Does Health Insurance Coverage or Improved Quality Protect Better Against Out-of-Pocket Payments? Experimental Evidence from the Philippines

Natascha Wagner[1], Stella Quimbo[2],Riti Shimkhada[3],John Peabody[4]

Abstract

Using data from the QIDS randomized policy experiment in the Philippines, we found thatinterventions to expand insurance coverage and improve provider qualityboth had an impact on out-of-pocket payments. Compared to controls, the expanded insurance interventionand the performance-based provider payments to improve quality both resulted in a decline in out-of-pocket spending (21 percent decline, p=0.072; and 24 percent decline, p=0.028, respectively). With lower out-of-pocket payments, monthly spending on personal hygiene rose by 0.9 and 0.6 US$ under the expanded insurance and provider payment interventions, respectively, amounting to roughly a 40 to 60 percent increase relative to the controls.

Keywords: health insurance, health care quality, universal health care coverage, out-of-

pocket payments, RCT

JEL: I11, I13, I15, O10

Word count abstract: 203

Word count main text: 7,712

1. Introduction

Out-of-pocket payments for health care are still the major source of health care financing in most developing countries. Such medical expenses increase the burden of poverty (McIntyre et al., 2006). Annually, an estimated 150 million people fall into poverty due to catastrophic health care expenditures (van Doorslaer et al. 2006, Xu et al., 2007). To better protect against unexpectedillness and unplanned health care expenditures,a range of health sector financing reforms are being implemented throughout developing countries with the aspiration ofprotecting poorer households. These public policy interventions range from the introduction of community-based health insurance (Jakab and Krishnan, 2001; Smith and Sulzbach, 2008;Mebratie et al., 2014) to health equity funds for the poor (Flores et al., 2011), to improved access to quality health care through set user fees (Litvack and Bodart, 1993), or the introduction of social health insurance programs for a large portion of the (so far uninsured) population (Limwattananon et al. 2015; Sparrow et al. 2013b; King et al., 2009; Thornton et al., 2010).

Existing research indicates that these financing reforms successfully increase access and utilization of health care services (Hou et al, 2013; Limwattananonet al., 2015).Financial protection—distinct from access—however, is not necessarily achieved by health care financing reforms (Xu et al., 2007; Wagstaff and Lindelow, 2008). While insurance expands access, the evidence does not demonstrate that these policies reduce out-of-pocket payments; indeed, they may even increase health expenditures among the poor. In Indonesia, while Health Insurance for the Poor (i.e., the Askeskin program) successfully increased access to health care it also increased out-of-pocket spending for new insurees who live in urban areas (Sparrow et al., 2013b). Likewise, the Chinese NCMS did not reduce out-of-pocket payments butin some cases even increased household spending (Lei and Lin, 2009; Liu and Tsegai, 2011; Wagstaff et al., 2009; Zhou et al., 2009). Possible explanations for these findings include overconsumption by the insured and overprovision of medical services by providers who are reimbursed by the insurance on a fee-for-service basis (Liu et al. 1999; Wagstaff and Lindelow, 2008). Despite this mixed evidence, during the last decade,most health policies to protect the poor have been insurance-based health reforms.

To determineif insurance confers financial protection, several aspects need to be considered. First, it needs to be determined whattypes of payments are affected by health financing reforms: is it financial support for hospitals, outpatients, provider services or medications? Second, what are the related out-of-pocket expenditures for and what is their impact onhousehold paymentsincluding health-related goods such as water and sanitation as well as non-health items such as transportation and education, and other consumption goods? (Pannarunothai and Mills, 1997; Gustafsson and Li, 2004).

Beyond characterizing (the lack of) financial protection at the user level, there is thecomplementing question of howfinancial risk protection may be achieved through supply-side policy interventions, such as the provision of accessible care and incentivizing better care. Established supply side policy interventions that providedirect care through public facilities primarilyrely on fee-for-service payments that either place the burden of payment on the user or misalign the incentives of hospital staff due toinflexible public repayment schemes (Ellis and McGuire, 1993; McIntyre et al., 2006). Consequently, supply-side interventions are not necessarily efficiency enhancing and tend to be neglected when health care reforms are designed (Ellis and McGuire, 1993; Smith and Sulzbach, 2008).Newer policy approaches, which hope to overcome the inadequacy of supply-side interventions, incentivize providers to provide more or better care. The experience of Thailand’s 2001 healthcare reform, “30 Baht.” demonstrates that carefully designed supply-side interventions can increase access and utilization of health care services among the poor, induce a switch from private to public hospitals and reduce out-of-pocket payments(Gruber et al., 2014).

There is—not surprisingly—an important dearth in understanding and alack of studies that contrast the impact of a demand-side, insurance-based reform versus a supply-side, provider payment reform; this further adds to policy uncertainty over how best to provide financial protection to the poor.

