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Catastrophic healthcare expenditure – Drivers and protection: The Portuguese case
Health Policy 115 (2014) 44–51
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Health Policy journal homepage: www.elsevier.com/locate/healthpol
Catastrophic healthcare expenditure – Drivers and protection: The Portuguese case Christoph Kronenberg a,∗ , Pedro Pita Barros b,c a b c
Centre for Health Economics, University of York, United Kingdom Universidade Nova Lisboa, Portugal CEPR, London, United Kingdom
a r t i c l e
i n f o
Article history: Received 12 November 2012 Received in revised form 3 October 2013 Accepted 8 October 2013
JEL classification: I140 H510 I320 Keywords: Catastrophic health expenditure Portuguese health system Healthcare financing Out of pocket payments Poverty
a b s t r a c t The objective of this paper is to assess the extent of catastrophic healthcare expenditure, which can lead to impoverishment, even in a country with a National Health Service, such as Portugal. The level of catastrophic healthcare expenditure will be identified before the determinants of these catastrophic payments are analyzed. Afterwards, the effects of existing exemptions to copayments in health care use will be tested and the relationship between catastrophe and impoverishment will be discussed. Catastrophe is calculated from the Portuguese Household Budget Surveys of 2000 and 2005, and then analyzed using logistic regression models. The results show that catastrophe due to healthcare out-of-pocket payments are a sizeable issue in Portugal. Exemptions from out-of-pocket expenses for medical care should be created to prevent vulnerable groups from facing catastrophic healthcare spending. These vulnerable groups include children, people with disabilities and individuals suffering from chronic conditions. Disability proxies offer straightforward policy options for an exemption for the elderly with recognized disabilities. An exemption of retired people with disabilities is therefore recommended to policymakers as it targets a vulnerable group with high risk of facing catastrophic healthcare expenditure. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction An influential publication on health inequalities is the Marmot Review [1]. It renewed interest in barriers to health care. The creation of a National Health Service in several countries aimed at eliminating, or reducing, these barriers, while providing care with efficient use of resources. Whether this is the case, or not, can be assessed using the concept of catastrophic healthcare expenditures. Many countries attempt to improve the efficiency of their healthcare systems. A popular way in economics and policy making to increase efficiency acting on the demand
∗ Corresponding author. Tel.: +44 1904321411. E-mail addresses: [email protected], [email protected] (C. Kronenberg). 0168-8510/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.healthpol.2013.10.001
side is to reduce overconsumption due to moral hazard. This is usually done by cost sharing either in the form of co-payments, a fixed fee for a good or service or coinsurance. These payments, if the household does not have supplementary insurance, are paid directly and called outof-pocket payments. Many studies over the last decades have discussed theoretically [2–4] and empirically [5–9] the necessity for the right type and amount of cost sharing in relation to demand-side moral hazard. The contribution of the paper is the assessment of the extent of catastrophic healthcare expenditures, which can lead to impoverishment, even in a country with a National Health Service, such as Portugal. The mere existence of a National Health Service is not sufficient to completely avoid the existence of catastrophic health expenditures. Only about ten years ago researchers started to consider the impoverishing side effects of OOP payments,
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highlighting the potential trade-off between OOP as an instrument for demand control and OOP as an impoverishment driver. That is, households are forced into poverty by paying catastrophic shares of their income for healthcare; spending of this kind is called catastrophic healthcare expenditure (CHE).1 For brevity’s sake it will often be referred to as catastrophe throughout the text. In the context of a National Health Service providing universal coverage, the presence of copayments is presented as management of demand pressure. This feature should imply a different role of out-of-pocket payments than in health systems where they result mainly from the absence of health insurance. Of course, catastrophic health expenditures can also be found when a considerable part of the population is uninsured. Studies have shown that this problem exists, and not only in poor countries. For the United States, Waters et al. [10] show that families that have an income below the official federal poverty level spend 13.5% of their income on OOP payments for healthcare. Himmelstein et al. [11] have shown that a staggering 46.2% of personal bankruptcies in the US are caused by healthcare expenditure. The paper proves, using data from the 2000 and 2005 Portuguese Household Survey, that also a National Health Service may face impoverishment by catastrophic health expenditures. A specific group, the elderly, was found to be associated with a higher likelihood of catastrophic health expenditures. A policy recommendation which arises from this finding is that an exemption from copayments for retired people with disability will contribute significantly to higher social protection. The paper is organized in the following way. In Section 2, provides a brief overview of the health care system in Portugal. In Section 3 presents data and methodology used. Results are reported in Section 4. Section 5 provides concluding remarks. 2. A brief on the Portuguese healthcare system Organizational reforms of different scales and with different objectives have been quite common in Portugal over the last decades. However, no major reform has tackled the financing side of the healthcare system since the 1990s [12]. The Portuguese healthcare system has three overlapping financing systems, the first being the NHS which, given article 64 of the 1976 constitution, provides universal and comprehensive services approximately free of charge [13]. The second layer of the Portuguese healthcare system is the occupational insurance system (health subsystems) pre-dating the NHS. Health subsystems are financed via employee and employer contributions, though employee contributions tend to be around 1–1.5% of wages, accounting to about 10–15% of total funding needs of each
1 The authors prefer to talk about catastrophic healthcare expenditure despite the usual term being catastrophic health expenditure. The reason is that health is determined by more than healthcare and health payments should therefore include food, physical exercise, living conditions, etc. However, that is beyond the scope of this paper.
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particular health subsystem, or sometimes do not exist at all. The whole set of health subsystems covers around 15–16% of the population [14]. Additionally, 10% of the people take out voluntary health insurance (VHI). The Portuguese Association of Insurers puts this value closer to 20%, a value roughly doubling what was found in the latest National Health Survey (2005/2006). A minority of 2% even has both health subsystems and VHI. VHIs do not discriminate by risk groups explicitly, but their services are often tailored toward low risk groups, explaining that average VHI members are between 20 and 54, live in urban areas, have medium to high income and work in medium to large companies [15]. Around a third of total expenditure is financed privately, mainly through OOP payments either in the form of user charges or co-insurance and to a smaller extent through premiums to private insurance schemes and mutual institutions. OOP payments for pharmaceuticals were organized in a co-insurance system based on therapeutic value ranging from 10% to 95% in four categories. Additionally, there were a few pharmaceuticals that were completely reimbursed by the government based on equity concerns. In 2004, the OOP payments for emergency services were D 6.9 in a central hospital and D 6.1 in a district hospital. For outpatient services a central hospital charged D 4.1, a district hospital D 2.7 and a primary care facility D 2 [16]. In the study time individuals were exempted from OOP payments if they fell into one of the following groups: children under 12, pregnant women, suffering from chronic conditions, unemployed, poor, or if they were deemed to create positive externalities for society. Being poor for the purpose of exemption granting was defined by the Portuguese National Health Service as earning less than the minimum wage, which was D 318.2 and D 374.4 for 2000 and 2005, respectively. Individuals with positive externalities such as fire-fighters or blood donors were also exempted. There have been some minor recent changes, which are ignored here due to the time scope of the paper. Around a third of total expenditure is financed privately, of which a sizeable fraction results from out-of-pocket payments.2 Therefore the question of whether a part of the population is subject to catastrophic health expenditure, or not, is relevant.
