Résumé

Objectif

Évaluer la comparabilité des paiements directs (PD) et estimer les dépenses de santé catastrophiques (DSC) à partir de différentes enquêtes menées auprès des ménages en Inde.

Méthodes

Les données recueillies dans toutes les grandes enquêtes nationales ou multi-régionales depuis 2000 et portant sur les DSC, les paiements directs pour hospitalisation interne et externe et autres dépenses, ont été comparées. Parmi ces études, figurent notamment deux enquêtes portant sur les dépenses des consommateurs (l'enquête nationale pour 2004–2005 [NSS 2004–05] et pour 2009–2010 [NSS 2009–10]), et trois enquêtes axées sur la santé (l'enquête sur la santé dans le monde 2003 [WHS 2003]; l'enquête nationale sur la morbidité, les soins de santé et la condition des personnes âgées 2004 [NSS 2004]; et l'étude sur le vieillissement et la santé des adultes 2007–2008 [SAGE 2007–08]). Toutes ces enquêtes, à l'exception de la NSS 2004–05 et de la NSS 2009–10, ont utilisé des questionnaires différents.

Résultats

Dans les enquêtes WHS 2003 et SAGE 2007–08, les DSC étaient deux fois plus élevées que celles relevées dans les enquêtes NSS 2004–05, NSS 2009–10 et NSS 2004. Les paiements directs pour une hospitalisation interne étaient deux fois plus élevés dans les enquêtes WHS 2003 et SAGE 2007–08, car, dans ces enquêtes, une proportion beaucoup plus élevée de ménages ont déclaré ces paiements. Cependant, d'autres dépenses estimées étaient deux fois moins élevées dans l'enquête WHS 2003 que dans les autres enquêtes, car un très petit nombre de questions ont été utilisées pour rendre compte de ces dépenses.

Conclusion

Les grandes variations observées dans les DSC et les estimations de paiements directs résultent de différences méthodologiques. Les méthodes d'enquête utilisées pour évaluer les DSC en Inde doivent être standardisées et validées pour évaluer les DCS avec précision et mesurer l'impact des politiques récentes pour les réduire.

Resumen

Objetivo

Evaluar la comparabilidad de las estimaciones del pago por el propio paciente (OOP) y los gastos sanitarios catastróficos (CHE) a partir de distintas encuestas a hogares de la India.

Métodos

Se compararon los datos sobre los gastos sanitarios catastróficos (CHE), los pagos por el propio paciente (OOP) por el cuidado sanitario ambulatorio y hospitalario, así como otros gastos de las principales encuestas nacionales o plurinacionales desde 2000. Estos incluyen dos encuestas sobre los gastos de consumo (la Encuesta Nacional por Muestreo de 2004-05 [ENM 2004-05] y 2009-10 [ENM 2009-10]) y tres encuestas sobre salud (la Encuesta Mundial de Salud de 2003 [EMS 2003], la Encuesta Nacional por Muestreo de morbilidad, cuidado sanitario y de las condiciones de la tercera edad de 2004 [ENM 2004], y el Estudio sobre el Envejecimiento mundial de la población y la salud de los adultos de 2007-08 [SAGE 2007-08]). Se emplearon cuestionarios diferentes en cada una de ellas, excepto para la ENM 2004–05 y la ENM 2009–10.

Resultados

Las estimaciones de los gastos sanitarios catastróficos (CHE) de la EMS 2003 y del Estudio sobre el Envejecimiento mundial de la población y la salud de los adultos (SAGE) de 2007-08 fueron dos veces más altas que las de la ENM 2004-05, ENM 2009-10 y ENM 2004. Las estimaciones del pago por el propio paciente (OOP) fueron dos veces más altas en la EMS 2003 y el SAGE 2007-08 debido a que en estos estudios una proporción mucho mayor de los hogares informó acerca de dichos pagos. Sin embargo, otros gastos estimados fueron la mitad en la EMS 2003 y en las otras encuestas porque se utilizó un número muy pequeño de elementos para captar dichos gastos.

Conclusión

Las grandes variaciones observadas en las estimaciones de los gastos sanitarios catastróficos (CHE) y los pagos por el propio paciente (OOP) se debieron a diferencias metodológicas. Es necesario estandarizar y validar los métodos de encuesta utilizados para evaluar los gastos sanitarios catastróficos (CHE) en la India a fin de realizar un seguimiento preciso sobre dichos gastos y evaluar el impacto de las políticas recientes para reducirlos.

ملخص

الغرض

تقييم قابلية مقارنة تقديرات مدفوعات المرضى من مالهم الخاص (OOP) والإنفاق الصحي الكارثي (CHE) من الدراسات الاستقصائية الأسرية المختلفة في الهند.

الطريقة

تم مقارنة البيانات المعنية بالإنفاق الصحي الكارثي ومدفوعات المرضى الخارجيين والداخليين من مالهم الخاص وغيرها من أوجه الإنفاق من جميع الدراسات الاستقصائية الرئيسية الوطنية أو متعددة الولايات منذ عام 2000. (الدراسة الاستقصائية للعينة الوطنية في الفترة من عام 2004 إلى 2005 [NSS 2004-05] وفي الفترة من عام 2009 إلى 2010 [NSS 2009-10]) والثلاثة استقصاءات المركزة على الصحة (استقصاء الصحة العالمية لعام 2003 [WHS 2003]؛ واستقصاء العينة الوطنية المعني بالمراضة والرعاية الصحية وحالة كبار السن لعام 2004 [NSS 2004]؛ والدراسة المعنية بالشيخوخة وصحة البالغين على الصعيد العالمي في الفترة من عام 2007 إلى 2008 [SAGE 2007-08]). واستخدمت جميع الدراسات الاستقصائية عدا الدراسة الاستقصائية للعينة الوطنية في الفترة من عام 2004 إلى 2005 وفي الفترة من عام 2009 إلى 2010 استبيانات مختلفة.

