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Showing content from https://pmc.ncbi.nlm.nih.gov/articles/PMC4428601/ below:

Catastrophic Expenditure to Pay for Surgery: A Global Estimate

. Author manuscript; available in PMC: 2016 Apr 27.

Approximately 150 million individuals face catastrophic expenditure each year from medical costs alone, and many more from the nonmedical costs of accessing care. The proportion of this expenditure arising from surgical conditions is unknown. Because World Bank has proposed eliminating medical impoverishment by 2030, the impact of surgical conditions on financial catastrophe must be quantified so that any financial risk protection mechanisms can appropriately incorporate surgery.

To determine the global incidence of catastrophic expenditure due to surgery, a stochastic model was built. The income distribution of each country, the probability of requiring surgery, and the medical and nonmedical costs faced for surgery were incorporated. Sensitivity analyses were run to test model robustness.

3.7 billion people risk catastrophic expenditure if they need surgery. Every year, 33 million of them are driven to financial catastrophe from the costs of surgery alone, and 48 million from nonmedical costs, leading to 81 million cases worldwide. The burden of catastrophic expenditure is highest in low- and middle-income countries; within any country, it falls on the poor. Estimates are sensitive to the definition of catastrophic expenditure and the costs of care. The inequitable burden distribution is robust to model assumptions.

Half the global population is at risk of financial catastrophe from surgery. Annually, 81 million individuals, especially the poor, face catastrophic expenditure due to surgical conditions, of which less than half is attributable to medical costs. These findings highlight the need for financial risk protection for surgery in health system design.

Partial funding for Dr. Shrime from NIH/NCI R25CA92203.

Access to health care is not always free, and its utilization is often not without risk of impoverishment. In many parts of the world, out-of-pocket (OOP) payments for health care remain the predominant form of health financing.¹ Approximately 150 million cases of catastrophic expenditure—defined as an expenditure of over 40% of non-food household expenditure² or 10% of overall household expenditure³— occur each year due to accessing care.² Relatively little, however, is known about the magnitude of catastrophic expenditure attributable to various portions of the health system—both at a global level and in countries at different stages of development. In particular, the contribution of surgical care to catastrophic health expenditure has not previously been estimated.

Access to surgery is increasingly recognized as a critical component of a functioning health system for countries at all stages of development.⁴ Approximately 30% of the global burden of disease is surgical, ⁵ and the delivery of basic, life-saving surgical care is highly cost-effective in both high-income countries (HICs) and low- and middle-income countries (LMICs) ⁶ However, cost-effectiveness at the population level does not translate into affordability for an individual patient. In the absence of financial risk protection measures, accessing surgery be catastrophically expensive for patients. Because common effectiveness measures (such as quality- or disability-adjusted life years) do not explicitly capture the potentially impoverishing impacts of care, these financial impacts on individuals have often been overlooked. The need for surgical care can be time-critical, unpredictable, and resource-intensive, and, consequently, surgery is difficult to plan or save for. In addition, seeking treatment for surgical conditions has been shown to be more impoverishing than for other conditions.⁷

Adding to the financial burden of paying for surgical services are the costs of getting to care. These non-medical costs of transportation, food, and lodging⁸ are substantial and can themselves drive patients into poverty.⁹ The high costs associated with accessing surgical care, then, not only increase the chance of catastrophic health expenditure, but can also act to prevent health-seeking behaviour, especially among the poor.¹⁰

Protecting households against catastrophic health expenditure has emerged as a leading policy goal for the post-2015 global health agenda. The World Health Organization (WHO)¹¹, the World Bank,¹² and the United Nations (UN)¹³ have recently renewed calls for the introduction of universal health coverage and the assurance of financial risk protection (FRP) against the costs of illness. The World Bank has issued a statement that, “By 2030, no one should fall into poverty because of out-of-pocket health care expenses.”¹⁴ As a result, a greater understanding of the financial catastrophe attributable to different health interventions, including surgical care, has become necessary to inform policy. Our aim was to estimate the number of patients worldwide that experience catastrophic expenditure each year due to accessing surgery. We also investigated how rates of catastrophic expenditure from surgery change according to national development status and individual wealth quintile. We hypothesized that a large portion of worldwide financial catastrophe due to medical care would be attributable to surgery, and that this burden would fall most heavily on the poor.