3. Data and methods 3.1. Survey description The 2000 and 2005 waves of the Portuguese Household Budget Survey (Inquérito aos Orcamentos Familiares) were used. The Portuguese Household Budget Survey (PHBS) contains nationally representative data. The survey is carried out by Statistics Portugal, the official body in charge of production of data on Portugal. The 2000 wave included
2 A table giving an overview of the financing mix of the health care sector in Portugal can be found in the online appendix at [add link].
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10,020 households and the 2005 version included 10,403 households.3 The surveys were conducted in face-to-face interviews over the whole year to account for temporal changes. For the expenditure part of the surveys, individuals filled in tables explaining what they bought, the cost, etc., for two weeks. The expenditure data includes all areas of life: food, clothing, furniture, kitchen equipment and electronics, utility and insurance bills, leisure and cultural expenditure as well as transportation expenditure. 3.2. Dependent variable In order to keep this study comparable with others, the same definition of catastrophe is used as in earlier studies [17,18]. A household is said to have incurred catastrophic healthcare expenditure if its OOP healthcare payments are higher than 40% of its capacity to pay. Capacity to pay is the subtraction of the subsistence need from the household expenditure. Subsistence need is defined as a percentage share of the average household food expenditure. Values vary widely with 40% being the WHO definition [18,19]. This study uses the WHO definition of 40% but also tests 10%, 20% and 30% as well as an extended basket which includes clothing and utility bills in addition to food expenditures, as suggested by Evetovits et al. [20]. As variables are reported with respect to different time horizons, they have to be normalized. Inflating the twoweek data on expenditure to match the income-reporting time horizon or reducing income to a two-week period to match the data on expenditures produces the same result, as it is a matter of scaling. Of course, this is not a perfect procedure. Someone with health expenditures in a given two-week period will not have it in the same amount every two-week period of the year. In the opposite direction, there will be people with positive health expenditures outside the two-week period and with zero value within the window of data recording. 3.3. Independent variables To identify the share of poor households a poverty line has been computed as 60% of median income. This type of poverty line and level of income have been used in previous research [21]. All financial variables from 2005 have been inflationadjusted to 2000 values. Additionally the household income has been adjusted using the equivalence square root method. This method recommends dividing household income by the square root of the household size.4 During the 2000s Portugal’s currency was the Portuguese Escudo, therefore financial variables in the 2000 wave had to be transformed into Euro at the exchange rate 200.482 Portuguese Escudo to 1 Euro.
3 Variable definitions and basic descriptive statistics about the variables used can be found below in an online appendix. 4 There is no generally accepted equivalence scale. The square root method results, however, do not differ substantially from the results of other scales. The square root scale has also been used in recent research into poverty and inequality, which is closely related to this research.
Other independent variables cover gender, education, size of household, urbanicity and the number of seniors and juniors in the household as previously used by Habicht et al. or Yardim et al. [18,22]. Regional dummies were also included to account for regional variation. As the PHBS is a budget survey, no health proxy variables were available. To account for the effect of some aspects of health, variables for disability were derived from the data. Two disability proxies were derived from employment status if the respondent reported: Incapacity to work or retired due to disability. The third disability proxy is the amount of sickness and disability benefits. In the 2005 dataset the employment variable was not available, rendering it impossible to create employment ratios or disability proxies. The 2005 dataset included new variables describing accommodation. These allowed the generation of dummy variables on running water, electricity and sanitation. The size of the accommodation in square meters was also available.5 3.4. Methods The percentage of the population with catastrophe is tabulated by income quintiles and region. The determinants of catastrophe were analyzed individually via a logistic regression model, to assess the significance of individual determinants identified via the literature.6 The determinants statistically significant in this first run were then used together in a multivariate logistic regression. The results are reported as odds ratios; the ratio of the odds of an event occurring in one group compared to the odds of that event occurring in another group. The change in predicted probability of occurring catastrophe between two levels is also reported.7 The difference between minimum and maximum levels as well as between minus and plus half the base were computed. Their interpretation is intuitive: if the difference between minimum and maximum income is −0.03, keeping all other factors constant, an individual with the highest income in the sample is 3% less likely to face catastrophe than an individual with the lowest income. Households are identified to have catastrophe-related impoverishment if their income is above the respective poverty line but if, after subtracting OOP payments, their income is below the poverty line and the household has been identified to have faced impoverishment. 4. Results 4.1. Descriptive statistics The amount of catastrophic cases differs by region and over time. Table 1 shows the mean percentage values for different measures of catastrophe and by region and
5 The definition of further independent variables can be found in the online appendix. 6 There is no econometric reason to choose probit over logit or the other way around. The logistic regression model has been chosen, because it is more common in the literature on catastrophe. 7 The Stata add-on prchange from Long and Freese [23] was used.
C. Kronenberg, P.P. Barros / Health Policy 115 (2014) 44–51 Table 1 Percentages of occurrence of catastrophe by measure, region and income. 2000
2005
Measures of catastrophic healthcare expenditure CHE 10% 29.04% 32.76% CHE 20% 16.74% 15.40% CHE 30% 10.98% 8.59% CHE 40% (WHO) 7.85% 5.03% CHE extended basket 19.77% 19.63% Region North 3.87% 4.29% Center 9.10% 6.00% Lisbon 6.78% 3.11% Alentjo 10.66% 7.38% Algarve 9.32% 4.42% Azores 8.74% 3.65% Madeira 7.27% 5.68% Income quintile 1st 2nd 3rd 4th 5th
22.26% 11.08% 3.64% 1.55% 0.75%
13.50% 7.16% 2.60% 1.30% 0.58%
Trend 3.72% −1.34% −2.38% −2.83% −0.14% 0.42% −3.10% −3.67% −3.27% −4.90% −5.10% −1.59% −8.75% −3.92% −1.04% −0.25% −0.17%
income quintile. The region and income quintile values are calculated with the WHO measure of catastrophe. Comparing the 2000 result with an earlier study [17] which found 2.95% catastrophe in Portugal in 1995, there is drastic increase in those five years. There are no methodological differences to this study. However, two possible explanations are that the studies are based on different surveys and that health care expenditures have outgrown GPD growth during this time [24]. However, this seems contradicted by the fact that income inequality as measured by the Gini coefficient has virtually not changed from 1995 to 2000 [25]. However, this might be misleading as since 1995 lower incomes have grown faster [26]. Therefore, the threshold of catastrophe might have increased and causing more individuals to be unable to cross it. For the different measures of catastrophe, it can be seen that all have decreased or stayed constant. The extended basket approach lies between the 10% and 20% approach, but closer to the 20% as was found by Evetovits et al. [20]. A split-up by region shows the heterogeneity within Portugal. The occurrence of catastrophe in the North region has nearly doubled in the five years, whereas it decreased in Lisbon area. In the Azores catastrophe more than halved. The Alentejo, Madeira, Center and Algarve regions showed increases of a few percentage points. Income differences within CHE are vast. The 8% average for 2000 is decomposed to a 22% average for the lowest income quintile of the population and 1% for the highest income quintile. Overall, there seems to be a slight decrease of catastrophe. The PHBS allows for decomposition of the OOP payments into three broad categories. Pharmaceuticals are by far the biggest spending category, with the share slightly increased from 2000 to 2005 (64.6–65.4%). Medical services, the second most important spending category causing catastrophe including GPs, dentists, etc., has somewhat decreased (33.4–32.6%). Hospital services only play a minor role (below 2%).