النتائج

بلغت تقديرات الإنفاق الصحي الكارثي من استقصاء الصحة العالمية لعام 2003 والدراسة المعنية بالشيخوخة وصحة البالغين على الصعيد العالمي في الفترة من عام 2007 إلى 2008 ضعفي تقديرات الإنفاق الصحي الكارثي من الدراسة الاستقصائية للعينة الوطنية في الفترة من عام 2004 إلى 2005 والدراسة الاستقصائية للعينة الوطنية في الفترة من عام 2009 إلى 2010 واستقصاء العينة الوطنية المعني بالمراضة والرعاية الصحية وحالة كبار السن لعام 2004. وزادت مدفوعات المرضى الداخليين من مالهم الخاص إلى الضعفين في استقصاء الصحة العالمية لعام 2003 والدراسة المعنية بالشيخوخة وصحة البالغين على الصعيد العالمي في الفترة من عام 2007 إلى 2008 بسبب إبلاغ نسبة عالية جداً من الأسر المعيشية عن هذه المدفوعات في هذه الاستقصاءات. وعلى الرغم من ذلك، بلغ الارتفاع في تقديرات الإنفاق الأخرى في استقصاء الصحة العالمية لعام 2003 نصف الارتفاع في الاستقصاءات الأخرى نتيجة استخدام عدد صغير جداً من البنود لاستخلاص هذه النفقات.

الاستنتاج

نتجت التفاوتات الواسعة التي لوحظت في تقديرات الإنفاق الصحي الكارثي ومدفوعات المرضى من مالهم الخاص من الاختلافات المنهجية. ويجب توحيد أساليب الاستقصاء المستخدمة لتقييم الإنفاق الصحي الكارثي في الهند واعتمادها بغية تتبع الإنفاق الصحي الكارثي بدقة وتقييم أثر السياسات الحديثة للحد منه.

摘要

目的

从印度不同家庭调查中评估自付(OOP)支出和大病支出(CHE)估计的可比性。

方法

对2000 年以来所有重要全国性或多个邦的调查所得到CHE、门诊和住院OOP费用及其他开支的数据进行比较。这些调查包括两个消费支出调查(2004-05 [NSS 2004-05]和2009-10 [NSS 2009-10]全国抽样调查)和三个以健康为中心的调查(2003 年世界卫生调查[WHS 2003]、2004 年全国老年人发病率、健康护理和状况抽样调查 [NSS 2004]以及全球老龄化与成人健康研究2007-08 [SAGE 2007-08])。除NSS 2004-05 和NSS 2009-10 以外,所有调查都使用不同的问卷。

结果

来自WHS 2003 和SAGE 2007-08 的CHE估算是NSS 2004-05、NSS 2009-10 和NSS 2004 的两倍。在WHS 2003 和SAGE 2007-08 中估计的住院护理OOP费用也是WHS 2003 和SAGE 2007-08 的两倍,这是因为这些调查中报告有此类费用的家庭比例高得多。然而,较之其他调查,WHS 2003 的估计费用仅为一半,原因是计算这些费用使用的项目数量很少。

结论

不同方法得到的CHE和OOP支出估算差异很大。用来评估印度CHE的方法需要标准化并经过验证,以准确地跟踪CHE并评估最近旨在减少开支的政策的效果。

Резюме

Цель

Оценить соотношение данных о размере оплаты медицинского обслуживания из собственных средств (ОСС) к уровню катастрофических расходов на медицинское обслуживание (КРМ), полученных в ходе нескольких социологических исследований в Индии.

Методы

Был выполнен сравнительный анализ данных по уровню КРМ, размеру ОСС в стационарном и амбулаторном здравоохранении, а также по другим расходам, полученных в рамках всех крупных национальных и международных социологических исследований, проводившихся с 2000 года, в том числе двух социологических исследований структуры потребительских расходов (Национальных выборочных исследований 2004-05 гг. [НВИ 2004–05] и 2009–10 гг. [НВИ 2009–10]) и трех социологических исследований медицинской направленности (Всемирного обследования состояния здоровья 2003 г. [ВОЗ 2003]; Национального выборочного социологического исследования по вопросам заболеваемости, здравоохранения и состояния здоровья пожилых 2004 г. [НВИ 2004]; и Исследования глобальной проблематики старения и здоровья взрослых 2007–08 гг. [ИГСП 2007–08]). Во всех исследованиях, кроме НВИ 2004–05 и НВИ 2009–10, использовались разные анкеты.

Результаты

Данные по уровню КРМ, полученные в ходе исследований ВОЗ 2003 и ИГСП 2007–08, превышают данные исследований НВИ 2004–05, НВИ 2009–10 и НВИ 2004 в два раза. Данные по уровню ОСС в стационарном здравоохранении, полученные в ходе исследований ВОЗ 2003 и ИГСП 2007–08, вдвое выше значений, полученных в ходе других исследований ввиду того, что доля домохозяйств, участвовавших в этих двух исследованиях и сообщивших об уплате ОСС, была существенно выше. Тем не менее, данные о других затратах, полученные в ходе исследования ВОЗ 2003, оказались вполовину ниже, чем по версии других исследований, поскольку число показателей, на основе которых осуществлялась их оценка, было крайне невелико.