Multiple thresholds have been proposed for catastrophic expenditure. In this paper, we have chosen to use a threshold of 10% of overall household expenditure,¹⁵ which we explore in sensitivity analyses, below.

An individual faces catastrophic expenditure when the out-of-pocket (OOP) costs faced to access care are greater than this threshold. That is:

where c is the total cost of a service, OOP represents the out-of-pocket proportion of that cost, y represents ex-ante (before care was sought) household expenditure, and t represents the threshold, expressed as a proportion of household expenditure, at which catastrophic expenditure is said to have occurred. For example, an individual whose pre-healthcare expenditure is $1000 would face catastrophic expenditure if she were required to pay more than $100 for healthcare.

The methodology behind the application of Equation (1) to the world population is given in detail in the Appendix. Briefly, the income distribution of a country's population is modeled, and the proportion of the population undergoing surgery estimated, by wealth quintile, from prior publications (see Data sources, below, and Table A1 in the Appendix). Individuals who require surgical services are assessed an OOP cost. If that amount is greater than 10% of their pre-surgery income, they are counted as having experienced catastrophic expenditure. This calculation is repeated across all countries to obtain a global estimate. To obtain an estimate of the number of individuals at risk of catastrophic expenditure, the same calculation was repeated, with the probability of getting surgery factored out.

World Bank data were used for each of the necessary parameters in 199 countries. Household expenditure was used where available.¹⁶ If not available, GDP per capita¹⁷ was used as a proxy. WHO-CHOICE estimates for the unit cost of a C-section were taken to represent costs of surgery,¹⁸ an assumption which was tested in sensitivity analyses, below. According to the WHO, this cost includes “… initiation of labour at referral level, diagnosis of obstructed labour and referral, Caesarean delivery associated devices and medicines, operative facility time, medical human resources time, management of shock including hysterectomy and blood transfusion (assumed for 1% of CS performed), postoperative hospital stay for stabilization … programme administration, training, and the corresponding office space, electricity and other services, as well as a variety of standard consumables and equipment.”¹⁸ In probabilistic sensitivity analysis, a multiplier was applied to this cost (see below). One country (Iceland) had a reported cost that was a significant outlier. Its cost was taken as the result of a linear regression on its GDP per capita.

OOP was calculated as a proportion of total health expenditure.¹⁹ The model was run with and without the inclusion of non-medical costs faced by patients when accessing surgical services (transportation, lodging, food, etc.). When included, we used a conservative estimate consistent with estimates from Ethiopia,²⁰ Bangladesh,^7,21-23 India,^24-29 and Vietnam³⁰; specifically, the non-medical costs were constructed as a multiplier to direct medical costs based on these data, with the introduction of error from the varied estimates. This was examined in detail in sensitivity analyses, below. All costs, expenditure, and income estimates were adjusted to 2007 international dollars, using UN purchasing power parity conversion factors and World Bank GDP deflators, following methods previously described.³¹ 2007 was chosen because it was the year for which the most robust primary data were available.

The probability of accessing surgery was taken from previously published estimates.³² For countries in which estimates of cases per capita were not available, regional estimates for countries with similar overall healthcare expenditure per capita were used instead. Similarly, when Gini indices for individual countries were unavailable, regional indices were used.³³ This average probability was broken down by quintiles as follows: access to Caesarean section was calculated for Ethiopia, India, and Bangladesh from their respective Demographic and Health Surveys.^34-36 Access by quintile was compared with mean access, and the average proportion of access faced by each quintile was then taken as an access multiplier for the model as a whole. The determination of the gradient was limited by the data available and is discussed below.

Sufficient data were not available for the following countries: The Cook Islands, North Korea, Nauru, Niue, Puerto Rico, Somalia, South Sudan, and Taiwan. Data were missing for these countries from both the World Bank economic and population indicators and from estimates of surgical delivery. The combined population of these countries represents only 1.03% of the overall global population; as a result, an assumption was made of missingness at random, and the results from the remaining countries were scaled up linearly to encompass the world's population.