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Based on these results and literature findings, potential target groups for exemption have been analyzed. In the year 2005, for example, 40% of all households that faced catastrophe were one-person households. Additionally, 63% were seniors and 65% were poor. Combining this, 26% of all cases of catastrophe were faced by alone-living seniors who were poor. This poverty among the elderly is likely to have increased with the economic crisis starting in 2008, as these elderly cannot expect their income to increase or to spread the risk of catastrophe with their other household members. 4.2. Determinants of catastrophic healthcare expenditure Table 2 presents the results of the multivariate logistic regression. Table 3 presents the changes in predicted probability and the means and standard deviations based on which these were calculated. The economic impact of income is small. The odds ratio is close to one implying that there is only a small difference between income levels with respect to catastrophe. For 2005 income could not been estimated precisely. The highest probabilities are very concentrated among the poorest individuals, as shown in Fig. 1. Very low income levels have lower predicted probabilities. This is most likely the case because these households do not take out healthcare in the first place. Concerning the odds ratio, the gender of the household head seems important. In 2000, the odds ratio is below one, implying that a male household head protects from catastrophe. In 2005, on the other hand, the odds ratio has increased to 2.1 implying that male household heads attract catastrophe. In probabilities the case for male household heads looks less dramatic; a switch from a female to a male household head only increases the probability of catastrophe by 1% in 2005. Age is a statistically significant factor at the 5% level in both years. The age of the household head has an odds ratio slightly above one in both years, showing a slight increase. The predicted probability increased from 3% to 4% from the youngest to the oldest household head. This is one of the biggest predicted probabilities impact. The disability proxies have the expected effect but a small magnitude. Incapacity to work has a slightly stronger effect than retired due to disability. Someone who is incapacitated to work is 1.5 times more likely to confront catastrophe than someone who is not incapacitated; this translates into a 1% predicted probability. All housing determinants have a protective effect and only running water and electricity are not significant. The size of the accommodation could be a proxy for both wealth and household size; however, they are separate variables and should therefore capture these effects, as well as income was adjusted for household size. 4.3. Imperfect policy test Given the data and the exemption rules to health care copayments and user charges, it is possible to check whether exempted population subgroups incurred no or very little catastrophe.
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Table 2 Determinants of catastrophe in the multivariate logit model. Characteristics
2000 Odds ratio
2005 Coefficients
Income (in 1000D ) Household head gender Household head age HHH completed education Amount of household members Senior ratio Semi urban Rural North Center Lisbon Alentejo Azores Madeira Algarve
0.92*** −0.08*** 0.69*** −0.37*** 1.14*** 0.13*** 0.77*** −0.26*** 0.86* −0.16* 1.81*** 0.59*** 0.38 −0.96 0.97 −0.03 0.54*** −0.61*** 0.90 −0.11 0.94 −0.07 0.92 −0.09 1.70*** 0.53*** 0.83 −0.18 Algarve is the reference category
2000 only variables Earner ratio Employed ratio Unemployed ratio Incapacity to work Retired due to disability
1.44 0.46*** 0.91 1.41** 1.14
2005 only variables Running water Electricity Sanitary Size (in m2 ) Construction year Constant N * ** ***
Odds ratio
Coefficients
0.95*** 2.45*** 1.21*** 0.43** 0.84** 1.64*** 0.28 1.28*** 1.13 1.14 0.97 1.26 1.10 1.54**
−0.05*** 0.89*** 0.19*** −0.83** −0.17** 0.50*** −1.28 0.25*** 0.12 0.13 −0.03 0.23 0.10 0.43**
0.68 0.57 0.63*** 0.86*** 0.93**
−0.38 −0.56 −0.46*** −0.16*** −0.07** 11.26*
0.37 −0.79*** −0.09 0.34** 0.13
−2.55*** 10,020
10,403
p < 0.10. p < 0.05. p < 0.01.
Nearly all of the relevant variables were only available for the year 2000, so the values presented here are for 2000 only. Four exemptions are testable, whereas usually there are 15–20 exemption types depending on which law is considered. It is important to note that exemptions do not involve pharmaceutical copayments. Only low-income pensioners have a lower copayment. In the remaining cases, exemptions of copayments apply to NHS direct provision of health care. The first testable exemption is age. Children below the age of twelve were exempted. Age is reported in five-year increments. The three relevant steps are children until the age of five, from five to nine and from nine to fourteen. The first two steps are combined as well as all three. Since the exact threshold of twelve years is not available, these present an underestimation as the first threshold does not include all exempted children. The second threshold overestimates the share of catastrophe. The second exemption type is aimed at unemployed individuals; both employment status and unemployment benefits were available to identify unemployed persons. Thirdly, pensioners receiving a pension below the minimum wage were exempted from OOP payments. The minimum wage was available for 251 individuals in 2000. Lastly, disabled individuals were completely exempted from OOP payments and the disability proxies were used to identify these individuals.
