|Ahead of print publication
The determinants of out-of-pocket health-care expenses for diabetes mellitus patients in India: An examination of a tertiary care government hospital in Delhi
Saurav Basu1, Suneela Garg1, Nandini Sharma1, M Meghachandra Singh1, Sandeep Garg2, Miqdad Asaria3
1 Department of Community Medicine, Maulana Azad Medical College, New Delhi, India
2 Department of Medicine, Maulana Azad Medical College, New Delhi, India
3 Department of LSE Health, London School of Economics and Political Science, London, UK
Department of Community Medicine, Maulana Azad Medical College, New Delhi
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Objective: To assess the determinants of out-of-pocket (OOP) expenses on diabetes-related treatment incurred in patients attending outpatient clinics in a tertiary care hospital in Delhi, India.
Study Design: A cross-sectional analysis of baseline data from a quasi-experimental study was conducted over 8 months in 2016 in a major tertiary care hospital in Delhi.
Methods: The study included 375 diabetes patients up to 65 years of age on treatment for at least a year without significant complications. Data were collected through a patient interview schedule.
Results: Of the previous six scheduled appointments, at least two missed appointments were seen in 267 (71.2%) patients. The average patient's OOP expenditure on diabetes-related medicines was ₹63.5 a month, a similar amount was spent on traveling to and from health facilities. Sixty-four (17.1%) patients took antidiabetic medication for <85% of the days in the previous 3 months.
Conclusion: There exists a high burden of missed clinic appointments among diabetes patients in tertiary care government health settings in India. This appears to be related to the high cost in terms of both time and money involved in attending appointments for the modest benefit of a dispensation of a 15-day drug refill. Health policy measures focused on strengthening medication coverage need to explore the balance of costs and benefits when determining the frequency of clinical appointments in these settings.
Keywords: Delhi, diabetes, India, medication adherence, out-of-pocket expenses
|How to cite this URL:|
Basu S, Garg S, Sharma N, Singh M M, Garg S, Asaria M. The determinants of out-of-pocket health-care expenses for diabetes mellitus patients in India: An examination of a tertiary care government hospital in Delhi. Perspect Clin Res [Epub ahead of print] [cited 2019 Sep 23]. Available from: http://www.picronline.org/preprintarticle.asp?id=257232
| Introduction|| |
Diabetes mellitus (DM) and its related complications constitute a significant cause of morbidity and mortality globally. Developing countries account for 80% of the global diabetes disease burden which is expected to further increase in the coming decades as these countries increasingly adopt unhealthy dietary patterns and sedentary lifestyles and are thus faced with an increasing obesity burden.,, India has the second highest diabetes disease burden in the world. However, there is considerable geographic and regional variation in this burden with urban areas and more particularly urban slums showing the highest prevalence of diabetes.,
The cost of diabetes management involves direct, indirect, and intangible costs. Direct medical costs are related to economic expenses for drug treatment of diabetes with oral hypoglycemic agents and insulin, routine investigation, and physician consultation. Indirect costs emerge from diabetic morbidity which reduces patient's creative, educational, and economic potential and productivity. The intangible costs refer to the pain and suffering of patients due to diabetes complications such as neuropathy and nephropathy that lower the patient's quality of life. In this study, we focused only on the direct costs of diabetes management.
The inability of uninsured DM patients to finance their direct medical costs by out-of-pocket (OOP) payments can result in poor medication adherence resulting in a suboptimal benefit of treatment with adverse health outcomes., Poorly controlled diabetes due to incomplete and ineffective treatment increases the likelihood of early onset of diabetic complications which involve considerable indirect and intangible costs., The magnitude of medical costs increases enormously in the management of vascular complications of DM such as cardiovascular disease and renal failure. Medication costs in India often exceed catastrophic expenditure limits in economically disadvantaged populations, rendering them at risk of further impoverishment which in turn creates a vicious cycle of poverty and ill health., Furthermore, in India, DM patients from the lower socioeconomic strata (SES) utilize a higher proportion of their household income on expenses related to diabetes care, contributing further to health inequity.