Sensitivity analyses were performed to test the robustness of our results. Because, as seen in equation (1), the cost of surgery is a driver of the model, and because the cost of a C-section may under represent the cost of surgery overall,³⁷ we ran a sensitivity analysis on average cost of an operation. We ran a similar sensitivity analysis on the average non-medical costs faced by patients, using both high and low estimates from the sources listed above. The WHO and World Bank have recently redefined catastrophic expenditure as any spending that is over 25% of post-subsistence expenditure.³⁸ Applying the linear transformation that previously converted thresholds based on post-subsistence expenditure to thresholds based on total expenditure, this new definition translates to 6.25% of overall expenditure. We performed a sensitivity analysis with this as the cutoff. Finally, the World Bank estimates of OOP expenditure differ from estimates published directly from an examination of national health accounts.³⁹ We substituted the latter estimates where available in a fourth sensitivity analysis.

First-order heterogeneity was modeled by using Monte Carlo simulations at each of the probability nodes. Second-order uncertainty was incorporated by drawing from probability distributions around c × OOP, the surgical cost scaling factor, and the proportion of the population accessing surgical care. Parameterization for each is given in Table A2, in the Appendix.

Two hundred parameter sets were drawn from each distribution; 1000 iterations of each parameter set were run, over all 199 countries, and for each of the three model versions. The mean and 95% posterior credible intervals (PCIs) are reported for overall numbers of cases of catastrophic expenditure and for cases per million in the population.

Model construction and data analysis were performed in Microsoft Excel 2011 (Microsoft Corporation) and R v3.0.2 (www.rproject.org).

No direct funding for this study. No entity besides the authors had a role in any aspect of this study, including conception and design, data acquisition, data analysis, manuscript preparation, manuscript revision, or the decision to submit. MGS received speaking fees from Ethicon for a talk unrelated to this present work.

Globally, 32.8 million (32.4 – 33.1 million) cases of catastrophic expenditure occur every year due to patients accessing surgical services. (Table 1) Approximately 3.7 billion individuals (3.2 – 4.2 billion) are at risk of financial catastrophe should they require surgical care. These individuals live primarily in sub-Saharan Africa, and South and Southeast Asia. (Figure 1)

Individuals in low- and middle-income countries are at high risk of catastrophic expenditure if surgical care is necessary. Red = high risk of catastrophic expenditure. Yellow = low risk of catastrophic expenditure.

Catastrophic expenditure due to medical costs falls primarily on the poorest, with differences by region. Worldwide, approximately 6.1% (5.2 – 7.0%) of the poorest patients who undergo surgery face catastrophic expenditure, while fewer than 0.1% (0.01 – 0.19%) of the richest do. In low-income countries, this gradient is nearly flat, while in upper-middle and high-income countries, nearly all catastrophic expenditure falls on the poor (Figure 2).

Catastrophic expenditure by wealth quintile and national income group, conditional on seeking surgery. Of all patients facing surgery, the poorest face the highest risk of catastrophic expenditure than the richest, and the risk of catstrophic expenditure is greatest in low- and low-middle income countries than in higher income countries. Panel (a): Global estimate. Panels (b)-(d): Estimates by World Bank country income groups.

When the multi-sectorial nature of health seeking is examined, the estimates of catastrophic expenditure increase greatly. Direct nonmedical costs to patients (transportation, lodging, food, and informal payments) produce an additional 48.4 million cases of catastrophic expenditure, leading to a total estimate of 81.2 million cases (80.8 – 81.7 million). When these costs are counted in the model, the gradient among wealth quintiles flattens somewhat. Globally, catastrophic expenditure risk increases, and does so relatively more in the richer quintiles. These results are summarized in Figure 2.

Prior estimates of catastrophic expenditure have used the threshold used in our base case analysis. Newer thresholds have, however, been proposed. When the threshold for counting a case of catastrophic expenditure was lowered to 6.25%, the estimated number of cases of catastrophic expenditure predictably increased. At this threshold, 63.3 million (61.6 – 64.9 million) cases of catastrophic expenditure were predicted due to medical costs alone, and 119.7 million cases predicted due to medical and non-medical costs together (117.5 – 122.1 million).

When the average cost of surgery was halved, the number of cases of catastrophic expenditure dropped to 7.7 million (direct medical; 7.3 – 8.1 million) and 28.0 million (medical + non-medical; 27.1 – 28.9 million). When the cost of an average surgery was estimated to be double that of a C-section, estimates increased to 79.2 million (77.5 – 80.9 million) and 135.6 million (133.2 – 138.1 million), respectively.

Our estimates were sensitive to assumptions around the magnitude of direct non-medical costs, but only moderately. Increasing these costs six-fold only increased the estimate of total cases of catastrophic expenditure four-fold. The higher the estimate of non-medical costs, the more catastrophic expenditure the richer quintiles faced.

