Financial protection of households against health shocks in Greece during the economic crisis

Accepted Manuscript Financial protection of households against health shocks in Greece during the economic crisis Athanasios E. Chantzaras, John N. Yfantopoulos PII:

S0277-9536(18)30332-0

DOI:

10.1016/j.socscimed.2018.06.024

Reference:

SSM 11811

To appear in:

Social Science & Medicine

Received Date: 28 November 2017 Revised Date:

13 May 2018

Accepted Date: 20 June 2018

Please cite this article as: Chantzaras, A.E., Yfantopoulos, J.N., Financial protection of households against health shocks in Greece during the economic crisis, Social Science & Medicine (2018), doi: 10.1016/j.socscimed.2018.06.024. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Cover page

Financial protection of households against health shocks in Greece during the

Athanasios E. Chantzarasa, John N. Yfantopoulosb a

and Kapodistrian University Athens, Greece, email:

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School of Economics and Political Sciences, National of Athens, 6 Themistokleous Street, 106 78 [email protected]. b School of Economics and Political Sciences, National of Athens, 6 Themistokleous Street, 106 78 [email protected].

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economic crisis

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and Kapodistrian University Athens, Greece, email:

Corresponding author: Chantzaras Athanasios, Research Associate, School of Economics and Political Sciences, National and Kapodistrian University of Athens, 6 Themistokleous Street, 106 78 Athens, Greece, email: [email protected], tel: +30

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6938639268.

ACCEPTED MANUSCRIPT Abstract Background: Harsh funding cutbacks along with measures shifting cost to patients have been implemented in the Greek health system in recent years. Our objective was

of-pocket payments (OOPP) during the economic crisis.

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to investigate the evolution of financial protection of Greek households against out-

Methods: National representative data of 33,091 households were derived from the

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Household Budget Surveys for the period 2008-2015. Financial protection was assessed by applying the approaches of catastrophic (CHE) and impoverishing OOPP.

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The determinants of CHE and impoverishment were examined using multivariate logistic regressions.

Results: OOPP dropped by 23.5% in real values between 2008 and 2015, though their share in households’ budget rose from 6.9% to 7.8%, with an increasing trend since

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2012. These outcomes were driven by significant increases in medical products (20.2%) and inpatient (63%) OOPP, while outpatient expenses decreased considerably (-62%). Both incidence and overshoot of CHE were significantly

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exacerbated. The additional burden was distributed progressively, hence, financial risk inequalities decreased. Food poverty increased, but its incidence still remains at

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very low levels. Both incidence and intensity of relative poverty increased considerably in real terms. The poverty impact of OOPP is exacerbating following 2012, and 1.9% of individuals were impoverished due to OOPP in 2015. Households of higher size, lower expenditure quintile, in urban areas, without disabled, elderly or young children members, and with younger or retired, better-educated breadwinners were significantly less vulnerable to CHE. Households in the lower-middle expenditure quintile, in rural regions and with elderly members were facing higher

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ACCEPTED MANUSCRIPT risk, while wealthier families exhibited considerable lower likelihood of impoverishment. Conclusions: The expansion of reliance of healthcare funding on OOPP has increased the financial risk and hardship of Greek households, which may disrupt their living

recognise the different social protection needs of households.

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Keywords:

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conditions and create barriers to healthcare access. Cost-sharing policies should

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Financial fairness, equity, healthcare financing, catastrophic health expenditure,

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poverty, impoverishment, out-of-pocket payment, Greece

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ACCEPTED MANUSCRIPT 1. Introduction The prolonged economic crisis has taken its toll on the Greek society as a whole, as many social indicators, such as GDP, unemployment, poverty and inequality have deteriorated (see Figure 1). This is exactly the type of emergency that the welfare

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state is designed to weather (Castles, 2010), however, the deep budget cuts have stripped the system off its capacity to respond to these crucial social challenges. The Greek health system has been in the spotlight of the economic adjustment

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programmes, with several structural reforms and harsh funding cutbacks implemented during the recent years (Karanikolos & Kentikelenis, 2016; Karanikolos et al., 2013).