Due to a lack of health insurance financing, health care in India is predominantly financed by OOP payments (62.4%)., An analysis of the diabetes-related OOP expenditure in India revealed that costs of medication constitute the predominant component of patient spending. Consequently, India's National Health Program for Prevention and Control of Diabetes, Cardiovascular diseases, Cancer and Stroke (NPPCDS) targets the achievement of universal health coverage (UHC) by mitigating OOP health expenses. Government health facilities provide free antidiabetic medications to diabetes patients availing outpatient department (OPD) or inpatient department services.
It is essential to ascertain OOP health expenditure in DM patients who receive treatment in public health facilities because large segments of the Indian population, mainly from the lower SES, mostly working in the informal sector of the economy, will continue to depend on public health facilities for diabetes care. This is because most of the Indian economy is informal; hence, much of the workforce lack social security benefits, limiting their opportunities for employer-based health coverage.
The tertiary care government hospitals as per the NPPCDS have been identified as the key drivers for the provision of comprehensive noncommunicable disease care through a hub-and-spoke (district) model. The objective of this study is to assess the OOP expenses on diabetes-related treatment incurred by diabetes patients attending the outpatient clinic of a tertiary care hospital in Delhi, India, and to understand the determinants of these OOP expenses.
| Methods|| |
This is a cross-sectional analysis of the baseline data from a quasi-experimental trial, part of a larger study that reported on the behavioral aspects of diabetes self-care.
The study was conducted in the outpatient clinic of a major tertiary care government hospital in Delhi, India. Diabetes Mellitus patients were dispensed antidiabetic medication refills for 15 days from the hospital pharmacy during their appointment. These medication refills include all the prescribed medications and are provided absolutely free of charge. Patients are expected to procure refills through regular follow-up appointments every 2 weeks after obtaining a written prescription order from any of the treating physicians at their designated OPDs.
Inclusion and exclusion criteria
Adult DM patients up to 65 years of age on treatment for at least 1 year were included in the study. Patients with comorbidities and/or complications indicating advanced cardiovascular disease, renal failure requiring dialysis, previous cardiovascular accident, blindness, and needing psychotropic drugs were excluded as their health needs were expected to significantly differ from those of uncomplicated DM patients.
The data in the study were collected during 8 months from February to September 2016. The sampling universe comprised DM patients registered at the diabetes and endocrinology OPDs of the hospital. The proportion of patients spending OOP was expected to be equal to the national estimate of 62.4%. The appropriate sample size for the study was calculated to be 371, targeting a 95% confidence level, 5% margin of error, and accounting for 10% nonresponders.
The respondents were enrolled through consecutive sampling, i.e., the DM patients were enrolled one after the other, with a maximum of 12 patients being enrolled in a day. Data were collected through face-to-face interviews using a pretested patient interview schedule. The patients were asked to self-report the OOP expenses incurred on diabetes-related medications in the previous 1-month period and the usual costs of transport for traveling to and returning from the hospital. Medications for treating hypertension and/or lipid disorders in the comorbid patients were included in the total diabetes-related OOP expenses.
Refill adherence among the patients was estimated as per the proportion of days covered (PDC) method for the previous 3 months from the day of the interview. The PDC is calculated as the number of days in which a medication was available with the patient divided by the total number of days in the data analysis period. Patient medical records were evaluated to assess the ongoing drug treatment and the number of missed appointments in the previous 3 months. Furthermore, to ascertain the actual refill adherence, patients also reported their medication coverage during the period of missed appointments. A refill adherence rate of ≥80% for antidiabetic medication is usually considered satisfactory in DM patients. However, since the PDC in our study also involved patient self-reporting which is subject to self-desirability bias, a higher refill adherence rate of ≥85% was accepted as the cutoff for satisfactory adherence.
The socioeconomic status of the patients was assessed using the modified Kuppuswamy classification updated using the Consumer Price Index of the Industrial Workers in India for 2016.
Written and informed consent was taken from all the patients before enrollment in the study. Confidentiality of the collected data was maintained during all stages of the study. Institutional ethical clearance was obtained.