Our results were robust to the source of OOP proportion estimates. Using estimates directly from national health accounts³⁹ did not change our estimates significantly.

Globally, 3.7 million people are at risk of financial catastrophe should they need surgery, and 33 million of them face catastrophic expenditure each year accessing and paying for surgical care. This represents approximately 22% of the 150 million individuals who face catastrophic expenditure accessing the health system every year,² commensurate with the proportion of global disease burden that is surgical.⁵ Financial catastrophe impacts individuals in low- and middle-income countries most severely; within any one country, the poorest fare the worst.

Accessing healthcare involves more than simply paying for services. Non-medical costs, such as transportation, lodging, and food, can be a significant contributor to catastrophic expenditure.²⁰ These costs, however, are often not included in many estimates of out-of-pocket financial expenditures.² When accounted for in our model, an additional 48.4 million cases of catastrophic expenditure are predicted, leading to an overall estimate of 81 million annual cases of catastrophic expenditure from accessing surgical care worldwide. These cases of financial catastrophe are not being created by the addition of these non-medical costs; they already exist, but have not been accounted for in models that only consider the direct cost of services.

Our results demonstrate an interesting, if initially somewhat counterintuitive, finding: financial catastrophe from surgical access is more common in individuals in lower-middle income countries than those in low-income countries. This is because, on average, the cost of surgery, as a function of income, is 15% higher in lower-middle income countries than in low-income countries, suggesting that, as the financial status of a country improves, the costs of seeking surgery may inflate more rapidly than average household income. This highlights the importance of incorporating financial risk protection into health systems at all stages of development, particularly as countries transition from a low-income to lower-middle income group. This is consistent with findings by the Commission on Investing in Health.⁴⁰

When non-medical costs are considered, catastrophic expenditure appears to accrue more heavily on the richer wealth quintiles. This is artifactual. The poor already hit the catastrophic expenditure threshold, whether or not non-medical costs are included—the depth of their impoverishment is not measured by this metric. The apparent increase in catastrophic expenditure among the rich is due simply to the fact that increased utilization among the rich makes the model more sensitive to catastrophic expenditure due to non-medical costs occurring in these individuals.

This analysis has a number of strengths and limitations. First, the definition of catastrophic expenditure itself is controversial;^3,9,15 we have chosen one definition in our base case and another in our sensitivity analysis, but we recognize that, in the absence of a universally accepted definition, these remain open for debate.

More importantly, however, catastrophic expenditure only captures individuals who actually pay for services. It captures neither patients who would need services but could not afford them, nor the impoverishment caused if services are not obtained by the primary income-earner in a household. This is an inherent weakness in most current measures of the financial burden of healthcare. Borrowed from the taxation literature, these metrics⁴⁰—any expenditure, amount of expenditure, catastrophic expenditure, poverty head count, poverty gap or squared poverty gap, indebtedness, and borrowing and selling to pay for care—all inherently count only payment made. They do not account for a lack of access, and do not include indirect costs such as lost wages and decreased economic productivity. As a result, the degree of financial impact that results from a lack of access cannot be fully captured by these metrics.

Secondly, our choice of threshold for catastrophic expenditure may lead to an underestimation in LMICs. Individuals who spend a significant amount of their expenditure on food would face financial ruin with smaller OOP spending than is captured in this model. In addition, expenditures for individuals near the poverty line might not count as “catastrophic”, by definition, but may still have devastating impacts on household welfare.

Thirdly, this is a model based on limited data. The income distribution in individual countries is approximated by a statistical distribution which, although it fits population income distributions on average,⁴¹ may do so more or less accurately in countries in which the bulk of the workforce is in the informal sector. Similarly, we base our surgical costs on the costs of C-section because this is the only procedure for which we had reliable global data. In the absence of other data, we chose to inflate and deflate this estimate probabilistically, as well as to examine it explicitly in sensitivity analyses, mitigating this weakness. Of note, although many countries have financing in place specifically for C-sections, this financing does not always protect against catastrophic expenditure.⁴² The gradient of utilization across nations comes from a small sample of countries, which introduces uncertainty into our equity results. Data on disease burden is limited as well. This limitation, however, does not impact our results: because this paper looks at current catastrophic expenditure from patients actually accessing the surgical system, as opposed to latent demand for surgical services, the studies on surgical disease burden that have been done will be important for any further scale up of a surgical system.⁴³

Data on insurance are also limited—although OOP data used in this paper will, by definition, take the presence of insurance into account, the purpose of insurance is to smooth an OOP function. Evidence suggests that the expansion of insurance does increase healthcare utilization, but with varying impacts on actual health outcomes.⁴⁴ Although the institution of an insurance scheme in countries without it may allay some catastrophic expenditure, it does so with an important caveat:, because surgical conditions are often associated with large up-front costs, even a patient whose OOP spending is reimbursed by his insurance will still face catastrophic expenditure before the reimbursement.