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These drastic healthcare spending curtailment from the Greek government can only force citizens to fill the healthcare system’s funding void from their own pockets. A health system’s financing arrangements is a key driver of population health and well-being (Yardim et al., 2010). Financial protection in healthcare has long been

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recognised as a major policy goal, since it relates to the broader issue of access to adequate and quality healthcare and equity in health status (Marmot, 2007). The abrupt character of ill-health (health shocks) may induce the allocation of large

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portions of household financial resources (budget shocks) to the purchase of health

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goods and services, thus disrupting their living conditions or even forcing them into poverty or deepening the poverty they have already been experiencing (Wagstaff, 2007). The reliance on out-of-pocket payments (OOPP) for healthcare induces considerable financial strain in households. In 2010, approximately 150 million people worldwide sustained financial catastrophe, and 100 million people were pushed below the poverty line due to unavoidable OOPP (WHO, 2013). Even further, relatively high OOPP may create access barriers for some poor individuals preventing them from seeking necessary treatment (Xu et al., 2007). Consequently, it is justified 3

ACCEPTED MANUSCRIPT to argue that in a fair and equitable health system, households should not be forced to more medical spending than that which exceeds a certain share of their budget, and financial risks must be shared equally within the society (Wagstaff, 2007; Xu et al., 2003a).

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The main purpose of this study was to investigate the evolution of financial protection of households against OOPP in the Greek health system during the economic crisis by applying the approaches of catastrophic and impoverishing OOPP. Furthermore, we

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sought to provide an initial examination of the vulnerability of households to financial

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risk and hardship due to OOPP. 2. Short overview of the Greek health system

In general, the Greek health system is among the most privatised in the European Union (EU), and OOPP have been the dominant form of private health expenses (Yfantopoulos, 2008). Even before the crisis, the Greek health system was suffering

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from a series of inefficiencies (Economou, 2010): high degree of centralisation, ineffective managerial structures, lack of planning and coordination, uneven and

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inefficient allocation of human and economic resources, fragmentation of care and absence of a referral and gatekeeping system, inequalities in access to services due to

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diverse benefit packages provided by multiple insurance funds, regressive funding mechanisms attributable to high private financing in the form of formal and informal payments, widespread tax and social security contribution evasion and excessive indirect taxation, anachronistic reimbursement system not related to performance, and lack of a health technology assessment system. These structural problems in conjunction with the amassed financial pressures and the failure to contain health expenditure growth during the previous decade placed the health sector at the center of the reform agenda of the economic adjustment programmes. Overall, current total 4

ACCEPTED MANUSCRIPT health expenditure dropped by 34.5% between 2009 and 2015 (Eurostat, 2017) (see Table S1 in online Appendix). This was mainly driven by cuts in public health spending (-43.5%), and particularly in social security funding (-54.3%). Since private health expenditure decreased by a smaller rate (-18.1%), its share of total health

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spending was enlarged.

The health system in Greece is a mixture of public integrated, public contract and public reimbursement systems (Economou, 2010), where the tax-funded Beveridge-

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type national health system (ESY), introduced in the early 1980s, goes hand in hand with a Bismarckian insurance fund structure (Yfantopoulos et al., 2016). In 2011, the

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majority of social insurance funds were unified under the umbrella of the National Organization for the Provision of Health Services (EOPYY), which now covers over 98% of the insured population (Karakolias & Polyzos, 2014). EOPYY purchases primary and secondary healthcare services for its insured members from both public

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and private healthcare suppliers. Another important reform concerned the reorganisation of primary healthcare (2014), which was moved from the jurisdiction of EOPYY to the ESY structure and the newly established National Primary Health

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Care Network (PEDY) (Economou et al., 2015). Major cost-containment measures

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included (Economou, 2015): cuts to public healthcare personnel salaries and replacement restrictions (2010), changes in procurement of health supplies (2010), mandatory e-prescription for all medical activities (2012), enhanced budgetary and operational monitoring and auditing of healthcare spending (2012) and establishment of a Diagnosis-Related Group (DRG) hospital payment system (2013). Furthermore, a series of horizontal cuts resulted in reductions in insurance coverage. Some expensive examinations were no longer reimbursed by the common benefits basket, and entitlement restrictions were also introduced in other cases (Economou, 2015). User