Data were analyzed using Statistical Package for the Social Sciences (SPSS for Windows, Version 17.0, SPSS Inc., Chicago, IL, USA). Categorical variables were expressed in frequency and percentage, whereas quantitative variables were expressed as mean ± standard deviation.
| Results|| |
A total of 375 adult Diabetes Mellitus patients comprising 201 males and 174 females were enrolled in the study. The median age of the patients was 50 years. A majority of the patients were not employed (62.1%) and belonged to the lower (lower middle, upper lower, and lower) SES (75.4%). Tablet therapy was prescribed to 257 (68.5%), whereas 118 (31.5%) patients were on insulin therapy [Table 1]. In addition to the prescribed antidiabetic medication, the use of alternative medication for control of diabetes was reported by 43 (11.5%) patients.
|Table 1: Distribution of sociodemographic and clinical characteristics in the DM patients (N=375)|
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Based on observation and patient testimony, the entire process for obtaining medication refills involving obtaining a clinic appointment, consulting the physician, and collection of medication from the hospital pharmacy ranged from 2 to 4 h of patient time.
The OOP health expenses for diabetes-related care in the patients expressed as Indian National Rupees (₹) is reported in [Table 2]. The average OOP expense incurred by the patients in the previous 1 month was ₹63.5 and ranged from ₹0 to ₹800. Transport costs were incurred by 305 (81.3%) patients during their clinic visits. The average transport cost per patient for each clinic visit was ₹32. This shows that, if the patients were provided long-duration medication refills, it would significantly reduce their OOP expenses. The reduction in expenses on transport if refills were provided for 90 days would be ₹160.
|Table 2: Distribution of out-of-pocket diabetes healthcare expenses in the previous 30 days in the diabetic patients (n=375)|
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Of the previous six scheduled appointments for diabetes care at the hospital OPD clinics in the previous 3 months, at least two missed appointments were seen in 267 (71.2%) patients. The patients belonging to the lower SES were more regular and missed fewer appointments compared to those belonging to the higher SES [Table 3].
|Table 3: Missed appointments in the previous 90 days stratified by socioeconomic status of the diabetic patients (n=375)|
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Patient perspectives on reasons for missed appointments identified including the long waiting queues: “ghaṇṭē laga jātē hai davā ki line mē (we need to spend several hours waiting in the queue of the hospital pharmacy), one patient remarked “pichalī bāra davā nahī milī mujhē. Itni zyādā bhīṛa thī” (I could not collect my medication the last time. It was so crowded!). Most patients preferred a longer duration of refill “kam SE kam mahinā bhar ki davā milani chahiye” (at least 1 month of refill should be provided, “hamē bahuta dūra SE ānā hōtā hai. Kabhī kabhī nahi aa pate. bahuta ārāma ho jāyega” (we have to come from far-off. Sometimes, we are unable to come for our appointment. It will become very comfortable [if we get longer refills]). Some patients complained, “itni bhīṛa mey doctor zyādā kucha nahī bāta kar pāte” (crowding in the clinics causes inadequate doctor–patient communication).
All the patients with health insurance (n = 29) had complete medication coverage. Noninsured patients, in the event of missed appointments, could potentially satisfy their antidiabetic medication requirements partially or completely either via another public health facility or by OOP spending [Figure 1].
|Figure 1: Flow diagram of potential sequelae after missed appointments in Diabetes Mellitus patients attending a tertiary care government hospital in Delhi, India|
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A total of 278 (74.1%) patients reported 100% PDC with antidiabetic medication, 64 (17.1%) reported <85% PDC, and 33 (8.8%) reported 85%–99% of PDC. The inability to replenish the exhausted antidiabetic drug stocks in the event of missed appointments was attributed to financial constraints by 69 (18.4%) patients, all of whom belonged to the lower SES.