The randomized health policy experiment, known as the Quality ImprovementDemonstration Study or QIDS collected household and, facility and provider data before and after two health policy reforms were introduced. Conducted in four central regions of the Philippines between 2003 to 2008,QIDS was a large-scale community level intervention carried out in 30 public hospitals. The hospitals were randomly assigned into a control site and two different policy intervention sites, one expanding access to health insurance and the other intervention incentivizing hospital staff through bonus payments. The overarching objective of QIDS was to evaluate the effects of these policies on the health status of children, through the utilization and quality channels. The focus was on children who were hospitalized due to pneumonia and diarrhea as these diseases are among the leading causes of morbidity and mortality among Filipino children (Department of Health, 2011). Previous reports on this large social experiment established that the two interventions both have significant health effects on the quality of hospital care and that these quality improvements could be linked to better outcomes (reduced wasting and improved self-reported health ratings) among children 4-10 weeks after hospital charge (Quimbo et al. 2011; Peabody et al. 2013; Peabody et al., 2011; Peabody et al. 2012).

In this paperwe will estimate the impact of theQIDS demand-side insurance intervention compared to the supply-side incentive intervention on out-of-pocket payments for hospital services, the costs of medical treatment that were incurred inside and outside the hospital, and on household spending for preventive care. To analyze expenditureswe will look at overall household health expenditures,preventative care, as captured by personal hygiene, water and sanitation, and whether expenses for food, education and durablegoods are reallocated with illness. This will allow investigationofpreventive care spending along with the household expenditure pathway to health, namely, will freed-up household resources due to reduced out-of-pocket payments increase food consumptionor expenditures on health-related items such as toiletries, clean water and improved sanitation?

The paper is organized as follows. The project background and experimental design are presented in Section 2. Section 3 introduces the study population by means of descriptive statistics. The conceptual framework is described in Section 4 and Section 5 provides the empirical specifications, which make use of the linear fixed effects and Poisson fixed effects model. Results are discussed in Section 6 and Section 7 summarizes the findings, provides some additional contextualization of the results and policy conclusions.

2. Background

2.1 Study setting and project background

In the Philippines, improving access to care has been a priority policy concern since the Health Sector Reform Agenda was launched in 1999 under the leadership of then Health Secretary Alberto Romualdez who did so through the National Health Insurance scheme, PhilHealth. PhilHealth's mandate is to provide universal health insurance coverage. The most recent reports indicate that PhilHealth covers 83 percent of the population (PhilHealth, 2013). Yet, the actual coverage rate could be smaller based on other national surveys. For example, the 2008 National Demographic and Health Survey indicates that 47.83 percent of surveyed households had at least one household member covered by PhilHealthrevealing that about 58% of the poorest children with acute respiratory illness receive medical treatment and, similarly, only about 37% of the poorest children with symptoms of diarrhea get oral rehydration therapy (NDHS 2008).

In PhilHealth’s aim to provide financial protection to the poor, the insurer targets selected population groups, i.e. the government-employees, indigent individuals, retirees, and overseas workers. To afford more financial protection, among these groups, the insurance premiums vary, for example,an averageformallyemployed individual pays a premium of3,370 pesos(≈ 76.5 US$)[5]per personandyear. For an averagehouseholdwith 4.6 members (2010 Censusof Population andHousing) thisamountsto a totalcostof 15,502 pesos (≈ 351.9 US$) per year. Indigent individuals are sponsored by the national and local governments and thus pay only 2,400 pesos (≈ 54.4US$) per household and year. The basic benefit package primarily covers inpatient care and not outpatient care or medications purchased outside of the hospital. Recently, to keep insurance pay-outs down,in-patient reimbursementshave been switched to what are called “case rates” or bundled payments. When costs exceed the case rates for a given hospitalization, patientsand their families have to shoulder the difference in the form of out-of-pocket payments.

PhilHealth finances only about 10 percent of the overall personal health care spending in the Philippines (NSCB 2013). Despite expanding coverage and subsidized premiums aimed towards the poor, the Philippine National Health Accounts (2011) indicate that out-of-pocket expenditures remain the most dominant form of health-care financing, accounting for 52.7% of total health-care expenditures in 2011 (NSCB 2013).

2.2 QIDS Experimental Design, Sample Frame and Data

The QIDS experiment, a 5-year project done jointly with PhilHealth, the Department of Health, the University of the Philippines and the University of California San Franciscowas undertaken to potentially benefit an estimated one million people. Launched in 2001, community level randomization was applied to 30 public hospitals that were organized into matched blocks of three and randomly assigned to either one of two interventions and a control group. The matching was done based on demand and supply characteristics of the hospitals such as population, average household income, number of beds, average case load, PhilHealth accreditation and insurance coverage of the households.