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ACCEPTED MANUSCRIPT charges for treatment and clinical tests in private clinics and laboratories contracted with EOPYY were increased, and exemptions were removed for some groups (e.g. chronically-ill people) (European Commission & Economic Policy Committee, 2016). Regarding ambulatory care, the fixed fee for outpatient care in public hospitals was

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increased from 3 to 5 euros between 2010 and 2015, when it was abolished altogether. In 2010, a mandatory all-day functioning of public hospitals was enacted, with the afternoon outpatient surgeries of public hospitals providing medical services to

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patients on a consultation fee ranging between 30 and 90 euros depending on physicians’ grades; a horizontal reduction of 20% was implemented on all fees in

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2013 (Economou, 2015; European Commission & Economic Policy Committee, 2016).

The curtailment of pharmaceutical expenditure was set as a priority by the economic adjustment programmes. On average, the annual growth rate of pharmaceutical

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expenditure was higher than that of GDP in Greece during the 1990’s and early 2000’s; their share of GDP was doubled from 0.9% to 1.8% between 1990 and 2004, while their share of current health expenditure increased from 14.9% to 23% in the

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same period (OECD, 2018). In 2009, Greece was associated with the highest share of

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(outpatient) pharmaceutical expenditure in GDP (2.6%) among all OECD countries, which was mainly financed through public schemes (78.1% of drug spending) (OECD, 2018). A recent qualitative study of the legislation revealed that 82.6% of the measures in the pharmaceutical sector were cost-containment efforts, and 59.8% of them reallocated cost to consumers (Yfantopoulos et al., 2017). As a result of the adoption of the undermentioned measures, the share of drug expenses in GDP dropped to 2.2% in 2015, and the public funding was restricted to 51.7% of pharmaceutical payments (OECD, 2018). 6

ACCEPTED MANUSCRIPT The introduction of a negative list for medicines (2011) withdrew the reimbursement status from many drugs, and significant increases in cost-sharing rates for several chronic diseases took place between 2012 and 2013 (Siskou et al., 2014). A new reimbursement system was established in 2012 based on an internal reference price

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system; in cases where the retail price was higher than the reimbursement price, besides the statutory cost-sharing rates, patients had to cover the full price difference. Also, a fixed fee of €1 per prescription paid as co-payment by patients has been

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imposed since 2014 (Gouvalas et al., 2016; Siskou et al., 2014). Other major costcontainment measures in the pharmaceutical sector included: reduction in VAT for

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medicines (2011), a new pricing system based on the average price of the three lowest-priced markets in the EU (2011), reductions in the profit margins of pharmacies and wholesalers (2011-2015), automatic rebates on private pharmacies (2011) and clawbacks on pharmaceutical companies (2012), compulsory prescription

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guidelines/protocols and prescription by active substance (2012), incentives and obligations to promote generics (2012), detailed monitoring on prescription patterns and pharmaceutical expenditure (2012), and spending cap and prescribing ceilings on

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physicians (2014) (Economou et al., 2015; European Commission & Economic Policy

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Committee, 2016).

In general, the introduction of or the increase in existent user fees or contributions and the restriction of entitlements entail additional economic burden for all healthcare users of public healthcare services, since they are not conditional on means-testing in Greece. However, households with more available resources are better equipped to absorb this extra direct cost without being exposed to higher financial risk or hardship. Some other measures designed to control demand, such as prescribing ceilings and spending limits may also be felt heavier among the economically weaker

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ACCEPTED MANUSCRIPT groups, as consumers could be compelled to use non-reimbursable services or to turn to the private sector. On the other hand, some price reductions that have been implemented on pharmaceuticals (or abolition of fees) alleviate the financial burden of ill-health, presumably relatively more in poorer households than in richer ones. It is

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not possible to discern the independent effect of each of the abovementioned reforms since they were implemented during a relatively short period. Therefore, the following analysis should be largely interpreted as the combined effect of the policy

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changes and the economic implications of the crisis. Nevertheless, the time of enactment of these reforms is important, hence, a figure is provided to function as a

(Figure 1). 3. Materials and Methods 3.1. Data source

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(simplified) timeline of the major reforms implemented between 2008 and 2015