Вывод

Причиной большого разброса данных об ориентировочной величине КРМ и ОСС является методика исследования. Точность наблюдений за КРМ в Индии и оценка эффективности проводимой в последнее время политики по его уменьшению требует стандартизации и верификации методов социологических исследований, применяемых для оценки КРМ.

Introduction

Out-of-pocket (OOP) payments are the primary source of health-care financing in many countries.¹1 Heijink R, Xu K, Saksena P, Evans D. Validity and comparability of out-of-pocket health expenditure from household surveys: a review of the literature and current survey instruments. Geneva: World Health Organization; 2011. In 2004–05, OOP payments in India were estimated to account for approximately two thirds of total health expenditure²2 National Health Accounts Cell. National Health Accounts, India, 2004–05. New Delhi: Ministry of Health and Family Welfare, Government of India; 2009. and fewer than 10% of households had health insurance for at least one member.³3 Kumar AK, Chen LC, Choudhury M, Ganju S, Mahajan V, Sinha A et al. Financing health care for all: challenges and opportunities. Lancet 2011;377:668–79. doi: http://dx.doi.org/10.1016/S0140-6736(10)61884-3 PMID:21227490
https://doi.org/10.1016/S0140-6736(10)61... OOP payments are considered “catastrophic” when they drive households into having to reduce expenditure on basic necessities.⁴4 Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet 2003;362:111–7. doi: http://dx.doi.org/10.1016/S0140-6736(03)13861-5 PMID:12867110
https://doi.org/10.1016/S0140-6736(03)13... The proportion of households that incur catastrophic health expenditure (CHE) in a country is widely used as an indicator of the extent to which the health system protects households needing health care against financial hardship. Offering such protection is a major goal of health systems and is the purpose behind universal health coverage.⁴4 Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet 2003;362:111–7. doi: http://dx.doi.org/10.1016/S0140-6736(03)13861-5 PMID:12867110
https://doi.org/10.1016/S0140-6736(03)13... ^–1010 Shahrawat R, Rao KD. Insured yet vulnerable: out-of-pocket payments and India’s poor. Health Policy Plan 2012;27:213–21. doi: http://dx.doi.org/10.1093/heapol/czr029 PMID:21486910
https://doi.org/10.1093/heapol/czr029...

In many countries, household surveys – some focused on consumer expenditure and others on health – are the main sources of data on households' OOP payments for health care.¹1 Heijink R, Xu K, Saksena P, Evans D. Validity and comparability of out-of-pocket health expenditure from household surveys: a review of the literature and current survey instruments. Geneva: World Health Organization; 2011. The estimates of OOP payments vary substantially between surveys depending on survey type, type of respondents and the survey methods used, such as the length of the recall period or the number of items included in the survey questionnaire.¹1 Heijink R, Xu K, Saksena P, Evans D. Validity and comparability of out-of-pocket health expenditure from household surveys: a review of the literature and current survey instruments. Geneva: World Health Organization; 2011.^,1111 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. doi: http://dx.doi.org/10.2471/BLT.08.054379 PMID:19377721
https://doi.org/10.2471/BLT.08.054379... ^–1717 Winter J. Response bias in survey-based measures of household consumption. Econ Bull 2004;3:1–12. In India, data on household expenditure are routinely available from National Sample Survey Organisation surveys on consumer expenditure and from special survey rounds on health.¹⁸18 National Sample Survey Office, National Statistical Organisation. Level and pattern of consumer expenditure - NSS 66th round, 2009–10. New Delhi: Ministry of Statistics and Programme Implementation, Government of India; 2011.^,1919 National Sample Survey Organisation. Morbidity, health care and the condition of the aged – NSS 60th round, 2004. New Delhi: Ministry of Statistics and Programme Implementation, Government of India; 2006. All of these surveys exert an important influence on health policy because they are the sources of data for programme and policy assessment⁹9 Selvaraj S, Karan AK. Why publicly-financed health insurance schemes are ineffective in providing financial risk protection. Econ Polit Wkly 2012;47:60–8.^,1010 Shahrawat R, Rao KD. Insured yet vulnerable: out-of-pocket payments and India’s poor. Health Policy Plan 2012;27:213–21. doi: http://dx.doi.org/10.1093/heapol/czr029 PMID:21486910
https://doi.org/10.1093/heapol/czr029... ^,2020 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. doi: http://dx.doi.org/10.1093/heapol/czn046 PMID:19095685
https://doi.org/10.1093/heapol/czn046... ^,2121 Providing outpatient healthcare to complement Rashtriya Swasthya Bima Yojana (RSBY), India’s national health insurance scheme. New Delhi: Ministry of Labour and Employment, Government of India; 2011. and for the preparation of the national health accounts.²2 National Health Accounts Cell. National Health Accounts, India, 2004–05. New Delhi: Ministry of Health and Family Welfare, Government of India; 2009.^,2222 National Health Accounts Cell. National Health Accounts, India, 2001–02. New Delhi: Ministry of Health and Family Welfare, Government of India; 2005. Other health-focused household surveys have also recently collected information on household expenditure.²³23 Health system performance assessment: World Health Survey India 2003. Mumbai: International Institute for Population Sciences & World Health Organization; 2006.^,2424 World Health Organization [Internet]. WHO study on global Ageing and adult health (SAGE). Geneva: WHO; 2013. Available from: http://www.who.int/healthinfo/systems/sage/en/index.html [accessed 23 June 2013].
http://www.who.int/healthinfo/systems/sa... Although these surveys have all been used to estimate CHE and OOP payments in India, no one has ever assessed whether the estimates obtained from them are comparable.