Finally, this paper is a static picture: the modeling techniques used are unable to project the impacts of financial catastrophe over time. Not enough data currently exist to construct a global model addressing future financial impacts.

The strengths of this study, however, lie in what it shows about the nature of global catastrophic health expenditure. This model does what many other estimates of catastrophic expenditure have not done. First, it includes the direct nonmedical costs that individuals face, giving a more realistic estimate of impoverishment. In the prior global estimate of medical impoverishment, Xu, et al., note, “no data on transportation costs were available, so the analysis underestimates the financial consequences of obtaining care.”² This particular underestimation is avoided in our analysis. As such, by considering the full costs of care, we are able to provide a more complete picture of the need for financial risk protection.

Secondly, the prior global estimate of medical impoverishment² did not break the impoverishment down by disease condition. To our knowledge, no study has proposed a global estimate for catastrophic expenditure due, for example, to chronic conditions or critical care. This is important—as the elimination of medical impoverishment is the stated goal of many world bodies,¹⁴ detailed information on the distribution of financial catastrophe is necessary to inform sound policies aimed at preventing it.

Thirdly, the nature of a stochastic model such as this allows for an explicit determination of heterogeneity and uncertainty around parameters. Any global estimate is fraught with uncertainty—this model makes the uncertainty explicit. The sensitivity analyses also document the factors in the model to which the results are reliant. A redefinition of the threshold at which catastrophic expenditure is counted, for example, drastically changes the number of individuals with catastrophic expenditure per year.

Finally, this paper approaches the question of financial catastrophe using a different modeling technique from other estimates; despite this, it arrives at estimates that are in line with prior estimates, lending credence both to this modeling technique and to the external validity of the estimates.

Surgery is a substantive part of any health system,^4,20,45 and surgical conditions often uniquely put patients at risk for financial catastrophe because they can be time-critical, life threatening, and fraught with large up-front costs. In January, 2014, the President of the World Bank, Jim Y Kim, detailed the Bank's goals for catastrophic expenditure: “The proposed target is, by 2020, to reduce by half the number of people who are impoverished due to out-of-pocket health care expenses. By 2030, no one should fall into poverty because of out-of-pocket health care expenses.”¹⁴ The results of this paper highlight the fact that, with over 30 million of the previously estimated 150 million cases of medical catastrophic expenditure coming from surgery, no zero target for impoverishment can be met without providing financial risk protection for surgical conditions. Surgery must therefore be considered an integral part in universal health coverage.

Importantly, this paper suggests that countries must consider the potential catastrophic impacts of both medical and non-medical costs in the design of policies. Simply making surgery free at the point of care will not completely alleviate the risk of financial catastrophe to patients; coverage of non-medical costs (for example, vouchers for travel, or some form of “negative user fees”)²⁰ may be necessary for full attainment of the goal of eliminating medically-driven financial catastrophe by 2030.

3.7 billion individuals risk catastrophic expenditure if they need surgery, and 33 million actually face it yearly when accessing surgical care. This represents 22% of all cases of catastrophic health expenditure. An additional 48 million (or 81 million total) face catastrophic expenditure as a result of the nonmedical costs required to seek care. The burden of catastrophic expenditure falls primarily on individuals in low- and middle-income countries and, within any country, on the poor. Universal health coverage policies must address the financial catastrophe faced by individuals seeking surgery.

01

We searched PubMed and Google Scholar for English-language publications, without restriction to date, with the terms “catastrophic expenditure”, “surgery”, and “surgical delivery”. We also identified pertinent references from the bibliographies of these publications. Catastrophic expenditure is one of many metrics to address the financial burden borne by individuals seeking health care. Like all similar metrics, it has moderate construct validity but has limitations, discussed in the text. Although catastrophic expenditure due to surgical conditions has been addressed in small series within individual countries, there has been no systematic review of this, and no attempt to construct a global estimate. Few studies have incorporated the financial burden of the cost of getting to care with the cost of care itself.

