(1)
Institute of Economic Growth, Delhi University, Delhi, India
Abstract
The preceding discussion has perhaps clearly underscored the fact that ailments and poor health conditions contribute heavily in exposing households to serious economic issues, press them hard to make OOP expenses, push a number of them to slip below the threshold poverty level (see the last two columns in Appendix Table 6.A.1) and render many to meet with serious catastrophic situations—amounting to curtailments in their normal consumption pattern and forcing them in certain cases to borrow from private moneylenders. All these make analysts to ask an obvious question: Why is there so much of OOP health spending, and what and where public policy interventions could be directed to ameliorate the situation? In certain countries, the answer to these questions rests with demographically mediated age structure changes and rapid population ageing (Dormont and Huber 2006; Dormont et al. 2006; Getzen 1992). Given the fact that in many cases health-care expenses are determined by the progressing age of the older adults, the growing share of 60 or 65+ is expected to increase the size of health expenditure both in a society and in a household. With ageing in India yet to reach the level achieved by many developed countries, a great deal of health expenditure in this or similar other countries may not be simply considered as age-driven or caused by the ailing olds. Components of health care, in particular, high costs of medicinal drugs and diagnostics, may as well play a role and make families incur a much greater spending on health. This has also been argued by the studies conducted on the initiative of the government including NCMH (2005, Sec. II) or the Annual Report to the People on Health (Ministry of Health & Family Welfare, Government of India, December 2011, Chapter VII).
The preceding discussion has perhaps clearly underscored the fact that ailments and poor health conditions contribute heavily in exposing households to serious economic issues, press them hard to make OOP expenses, push a number of them to slip below the threshold poverty level (see the last two columns in Appendix Table 6.A.1) and render many to meet with serious catastrophic situations—amounting to curtailments in their normal consumption pattern and forcing them in certain cases to borrow from private moneylenders. All these make analysts to ask an obvious question: Why is there so much of OOP health spending, and what and where public policy interventions could be directed to ameliorate the situation? In certain countries, the answer to these questions rests with demographically mediated age structure changes and rapid population ageing (Dormont and Huber 2006; Dormont et al. 2006; Getzen 1992). Given the fact that in many cases health-care expenses are determined by the progressing age of the older adults, the growing share of 60 or 65+ is expected to increase the size of health expenditure both in a society and in a household. With ageing in India yet to reach the level achieved by many developed countries, a great deal of health expenditure in this or similar other countries may not be simply considered as age-driven or caused by the ailing olds. Components of health care, in particular, high costs of medicinal drugs and diagnostics, may as well play a role and make families incur a much greater spending on health. This has also been argued by the studies conducted on the initiative of the government including NCMH (2005, Sec. II) or the Annual Report to the People on Health (Ministry of Health & Family Welfare, Government of India, December 2011, Chapter VII).
This chapter is therefore designed to decompose the expenses on health by households into four broader components: (i) fee paid to physician or medical consultant, (ii) cost of drugs and medicines (both prescription and self-medicated), (iii) expenses on diagnostic tests and (iv) money spent on transportation and stay. Most likely, the results of this analysis would help in identifying areas of major public concern and see if there are possible ways for the government to reduce the expenses incurred by households on items costing most to their health budget. Three interconnected exercises are presented. These include:
A detailed distribution of OOP health-care expenditure by sample household into four broad categories listed above
A similar distribution of households regrouped into five quintile groups, ranging from the poorest 20 % to the richest 20 %
Decomposition of OOP expenses into four broad expenditure items incurred by households facing lowest (z = 5 %) and highest (z = 25 %) levels of catastrophe based on the total (i.e. food + nonfood) consumption criterion (refer to the discussion in Chap. 5 on z values)
All the results are presented separately for households drawn from rural and urban areas of both the districts in the two major states of UP and Rajasthan.1 The same for Delhi was described by making a distinction between slum and non-slum households. The small-sample bias must nevertheless be kept in mind while interpreting the results.
6.1 Decomposition of Health-Care Expenditure: Share of Spending on Drugs, Diagnostics and Other Components
A great deal of literature on private financing of health care in India suggests drugs forming almost three-quarters or even more of the total private spending on health. This has particularly been noticed for the rural households (Sakthivel 2005).2 Obviously, with such a huge share of drugs and medicines in the total OOP budget, any policy intervention to reduce the cost of health care may not be considered without capping the drug prices and reducing their weight in the overall health spending of rural or urban households. Despite a growing realisation of this fact (Rane 1999), it may not be easy to implement any significant price reduction in India or elsewhere due to changes in drug policy regime, adopted in compliance with a mix of external and internal forces including demand for liberalisation in drug control policies,3 product patent regime, WTO patenting obligations and TRIPS.4 Some recent studies have already raised concern about these changes followed by substantial increase in drug prices causing escalations in OOP expenses and erosion in health-care affordability (Kamiike and Sato 2011; Watal 2000; Srinivasan 1999).