For this paper, we generated household OOP payments and CHE estimates using data from five national and multi-state household surveys conducted in India since the year 2000 and we compared the results. We also examined and compared the number and type of household expenditure items included in each survey questionnaire to try to explain the variability in OOP payment and CHE estimates across surveys. This exercise may prove useful in standardizing survey methods to obtain CHE estimates that are valid and consistent.

Methods

Data sources

Table 1 shows the characteristics of the five surveys that have collected data on health expenditure and other expenditure in India since the year 2000. The surveys are of two types: consumer expenditure surveys and health-focused surveys.

Consumer expenditure surveys

We obtained data from the National Sample Survey on Household Consumer Expenditure, which was conducted in all Indian states in 2004–05 (NSS 2004–05)²⁵25 National Sample Survey Organisation. Level and pattern of consumer expenditure – NSS 61st round, 2004–05. New Delhi: Ministry of Statistics and Programme Implementation, Government of India; 2006. and 2009–10¹⁸ (NSS 2009–10). These surveys collected data on expenditure for any health service, whether or not the household paid for the service. The expenditure data thus collected is considered an approximation of OOP payments, since most private payments for health care in India are made out of pocket. NSS 2009–10 was conducted in two parts – Type I and Type II – with a different questionnaire for each one. The Type I survey used the same questionnaire as NSS 2004–05 and hence was used for all analyses; in the Type II survey, the recall period for food expenditure differed from the one that was used in the Type I survey.

Health-focused surveys

We analysed data on OOP payments from the World Health Survey conducted in 2003 (WHS 2003);²³23 Health system performance assessment: World Health Survey India 2003. Mumbai: International Institute for Population Sciences & World Health Organization; 2006. the National Sample Survey on Morbidity, Health Care and the Condition of the Aged conducted in 2004 (NSS 2004),¹⁹19 National Sample Survey Organisation. Morbidity, health care and the condition of the aged – NSS 60th round, 2004. New Delhi: Ministry of Statistics and Programme Implementation, Government of India; 2006. and the Study on Global Ageing and Adult Health conducted in 2007–08 (SAGE 2007–08).²⁴24 World Health Organization [Internet]. WHO study on global Ageing and adult health (SAGE). Geneva: WHO; 2013. Available from: http://www.who.int/healthinfo/systems/sage/en/index.html [accessed 23 June 2013].
http://www.who.int/healthinfo/systems/sa... The WHS 2003 and SAGE 2007–08 were conducted in six states that were selected to be representative of India geographically and in level of development;²³23 Health system performance assessment: World Health Survey India 2003. Mumbai: International Institute for Population Sciences & World Health Organization; 2006. the NSS 2004 was conducted in all Indian states. In WHS 2003 and SAGE 2007–08, data on OOP payments were collected from a household informant; in the NSS 2004, such data were collected from the individual treated for each episode of illness.

Expenditure variables

Table 2 shows the number of survey items or questions used to collect household expenditure data in each survey; Table 3 presents the health items recorded. NSS 2004 was the only survey that used a single question to investigate total household expenditure. As a result, it did not collect data on food expenditure separately. Since NSS 2004–05 and NSS 2009–10 were consumer expenditure surveys, they collected expenditure data on a wider variety of household items than WHS 2003 and SAGE 2007–08. The items included in the outpatient and inpatient expenditure categories varied across surveys (Table 3).

Data analysis

We measured CHE using two definitions commonly used in the literature.⁴4 Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet 2003;362:111–7. doi: http://dx.doi.org/10.1016/S0140-6736(03)13861-5 PMID:12867110
https://doi.org/10.1016/S0140-6736(03)13... ^,77 Somkotra T, Lagrada LP. Which households are at risk of catastrophic health spending: experience in Thailand after universal coverage. Health Aff (Millwood) 2009;28:w467–78. doi: http://dx.doi.org/10.1377/hlthaff.28.3.w467 PMID:19336470
https://doi.org/10.1377/hlthaff.28.3.w46... ^,99 Selvaraj S, Karan AK. Why publicly-financed health insurance schemes are ineffective in providing financial risk protection. Econ Polit Wkly 2012;47:60–8.^,2626 Bonu S, Bhushan I, Rani M, Anderson I. Incidence and correlates of ‘catastrophic’ maternal health care expenditure in India. Health Policy Plan 2009;24:445–56. doi: http://dx.doi.org/10.1093/heapol/czp032 PMID:19687135
https://doi.org/10.1093/heapol/czp032... ^–2929 van Doorslaer E, O’Donnell O, Rannan-Eliya RP, Somanathan A, Adhikari SR, Garg CC et al. Catastrophic payments for health care in Asia. Health Econ 2007;16:1159–84. doi: http://dx.doi.org/10.1002/hec.1209 PMID:17311356
https://doi.org/10.1002/hec.1209... Under the first definition, OOP payments were estimated as a proportion of household capacity to pay; under the second, they were estimated as a proportion of total household expenditure. Household capacity to pay was calculated as the total household expenditure less subsistence expenditure, in accordance with the method described by Xu et al.⁴4 Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet 2003;362:111–7. doi: http://dx.doi.org/10.1016/S0140-6736(03)13861-5 PMID:12867110
https://doi.org/10.1016/S0140-6736(03)13... Subsistence expenditure – defined as the mean food expenditure of households falling between the 45th and 55th percentiles of the total sample in terms of the share of total household expenditure spent on food – was estimated for each survey separately.⁴4 Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet 2003;362:111–7. doi: http://dx.doi.org/10.1016/S0140-6736(03)13861-5 PMID:12867110
https://doi.org/10.1016/S0140-6736(03)13... We classified a household as having incurred CHE if it had spent out of pocket on health 40% or more of its capacity to pay or 10% or more of its total household expenditure.⁷7 Somkotra T, Lagrada LP. Which households are at risk of catastrophic health spending: experience in Thailand after universal coverage. Health Aff (Millwood) 2009;28:w467–78. doi: http://dx.doi.org/10.1377/hlthaff.28.3.w467 PMID:19336470
https://doi.org/10.1377/hlthaff.28.3.w46... ^,99 Selvaraj S, Karan AK. Why publicly-financed health insurance schemes are ineffective in providing financial risk protection. Econ Polit Wkly 2012;47:60–8.^,2626 Bonu S, Bhushan I, Rani M, Anderson I. Incidence and correlates of ‘catastrophic’ maternal health care expenditure in India. Health Policy Plan 2009;24:445–56. doi: http://dx.doi.org/10.1093/heapol/czp032 PMID:19687135
https://doi.org/10.1093/heapol/czp032... ^,2828 Ranson MK. Reduction of catastrophic health care expenditures by a community-based health insurance scheme in Gujarat, India: current experiences and challenges. Bull World Health Organ 2002;80:613–21. PMID:12219151^,3030 Devadasan N, Criel B, Van Damme W, Ranson K, Van der Stuyft P. Indian community health insurance schemes provide partial protection against catastrophic health expenditure. BMC Health Serv Res 2007;7:43. doi: http://dx.doi.org/10.1186/1472-6963-7-43 PMID:17362506
https://doi.org/10.1186/1472-6963-7-43... We applied both definitions to estimate CHE from all the surveys except NSS 2004, where we used only the second definition because the survey did not collect food expenditure.