Table 4 presents the results, which have to be treated very carefully. Since children do not have expenditure, the average per person expenditure values for their household has been used. This might introduce a bias. The direction of the bias is ambiguous. In rich households with a low amount of children, the average expenditure per person might be higher than the actual expenditure for the child. Nevertheless, in households with low income and many children the average might be an overestimation. For households with middle incomes it is ambiguous. The distribution of these households is another factor. The share of unemployed in the sample is lower than the real population value. The amount of catastrophe in this subgroup is substantial. The sample contains 265 unemployed individuals of which 169 faces catastrophe. Pensioners as well have a considerable share of catastrophe despite their exempted status 54% of pensioners with low pensions face catastrophe. The two disability proxies also generate sizeable shares of catastrophe within their subgroup. The disability proxies are proxies and do not present an official status as disabled, so they might be an overestimation. However, nearly three quarters of individuals reporting to have retired due to disability are confronted with catastrophic healthcare expenditure. While for individuals reporting to be unable to work the rate is only slightly lower with every two out of three individuals. There might be some reporting
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Table 3 Differences in predicted probabilities between minimun and maximum values and ± half the base of the independent variables. Variable
2000 Min → max
−1/2 → 1/2
Mean
SD
2005 Min → max
−1/2 → 1/2
Mean
SD
Financial determinants Income Expenditure Ratio of earners in the HH Ratio of employed in the HH Ratio of unemployed in the HH
−0.03 −0.06 0.00 −0.01 0.01
0.00 0.00 0.01 −0.01 0.01
12,285 11,918 0.77 0.35 0.02
11,130 10,546 0.26 0.33 0.10
−0.03 0.06 N/A N/A N/A
0.00 0.00 N/A N/A N/A
16,459 16,184 N/A N/A N/A
15,968 12,247 N/A N/A N/A
Household head Gender Age Category Completed Education Household Amount of Household members Senior Ratio Junior Ratio Urbanicity
0.00 0.02 −0.02 −0.02 0.01 0.02 0.00
0.00 0.00 0.00 0.00 0.01 0.02 0.00
0.74 9.07 2.34 2.83 0.42 0.02 2.20
0.44 3.00 1.57 1.48 0.42 0.08 0.80
0.01 0.04 −0.01 −0.01 0.00 −0.01 0.01
0.01 0.00 −0.01 0.00 0.00 −0.02 0.00
0.94 7.84 1.12 2.72 0.22 0.10 1.54
0.23 2.99 0.55 1.34 0.38 0.18 0.78
−0.01 0.00 0.00 0.00 −0.01 0.01
−0.01 0.00 0.00 0.00 −0.01 0.01
0.18 0.13 0.11 0.13 0.15 0.14
0.38 0.00 0.00 0.34 0.00 0.00 0.32 0.00 0.00 0.34 0.00 0.00 0.35 0.01 0.01 0.36 0.00 0.00 (omitted categorical variable)
0.19 0.16 0.13 0.15 0.13 0.09
0.39 0.36 0.33 0.36 0.34 0.29
Health proxies Smoking Drinking Incapacity to work Retired due to Disability
−0.02 −0.01 0.01 0.00
−0.02 −0.01 0.01 0.00
0.29 0.44 0.05 0.17
0.45 0.50 0.21 0.37
−0.01 −0.01 N/A N/A
−0.01 −0.01 N/A N/A
0.26 0.44 N/A N/A
0.44 0.50 N/A N/A
Housing determinants Running water Electricity Sanitary Size (in m2 ) Construction Year
N/A N/A N/A N/A N/A
N/A N/A N/A N/A N/A
N/A N/A N/A N/A N/A
N/A N/A N/A N/A N/A
−0.01 −0.01 −0.01 −0.03 −0.01
−0.01 −0.01 −0.01 0.00 0.00
0.98 1.00 0.95 6.62 196.96
0.14 0.07 0.22 1.93 1.83
Region North Center Lisbon Alentejo Madeira Azores Algarve
Fig. 1. Scatterplot of predicted probabilities of catastrophe by income on a log scale.
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Table 4 Catastrophe within protected groups. Exemption group
% of the sample
% CHE in this subgroup
Average value of the N exemption
N
Children Until the age of 9 Until the age of 14
8.68 14.90
62.30 58.21
N/A N/A
N/A N/A
Unemployment
2.64
63.37
2,586 D
7
Low Pensions
0.82
53.88
747 D
4
Disability Proxy 1 – Incapacity to work Proxy 2 – Retired due to disability
1.83 6.85
63.51 71.74
2,842 D 1,015 D
20 204
error in these numbers, but their sheer size is surprising and points out that the disabled seems to be at risk of facing catastrophe more than the general population. 4.4. Impoverishment The self-created poverty line and the deflated minimum wage produce the following values for catastrophe related impoverishment for the year 2000: 0.71% and 0.87%, respectively. For 2005 the values are 0.87% and 1.05%. Overall values around 0.9% seem small. With a population of 10.5 million this means around 74,450–91,350 people were impoverished in 2000 through healthcare. For the year 2005, the value ranges between 91,350 and 110,250 persons. 4.5. Sensitivity analysis To test whether the results here are stable, the same model from Table 2 has been run with catastrophic healthcare expenditure at a threshold of 10%, 20%, 30% and with an extended basket. Determinants of catastrophic healthcare expenditure are broadly the same across variants of the model. Statistical significance of the main effects is robust across specifications. All forms are equally significant.8 5. Conclusions The analysis of catastrophic health expenditures allows identifying vulnerable population groups. Protecting these groups should be a concern for policy makers. The analysis confirms that the elderly are a group that needs protection against catastrophe. An intuitive hypothesis explaining why old age seems to increase the probability of catastrophe is that older persons are more likely to become sick and to become sick with expensive diseases. A high correlation between poverty and old age has been observed. It is unknown whether old individuals get poor through catastrophe or have a higher propensity for catastrophe because they are more likely to be poor. The finding that old individuals require protection is a robust, policy-relevant finding. The exemption categories from year 2000 Portugal are supported by the results. Still, the levels of catastrophe
8
Results available in the web appendix.
found in groups that should be protected are quite surprising so that further policy action seems necessary. Retirement due to disability is a significant proxy for the risk of CHE in all measures, for the year 2000. As an exemption category disability is hard to fool, as one need to see a doctor and it is also easy to document for administrative purposes. Incapacity to work, the second proxy, is as well only available in the year 2000 and produces similar inference. We therefore recommend these proxies as exemption categories from OOP payments. Another policy which will have a positive effect on the problem of catastrophe in the elderly is underway in current reforms in Portugal, which is promoting generics to reduce prices of pharmaceutical products. This policy seems to be justified, as pharmaceuticals are still the single most important reason for OOP payments in Portugal. Acknowledgments The authors thank Tamás Evetovits, Susana Peralta, the editor, Wilm Quentin, and two anonymous referees for useful comments. All remaining errors, however, are the authors’ only. The authors would like to thank INE - Statistics Portugal for supplying the data, the usual disclaimers apply. This project started during a visit of Christoph Kronenberg at Nova School of Business and Economics in Lisbon. Christoph would like to extends his thanks to Pedro Pita Barros and NOVA SBE for hosting him. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.healthpol.2013.10.001. References [1] Marmot M, Allen J, Goldblatt P, Bocye T, McNeish D, Grady M. Fair society, healthy lives: strategic review of health inequalities in England post-2010. London: The Marmot Review; 2010. [2] Arrow KJ. Uncertainty and the welfare economics of medical-care. American Economic Review 1963;53(5):941–73. [3] Pauly MV. Economics of moral hazard – reply. American Economic Review 1968;58(3):531–7. [4] Nyman JA. The economics of moral hazard revisited. Journal of Health Economics 1999;18(6):811–24. [5] Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz A. Health-insurance and the demand for medical-care – evidence from a randomized experiment. American Economic Review 1987;77(3):251–77.
