Yeshswani Health Insurance Scheme.[11] Among the government-sponsored schemes, most of the schemes are state specific or for a particular group, for example, Awaz Health Insurance Scheme is for migrant population in Kerala and Central Government Health Scheme is for the employees working in the Central Government organizations. Government-sponsored insurance schemes in the past have had limited success in reducing the out-of-pocket expenditure and financial burden of the population. One of the reasons for this could be the coverage being limited to in-patient hospitalization costs and not taking outpatient costs into consideration. Outpatient costs like consultation fees, medicine, and laboratory tests expenses also considerably impact the out-of-pocket expenditure. To address the need for a comprehensive health coverage and reduce out-of-pocket expenditure, Pradhanmantri Jan Arogya Yojna (PMJAY) was launched under the Ayushman Bharat Yojna.[12] However, it is yet to be implemented in all states of India. A preliminary study conducted in Chhattisgarh by Garg and colleagues have reported minimal benefits of PMJAY so far.[13]

The Rashtriya Swasthya Bima Yojana (RSBY) or National Health Insurance Program launched by the Indian Ministry of Labor and Employment (currently under the purview of the Department of Health and Family Welfare)[14] to provide insurance coverage for inpatient care to “Below Poverty Line” families and the unorganized labor force does not cover outpatient care expenses. Prior studies have also mentioned that not including expenditure on medicines, laboratory testing, and outpatient visits has limited the role of RSBY in mitigating financial risk among the beneficiaries.[15]

While exhaustive studies from Europe have been carried out on the expenditures for DM care and comorbidities, there is a paucity of data from the Indian subcontinent on the expenditure pattern of DM patients with comorbid conditions, especially in the primary care setting.

Therefore, there is a need to explore the costs related to DM care at the primary care level. The present study provides an overview of the healthcare utilization and out-of-pocket expenditure of T2DM patients attending primary health care facilities in Bhubaneswar, India. We examined the impact of comorbidity on healthcare utilization and costs borne by T2DM patients and the effects of different comorbidities on health care utilization and out-of-pocket expenditures.

Material and Methods

Study design and setting

A cross-sectional interview survey was conducted in all 17 urban primary healthcare centers in Bhubaneswar, the capital city of Odisha with a population of 900,000 inhabitants.[16] According to the National Sample Survey Office’s 71^st round on social consumption of health, about 72% of outpatient care in Odisha is provided by public healthcare professionals.[17] The public health care system has a three-tier structure (primary, secondary and tertiary levels). Primary Health Care Centers are involved in delivering primary care while district hospitals and subdivisional hospitals provide secondary care. Tertiary health care is provided by medical college hospitals. The sampling design for this study was two stages. First, all the 17 primary health centers under Capital Hospital were selected for the study. Second, diabetes patients were randomly recruited from each center. Details of the sampling design are given in [Appendix 1].

Study participants

Patients attending a primary healthcare center between September 2014 and February 2015 who had been diagnosed by a physician with T2DM for more than 6 months according to their personal medical record were eligible to be included in the study. The inclusion criterion of diabetes duration of at least 6 months was applied because we needed information about healthcare utilization for diabetes. Patients too ill to participate or with emergency health conditions were excluded from the study. Anonymized details of all patients excluded (age, gender, reason for exclusion) were recorded to compare the characteristics of the participants with the nonparticipants.

Ethics approval and consent to participate

The Odisha State Research and Ethics Committee granted ethical approval for the study (letter no. 161/SHRMU dated 16/05/2014). Respondents were informed about the purpose of the study and the information used. We collected their signature or thumb impression on the informed consent form. The data were coded, and the identities of the respondents were kept confidential.