Since WHS 2003 and SAGE 2007–08 sampled only six states in India and the other surveys sampled all states, we examined the possibility that any differences in CHE estimates were due to this difference in sample coverage. We did this by comparing the CHE estimates from NSS 2004–05, NSS 2009–10 and NSS 2004 for all states with CHE estimates from these same surveys for the six states sampled in WHS 2003 and SAGE 2007–08. Our premise was that if the estimates for all states turned out to be similar to those for the six states, this would indicate that CHE estimates were not affected by the difference in sample coverage.

Because the differences between surveys in CHE estimates could be due to differences in OOP payment and total household expenditure estimates, these estimates were compared. The OOP payments reported in the surveys were divided into outpatient and inpatient expenditure. Expenditure on food and “other” expenditure were also investigated. “Other” expenditure comprised all household expenditure other than out-of-pocket health-care payments and food expenditure; it included specific items under prepaid health expenditure, such as health insurance, in WHS 2003 and SAGE 2007–08, and durable items in NSS 2004–05, NSS 2009–10 and SAGE 2007–08.

The mean, median and first and third quartiles of outpatient and inpatient OOP payments, food expenditure, other expenditure and total household expenditure, documented in Indian rupees (INR), were converted to 2009–10 prices using gross domestic product deflators and then to United States dollars (US$; exchange rate: US$ 1 = 46.7 INR).³¹31 International Monetary Fund [Internet]. World Economic Outlook Database. Washington: IMF; 2011. Available from: http://www.imf.org/external/pubs/ft/weo/2011/01/weodata/weoselgr.aspx [accessed 23 June 2013].
http://www.imf.org/external/pubs/ft/weo/... ^,3232 IMF.Stat [Internet]. Enhanced Principal Global Indicators (PGI). Washington: International Monetary Fund; 2013. Available from: http://imfstatext.imf.org/wbos-query/Index.aspx [accessed 23 June 2013].
http://imfstatext.imf.org/wbos-query/Ind... The interquartile range was defined as the interval between the third and first quartiles. Since outpatient and inpatient OOP payments can be affected by the proportion of households reporting this expenditure, we also compared the proportions of households that reported such payments in the different surveys and the mean and median outpatient and inpatient OOP payments of the reporting households. Since different recall periods were used in the surveys for different items of expenditure, we prorated the reported expenditures to correspond to the same recall period to facilitate direct comparisons between surveys. Thus, for inpatient OOP payments we used a recall period of one year for all surveys. Outpatient OOP was reported for the most recent month in all surveys except NSS 2004, which used a 15-day recall period. For food expenditures, “other” expenditures and total expenditures we used a one-year recall period to allow comparison between surveys. Because the two parts of NSS 2009–10 used a different recall period for food expenditure, we assessed estimates of food expenditure from both parts.

We conducted all analyses at the household level and applied survey sampling weights. To calculate the 95% confidence intervals (CIs) of the proportions, we took into account survey design features such as stratification and clustering in estimating the variance with Taylor linearization.³³33 Woodruff R. A simple method for approximating the variance of a complicated estimate. J Am Stat Assoc 1971;66:411–4. doi: http://dx.doi.org/10.1080/01621459.1971.10482279
https://doi.org/10.1080/01621459.1971.10... Data were analysed using SAS version 9.2 (SAS Inc., Cary, United States of America).