Against this backdrop, we present in Table 6.1 the distribution of OOP spending on drugs and other health-care components to reiterate further the primacy of the former in overall health-care budgets. This has been noticed all across the sample of households—rural, urban, slum or non-slum and irrespective of the districts or states they were located in. Our results are also to a large extent in the vicinity of the earlier findings (Sakthivel 2005; Bonu et al. 2007), suggesting that more than three-fourths of the money spent on health care invariably goes to allopathic medicines. Share of other forms of treatment—and hence medicines—is minuscule as may be noticed from the discussion in the next chapter.
Table 6.1
Shares of drug and non-drug expenses in OOP expenditure on health: hospitalised and nonhospitalised care (%)
Nonhospitalisation | Hospitalisation | |||||||
|---|---|---|---|---|---|---|---|---|
UP | Rajasthan | Delhi | Total | UP | Rajasthan | Delhi | Total | |
Panel A: rural HHDs | ||||||||
Doc. fee | 6.3 | 7.0 | – | 6.5 | 6.8 | 4.8 | – | 5.8 |
Drugs | 81.5 | 81.3 | – | 81.4 | 80.5 | 83.2 | – | 81.8 |
Transport | 7.4 | 6.9 | – | 7.2 | 6.7 | 6.5 | – | 6.6 |
Diagnostics | 4.9 | 4.8 | – | 4.9 | 6.1 | 5.5 | – | 5.8 |
Total | 100.0 | 100.0 | – | 100.0 | 100.0 | 100.0 | – | 100.0 |
Panel B: urban HHDs | ||||||||
Doc. fee | 9.5 | 10.1 | – | 9.7 | 19.8 | 4.1 | – | 16.0 |
Drugs | 77.7 | 77.3 | – | 77.5 | 67.4 | 87.5 | – | 72.2 |
Transport | 5.7 | 6.8 | – | 6.0 | 3.7 | 5.0 | – | 4.0 |
Diagnostics | 7.2 | 5.8 | – | 6.8 | 9.2 | 3.5 | – | 7.8 |
Total | 100.0 | 100.0 | – | 100.0 | 100.0 | 100.0 | – | 100.0 |
Panel C: slums HHDs | ||||||||
Doc. fee | – | – | 1.7 | 1.7 | – | – | 2.7 | 2.7 |
Drugs | – | – | 84.1 | 84.1 | – | – | 86.7 | 86.7 |
Transport | – | – | 6.6 | 6.6 | – | – | 3.0 | 3.0 |
Diagnostics | – | – | 7.7 | 7.7 | – | – | 7.6 | 7.6 |
Total | – | – | 100.0 | 100.0 | – | – | 100.0 | 100.0 |
Panel D: non-slum HHDs | ||||||||
Doc. fee | – | – | 5.4 | 5.4 | – | – | 0.5 | 0.5 |
Drugs | – | – | 83.1 | 83.1 | – | – | 88.8 | 88.8 |
Transport | – | – | 4.5 | 4.5 | – | – | 1.3 | 1.3 |
Diagnostics | – | – | 7.0 | 7.0 | – | – | 9.4 | 9.4 |
Total expenditure | – | – | 100.0 | 100.0 | – | – | 100.0 | 100.0 |
Panel E: total HHDs | ||||||||
Doc. fee | 7.0 | 7.6 | 4.8 | 6.3 | 13.5 | 4.6 | 1.1 | 7.4 |
Drugs | 80.6 | 80.6 | 83.3 | 81.6 | 73.7 | 84.3 | 88.2 | 80.9 |
Transport | 7.0 | 6.8 | 4.8 | 6.2 | 5.1 | 6.1 | 1.7 | 4.3 |
Diagnostics | 5.4 | 5.0 | 7.1 | 5.9 | 7.7 | 5.0 | 9.0 | 7.3 |
Total | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 |
Without too much of variations, Table 6.1 indicates almost a similar distribution pattern of health budgets across all the study areas (see also Fig. 6.1) with around four-fifths of the total OOP expenditure going to drugs followed by another 5–10 % (depending upon rural–urban and inpatient or outpatient treatment) of the total expenses going to medical practitioners (both qualified and others) as their consultation fee. Expenditure on diagnostics remains in most cases between 5 and 7 % of the total budget, and almost an equal amount (between another 5 and 7 %) is devoted to meet a few sundry expenses, especially transportation (see Fig. 6.1a–c).