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[16] Barros PP. Health policy reform in tough times: the case of Portugal. Health Policy 2012;106(1):17–22. [17] Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet 2003;362(9378):111–7. [18] Yardim MS, Cilingiroglu N, Yardim N. Catastrophic health expenditure and impoverishment in Turkey. Health Policy 2010;94(1):26–33. [19] Wagstaff A, van Doorslaer E. Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993–1998. Health Economics 2003;12(11):921–34. [20] Evetovits T, Gaal P, Szigeti S, Lindeisz F, Habicht J. Informing decision makers: Analysis of financial protection over time in the context of policy change. In: European Conference on Health Economics. 2012. [21] Foerster MF. Measurement of low income and poverty in a perspective of international comparisons, OECD Occasional Papers OECD.; 1994. [22] Habicht J, Xu K, Couffinhal A, Kutzin J. Detecting changes in financial protection: creating evidence for policy in Estonia. Health Policy and Planning 2006;21(6):421–31. [23] Long JS, Freese J. Regression models for categorical outcomes using stata, 2nd ed. College Station, Texas: Stata Press; 2005. [24] OECD. OECD Health Statistics; 1995. [25] PorData. Gini index in Portugal. Available from: http://www.pordata. pt/en/Portugal/Gini+index-2166; 2013. [26] Alves N. Novos factos sobre a pobreza em portugal. Banco de Portugal: Boletim Económico; 2009. p. 29.
Contents lists available at ScienceDirect
Health Policy journal homepage: www.elsevier.com/locate/healthpol
Catastrophic healthcare expenditure – Drivers and protection: The Portuguese case Christoph Kronenberg a,∗ , Pedro Pita Barros b,c a b c
Centre for Health Economics, University of York, United Kingdom Universidade Nova Lisboa, Portugal CEPR, London, United Kingdom
a r t i c l e
i n f o
Article history: Received 12 November 2012 Received in revised form 3 October 2013 Accepted 8 October 2013
JEL classification: I140 H510 I320 Keywords: Catastrophic health expenditure Portuguese health system Healthcare financing Out of pocket payments Poverty
a b s t r a c t The objective of this paper is to assess the extent of catastrophic healthcare expenditure, which can lead to impoverishment, even in a country with a National Health Service, such as Portugal. The level of catastrophic healthcare expenditure will be identified before the determinants of these catastrophic payments are analyzed. Afterwards, the effects of existing exemptions to copayments in health care use will be tested and the relationship between catastrophe and impoverishment will be discussed. Catastrophe is calculated from the Portuguese Household Budget Surveys of 2000 and 2005, and then analyzed using logistic regression models. The results show that catastrophe due to healthcare out-of-pocket payments are a sizeable issue in Portugal. Exemptions from out-of-pocket expenses for medical care should be created to prevent vulnerable groups from facing catastrophic healthcare spending. These vulnerable groups include children, people with disabilities and individuals suffering from chronic conditions. Disability proxies offer straightforward policy options for an exemption for the elderly with recognized disabilities. An exemption of retired people with disabilities is therefore recommended to policymakers as it targets a vulnerable group with high risk of facing catastrophic healthcare expenditure. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction An influential publication on health inequalities is the Marmot Review [1]. It renewed interest in barriers to health care. The creation of a National Health Service in several countries aimed at eliminating, or reducing, these barriers, while providing care with efficient use of resources. Whether this is the case, or not, can be assessed using the concept of catastrophic healthcare expenditures. Many countries attempt to improve the efficiency of their healthcare systems. A popular way in economics and policy making to increase efficiency acting on the demand
∗ Corresponding author. Tel.: +44 1904321411. E-mail addresses: [email protected], [email protected] (C. Kronenberg). 0168-8510/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.healthpol.2013.10.001
side is to reduce overconsumption due to moral hazard. This is usually done by cost sharing either in the form of co-payments, a fixed fee for a good or service or coinsurance. These payments, if the household does not have supplementary insurance, are paid directly and called outof-pocket payments. Many studies over the last decades have discussed theoretically [2–4] and empirically [5–9] the necessity for the right type and amount of cost sharing in relation to demand-side moral hazard. The contribution of the paper is the assessment of the extent of catastrophic healthcare expenditures, which can lead to impoverishment, even in a country with a National Health Service, such as Portugal. The mere existence of a National Health Service is not sufficient to completely avoid the existence of catastrophic health expenditures. Only about ten years ago researchers started to consider the impoverishing side effects of OOP payments,
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highlighting the potential trade-off between OOP as an instrument for demand control and OOP as an impoverishment driver. That is, households are forced into poverty by paying catastrophic shares of their income for healthcare; spending of this kind is called catastrophic healthcare expenditure (CHE).1 For brevity’s sake it will often be referred to as catastrophe throughout the text. In the context of a National Health Service providing universal coverage, the presence of copayments is presented as management of demand pressure. This feature should imply a different role of out-of-pocket payments than in health systems where they result mainly from the absence of health insurance. Of course, catastrophic health expenditures can also be found when a considerable part of the population is uninsured. Studies have shown that this problem exists, and not only in poor countries. For the United States, Waters et al. [10] show that families that have an income below the official federal poverty level spend 13.5% of their income on OOP payments for healthcare. Himmelstein et al. [11] have shown that a staggering 46.2% of personal bankruptcies in the US are caused by healthcare expenditure. The paper proves, using data from the 2000 and 2005 Portuguese Household Survey, that also a National Health Service may face impoverishment by catastrophic health expenditures. A specific group, the elderly, was found to be associated with a higher likelihood of catastrophic health expenditures. A policy recommendation which arises from this finding is that an exemption from copayments for retired people with disability will contribute significantly to higher social protection. The paper is organized in the following way. In Section 2, provides a brief overview of the health care system in Portugal. In Section 3 presents data and methodology used. Results are reported in Section 4. Section 5 provides concluding remarks. 2. A brief on the Portuguese healthcare system Organizational reforms of different scales and with different objectives have been quite common in Portugal over the last decades. However, no major reform has tackled the financing side of the healthcare system since the 1990s [12]. The Portuguese healthcare system has three overlapping financing systems, the first being the NHS which, given article 64 of the 1976 constitution, provides universal and comprehensive services approximately free of charge [13]. The second layer of the Portuguese healthcare system is the occupational insurance system (health subsystems) pre-dating the NHS. Health subsystems are financed via employee and employer contributions, though employee contributions tend to be around 1–1.5% of wages, accounting to about 10–15% of total funding needs of each
1 The authors prefer to talk about catastrophic healthcare expenditure despite the usual term being catastrophic health expenditure. The reason is that health is determined by more than healthcare and health payments should therefore include food, physical exercise, living conditions, etc. However, that is beyond the scope of this paper.
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particular health subsystem, or sometimes do not exist at all. The whole set of health subsystems covers around 15–16% of the population [14]. Additionally, 10% of the people take out voluntary health insurance (VHI). The Portuguese Association of Insurers puts this value closer to 20%, a value roughly doubling what was found in the latest National Health Survey (2005/2006). A minority of 2% even has both health subsystems and VHI. VHIs do not discriminate by risk groups explicitly, but their services are often tailored toward low risk groups, explaining that average VHI members are between 20 and 54, live in urban areas, have medium to high income and work in medium to large companies [15]. Around a third of total expenditure is financed privately, mainly through OOP payments either in the form of user charges or co-insurance and to a smaller extent through premiums to private insurance schemes and mutual institutions. OOP payments for pharmaceuticals were organized in a co-insurance system based on therapeutic value ranging from 10% to 95% in four categories. Additionally, there were a few pharmaceuticals that were completely reimbursed by the government based on equity concerns. In 2004, the OOP payments for emergency services were D 6.9 in a central hospital and D 6.1 in a district hospital. For outpatient services a central hospital charged D 4.1, a district hospital D 2.7 and a primary care facility D 2 [16]. In the study time individuals were exempted from OOP payments if they fell into one of the following groups: children under 12, pregnant women, suffering from chronic conditions, unemployed, poor, or if they were deemed to create positive externalities for society. Being poor for the purpose of exemption granting was defined by the Portuguese National Health Service as earning less than the minimum wage, which was D 318.2 and D 374.4 for 2000 and 2005, respectively. Individuals with positive externalities such as fire-fighters or blood donors were also exempted. There have been some minor recent changes, which are ignored here due to the time scope of the paper. Around a third of total expenditure is financed privately, of which a sizeable fraction results from out-of-pocket payments.2 Therefore the question of whether a part of the population is subject to catastrophic health expenditure, or not, is relevant.