Results

Catastrophic health expenditure

Fig. 1 shows the estimated proportion of households that had CHE according to each of the two definitions of CHE used. The estimates of CHE for NSS 2004–05 and NSS 2009–10 were the same and, since these surveys used the same questionnaire to document expenditure, there was no change in CHE from 2004–05 to 2009–10. When defined as the proportion of a household's capacity to pay, CHE was most frequently found in WHS 2003 (33.9% of households; 95% CI: 31.6–36.2) and SAGE 2007–08 (20.0%; 95% CI: 18.8–21.3). These figures were markedly higher than for NSS 2004–05 (3.8%; 95% CI: 3.6–3.9) and NSS 2009–10 (3.5%; 95% CI: 3.3–3.7). When defined as the proportion of a household's total expenditure, CHE was, again, most frequently found in WHS 2003 (43.5% of households; 95% CI: 41.3–45.8) and SAGE 2007–08 (31.9%; 95% CI: 30.2–33.7). In NSS 2004, 20.2% of households (95% CI: 19.7–20.6) were found to have incurred CHE. This was a higher rate than the rates found in NSS 2004–05 (14.0%; 95% CI: 13.4–14.3) and NSS 2009–10 (13.9%; 95% CI: 13.4–14.3).

When we assessed the relative contribution of inpatient and outpatient OOP payments to CHE, we found outpatient OOP payments to be responsible for a large proportion of the households with CHE: 73.1% in NSS 2009–10 to 84.6% in SAGE 2007–08 when CHE was defined as the proportion of a household's capacity to pay; 73.2% in NSS 2004 to 78.6% in NSS 2004–05 when CHE was defined as the proportion of a household's total expenditure.

Table 4 shows the proportion of households that incurred CHE in NSS 2004–05, NSS 2009–10 and NSS 2004 in all states of India and in only the six states that were sampled in WHS 2003 and SAGE 2007–08. CHE estimates were slightly higher for the six states than for all states in NSS 2004–05 and NSS 2004, but not in NSS 2009–10. However, these small differences do not explain why the estimates from WHS 2003 and SAGE 2007–08 were much higher than those from the other surveys.

Outpatient care

Table 5 shows the mean and median OOP payments for outpatient care reported by all households over the most recent month. Such payments were approximately 2.6 to 3.8 times higher in WHS 2003 and SAGE 2007–08 than in NSS 2004–05 and NSS 2009–10. The interquartile ranges for WHS 2003 and SAGE 2007–08 were 2.5 to 3.8 times higher than for NSS 2004–05 and NSS 2009–10. In NSS 2004, the mean OOP payment for outpatient care in the most recent 15 days was US$ 3.1 and the median was zero.

The proportion of households that reported OOP payments for outpatient care in the most recent month varied by only 13 percentage points between the surveys; the highest proportion was found in SAGE 2007–08 (75.2%) and the lowest in NSS 2004–05 (62%) (Table 6). When OOP payments for outpatient care were considered only for the households that reported them, WHS 2003 and SAGE 2007–08 again showed substantially higher estimates (2.3 to 2.8 times higher) than NSS 2004–05 and NSS 2009–10. This suggests that the use of different items to assess how much households spent out of pocket on outpatient care had a significant impact on estimates. In NSS 2004, with a recall period of 15 days, 32.9% (95% CI: 32.3–33.4) of the households reported OOP payments for outpatient care, and the mean and median amounts paid out of pocket for such care by these households were US$ 9.4 and US$ 4.2, respectively.

Inpatient care

The OOP payments for inpatient care in the most recent year were 1.6 to 2.8 times higher in WHS 2003 and SAGE 2007–08, respectively, than in NSS 2004–05 and NSS 2009–10 (Table 5). The OOP payment for inpatient care in NSS 2004 was 0.5 and 1.1 times as high as the payment in NSS 2004–05 and NSS 2009–10, respectively.

The proportion of households that reported paying out of pocket for inpatient care varied substantially between surveys. This proportion was much higher in WHS 2003 (25.9%) and SAGE 2007–08 (25%) than in NSS 2004–05 (9.2%), NSS 2004 (12.8%) and NSS 2009–10 (13.2%) (Table 6). Interestingly, when expenditure for inpatient care was examined for the households that reported it, the lowest median (US$ 68.4, NSS 2009–10) was only 26% lower than the highest median (US$ 92.8, WHS 2003) (Table 6). In contrast, the highest median expenditure for all sampled households was three times larger than the smallest median (Table 5), which suggests that the methods used in each survey had a greater effect on the frequency with which households reported having paid out of pocket for inpatient care than on the amount reported.

Food expenditure

Food expenditure in the most recent year is shown in Table 5. The surveys that used a one-month recall period (NSS 2004–05, NSS 2009–10 Type I and WHS 2003) had median food expenditure estimates that were from 14% to 36% lower than the surveys that used a one-week recall period, alone or in combination with a one-month recall period (NSS 2009–10 Type II and SAGE 2007–08). NSS 2009–10 Type II and SAGE 2007–08, both of which had a one-week recall period for some or all items, had similar median expenditure, even though SAGE 2007–08 used only 9 items to capture food expenditure and NSS 2009–10 Type II used 142. However, WHS 2003, which only used one item, had a higher median food expenditure estimate than the other surveys with the same recall period, namely NSS 2004–05 and NSS 2009–10 Type I. The interquartile range for food expenditure in WHS 2003 (US$ 604.6) and SAGE 2007–08 (US$ 576.6) was higher than in NSS 2004–05 (US$ 378.6), NSS 2009–10 Type I (US$ 408.2) and Type II (US$ 486.0).

“Other” and total household expenditure

“Other” expenditure was lowest in WHS 2003; it was about 1.5 to 2 times higher in SAGE 2007–08, NSS 2004–05 and NSS 2009–10 (Table 5). WHS 2003 used the least number of items to assess “other” expenditure; it also used a one-month recall period for all items and it used no items to specifically document expenditure on durables. Thus, “other” expenditure is higher in surveys with a higher number of items and a variety of recall periods. The low “other” expenditure estimate in WHS 2003 would have contributed to the fact that CHE estimates for WHS 2003 were higher than for the other surveys. The total household expenditure in the most recent year was lowest for NSS 2004 (median: US$ 829.6), a survey that did not collect disaggregated household expenditure data like the other surveys (Table 5).