Measurements

The participating patients were interviewed in a separate private room using a predesigned and pretested questionnaire, Diabetes Co-morbidity Evaluation Tool in Primary Care (DCET- PC). The DCET-PC is derived from “Multimorbidity Assessment Questionnaire for Primary Care,” a validated questionnaire, which was pretested and the feedback was used to adapt the questionnaire for our study.[18] Two graduate nurses trained in patient history-taking and interview techniques carried out the interviews, and 10% of the interviews were done in the presence of the first author. The DCET-PC [Appendix 2] included questions about the existence of comorbid conditions, eliciting information on whether the patient had any of the15 listed chronic conditions, and socio-demographic details, i.e., age, sex, residence (rural, semi-urban, urban), ethnicity (general, scheduled caste and tribe, other backward classes), religion (Hindu, Muslim, Christian, others), educational level (no education, primary level, secondary, graduate and above), marital status (single, married), annual family income (categorized into five quintiles), and household status (above poverty line, below poverty line). The details of the development and domains of the DCET-PC questionnaire were described in our previous paper.[1]

We estimated comorbidity as the presence or absence of any comorbidity, which was further categorized into the number of comorbid conditions (zero, one, two, three, four or more chronic conditions). The presence of a pattern of comorbidity combination in one individual patient was derived using a simple combination for two more chronic conditions. Healthcare utilization was operationalized as the reported number of visits to any healthcare facility in the last 6 months for any reason. Expenditure was measured in Indian Rupees (INR) by asking about expenses incurred in the last 6 months separately for outpatient consultation fees, medicines (for DM and other diseases separately), travelling to those healthcare facilities, and diagnostic tests (for DM and other diseases separately). Total out-of-pocket expenditure was defined as the sum of these costs and rounded to the nearest absolute number.

Analysis

To estimate the healthcare utilization, median (interquartile ranges) number of visits done by the patient to any healthcare facility during last 6 months were calculated. Healthcare utilization and out-of-pocket expenditure were further described across the number of comorbid conditions and the prevalence of leading comorbidities. Bivariate comparison was performed using a Kruskal–Wallis test for quantitative data (based on median values) and a Chi-square test for categorical data. Furthermore, we calculated the median and interquartile ranges of out-of-pocket expenditure by comorbidity status (Yes/No). The difference in mean out-of-pocket expenditure and healthcare utilization across the comorbidity groups was tested using Kruskal–Wallis test.

Both the outcomes in our study were count data and with less than 5% patients had “zero” values. Therefore, a Poisson regression model in multilevel mixed-effects methods was used with two levels (health center and patient) for multivariate analysis to assess the independent contribution of comorbidity on healthcare utilization and out-of-pocket expenditure. The collinearity between the variables was tested before including them in multivariate analysis. Adjusted risk ratio was calculated for each predictor for estimating health care utilization and expenditure. The details of the model fit statistics, variances across the levels, and the intraclass coefficient for each adjusted model are provided in [Appendix 3]. A P value of < 0.05 was considered statistically significant. Analyses were performed in STATA Corp-12 Tx.

Results

Participants

We approached 942 T2DM patients, of whom 912 (97%) consented to be interviewed. The reasons cited for not participating were lack of time and unwillingness to answer. Of all respondents, 575 (63%) were male. The highest number of respondents was in the age group 40–69 years [N = 766 (83%)]. The mean age of the respondents was 55 years. The mean number of health facility visits was 7.1 (SD: 11.7) and the median was 4 (IQR: 3–7). The mean total healthcare expenditure was INR 2653 (SD: 2975) and the median was INR 1810 (IQR: 1050–3140 INR). Nearly 84% of patients had comorbidity, 29% had single comorbidity, 25% had two comorbidities, 17% reported having three, and 14% had four or more comorbidities [Table 1].

Table 1.

Basic characteristics of type 2 diabetes patients by comorbidity status (n=912)