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This study utilised the microdata collected by the Greek Household Budget Surveys (HBS) for the years 2008-2015, which were provided by the Hellenic Statistical

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Authority. The HBS is a cross-sectional survey comprising nationally representative data covering all non-institutional households in the country, and it is carried out with

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a two-stage stratified sampling. Our overall sample consisted of 33,091 households and their 81,649 members. In the Greek HBS, household consumption expenditure encompasses both monetary and non-monetary payments on all goods and services as well as the monetary value of the consumption of household’s own production during the reference period. OOPP is an expense incurred directly by a household in return for a healthcare service net of any insurance reimbursement. In the case of HBS, its main contributors are expenses for a) medical products, appliances and equipment, b) out-patient and c) hospital services. Pharmaceutical expenditure is the largest 8

ACCEPTED MANUSCRIPT component of medical products (82.4-91.4%, depending on the year; see also Table S2 in online Appendix), and it involves outpatient drug expenses incurred by patients, hence it doesn’t capture hospital pharmaceutical spending. 3.2. Statistical analysis

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The extent of the protection of households against the financial implications of illhealth is explored through the approaches of catastrophic health expenditures (CHE) and impoverishment as a result of OOPP (O'Donnell et al., 2008; Wagstaff &

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Doorslaer, 2003; Xu & World Health Organization/Department of Health System

Health

Organization

(WHO)

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Financing, 2005). Our analysis followed the methodological framework of the World (Xu

et

al.,

2003a;

Xu

&

World

Health

Organization/Department of Health System Financing, 2005) and O'Donnell et al. (2008). These approaches describe two different dimensions. While better-off households may incur large CHE without ever being impoverished or experiencing

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any hardship, households living on the verge of poverty may become poor with even a small amount of health payments that may not be declared as catastrophic in relation

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to their share of household resources (Xu et al., 2003a). All expenditures were deflated (2015=100) with the price index obtained from

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Eurostat (2017) and were equivalised considering the economy scale of household consumption (Xu et al., 2003a). The complex sample design was also incorporated into our analyses. 3.2.1. CHE

A CHE occurs if OOPP surpasses a predefined threshold share (zcat) of household’s standard of living (O'Donnell et al., 2008; Xu et al., 2003a). The value of zcat denotes the point at which greater medical spending may be at the expense of other basic necessities. However, there is no consensus among previous studies regarding the 9

ACCEPTED MANUSCRIPT level of this threshold. We opted to use a range of thresholds (10%, 20%, 30%, 40%) to improve the robustness of our findings, and let the readers choose where to ascribe more weight. This study adopts a definition of CHE in relation to household capacity to pay (CTP).

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A household’s CTP is given by its effective income (total consumption expenditure) net of basic subsistence needs. Subsistence expenditure was approximated on the basis of the share of total expenditure spent on food (Xu & World Health

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Organization/Department of Health System Financing, 2005). As suggested by O'Donnell et al. (2008), we defined an indicator E, which equals 1 if the share of

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OOPP in the CTP of the ith household is equal to or larger than the predefined threshold zcat. Then an estimate of headcount (H) is: =

1

20%, 30% and 40%.

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Where N is the sample size and zcat are the specific thresholds explored, i.e. 10%,

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Let Oi be the catastrophic overshoot, i.e. the percentage points by which household

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spending on health exceeds the threshold zcat as: =

(

−

)

Then the overshoot, i.e. the average excess of health payment budget share in the whole sample, is:

=

1

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ACCEPTED MANUSCRIPT The incidence (H) and intensity (O) of CHE are related through the mean positive overshoot (MPO), which expresses the average excess of health payment budget share of those households with CHE, i.e. the intensity of excess, and it is defined as:

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=

The above measures are insensitive to the distribution of CHE. To account for the differential opportunity cost of health spending between the poor and the rich,

=

(1 −

)

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overshoot measures as follows:

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Wagstaff and Doorslaer (2003) introduced the rank-weighted headcount and

Where Hw is the weighted headcount and CE is the concentration index for Ei, and =

(1 −

!