Discussion

CHE is an important indicator of the financial protection offered to patients by a health system and has been estimated for health systems throughout the world using a variety of survey instruments. Although two publications in 2009 highlighted some of the difficulties of measuring OOP payments in household surveys,¹¹11 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. doi: http://dx.doi.org/10.2471/BLT.08.054379 PMID:19377721
https://doi.org/10.2471/BLT.08.054379... ^,1212 Xu K, Ravndal F, Evans DB, Carrin G. Assessing the reliability of household expenditure data: results of the World Health Survey. Health Policy 2009;91:297–305. doi: http://dx.doi.org/10.1016/j.healthpol.2009.01.002 PMID:19217184
https://doi.org/10.1016/j.healthpol.2009... CHE continues to be estimated with survey methods that have not been validated. Our study demonstrates that CHE estimates can vary dramatically depending on the survey instrument used. This has major implications for health policy planning not only in India, but also in other low- and middle-income countries, especially if they are striving to offer universal health coverage.

The wide variation seen between surveys in the estimates of CHE was the result of differences in OOP payments for health care and in “other” household expenditure. In WHS 2003 and SAGE 2007–08, OOP payments for outpatient and inpatient care were two to three times higher than in the other surveys. Our results suggest that most of the variation in OOP payments for outpatient care resulted from the expenditure amount reported. On the other hand, much of the variation in OOP payments for inpatient care resulted from the proportion of households that reported having incurred such payments in the most recent year. This proportion was substantially higher in WHS 2003 and SAGE 2007–08 than in the other surveys. These findings suggest that survey design has a different effect on recall in the case of outpatient and inpatient OOP payments.

The types of items used to document outpatient OOP probably influenced their estimates. More items and more specific probing can improve respondent recall, particularly with respect to minor events.¹¹11 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. doi: http://dx.doi.org/10.2471/BLT.08.054379 PMID:19377721
https://doi.org/10.2471/BLT.08.054379... ^,1717 Winter J. Response bias in survey-based measures of household consumption. Econ Bull 2004;3:1–12.^,3434 Browning M, Rossley T, Weber G. Asking consumption questions in general purpose surveys. Econ J 2003;113:F540–67. doi: http://dx.doi.org/10.1046/j.0013-0133.2003.00168.x
https://doi.org/10.1046/j.0013-0133.2003... WHS 2003 and SAGE 2007–08 both had specific questions about dental care and care by traditional healers, whereas the consumer expenditure surveys did not. This may account for the higher outpatient OOP payments found in WHS 2003 and SAGE 2007–08. Additionally, in lengthy questionnaires respondents tend to invest less time in trying to recall events, and this may have been true for the consumer expenditure surveys.³⁵35 Bhandari A, Wagner T. Self-reported utilization of health care services: improving measurement and accuracy. Med Care Res Rev 2006;63:217–35. doi: http://dx.doi.org/10.1177/1077558705285298 PMID:16595412
https://doi.org/10.1177/1077558705285298... Conversely, it is possible that surveys focused on health, such as WHS 2003 and SAGE 2007–08, prime respondents to report events beyond the recall period, and this may lead them to overestimate OOP payments.¹1 Heijink R, Xu K, Saksena P, Evans D. Validity and comparability of out-of-pocket health expenditure from household surveys: a review of the literature and current survey instruments. Geneva: World Health Organization; 2011.^,3535 Bhandari A, Wagner T. Self-reported utilization of health care services: improving measurement and accuracy. Med Care Res Rev 2006;63:217–35. doi: http://dx.doi.org/10.1177/1077558705285298 PMID:16595412
https://doi.org/10.1177/1077558705285298... However, studies have also shown that health care use is more commonly underreported than overreported.³⁶36 Clarke PM, Fiebig DG, Gerdtham UG. Optimal recall length in survey design. J Health Econ 2008;27:1275–84. doi: http://dx.doi.org/10.1016/j.jhealeco.2008.05.012 PMID:18667254
https://doi.org/10.1016/j.jhealeco.2008.... Such factors may have also contributed to the substantially higher proportion of households that reported OOP payments for inpatient care in WHS 2003 and SAGE 2007–08, by comparison with the other surveys. The health survey NSS 2004 documented every event involving inpatient care separately. The fact that it collected the data from the person who was treated might lead respondents to recall each event more accurately. Interestingly, however, in this survey, the proportion of households that reported inpatient OOP payments was practically the same as in NSS 2004–05 and NSS 2009–10. Since OOP payments for inpatient care in those households that reported such expenditure were similar across surveys, one might conclude that OOP payments for inpatient care are less sensitive to the number of items in the questionnaire than OOP payments for outpatient care. The obvious reason is that being an inpatient is a major event and hence any expenditure associated with this event is more accurately remembered by households.¹1 Heijink R, Xu K, Saksena P, Evans D. Validity and comparability of out-of-pocket health expenditure from household surveys: a review of the literature and current survey instruments. Geneva: World Health Organization; 2011.^,1313 Clark RE, Ricketts SK, McHugo GJ. Measuring hospital use without claims: a comparison of patient and provider reports. Health Serv Res 1996;31:153–69. PMID:8675437^,3535 Bhandari A, Wagner T. Self-reported utilization of health care services: improving measurement and accuracy. Med Care Res Rev 2006;63:217–35. doi: http://dx.doi.org/10.1177/1077558705285298 PMID:16595412
https://doi.org/10.1177/1077558705285298... It should be noted that the indirect costs of health care, such as transportation and lost earnings, also contribute to the financial burden incurred by households, but we did not assess them because they were not consistently documented in the surveys.