3. Data and methods 3.1. Survey description The 2000 and 2005 waves of the Portuguese Household Budget Survey (Inquérito aos Orcamentos Familiares) were used. The Portuguese Household Budget Survey (PHBS) contains nationally representative data. The survey is carried out by Statistics Portugal, the official body in charge of production of data on Portugal. The 2000 wave included
2 A table giving an overview of the financing mix of the health care sector in Portugal can be found in the online appendix at [add link].
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10,020 households and the 2005 version included 10,403 households.3 The surveys were conducted in face-to-face interviews over the whole year to account for temporal changes. For the expenditure part of the surveys, individuals filled in tables explaining what they bought, the cost, etc., for two weeks. The expenditure data includes all areas of life: food, clothing, furniture, kitchen equipment and electronics, utility and insurance bills, leisure and cultural expenditure as well as transportation expenditure. 3.2. Dependent variable In order to keep this study comparable with others, the same definition of catastrophe is used as in earlier studies [17,18]. A household is said to have incurred catastrophic healthcare expenditure if its OOP healthcare payments are higher than 40% of its capacity to pay. Capacity to pay is the subtraction of the subsistence need from the household expenditure. Subsistence need is defined as a percentage share of the average household food expenditure. Values vary widely with 40% being the WHO definition [18,19]. This study uses the WHO definition of 40% but also tests 10%, 20% and 30% as well as an extended basket which includes clothing and utility bills in addition to food expenditures, as suggested by Evetovits et al. [20]. As variables are reported with respect to different time horizons, they have to be normalized. Inflating the twoweek data on expenditure to match the income-reporting time horizon or reducing income to a two-week period to match the data on expenditures produces the same result, as it is a matter of scaling. Of course, this is not a perfect procedure. Someone with health expenditures in a given two-week period will not have it in the same amount every two-week period of the year. In the opposite direction, there will be people with positive health expenditures outside the two-week period and with zero value within the window of data recording. 3.3. Independent variables To identify the share of poor households a poverty line has been computed as 60% of median income. This type of poverty line and level of income have been used in previous research [21]. All financial variables from 2005 have been inflationadjusted to 2000 values. Additionally the household income has been adjusted using the equivalence square root method. This method recommends dividing household income by the square root of the household size.4 During the 2000s Portugal’s currency was the Portuguese Escudo, therefore financial variables in the 2000 wave had to be transformed into Euro at the exchange rate 200.482 Portuguese Escudo to 1 Euro.
3 Variable definitions and basic descriptive statistics about the variables used can be found below in an online appendix. 4 There is no generally accepted equivalence scale. The square root method results, however, do not differ substantially from the results of other scales. The square root scale has also been used in recent research into poverty and inequality, which is closely related to this research.
Other independent variables cover gender, education, size of household, urbanicity and the number of seniors and juniors in the household as previously used by Habicht et al. or Yardim et al. [18,22]. Regional dummies were also included to account for regional variation. As the PHBS is a budget survey, no health proxy variables were available. To account for the effect of some aspects of health, variables for disability were derived from the data. Two disability proxies were derived from employment status if the respondent reported: Incapacity to work or retired due to disability. The third disability proxy is the amount of sickness and disability benefits. In the 2005 dataset the employment variable was not available, rendering it impossible to create employment ratios or disability proxies. The 2005 dataset included new variables describing accommodation. These allowed the generation of dummy variables on running water, electricity and sanitation. The size of the accommodation in square meters was also available.5 3.4. Methods The percentage of the population with catastrophe is tabulated by income quintiles and region. The determinants of catastrophe were analyzed individually via a logistic regression model, to assess the significance of individual determinants identified via the literature.6 The determinants statistically significant in this first run were then used together in a multivariate logistic regression. The results are reported as odds ratios; the ratio of the odds of an event occurring in one group compared to the odds of that event occurring in another group. The change in predicted probability of occurring catastrophe between two levels is also reported.7 The difference between minimum and maximum levels as well as between minus and plus half the base were computed. Their interpretation is intuitive: if the difference between minimum and maximum income is −0.03, keeping all other factors constant, an individual with the highest income in the sample is 3% less likely to face catastrophe than an individual with the lowest income. Households are identified to have catastrophe-related impoverishment if their income is above the respective poverty line but if, after subtracting OOP payments, their income is below the poverty line and the household has been identified to have faced impoverishment. 4. Results 4.1. Descriptive statistics The amount of catastrophic cases differs by region and over time. Table 1 shows the mean percentage values for different measures of catastrophe and by region and
5 The definition of further independent variables can be found in the online appendix. 6 There is no econometric reason to choose probit over logit or the other way around. The logistic regression model has been chosen, because it is more common in the literature on catastrophe. 7 The Stata add-on prchange from Long and Freese [23] was used.
C. Kronenberg, P.P. Barros / Health Policy 115 (2014) 44–51 Table 1 Percentages of occurrence of catastrophe by measure, region and income. 2000
2005
Measures of catastrophic healthcare expenditure CHE 10% 29.04% 32.76% CHE 20% 16.74% 15.40% CHE 30% 10.98% 8.59% CHE 40% (WHO) 7.85% 5.03% CHE extended basket 19.77% 19.63% Region North 3.87% 4.29% Center 9.10% 6.00% Lisbon 6.78% 3.11% Alentjo 10.66% 7.38% Algarve 9.32% 4.42% Azores 8.74% 3.65% Madeira 7.27% 5.68% Income quintile 1st 2nd 3rd 4th 5th
22.26% 11.08% 3.64% 1.55% 0.75%
13.50% 7.16% 2.60% 1.30% 0.58%
Trend 3.72% −1.34% −2.38% −2.83% −0.14% 0.42% −3.10% −3.67% −3.27% −4.90% −5.10% −1.59% −8.75% −3.92% −1.04% −0.25% −0.17%
income quintile. The region and income quintile values are calculated with the WHO measure of catastrophe. Comparing the 2000 result with an earlier study [17] which found 2.95% catastrophe in Portugal in 1995, there is drastic increase in those five years. There are no methodological differences to this study. However, two possible explanations are that the studies are based on different surveys and that health care expenditures have outgrown GPD growth during this time [24]. However, this seems contradicted by the fact that income inequality as measured by the Gini coefficient has virtually not changed from 1995 to 2000 [25]. However, this might be misleading as since 1995 lower incomes have grown faster [26]. Therefore, the threshold of catastrophe might have increased and causing more individuals to be unable to cross it. For the different measures of catastrophe, it can be seen that all have decreased or stayed constant. The extended basket approach lies between the 10% and 20% approach, but closer to the 20% as was found by Evetovits et al. [20]. A split-up by region shows the heterogeneity within Portugal. The occurrence of catastrophe in the North region has nearly doubled in the five years, whereas it decreased in Lisbon area. In the Azores catastrophe more than halved. The Alentejo, Madeira, Center and Algarve regions showed increases of a few percentage points. Income differences within CHE are vast. The 8% average for 2000 is decomposed to a 22% average for the lowest income quintile of the population and 1% for the highest income quintile. Overall, there seems to be a slight decrease of catastrophe. The PHBS allows for decomposition of the OOP payments into three broad categories. Pharmaceuticals are by far the biggest spending category, with the share slightly increased from 2000 to 2005 (64.6–65.4%). Medical services, the second most important spending category causing catastrophe including GPs, dentists, etc., has somewhat decreased (33.4–32.6%). Hospital services only play a minor role (below 2%).