To the best of our knowledge, this is the first systematic study to determine a global estimate of financial catastrophe due to accessing surgical services and the first to evaluate medical impoverishment from specific segments of the health sector. We find that catastrophic expenditure from surgery is large, making up approximately 22% of the overall incidence of financial catastrophe due to medical care in general. When the non-medical costs of getting to care are added, this burden more than doubles. The financial burden falls most heavily on the poor. Because impoverishment plays such a large role in access to surgery—and as a barrier to that same access—our hope is that these findings will bring surgery to the forefront of the discussion as countries develop policies to assure financial risk protection from catastrophic health expenses and move toward universal health coverage, as well as to spur more study into the interplay between health improvement and financial catastrophe.

Author contributions: MGS: study conception and design, data acquisition, data analysis, manuscript preparation and revision, final approval of the version to be submitted, and accountability for the results.

AD: study conception, data acquisition, data analysis, manuscript revision, final approval of the version to be submitted, and accountability for the results

BCA: study conception and design, data acquisition, data analysis, manuscript revision, final approval of the version to be submitted, and accountability for the results.

KO: data acquisition, manuscript revision, final approval of the version to be submitted.

JGM: study conception and design, manuscript revision, final approval of the version to be submitted, and accountability for the results.

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• 1.Scheiber GJ, Gottret P, Fleisher LK, Leive AA. Financing global health: Mission unaccomplished. Health Aff. 2007;26(4):921–34. doi: 10.1377/hlthaff.26.4.921. [DOI] [PubMed] [Google Scholar]
• 2.Xu K, Evans DB, Carrin G, Aguilar-Rivera AM, Musgrove P, Evans T. Protecting households from catastrophic health spending. Health affairs (Project Hope) 2007;26(4):972–83. doi: 10.1377/hlthaff.26.4.972. [DOI] [PubMed] [Google Scholar]
• 3.Wagstaff A, van Doorslaer E. Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993-1998. Health Economics. 2003;12:921–34. doi: 10.1002/hec.776. [DOI] [PubMed] [Google Scholar]
• 4.Farmer PE, Kim JY. Surgery and global health: A view from the OR. World J Surg. 2008;32:533–6. doi: 10.1007/s00268-008-9525-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 5.Shrime MG, Bickler SW, Alkire BC, Mock C. Thirty percent of the global burden of disease is surgical. Lancet Global Health. 2015 doi: 10.1016/S2214-109X(14)70384-5. Accepted for publication. [DOI] [PubMed] [Google Scholar]
• 6.Chao TE, Sharma K, Mandigo M, et al. Cost-effectiveness of surgery and its policy implications for global health: a systematic review and analysis. Lancet Global Health. 2014;2(6):e334–e45. doi: 10.1016/S2214-109X(14)70213-X. [DOI] [PubMed] [Google Scholar]
• 7.Hamid SA, Ahsan SM, Begum A. Disease-specific impoverishment impact of out-of-pocket payments for health care: evidence from rural Bangladesh. Applied Health Economics and Health Policy. 2014 doi: 10.1007/s40258-014-0100-2. [DOI] [PubMed] [Google Scholar]
• 8.Bakeera SK, Wamala SP, Galea S, State A, Peterson S, Pariyo GW. Community perceptions and factors influencing utilization of health services in Uganda. International journal for equity in health. 2009;8:25. doi: 10.1186/1475-9276-8-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 9.van Doorslaer E, O'Donnell O, Rannan-Eliya RP, et al. Effect of payments for health care on poverty estimates in 11 countries in Asia: an analysis of household survey data. Lancet. 2006;368:1357–64. doi: 10.1016/S0140-6736(06)69560-3. [DOI] [PubMed] [Google Scholar]