	Without comorbidity (n=146) % [95% CI]	With comorbidity (n=766) % [95% CI]
Age group (years)
18-29	1.3 [0.0-3.2]	0.1 [0.0-0.4]
30-39	8.7 [4.1-13.2]	6.3 [4.5-8.0]
40-49	28.7 [21.4-35.9]	20.3 [17.4-23.1]
50-59	39.3 [31.5-47.2]	33.4 [30.1-36.7]
60-69	17.3 [11.2-23.4]	29.5 [26.2-32.7]
>=70	4.7 [1.3-8.1]	10.5 [8.3-12.6]
Gender
Male	74.0 [66.9-81.1]	61.0 [57.5-64.5]
Female	26.0 [18.9-33.1]	39.0 [35.5-42.5]
Place of residence
Urban	76.0 [69.1-82.9]	78.4 [75.5-81.3]
Semi Urban	8.7 [4.1-13.2]	11.4 [9.1-13.6]
Rural	15.3 [9.5-21.1]	10.2 [8.0-12.4]
Ethnicity
Schedule Caste	14.7 [8.9-20.3]	31.5 [28.2-34.8]
Schedule Tribe	5.3 [1.7-8.9]	13.1 [10.7-15.5]
Other Backward Caste	22.7 [15.9-29.4]	12.6 [10.2-14.9]
Others	57.3 [49.4-65.3]	42.8 [39.3-46.3]
Socioeconomic status
Above Poverty Line	36.2 [24.8-47.7]	70.5 [66.4-74.6]
Below Poverty Line	63.8 [52.3-75.2]	29.5 [25.4-33.6]
Highest Education
Illiterate	8.7 [4.1-13.2]	8.4 [6.4-10.3]
Primary	22.0 [15.3-28.7]	16.0 [13.4-18.6]
Secondary	32.7 [25.1-40.2]	34.8 [31.4-38.2]
University	36.7 [28.9-44.4]	40.8 [37.3-38.2]
Marital Status
Single	13.2 [10.8-15.6]	8.7 [4.1-13.2]
Married	86.8 [84.4-89.2]	91.3 [86.8-95.5]
Religion
Hindu	92.0 [87.6-96.4]	88.4 [86.1-90.6]
Other	8.0 [6.6-12.4]	11.6 [9.4-13.9]
Family history of diabetes mellitus
Yes	10.7 [5.7-15.6]	24.7 [21.7-27.8]
No	89.3 [84.4-94.3]	75.3 [72.2-78.3]
Risk Factor: BMI
Underweight	4.7 [1.3-8.1]	2.1 [1.1-3.1]
Normal	40.0 [32.1-47.9]	20.0 [17.2-22.9]
Overweight	19.3 [13.0-25.7]	19.4 [16.5-22.2]
Obese	36.0 [28.3-43.7]	58.5 [55.0-62.0]
Health facility visits in last 6 months
Never	2.0 [0.1-4.2]	1.0 [0.3-1.8]
1-2 visits	26.7 [19.5-33.8]	16.7 [14.1-19.4]
3-4 visits	20.0 [13.6-26.4]	36.4 [32.9-39.8]
5-6 visits	24.7 [17.7-31.6]	20.1 [17.2-22.9]
7-8 visits	15.3 [9.5-21.1]	12.4 [10.1-14.8]
9 or more visits	11.3 [6.2-16.4]	13.3 [11.0-15.8]
Total expenditure (INR)
Zero	4.00 [1.81-8.62]	0.65 [0.27-1.56]
1-500	20.0 [14.3-27.2]	6.7 [5.1-8.7]
501-1000	24.0 [17.8-31.5]	11.5 [9.4-14.0]
1001-2000	35.3 [28.1-43.3]	30.6 [27.4-34.0]
2001-3000	9.3 [5.6-15.1]	19.8 [17.1-22.7]
>3000	7.3 [4.1-12.8]	30.7 [27.5-34.0]

1000 INR=13.4 USD (as on 30.06.2021); BMI - Body Mass index; INR - Indian Rupees

Health care utilization

The median number of visits of T2DM patients without any comorbidity in 6 months was 5 (IQR = 5) and 4 (IQR = 4) for patients having any comorbidity and 5 (IQR = 5) for diabetes patients with four or more comorbidities [Table 2]. Among DM patients with comorbidity, the median number of visits was highest for patients with depression 6 (IQR = 4), acid peptic disease (APD) 6 (IQR = 5), auditory impairment/deafness 6 (IQR = 5), stroke 6 (IQR = 17), followed by thyroid disease 4.5 (IQR = 5), cancer 4.5 (IQR = 5), and visual impairment/blindness 4 (IQR = 5) [Table 3].

Table 2.

Healthcare utilization by number of comorbidities

Number of comorbidities	Number of visits to health facility in last 6 months Median [Range]
Zero	5 (0-55)
1	4 (0-56)
2	4 (0-59)
3	4 (0-46)
>=4	5 (0-57)
Total	4 (0-59)
Diabetes with any Comorbidity	4 (0-59)
Kruskal-Wallis test, F (P)	F=0.707, P=0.587

Table 3.