A total of 309 (82.4%) patients were unable to access a functional glucometer. Furthermore, among the patients on insulin therapy, only 38 (32.2%) were operating a personal glucometer, whereas the rest were dependent on their nearby local health facilities for self-monitoring of blood glucose. Nearly 181 (48.3%) patients reported difficulty in bearing the expenses related to glucometer strips required for adhering to regular self-monitoring of blood glucose. Plasma glucose monitoring frequency was reported as monthly by 74 (19.7%), quarterly by 260 (69.3%), and irregularly by 41 (11%) patients.
| Discussion|| |
The treatment of diabetes involves high economic costs for patients. Government health facilities providing free medication and treatment, therefore, comprise a key element for achieving UHC, especially among the low SES patients. However, our study revealed that nearly half of the DM patients who avail outpatient diabetic care in a major government tertiary hospital incur OOP expenses for antidiabetic medications. Refill nonadherence usually occurs in patients from lower SES groups when they miss their scheduled appointments.
The average monthly OOP expenses (direct costs) on diabetes-related medication incurred in our study are much lower than those reported in the study by Katam et al. conducted in a government-funded hospital in North India. The Katam et al.'s study was restricted to DM patients on insulin therapy for whom medicines were subsidized compared to market rates but not provided free of cost, unlike the present study. Another study by Sharma et al. in South India found direct costs among urban DM patients in government outpatient settings to be ₹1856 annually equivalent to ₹156 per month.
The current mechanism of drug dispensing for a short duration of 15 days for a chronic disorder like diabetes is detrimental from a systems approach view of health-care delivery. Most patients had to bear transport costs and wait in queues for many hours. Such health facility congestion can potentially compromise the quality of patient–provider communication that negatively influences patients' diabetes-related knowledge. Moreover, nearly three in four patients reported missing more than one of their previous six scheduled appointments. In this regard, policy interventions for initiating long-duration dispensing of diabetes-related medications warrant consideration. Future studies should generate evidence regarding whether or not these long-duration refill-dispensing practices translate into expected gains from decongestion at clinic sites, reduction in refill nonadherence, increased treatment satisfaction, and improved health outcomes in DM patients.
The reduction in the number of planned patient appointments can also be achieved by linking patients to their nearby primary-level health facilities for routine follow-up and medication refills while reserving tertiary care hospital visits needed for specialist care at less frequent intervals. The feasibility of such health-care linkage should be explored through operational research studies.
In our study, the mean OOP expenses in the comorbid patients were nearly 30% higher than that of noncomorbid patients although, based on retail prices, this gap should be wider. This suggests that, in the comorbid patients, nonadherence to medicines for treating hypertension or lipid disorders is unlikely to correlate with antidiabetic medications even if they are prescribed to be taken together. Future studies should evaluate adherence rates for these comorbid conditions separately. A further limitation of our study was the inability to ascertain the OOP expenses for blood investigations included in diabetes follow-up management due to the variable testing frequency observed in the study patients. Because the costs of procuring the glucometer strips were perceived by the patients as a major barrier against self-monitoring of blood glucose, the feasibility and cost-effectiveness of dispensing glucometer strips to DM patients without charge from public health facilities in India warrants exploration.
The assessment of refill adherence in our study was ascertained using a combination of approaches which included self-reporting by patients to account for gaps due to missed appointments. Self-reporting is prone to recall bias, especially when assessed over longer recall periods. Furthermore, the self-desirability bias of the patients could have resulted in a potential overestimation of the adherence levels. Some patients, especially women, who did not directly engage in the purchase of medications provided tentative estimates for their diabetes-related OOP expenses, which increases the chances of information bias. Nearly one in ten patients used alternative medication for diabetes control; however, these costs were not estimated.
The study was conducted in a single tertiary care government hospital which caters to only a small proportion of DM patients, thereby reducing the generalizability of our research findings. Nevertheless, despite the heterogeneity of health settings, these findings are still likely to be applicable across other public health facilities in India that require compliance with a similar short twice-weekly pattern of refill dispensing.
| Conclusion|| |
The present study conducted in the outpatient setting of a tertiary care government hospital in Delhi, India, among uncomplicated patients with diabetes found a high burden of missed clinic appointments that was associated with the incurrence of OOP expenses. In the event of missed appointments, suboptimal refill adherence was attributed to financial constraints by patients from lower SES.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
International Diabetes Federation. IDF Diabetes Atlas. 6th
ed. Brussels: International Diabetes Federation; 2015.