Two interventions were implemented and financed by PhilHealth. The first is the demand side intervention. It is known as the “Access” intervention and consisted of increased enrollment of households in the PhilHealth program and zero copayments for PhilHealth-covered children under 5 years when using services in the participating district hospitals. The policy objective of the “Access” intervention was to increase existing PhilHealth coverage.A novel method was used for expanding insurance enrollment in this intervention: medical doctors were deployed as policy navigators and promoted PhilHealth enrollment through regular one-on-one meetings with heads of local government units to rigorously and systematically follow-up on PhilHealth premium sponsorships (Solon et al. 2009). The second intervention targets the supply side and is known as the “Bonus” intervention. It consisted of a scheme of quality measurement with pay-for-performance. District hospital staff usually received fixed salaries but under the “Bonus” intervention they could increase their incomes with bonus payments once the hospital was assessed to have met preset quality standards. These bonus payments were given quarterly from 2004 to 2007. Throughout this paper we refer to the “Access” intervention as “intervention A” and the “Bonus” intervention as “intervention B”. The hospitals in the control group, referred to as “C sites”, continued with the existing policies and practices. The three types of randomly selected hospitals in A, B, and C sites constitute the primary sampling unit for the evaluation of the QIDS interventions.

Data for this paper were obtained from two QIDS sources: (i) a patient exit survey and (ii) a follow-home survey 4-6 weeks after the child patient was discharged from the hospital. The patient exit and household surveys were conducted at baseline (2003/04) and at the end of the project (2007/08). By the time the second follow-up survey commenced, interventions had been in place for close to 2 years. The patient exit surveys were administered among the parents of child patientsup to the age of5 years. A total of 6,042 children were surveyed. Among those children, pneumonia and diarrhea patients were eligible for the follow home survey. Altogether, 3,183children were revisited at home of whom we have complete information for 3,121 children[6]. The follow home surveys in both rounds provided a detailed socioeconomic profile of the household including information on spending patterns.

3. Study Population

The total study population consists of 3,121 household observations, each of which had a child-patient in one of the 30 QIDS hospitals either at baseline in 2003/04 or at the follow-up in 2007/08. The observations are equally split across the three types of sites: In the A sites we have information about 1,036 patients; B sites comprise 1,055 observations; and the remaining 1,030 patients frequented the C sites. In the first survey round, 1,393 child patients participated; the follow-up survey covered 1,728 patients.

Basic descriptive statistics illustrating the features of the sampled child patients and their households are presented in Table 1. Of the total number of children 43.5 percent are girls. The children are on average slightly older than one and a halfyears and stay 4 days in the hospital. By design, the sample is equally split among pneumonia and diarrhea patients. Every household has about six members on average and at least one child below the age of 14 for every working adult. On average, the households have a monthly per capita income of 1,054 pesos (≈ 23.9 US$). As the primary sampling unit for the randomization is hospitals, we test for the balancing of the baseline child and household characteristics across the three hospital groups. Comparing child and household characteristics at baseline for each of the three cohorts, we find that the two intervention groups and the control site are comprised of children with statistically similar characteristics at the 5 percent significance level in all the 27 comparisons. Maternal education and per capita family income are slightly higher at baseline in the insurance expansion group compared to the control group (p-value=0.064 and 0.058, respectively). No significant differences can be found for the pay-for-performance group. Moreover, child characteristics such as age and gender and severity of disease are similar across all three (intervention and control) groups. Household size is also similar across sites. Yet, to account for child and household heterogeneity in the analysis, we jointly include these variables as controls in our empirical model.

The descriptive statistics for the outcome variables show that out-of-pocket payments for the hospitalization of the child patient amounted to 2,212 pesos (≈ 50.2 US$) on average (See Table 2). Average out-of-pocket payments are slightly higher than total medical charges associated with the hospitalization because the former also cover transportation and food expenditures. Total charges are mainly made up of costs incurred inside the hospital, namely an average of 1,422 pesos (≈ 32.3 US$). Charges for health services used outside the hospital amount to an average of 677 pesos (≈ 15.4 US$).

For household level expenditures we did not only consider the costs of the hospitalization of the child patient but also at the costs of health expenditures incurred during the last six months prior to the survey (not including the costs of the child hospitalization under study). These household health expenditures are presented in per capita and per month terms; they include drugs and medicines, hospital room charges, medical and dental charges, and other medical goods and supplies. Per capita health expenditures are 88 pesos (≈ 2.0 US$), accounting for 8.6 percent of the total monthly expenditure per individual. We observe that on average, per capita health expenditures are of similar range as the per capita costs for toiletries, water and sanitation that amount to 82 pesos (≈ 1.9 US$).[7]Taken together, monthly health expenditures and those for personal hygiene make up for 16 percent of the total monthly per capita expenditures. Relative to these16 percent constituted by the regular health care and preventive care spending, the out-of-pocket payments for the child hospitalization amount to more than twice the monthly expenditures per household member.