Out-of-pocket expenditure and healthcare utilization across comorbid conditions

Conditions Combinations	Number of visits in last 6 months median (range)	Total expenditure (In INR) median (range)
DM + Hypertension (n=181)	4 (0-53)	2100 (115-25700)
DM + Acid Peptic Disease (n=74)	6 (0-59)	1630 (115-25700)
DM + Obesity (n=54)	4 (0-55)	870 (0-9100)
DM + Backpain (n=48)	4 (1-44)	2000 (450-7200)
DM + Arthritis (n=39)	3.5 (0-59)	1715 (550-7200)
DM + Visual impairment/Blindness (n=25)	4 (2-53)	1930 (280-5990)
DM + Thyroid (n=22)	4.5 (2-59)	1980 (350-10150)
DM + Lung Disease (n=16)	4 (2-12)	2030 (410-6900)
DM + Heart Disease (n=13)	4 (2-52)	3600 (2600-16100)
DM + Stroke (n=7)	6 (3-39)	4220 (800-10150)
DM + Kidney Disease (n=6)	4 (2-9)	3167.5 (1740-18100)
DM + Epilepsy (n=6)	3.5 (1-5)	1565 (350-2130)
DM + Cancer (n=6)	4.5 (2-8)	2685 (1210-6020)
DM + Deafness (n=5)	6 (2-8)	2480 (1200-21000)
DM + Depression (n=3)	6 (4-8)	1860 (1300-2020)

DM - Diabetes Mellitus; INR - Indian Rupees

Out-of-pocket expenditure

We found a linear increase in total expenditure along with costs for medicines on diabetes, medicines for other diseases, and laboratory testing for other diseases with the number of comorbidities, which was statistically significant [Table 4]. No significant association was found between expenditures for travels to hospital and laboratory investigation for diabetes, and the number of comorbid conditions. Patients with any comorbidity spent two times more compared to those having no comorbidity. Diabetes patients with any comorbid condition had a two times higher expenditure for medicines (for diabetes and comorbidity) than patients with only diabetes. Among patients having one chronic condition, the median total expenditure ranged from 1565 INR to 4220 INR. The total median expenditure was higher for patients having stroke, heart diseases, kidney diseases, and cancer compared to other comorbid conditions [Table 4].

Table 4.

Out-of-pocket expenditure by number of comorbidities

Number of comorbidities	Median
	Medicine Diabetes	Medicine Other disease	Travel to Hospital	Test Cost for Diabetes	Test Cost for Other diseases	Total Expenditure
Zero	500	0	50	300	0	1045
1	600	200	50	331.72	0	1400
2	800	500	40	300	0	2000
3	1000	500	40	300	200	2460
>=4	1000	1000	5	400	400	3110
Diabetes with any comorbidity	800	440	40	300	100	2030
Kruskal-Wallis test (F, P)	11.14, <0.001	11.31, <0.001	0.80, 0.524	1.94, 0.102	13.42, <0.001	14.21, <0.001

Multivariate analyses

Multivariate adjusted multilevel mixed-effect Poisson regression analyses showed a strong positive association of diabetes patients with comorbidities with healthcare utilization (RR 1.33; 95% CI 1.24–1.43) and out-of-pocket expenditure (RR 1.97; 95% CI 1.96–1.98) [Table 5] compared to diabetes patients without comorbidity.

Table 5.

Predictors of healthcare utilization and total out-of-pocket expenditure of diabetes patients (n=912) using multilevel mixed-effect Poisson modelling (adjusting for clustering)

Variables	Categories	Healthcare utilization		Total expenditure
		Unadjusted RR [95%CI]	Adjusted RR# [95%CI]	Unadjusted RR [95%CI]	Adjusted RR# [95%CI]
Comorbidity	Only Diabetes	Reference	Reference	Reference	Reference
	DM with comorbidity	1.31 [1.22-1.40]*	1.33 [1.24-1.43]*	2.20 [2.19-2.21]*	1.97 [1.96-1.98]*

^#Adjusted for patient characteristics (diabetes duration, age, sex, educational status, income, and marital status); *P<0.05; CI-Confidence interval; DM - Diabetes Mellitus; RR - relative risk