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Based on these results and literature findings, potential target groups for exemption have been analyzed. In the year 2005, for example, 40% of all households that faced catastrophe were one-person households. Additionally, 63% were seniors and 65% were poor. Combining this, 26% of all cases of catastrophe were faced by alone-living seniors who were poor. This poverty among the elderly is likely to have increased with the economic crisis starting in 2008, as these elderly cannot expect their income to increase or to spread the risk of catastrophe with their other household members. 4.2. Determinants of catastrophic healthcare expenditure Table 2 presents the results of the multivariate logistic regression. Table 3 presents the changes in predicted probability and the means and standard deviations based on which these were calculated. The economic impact of income is small. The odds ratio is close to one implying that there is only a small difference between income levels with respect to catastrophe. For 2005 income could not been estimated precisely. The highest probabilities are very concentrated among the poorest individuals, as shown in Fig. 1. Very low income levels have lower predicted probabilities. This is most likely the case because these households do not take out healthcare in the first place. Concerning the odds ratio, the gender of the household head seems important. In 2000, the odds ratio is below one, implying that a male household head protects from catastrophe. In 2005, on the other hand, the odds ratio has increased to 2.1 implying that male household heads attract catastrophe. In probabilities the case for male household heads looks less dramatic; a switch from a female to a male household head only increases the probability of catastrophe by 1% in 2005. Age is a statistically significant factor at the 5% level in both years. The age of the household head has an odds ratio slightly above one in both years, showing a slight increase. The predicted probability increased from 3% to 4% from the youngest to the oldest household head. This is one of the biggest predicted probabilities impact. The disability proxies have the expected effect but a small magnitude. Incapacity to work has a slightly stronger effect than retired due to disability. Someone who is incapacitated to work is 1.5 times more likely to confront catastrophe than someone who is not incapacitated; this translates into a 1% predicted probability. All housing determinants have a protective effect and only running water and electricity are not significant. The size of the accommodation could be a proxy for both wealth and household size; however, they are separate variables and should therefore capture these effects, as well as income was adjusted for household size. 4.3. Imperfect policy test Given the data and the exemption rules to health care copayments and user charges, it is possible to check whether exempted population subgroups incurred no or very little catastrophe.
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Table 2 Determinants of catastrophe in the multivariate logit model. Characteristics
2000 Odds ratio
2005 Coefficients
Income (in 1000D ) Household head gender Household head age HHH completed education Amount of household members Senior ratio Semi urban Rural North Center Lisbon Alentejo Azores Madeira Algarve
0.92*** −0.08*** 0.69*** −0.37*** 1.14*** 0.13*** 0.77*** −0.26*** 0.86* −0.16* 1.81*** 0.59*** 0.38 −0.96 0.97 −0.03 0.54*** −0.61*** 0.90 −0.11 0.94 −0.07 0.92 −0.09 1.70*** 0.53*** 0.83 −0.18 Algarve is the reference category
2000 only variables Earner ratio Employed ratio Unemployed ratio Incapacity to work Retired due to disability
1.44 0.46*** 0.91 1.41** 1.14
2005 only variables Running water Electricity Sanitary Size (in m2 ) Construction year Constant N * ** ***
Odds ratio
Coefficients
0.95*** 2.45*** 1.21*** 0.43** 0.84** 1.64*** 0.28 1.28*** 1.13 1.14 0.97 1.26 1.10 1.54**
−0.05*** 0.89*** 0.19*** −0.83** −0.17** 0.50*** −1.28 0.25*** 0.12 0.13 −0.03 0.23 0.10 0.43**
0.68 0.57 0.63*** 0.86*** 0.93**
−0.38 −0.56 −0.46*** −0.16*** −0.07** 11.26*
0.37 −0.79*** −0.09 0.34** 0.13
−2.55*** 10,020
10,403
p < 0.10. p < 0.05. p < 0.01.
Nearly all of the relevant variables were only available for the year 2000, so the values presented here are for 2000 only. Four exemptions are testable, whereas usually there are 15–20 exemption types depending on which law is considered. It is important to note that exemptions do not involve pharmaceutical copayments. Only low-income pensioners have a lower copayment. In the remaining cases, exemptions of copayments apply to NHS direct provision of health care. The first testable exemption is age. Children below the age of twelve were exempted. Age is reported in five-year increments. The three relevant steps are children until the age of five, from five to nine and from nine to fourteen. The first two steps are combined as well as all three. Since the exact threshold of twelve years is not available, these present an underestimation as the first threshold does not include all exempted children. The second threshold overestimates the share of catastrophe. The second exemption type is aimed at unemployed individuals; both employment status and unemployment benefits were available to identify unemployed persons. Thirdly, pensioners receiving a pension below the minimum wage were exempted from OOP payments. The minimum wage was available for 251 individuals in 2000. Lastly, disabled individuals were completely exempted from OOP payments and the disability proxies were used to identify these individuals.