CHE estimates will be inaccurate if the estimated expenditure on “other” household items is not accurately captured. If estimates of this other expenditure are too low, CHE may be overestimated because the denominator will be small. WHS 2003, which had the least number of items, lacked specific items for durable goods and had only a one-month recall period, was the survey that yielded the lowest estimates of “other” expenditure. Although the evidence suggests that estimates of household expenditure increase as the number of items in the questionnaire increases,¹⁷17 Winter J. Response bias in survey-based measures of household consumption. Econ Bull 2004;3:1–12. in a health-focused survey it is highly impractical to ask questions as detailed as those that are included in consumer expenditure surveys. Thus, it is useful to note that SAGE 2007–08, which had 13 items, including durable goods, and various recall periods, had a higher estimate of “other” expenditure than WHS 2003, which included only four items. A single question, as in NSS 2004, does not appear to be enough to capture total household expenditure or expenditure on food. However, the 9 items used in SAGE 2007–08 for food expenditure provided an estimate similar to the estimates yielded by the consumer expenditure surveys. A one-week recall period yielded higher estimates of food expenditure. Other studies also suggest that one week is a more appropriate recall period for food expenditure than one month.³⁷37 National Sample Survey Organisation. Results of a pilot survey on suitability of different reference periods for measuring household consumption. New Delhi: Ministry of Statistics and Programme Implementation, Government of India; 2002.

We cannot comment on the accuracy of the CHE estimates derived from the different surveys since none of the surveys we examined can serve as a gold standard for measuring CHE. This highlights the need for validation studies to determine what questions and methods can most accurately capture CHE. These validation studies should not only examine the accuracy of the data, but also how to best use the data on OOP payments for outpatient care based on a relatively short recall period. Although a short recall period reduces recall error, it does not provide information about OOP payments for outpatient care in the population over a time frame more relevant for policy decisions, such as 6 months or one year.³⁶36 Clarke PM, Fiebig DG, Gerdtham UG. Optimal recall length in survey design. J Health Econ 2008;27:1275–84. doi: http://dx.doi.org/10.1016/j.jhealeco.2008.05.012 PMID:18667254
https://doi.org/10.1016/j.jhealeco.2008.... Simply multiplying the reported expenditure by as many times as necessary to obtain an estimate for the longer period, as we have done in this study, is equivalent to assuming that the expenditure is a recurrent one within a household, which is seldom the case. Hence, it probably caused overestimation of OOP payments in a one-year period for those households that reported such expenditure for a short recall period, and underestimation of OOP payments in the remaining households. This approach, which was used by others before us as well,⁹9 Selvaraj S, Karan AK. Why publicly-financed health insurance schemes are ineffective in providing financial risk protection. Econ Polit Wkly 2012;47:60–8.^,1010 Shahrawat R, Rao KD. Insured yet vulnerable: out-of-pocket payments and India’s poor. Health Policy Plan 2012;27:213–21. doi: http://dx.doi.org/10.1093/heapol/czr029 PMID:21486910
https://doi.org/10.1093/heapol/czr029... ^,2929 van Doorslaer E, O’Donnell O, Rannan-Eliya RP, Somanathan A, Adhikari SR, Garg CC et al. Catastrophic payments for health care in Asia. Health Econ 2007;16:1159–84. doi: http://dx.doi.org/10.1002/hec.1209 PMID:17311356
https://doi.org/10.1002/hec.1209... also leads to an overestimation of the contribution made to CHE by OOP payments for outpatient care. It might be possible to more accurately estimate how much OOP payments for outpatient care contribute to CHE, by performing longitudinal panel surveys that assess the distribution of outpatient care in households across the population over a one-year period, but studies of this kind are too costly to conduct on a regular basis. However, an occasional longitudinal study can provide validation data that would allow cross-sectional survey data for outpatient OOP payments based on a one-month recall period to be adjusted to a one-year period more accurately than simple multiplication.

Because CHE estimates and OOP payments for health care varied widely across surveys, only data from surveys with comparable methods should be used to make longitudinal comparisons. Policy-makers should consider this limitation when formulating policies and programmes that depend on data from household surveys. Survey methods for estimating OOP payments for health care must undergo standardization to allow effective tracking and monitoring of the impact of policies designed to improve financial risk protection. With universal health coverage and financial risk protection being recognized as goals for health systems in many low- and middle-income countries, comparisons of CHE estimates from different household surveys, like the ones in this study, should be the first step towards planning validation studies of OOP payment data in these countries. This is especially important in India, given the launch of government-subsidized health insurance programmes for poor households²¹21 Providing outpatient healthcare to complement Rashtriya Swasthya Bima Yojana (RSBY), India’s national health insurance scheme. New Delhi: Ministry of Labour and Employment, Government of India; 2011.^,3838 India tries to break cycle of health-care debt. Bull World Health Organ 2010;88:486–7. doi: http://dx.doi.org/10.2471/BLT.10.020710 PMID:20616965
https://doi.org/10.2471/BLT.10.020710... and the recommendations to reduce OOP payments made by the High Level Expert Group on Universal Health Coverage.³⁹39 High Level Expert Group Report on Universal Health Coverage for India: instituted by the Planning Commission of India. New Delhi: Planning Commission of India; 2011.

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Competing interests:

References

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