Discussion

The present study assessed the healthcare utilization and out-of-pocket expenditure among patients with type 2 diabetes with and without comorbidities attending primary healthcare centers in India. Our study indicates the substantially larger number of visits to healthcare facilities among T2DM patients with comorbidity compared to those without comorbidity, which is similar to findings of prior studies outside India.[4,19] We also found that the largest proportion of the total out-of-pocket expenditure went on medicines. Sum et al.[20] also concluded in their study on multimorbidity and out-of-pocket expenditure that the costs of medicines contributed to a substantial share of total expenditure. Another major finding of our study is that T2DM patients with any additional comorbidity had increased total out-of-pocket expenditure along with costs for medicines for diabetes, medicines for other diseases, and laboratory testing for other diseases. This expenditure increased with the number of comorbidities. The other finding is the higher prevalence of diabetes comorbidities among the above poverty line participants, which is contrary to the finding from studies in developed countries but similar to findings of studies conducted in India.[21,22]

Wang et al.[3] found a linear increase in outpatient hospital visits for each successive diabetic complication. Similarly, Gruneir et al.[23] inferred that there is increased utilization of all health services with an increase in the number of comorbid conditions. Comparable to other studies, our study confirms the higher number of visits to health facilities in the previous 6 months among T2DM patients with APD, stroke, deafness, and depression.[24] The higher healthcare utilization of T2DM patients with comorbid depression was also noted by Egede et al.[25] in their study. Calderón-Larrañaga and colleagues found an increased healthcare utilization among diabetes patients with mental and discordant comorbidities.[26]

Our finding of increased expenditure due to comorbidity among T2DM patients is consistent with the results from previous studies in middle- and low-income countries.[27,28,29] Tharkar et al.[30] concluded that the presence of an additional comorbid condition further enhances the cost burden among diabetes patients. Similarly, Akari et al.[31] analyzed the healthcare costs by calculating the direct and indirect costs of DM with comorbidities among hospitalized patients in a tertiary care hospital and concluded that higher expenses were incurred by diabetes patients with three or more comorbidities and also those with macro-vascular complications. Acharya et al.[32] assessed the costs of illness for DM patients with or without complications hospitalized in a tertiary care hospital; they concluded that diabetes patients with renal and cardiac complications incurred greater expenses than those with other chronic complications. These studies only investigated the cost of concordant comorbidities and complications associated with diabetes. As our study has considered both concordant and discordant comorbidities, comparability to these studies is limited. Our study has reported that out-of-pocket expenditure and healthcare utilization due to discordant comorbidity are almost equal to concordant comorbidities among diabetes patients. Piette and Kerr[33] classified comorbid conditions as concordant or discordant and concluded that concordant conditions resulted in better diabetes care but the clinically dominant condition may lead to worse diabetes management. Other studies from developed countries have also reported findings similar to the present study.[34,35]

Our study findings have special relevance for the practice of primary care physicians. The study finding that higher costs are associated with different types and number of comorbidity among diabetes patients can help in guiding primary care physicians in designing appropriate management plan of those patterns of comorbidities in diabetes patients. It would help in identifying the components incurring maximum expense, for example, drugs for various chronic conditions are highly expensive than laboratory tests. These findings will help primary care physicians to devise a rational and affordable treatment plan. Similarly, early and low-cost screening with preventive steps for diabetes patients with hypertension can be prescribed by the primary care physicians to prevent the onset of further conditions like chronic kidney disease and stroke incurring high expenses for management.

Strengths and limitations

This is the first study in India assessing the healthcare utilization and out-of-pocket expenditure among diabetes patients attending primary health care facilities and also taking a wide range of comorbidities into account, i.e., both concordant and discordant comorbid conditions. The findings are generally representative of urban primary care users in India.

Self-reported comorbidity status as reported by the patients is one of our study limitations. Patients who had not been diagnosed or had conditions that were not reported were not included. The exclusion of undiagnosed type 2 diabetes mellitus patients is the other limitation of this study. As it is a cross-sectional study, it shows associations but not causal relations. The lack of glycated hemoglobin (HbA1c) data, which would have helped in studying the impact of glycemic control and healthcare utilization and out-of-pocket utilization, is another limitation. Because of the smaller sample size in the pattern of comorbidities, a further detailed analysis was not possible.

Impact on policy and future research

The draft of India`s National Health Policy states that 63 million people have been pushed into poverty due to out-of-pocket expenditure on healthcare. As India progresses toward Universal Health Coverage, the financial burden posed by comorbidity among diabetes patients need to be considered in greater detail. The findings from the ICMR-INDIAB study by Anjana and colleagues[36] confirmed the higher prevalence of diabetes among low socioeconomic sections in urban areas. In the light of the findings of our study, this reiterates the need for a more comprehensive and robust policy to address out-of-pocket expenditures.