Table 4 presents the results, which have to be treated very carefully. Since children do not have expenditure, the average per person expenditure values for their household has been used. This might introduce a bias. The direction of the bias is ambiguous. In rich households with a low amount of children, the average expenditure per person might be higher than the actual expenditure for the child. Nevertheless, in households with low income and many children the average might be an overestimation. For households with middle incomes it is ambiguous. The distribution of these households is another factor. The share of unemployed in the sample is lower than the real population value. The amount of catastrophe in this subgroup is substantial. The sample contains 265 unemployed individuals of which 169 faces catastrophe. Pensioners as well have a considerable share of catastrophe despite their exempted status 54% of pensioners with low pensions face catastrophe. The two disability proxies also generate sizeable shares of catastrophe within their subgroup. The disability proxies are proxies and do not present an official status as disabled, so they might be an overestimation. However, nearly three quarters of individuals reporting to have retired due to disability are confronted with catastrophic healthcare expenditure. While for individuals reporting to be unable to work the rate is only slightly lower with every two out of three individuals. There might be some reporting
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Table 3 Differences in predicted probabilities between minimun and maximum values and ± half the base of the independent variables. Variable
2000 Min → max
−1/2 → 1/2
Mean
SD
2005 Min → max
−1/2 → 1/2
Mean
SD
Financial determinants Income Expenditure Ratio of earners in the HH Ratio of employed in the HH Ratio of unemployed in the HH
−0.03 −0.06 0.00 −0.01 0.01
0.00 0.00 0.01 −0.01 0.01
12,285 11,918 0.77 0.35 0.02
11,130 10,546 0.26 0.33 0.10
−0.03 0.06 N/A N/A N/A
0.00 0.00 N/A N/A N/A
16,459 16,184 N/A N/A N/A
15,968 12,247 N/A N/A N/A
Household head Gender Age Category Completed Education Household Amount of Household members Senior Ratio Junior Ratio Urbanicity
0.00 0.02 −0.02 −0.02 0.01 0.02 0.00
0.00 0.00 0.00 0.00 0.01 0.02 0.00
0.74 9.07 2.34 2.83 0.42 0.02 2.20
0.44 3.00 1.57 1.48 0.42 0.08 0.80
0.01 0.04 −0.01 −0.01 0.00 −0.01 0.01
0.01 0.00 −0.01 0.00 0.00 −0.02 0.00
0.94 7.84 1.12 2.72 0.22 0.10 1.54
0.23 2.99 0.55 1.34 0.38 0.18 0.78
−0.01 0.00 0.00 0.00 −0.01 0.01
−0.01 0.00 0.00 0.00 −0.01 0.01
0.18 0.13 0.11 0.13 0.15 0.14
0.38 0.00 0.00 0.34 0.00 0.00 0.32 0.00 0.00 0.34 0.00 0.00 0.35 0.01 0.01 0.36 0.00 0.00 (omitted categorical variable)
0.19 0.16 0.13 0.15 0.13 0.09
0.39 0.36 0.33 0.36 0.34 0.29
Health proxies Smoking Drinking Incapacity to work Retired due to Disability
−0.02 −0.01 0.01 0.00
−0.02 −0.01 0.01 0.00
0.29 0.44 0.05 0.17
0.45 0.50 0.21 0.37
−0.01 −0.01 N/A N/A
−0.01 −0.01 N/A N/A
0.26 0.44 N/A N/A
0.44 0.50 N/A N/A
Housing determinants Running water Electricity Sanitary Size (in m2 ) Construction Year
N/A N/A N/A N/A N/A
N/A N/A N/A N/A N/A
N/A N/A N/A N/A N/A
N/A N/A N/A N/A N/A
−0.01 −0.01 −0.01 −0.03 −0.01
−0.01 −0.01 −0.01 0.00 0.00
0.98 1.00 0.95 6.62 196.96
0.14 0.07 0.22 1.93 1.83
Region North Center Lisbon Alentejo Madeira Azores Algarve
Fig. 1. Scatterplot of predicted probabilities of catastrophe by income on a log scale.
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Table 4 Catastrophe within protected groups. Exemption group
% of the sample
% CHE in this subgroup
Average value of the N exemption
N
Children Until the age of 9 Until the age of 14
8.68 14.90
62.30 58.21
N/A N/A
N/A N/A
Unemployment
2.64
63.37
2,586 D
7
Low Pensions
0.82
53.88
747 D
4
Disability Proxy 1 – Incapacity to work Proxy 2 – Retired due to disability
1.83 6.85
63.51 71.74
2,842 D 1,015 D
20 204
error in these numbers, but their sheer size is surprising and points out that the disabled seems to be at risk of facing catastrophe more than the general population. 4.4. Impoverishment The self-created poverty line and the deflated minimum wage produce the following values for catastrophe related impoverishment for the year 2000: 0.71% and 0.87%, respectively. For 2005 the values are 0.87% and 1.05%. Overall values around 0.9% seem small. With a population of 10.5 million this means around 74,450–91,350 people were impoverished in 2000 through healthcare. For the year 2005, the value ranges between 91,350 and 110,250 persons. 4.5. Sensitivity analysis To test whether the results here are stable, the same model from Table 2 has been run with catastrophic healthcare expenditure at a threshold of 10%, 20%, 30% and with an extended basket. Determinants of catastrophic healthcare expenditure are broadly the same across variants of the model. Statistical significance of the main effects is robust across specifications. All forms are equally significant.8 5. Conclusions The analysis of catastrophic health expenditures allows identifying vulnerable population groups. Protecting these groups should be a concern for policy makers. The analysis confirms that the elderly are a group that needs protection against catastrophe. An intuitive hypothesis explaining why old age seems to increase the probability of catastrophe is that older persons are more likely to become sick and to become sick with expensive diseases. A high correlation between poverty and old age has been observed. It is unknown whether old individuals get poor through catastrophe or have a higher propensity for catastrophe because they are more likely to be poor. The finding that old individuals require protection is a robust, policy-relevant finding. The exemption categories from year 2000 Portugal are supported by the results. Still, the levels of catastrophe
8
Results available in the web appendix.
found in groups that should be protected are quite surprising so that further policy action seems necessary. Retirement due to disability is a significant proxy for the risk of CHE in all measures, for the year 2000. As an exemption category disability is hard to fool, as one need to see a doctor and it is also easy to document for administrative purposes. Incapacity to work, the second proxy, is as well only available in the year 2000 and produces similar inference. We therefore recommend these proxies as exemption categories from OOP payments. Another policy which will have a positive effect on the problem of catastrophe in the elderly is underway in current reforms in Portugal, which is promoting generics to reduce prices of pharmaceutical products. This policy seems to be justified, as pharmaceuticals are still the single most important reason for OOP payments in Portugal. Acknowledgments The authors thank Tamás Evetovits, Susana Peralta, the editor, Wilm Quentin, and two anonymous referees for useful comments. All remaining errors, however, are the authors’ only. The authors would like to thank INE - Statistics Portugal for supplying the data, the usual disclaimers apply. This project started during a visit of Christoph Kronenberg at Nova School of Business and Economics in Lisbon. Christoph would like to extends his thanks to Pedro Pita Barros and NOVA SBE for hosting him. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.healthpol.2013.10.001. References [1] Marmot M, Allen J, Goldblatt P, Bocye T, McNeish D, Grady M. Fair society, healthy lives: strategic review of health inequalities in England post-2010. London: The Marmot Review; 2010. [2] Arrow KJ. Uncertainty and the welfare economics of medical-care. American Economic Review 1963;53(5):941–73. [3] Pauly MV. Economics of moral hazard – reply. American Economic Review 1968;58(3):531–7. [4] Nyman JA. The economics of moral hazard revisited. Journal of Health Economics 1999;18(6):811–24. [5] Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz A. Health-insurance and the demand for medical-care – evidence from a randomized experiment. American Economic Review 1987;77(3):251–77.
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