• 10.Meremikwu MM, Ehiri JE, Nkanga DG, Udoh EE, Ikpatt OF, Alaje EO. Socioeconomic constraints to effective management of Burkitt's lymphoma in south-eastern Nigeria. Tropical medicine & international health : TM & IH. 2005;10(1):92–8. doi: 10.1111/j.1365-3156.2004.01348.x. [DOI] [PubMed] [Google Scholar]
• 11.World Health Organization. Everybody's business: strengthening health systems to improve health outcomes: WHO's framework for action. [accessed 9 December 2013];2007 http://www.who.int/healthsystems/strategy/everybodys_business.pdf.
• 12.Kutzin J. Towards Universal Health Care Coverage : Goal-oriented Framework for Policy Analysis. Washington, DC; World Bank: 2000. [Google Scholar]
• 13.Vega J. Universal health coverage: the post-2015 development agenda. Lancet. 2013;381(9862):179–80. doi: 10.1016/S0140-6736(13)60062-8. [DOI] [PubMed] [Google Scholar]
• 14.Kim JY. Speech by World Bank Group President Jim Yong Kim on universal health coverage in emerging economies. [accessed 31 August 2014];2014 http://www.worldbank.org/en/news/speech/2014/01/14/speech-world-bank-group-president-jim-yong-kim-health-emerging-economies.
• 15.Pradhan M, Prescott N. Social risk management options for medical care in Indonesia. Health Economics. 2002;11:431–46. doi: 10.1002/hec.689. [DOI] [PubMed] [Google Scholar]
• 16.World Bank. World Development Indicators, Household final expenditure. [accessed 4 July 2014];2013 http://data.worldbank.org/indicator/NE.CON.PRVT.PC.KD.
• 17.World Bank. World Development Indicators, GDP per capita. [accessed 4 July 2014];2013 http://data.worldbank.org/indicator/NY.GDP.PCAP.CD.
• 18.World Health Organization. WHO-CHOICE, Quantities, units, and prices. [accessed 4 July 2014]; http://www.who.int/choice/cost-effectiveness/inputs/en/
• 19.World Bank. World Development Indicators, Out of pocket health expenditure (as a percentage of total health expenditure. [accessed 4 July 2014];2013 http://data.worldbank.org/indicator/SH.XPD.OOPC.TO.ZS.
• 20.Shrime MG, Verguet S, Johansson KA, Jamison DT, Kruk ME. Task-shifting, universal public finance, or both for the expansion of surgical access in rural Ethiopia: an extended cost-effectiveness analysis. In: Jamison DT, editor. Disease Control Priorities in Developing Countries. 3rd. 2014. [Google Scholar]
• 21.Borghi J, Sabina N, Blum LS, Hoque ME, Ronsmans C. Household costs of healthcare during pregnancy, delivery, and the postpartum period: a case study from Matlab, Bangladesh. Journal of Health Population and Nutrition. 2006;24(4):446–55. [PMC free article] [PubMed] [Google Scholar]
• 22.Khan SH. Free does not mean affordable: maternity patient expenditures in a public hospital in Bangladesh. Cost effectiveness and resource allocation : C/E. 2005;3(1):1. doi: 10.1186/1478-7547-3-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 23.Mashreky SR, Rahman A, Khan TF, Faruque M, Svanstrom L, Rahman F. Hospital burden of road traffic injury: major concern in primary and secondary level hospitals in Bangladesh. Public health. 2010;124(4):185–9. doi: 10.1016/j.puhe.2010.01.004. [DOI] [PubMed] [Google Scholar]
• 24.Dhar RS, Nagpal J, Sinha S, Bhargava VL, Sachdeva A, Bhartia A. Direct cost of maternity-care services in south Delhi: a community survey. Journal of Health Population and Nutrition. 2009;27(3):368–78. doi: 10.3329/jhpn.v27i3.3379. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 25.Kumar GA, Dilip TR, Dandona L, Dandona R. Burden of out-of-pocket expenditure for road traffic injuries in urban India. BMC Health Services Research. 2012;12:285. doi: 10.1186/1472-6963-12-285. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 26.Mohanty SK, Srivastava A. Out-of-pocket expenditure on institutional delivery in India. Health policy and planning. 2013;28(3):247–62. doi: 10.1093/heapol/czs057. [DOI] [PubMed] [Google Scholar]
• 27.Pakseresht S, Ingle GK, Garg S, Singh MM. Expenditure audit of women with breast cancer in a tertiary care hospital of Delhi. Indian journal of cancer. 2011;48(4):428–37. doi: 10.4103/0019-509X.92263. [DOI] [PubMed] [Google Scholar]
• 28.Reddy GM, Negandhi H, Singh D, Singh AJ. Extent and determinants of cost of road traffic injuries in an Indian city. Indian journal of medical sciences. 2009;63(12):549–56. [PubMed] [Google Scholar]