There is a need to assess the components of expenditure incurred and to identify components having the maximum impact on expenses, for example, spending by the category of drugs, laboratory investigation or visits to multiple centers to manage their various comorbidities. The indirect expenses like loss of wages and disability-adjusted life years (DALY) should also be studied.

The present study indicates higher healthcare utilization among T2DM patients with comorbid depression. With the government of India`s thrust to expand the National Mental Health Program, the provision of mental health counselors at the primary care level could go a long way toward better management. As our study suggests that T2DM patients with comorbidities incur considerable out-of-pocket expenses, even in public primary care facilities, it can be expected that the expenses will be higher in private healthcare facilities. Hence, increased insurance coverage that includes outpatient services would help in alleviating the expenditure burden.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Appendix-1 Sampling design

Appendix-2

DCET-PC Questionnaire

1. Date:

2. Code No........

3. Age ( in years).......

4. Sex.....................

5. Completed education... [_] Illiterate [_]Primary level [_]High School or Secondary level [_] Graduation or above

6. Religion [_]Hindu [_] Muslim[_]Christian [_]Others

7. Marital Status [_]Unmarried [_]Married [_}Widow[_]Separated

8. Ethnicity [_]SC [_]ST [_]OBC[_] Others

9. No. of people living in your household (including you)...........

10. Employment status-[_] Employed[_]Unemployed[_]Homemaker[_]Retired

11. On an average how many hours do you work in a day ...............(hrs)

12. Net monthly income..[_] <10k [_] 10k-20k [_] 20k-30k [_] <30kINR

13. Net monthly income of your household ..................INR

14. APL/BPL (as per ration card)....................................

15. Housing type [_]Kutcha [_]Pucca [_]Semi Pucca

16. Place of living [_] Urban [_] Semi Urban [_]Rural

17. Where have you spent major part of your adult life [_] Urban [_] Semi Urban [_] Rural

18. Family history of diabetes (parents or siblings suffer from diabetes) [_] Yes [_] No

19. Date on which you were diagnosed with diabetes mellitus __/_ _/ _ _

20. Where were you first diagnosed with diabetes [_]Public health care facility [_] Private health care facility

21. Presently are you taking any prescribed medicines for diabetes [_]Yes [_]No.

22. If yes, are you taking [_]Oral antidiabetes pills[_] Insulin [_]Both

23. Are you taking any alternative medicines for diabetes [_]Ayurvedic [_]Homeopathic [_]Others [_]None

24. Co existing Conditions		Yes/ No
Arthritis	A. Have you ever been diagnosed by a doctor with Arthritis?	Yes[_] No[_]
	In thelast 12 months have you experiencedpain, aching, stiffness or swelling in or around the joints (like arms, hands, legs or feet)which were not related to an injury and lasted for more than a month?	Yes[_] No[_]
	If yes, then are you taking any prescribed medications?	Yes[_] No[_]
Hypertension	Have you ever beendiagnosedby a doctor withhigh blood pressure?	Yes[_] No[_]
	If Yes, then are you taking any prescribed medicines for high blood pressure (hypertension)?	Yes[_]No[_]
	Not Applicable	NA
Chronic Lung Diseases(Including Asthma)	Have you ever beendiagnosed withChronic Lung Disease (Emphysema, Bronchitis, Asthma, COPD)?	Yes[_]No[_]
	If yes, then are you taking any prescribed medications for it?	Yes[_]No[_]
	Not Applicable	NA
Acid Peptic Disease	Have you ever beendiagnosedby a doctor withAcid -Peptic Ulcer disease(Gastritis) in thelast 12 months?	Yes[_] No[_]
	Not Applicable	NA
Chronic Back ache	In the last 12 months have you beendiagnosedby a doctor with chronic back pain?	Yes[_] No[_]
	In the last 12 months, have you had continuous Back pain for more than 3 weeks?	Yes[_] No[_]
Heart disease	Have you ever been diagnosed by a doctor with Angina/heart attack/heart disease?	Yes[_] No[_]
	If yes then are you taking any prescribed medicines for it?	Yes[_] No[_]