Hu FB. Globalization of diabetes: The role of diet, lifestyle, and genes. Diabetes Care 2011;34:1249-57.
Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care 2004;27:1047-53.
Misra A, Pandey RM, Devi JR, Sharma R, Vikram NK, Khanna N, et al.
High prevalence of diabetes, obesity and dyslipidaemia in urban slum population in Northern India. Int J Obes Relat Metab Disord 2001;25:1722-9.
Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al.
High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia 2001;44:1094-101.
Narayan KM, Zhang P, Kanaya AM, Williams DE, Engelgau MM, Imperatore G, et al
. Diabetes: The pandemic and potential solutions. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al
. editors. Disease Control Priorities in Developing Counries. 2nd
ed., Ch. 30. Washington, (DC): World Bank; 2006. Available from: http://www.ncbi.nlm.nih.gov/books/NBK11777/
. [Last accessed on 2018 Jan 26].
Basu S, Khobragade M, Kumar A, Raut DK. Medical adherence and its predictors in diabetes mellitus patients attending government hospitals in the Indian capital, Delhi, 2013: A cross sectional study. Int J Diabetes Dev Ctries 2015;35 Suppl 2:95-101.
World Health Organization. Global Report on Diabetes. Geneva: World Health Organization; 2016.
Sabatâe E. Adherence to Long-Term Therapies: Evidence for Action. 1st
ed. Geneva: World Health Organization; 2003.
Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005;353:487-97.
Kapur A. Economic analysis of diabetes care. Indian J Med Res 2007;125:473-82.
] [Full text]
Garg CC, Karan AK. Reducing out-of-pocket expenditures to reduce poverty: A disaggregated analysis at rural-urban and state level in India. Health Policy Plan 2009;24:116-28.
Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ, et al.
Household catastrophic health expenditure: A multicountry analysis. Lancet 2003;362:111-7.
Ramachandran A, Ramachandran S, Snehalatha C, Augustine C, Murugesan N, Viswanathan V, et al.
Increasing expenditure on health care incurred by diabetic subjects in a developing country: A study from India. Diabetes Care 2007;30:252-6.
National Sample Survey Office. Health in India – NSS 71st
Round. National Sample Survey Office; January-June, 2014.
Yesudian CA, Grepstad M, Visintin E, Ferrario A. The economic burden of diabetes in India: A review of the literature. Global Health 2014;10:80.
Rao KD, Bhatnagar A, Murphy A. Socio-economic inequalities in the financing of cardiovascular and diabetes inpatient treatment in India. Indian J Med Res 2011;133:57-63.
] [Full text]
Prinja S, Kaur M, Kumar R. Universal health insurance in India: Ensuring equity, efficiency, and quality. Indian J Community Med 2012;37:142-9.
] [Full text]
Basu S, Garg S, Sharma N, Singh MM, Garg S. Adherence to self-care practices, glycemic status and influencing factors in diabetes patients in a tertiary care hospital in Delhi. World J Diabetes 2018;9:72-9.
Lu C, Chin B, Li G, Murray CJ. Limitations of methods for measuring out-of-pocket and catastrophic private health expenditures. Bull World Health Organ 2009;87:238-44, 244A-D.
Singh T, Sharma S, Nagesh S. Socio-economic status scales updated for 2017. Int J Res Med Sci 2017;5:3264-7.
Katam KK, Bhatia V, Dabadghao P, Bhatia E. High direct costs of medical care in patients with type 1 diabetes attending a referral clinic in a government-funded hospital in Northern India. Natl Med J India 2016;29:64-7.
] [Full text]
Sharma KM, Ranjani H, Zabetian A, Datta M, Deepa M, Moses CR, et al.
Excess cost burden of diabetes in Southern India: A clinic-based, comparative cost-of-illness study. Glob Health Epidemiol Genom 2016;1:e8.
[Table 1], [Table 2], [Table 3]