• 29.Reddy GM, Singh A, Singh D. Community based estimation of extent and determinants of cost of injuries in a north Indian city. Indian journal of medical sciences. 2012;66(1-2):23–9. [PubMed] [Google Scholar]
• 30.Nguyen H, Ivers R, Jan S, Pham C. Financial catastrophe, treatment discontinuation and death associated with surgically operable cancer in South-East Asia: results from the ACTION Study. Lancet. 2015 doi: 10.1016/j.surg.2015.02.012. Accepted for publication. [DOI] [PubMed] [Google Scholar]
• 31.Schreyer P, Koechlin F. Purchasing power parity--measurement and uses. [accessed 4 July 2014];OECD Statistics Brief. 2002 http://www.oecd.org/std/prices-ppp/2078177.pdf.
• 32.Weiser TG, Regenbogen SE, Thompson KD, et al. An estimation of the global volume of surgery: a modelling strategy based on available data. Lancet. 2008;372:139–44. doi: 10.1016/S0140-6736(08)60878-8. [DOI] [PubMed] [Google Scholar]
• 33.Ortiz I, Cummins M. Global inequality: Beyond the bottom billion. [accessed 8 July 2014];UNICEF social and economic policy working paper. 2011 http://www.unicef.org/socialpolicy/files/Global_Inequality_REVISED_-_5_July.pdf.
• 34.Central Statistical Agency [Ethiopia], ICF International. Ethiopia Demographic and Health, 2011. Addis Ababa, Ethiopia and Calverton, MD: Central Statistical Agency and ICF International; 2012. [Google Scholar]
• 35.International Insititute for Population Sciences, International M. National Family and Health Survey (NFHS-3), 2005-06, India. Mumbai: IIPS; 2007. [Google Scholar]
• 36.National Institute of Population Research and Training, Mitra and Associates, ICF International. Bangladesh Demographic and Health Survey. Dhaka, Bangladesh, and Calverton, Maryland, USA: NIPORT, Mitra and Associates, and ICF International; 2013. [Google Scholar]
• 37.Prinja S, Nandi A, Laxminarayan R. Chapter 18: Costs, effectiveness, and cost-effectiveness of selected surgical procedures and platforms: A summary from across the volume. In: Debas HT, Gawande AA, Jamison DT, Kruk ME, Mock C, Donkor P, editors. Disease Control Priorities in Developing Countries. 3rd. 2014. [PubMed] [Google Scholar]
• 38.World Health Organization, World Bank. Monitoring progress towards universal health coverage at country and global levels: Framework, measures and targets. 2014. accessed. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 39.Institute for Health Metrics and Evaluation. National health accounts visualization. [accessed 31 August 2014];2014 http://vizhub.healthdata.org/nha/
• 40.Jamison DT, Summers LH, Alleyne G, et al. Global health 2035: a world converging within a generation. Lancet. 2013;382:1898–955. doi: 10.1016/S0140-6736(13)62105-4. [DOI] [PubMed] [Google Scholar]
• 41.Salem ABZ, Mount TD. A convenient descriptive model of income distribution: the Gamma distribution. Econometrica. 1974;42(6):1115–27. [Google Scholar]
• 42.Arsenault C, Fournier P, Philibert A, et al. Emergency obstetric care in Mali: catastrophic spending and its impoverishing effects on households. Bulletin of the World Health Organization. 2013;91(3):207–16. doi: 10.2471/BLT.12.108969. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 43.Groen R, Samai M, Stewart KA, et al. Untreated surgical conditions in Sierra Leone: a cluster randomised, cross-sectional, countrywide survey. The Lancet. 2012;380(9847):1082–7. doi: 10.1016/S0140-6736(12)61081-2. [DOI] [PubMed] [Google Scholar]
• 44.Baicker K, Taubman SL, Allen HL, et al. The Oregon experiment--effects of Medicaid on clinical outcomes. NEJM. 2013;368(18):1713–22. doi: 10.1056/NEJMsa1212321. [DOI] [PMC free article] [PubMed] [Google Scholar]
• 45.Shrime MG, Sleemi A, Thulasiraj RD. Charitable platforms in global surgery: a systematic review of their effectiveness, cost-effectiveness, sustainability, and role in training. World journal of surgery. 2014 doi: 10.1007/s00268-014-2516-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

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