	In the last 12 months have you experienced any pain or discomfort in your chest when you walk uphill or hurry or normal walking?	Yes[_] No[_]
Stroke	Have you ever been told by a health professional that you have had a Stroke?	Yes[_] No[_]
	If yes, are you taking any prescribed medication for it?	Yes[_] No[_]
	In the last 12 months have you suffered from sudden onset of paralysis or weakness in your arms or legs on one side of your body for more than 24 hours?	Yes[_] No[_]
Blindness	Have you been diagnosed by a doctor with blindness?	Yes[_] No[_]
	Do you have difficulty with vision (Answer No if you can see OK with glasses)?	Yes[_] No[_]
Deafness	In the last 12 monthshave you been diagnosed by a doctor with deafness?	Yes[_] No[_]
	In the last 12 monthsdo you have Deafness or difficulty in hearing for more than 3 months?	Yes[_] No[_]
Cancer	Have you ever been diagnosedby a doctor with any type of cancer?	Yes[_] No[_]
Chronic Kidney Diseases	Have you ever been diagnosedby a doctor with long term kidney problem?	Yes[_] No[_]
	Have you ever been on dialysis?	Yes[_] No[_]
Epilepsy	Have you ever been toldby a health professional that you have Epilepsy?	Yes[_] No[_]
	If yes, are you taking any prescribed medications?	Yes[_] No[_]
	Have you ever sufferedfrom sudden onset of seizurewhile at work or at rest?	Yes[_] No[_]
Thyroid Disease	Have you ever been diagnosedby a doctor with Thyroid diseases?	Yes[_] No[_]
	If yes are you taking any prescribed medication?	Yes[_] No[_]
Tuberculosis	Do you suffer from TB?	Yes[_] No[_]
	Are you underany treatment for TB?	Yes[_] No[_]
Depression	Have you ever been diagnosedby a doctor with depression?	Yes[_]No[_]
	In the last twelve months have you consulted a doctor for feeling sad or depressed, worried or anxiety?	Yes[_]No[_]
	If yes, are you taking any prescribed medications for depression?	Yes[_]No[_]
	Not Applicable	NA

25. Do you suffer from any other chronic health problems? [_] Yes [_] No

If yes, name them-

1......................................................................

-Is it diagnosed by a doctor? Yes [_]No[_]

-Are you taking any prescribed medications for it? Yes [_]No[_]

-How much is it limiting you in your daily life? 1. Not at all [_] 2.A little [_] 3.Somewhat [_] 4. Quite a bit [_] 5. A lot [_]

2.......................................................................

-Is it diagnosed by a doctor? Yes[_]No[_]

-Are you taking any prescribed medications for it? Yes[_]No[_]

-How much is it limiting you in your daily life? 1. Not at all [_] 2.A little [_] 3.Somewhat [_]

4. Quite a bit [_] 5. A lot [_]

26. How much do you spend on medications per month?

1. For diabetes-...............INR

2. For other diseases.............INR

27. How much money do you spend on travel expenses for every visit to the health care facility...............INR

28. How many times in the last 6 months have you visited a health care facility? ....................................

29. How much money on an average do you spend in 6 months on laboratory investigations and tests for diabetes (blood sugar test, glycated haemoglobin estimation)? .........................................INR

-How much time do you spend on these tests? .............................

30. How much money do you spend on laboratory tests for other chronic conditions?......................INR

-How much time do you spend for these tests?......................................

31. Height.........................

Weight..................

BMI............................

>25 Yes[_] No[_]

<25 Yes[_] No[_]

Appendix-3 Model statistics (Multilevel mixed effect Poisson regression)

	Healthcare utilization		Health expenditure
	% of variance (95% CI)	Intraclass coefficient (95% CI)	% of variance (95% CI)	Intraclass coefficient (95% CI)
Level (Practice)	4.3 (1.5-12.2)	16.1 (12.8-19.7)	5.2 (2.1-13.5)	14.3 (9.2-20.4)
Level (Patient)	46.7 (41.3-52.7)	64.2 (52.5-76.8)	42.9 (38.5-47.1)	58.7 (45.9-71.3)
Goodness of fit	Chi square=3434.54; P<0.001		Chi square=6574.93; P<0.001