Fig. 6.1
Share of expenses on drugs, medical services and transportation in hospitalised and nonhospitalised care: rural–urban and slum–non-slum households (%) (Source: Table 6.1)
Between the two samples of households drawn from UP and Rajasthan, the share of expenditure gone to consultation fee is shown to be much higher in the former, particularly in sickness episodes requiring hospitalisation. Relatively, however, their expenses on drugs are much less. Both of them however follow almost a similar expenditure pattern in cases where hospitalisation was not required.
Moving to the OOP distribution for slum and non-slum households in Delhi, it is clear both from Table 6.1 (panels C and D) and Fig. 6.1c that the former are almost at a competing level with the latter in terms of their percentage expenditure on drugs and two other major medical services, namely, consultation and diagnostics. However, the share of expenditure on consultation fee is relatively higher for slum households, i.e. 2.7 % as against 0.5 % for the non-slum households (Table 6.1, panels C and D). Also, they are shown to incur a larger share of expenditure on transportation than the non-slum households.
From these results, which tend to portray certain degrees of equity between the slum and non-slum households in distribution of their health budgets, follow two significant questions: (i) Does this equity represent certain peculiarities of Delhi alone or is it a wider phenomenon and the poor in general encounter a similar situation in other places as well, and (ii) is there a safeguard to protect them?
Regarding the second question, safeguard perhaps lies in pooling the risk and offering certain form of health insurance mechanism—if not to all, at least to the poor.5 Another important safeguard derives from lowering inflation in the drug sector and pro-poor negotiations in the WTO. Particularly, most generic medicines and formulations need protection from strict patenting and royalty laws. This is particularly essential because of a very large share of medicines in overall household budgets on health. Reverting to the first question, we extend this analysis, as was already noted in the beginning, by briefly describing the OOP budget distributions at two levels: (i) by five consumption quintile groups (poorest 20 %, next 20 %, middle, rich and the richest) and (ii) by two catastrophic groups (z = 5 and 25 %).
6.2 Share of Drugs and Non-drugs in OOP Budget: Households by Consumption Quintiles
Using unit-level consumption data, Table 6.2 distributes the health-care expenditure of sample households arranged in ascending order into five quintile groups—from the poorest 20 % to the richest 20 %. Expenditure items in all the calculations remain identical.
Table 6.2
Shares of drug and non-drug expenses in hospitalised and nonhospitalised care: households by consumption quintiles (%)
Consumption quintiles | OOP expenditure: nonhospitalised care | OOP expenditure: hospitalisation cases | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
Doc. fee | Drugs | Transport | Diagnostic | Total | Doc. fee | Drugs | Transport | Diagnostic | Total | |
Panel A: rural UP and Rajasthan | ||||||||||
Poorest 20 % households | 4.9 | 85.1 | 8.1 | 1.9 | 100.0 | 3.1 | 77.6 | 18.8 | 0.5 | 100.0 |
Next | 5.6 | 83.0 | 8.8 | 2.6 | 100.0 | 4.9 | 79.8 | 9.2 | 6.2 | 100.0 |
Middle | 8.2 | 82.2 | 7.3 | 2.3 | 100.0 | 4.7 | 85.5 | 7.2 | 2.5 | 100.0 |
Rich | 9.2 | 77.6 | 7.9 | 5.3 | 100.0 | 9.9 | 78.1 | 5.2 | 6.8 | 100.0 |
Richest 20 % households | 5.3 | 81.9 | 6.5 | 6.3 | 100.0 | 4.4 | 82.9 | 6.8 | 5.9 | 100.0 |
Total sample | 6.5 | 81.4 | 7.2 | 4.9 | 100.0 | 5.8 | 81.8 | 6.6 | 5.8 | 100.0 |
Panel B: urban UP and Rajasthan | ||||||||||
Poorest 20 % households | 10.4 | 80.6 | 4.9 | 4.2 | 100.0 | 1.3 | 85.5 | 5.0 | 8.2 | 100.0 |
Next 20 % | 11.8 | 74.6 | 8.2 | 5.4 | 100.0 | 8.1 | 85.0 | 4.9
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