)

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Where Ow is the weighted overshoot and CE is the concentration index for Oi. The concentration index is a measure of socioeconomic inequality, and can be defined as (O'Donnell et al., 2008):

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2#

%$ℎ = ( + *+ + ,-$ '

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Where σ2 is the variance of the fractional rank (r) of household i in the equivalised expenditure distribution, hi is the indication CHE or overshoot for household i, µ is its mean, β is an estimate of the concentration index and ε is an error term. The concentration index is bounded between -1 and +1. If the value is negative, namely the incidence or intensity is more concentrated on poor households, higher weights are ascribed to headcount and overshoot ratios to account for the fact that low-income households are the ones experiencing more catastrophe, and HW or Ow are greater than H and O, respectively. If the value is positive, the incidence or intensity is more 11

ACCEPTED MANUSCRIPT concentrated on rich households, and the weighted estimates will be lower than the unweighted (Wagstaff & Doorslaer, 2003). The aforementioned measures were further disaggregated with respect to households’ expenditure quintile, and we also calculated quintile ratios (poorest/richest

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households) and performed χ2 and t-tests of the differences between 2008 and 2015.

Also, we calculated a summary index of the fairness in financial contribution of households, i.e. the Financial Fairness Contribution index (FFCI) (Murray et al.,

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2003; Xu & World Health Organization/Department of Health System Financing, 2005):

∑ − ∑

2

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.. / = 1 −

0∑

4

3

2

The FFCI compares the distribution of OOPP to a norm reference level, where

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households contribute the same share of their CTP (principle of equal burden), and it corresponds to the ratio of total OOPP to total CTP of households. Therefore, this is a measure of departure from proportionality. The index ranges between 0 (maximum

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departure from proportionality or inequality) and 1 (minimum departure from proportionality or inequality). Deviations from perfect financial fairness can be

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decomposed into a vertical (households with different CTP contribute different proportions of their CTP) and a horizontal (households with similar CTP pay different proportions of their CTP) effect. However, a limitation of this index is that it reflects both vertical and horizontal equity, without distinguishing between them, though the two imply quite different policy implications, as Wagstaff (2002) notices. For instance, not all horizontal discrepancies are inequitable, as they may arise from disparate utilisation levels (partly attributed to differences in illness) or unequal unit

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ACCEPTED MANUSCRIPT prices, which may reflect quality differences or fee exemptions. It might be argued that two individuals with similar health problems may decide to allot different portions of their CTP to health services. Furthermore, any departure from proportionality is considered unfair by this index, whether it is progressive or

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regressive. Nevertheless, the redistributive impact of OOPP on household income is not part of the objectives of this study, which focuses on the financial burden of OOPP based on the equal burden principle (Xu et al., 2003b), though the distribution

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of CHE is taken into account by the weighted measures of catastrophe.

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3.2.2. Impoverishment

Impoverishment is the process whereby a household falls below the poverty line as a result of OOPP (O'Donnell et al., 2008). We used two poverty lines (PL). A food poverty line was calculated based on the subsistence expenditure per (equivalent) capita in 2008 (anchored), following the methodology proposed by WHO (Xu &

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World Health Organization/Department of Health System Financing, 2005), and it was estimated at 2,620.64 euros. A relative poverty line was estimated as the 60% of the median per capita consumption expenditure in 2008 (anchored), a definition that

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has been historically used in the EU to identify people at-risk-of-poverty and it is part

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of the Europe 2020 Strategy (Bradshaw & Mayhew, 2011); it was calculated at 9,365.99 euros (pre-payment poverty line). In the case of relative poverty lines, an amount should be subtracted from the estimated (pre-payment) poverty line to arrive at the post-payment poverty line, as the pre-payment poverty line indirectly allows for resources required to cover healthcare needs. A partial solution is to deduct from the poverty line the average health spending of households with total expenditure in the region of the poverty line (O'Donnell et al., 2008; Wagstaff & Doorslaer, 2003). Therefore, the post-payment relative poverty line was set at 9,057.35 euros. 13

ACCEPTED MANUSCRIPT As suggested by O'Donnell et al. (2008), the poverty impact of OOPP can be measured by adjusting indicators from the general poverty literature to the concept of impoverishment due to OOPP, namely to compare poverty estimates before and after OOPP.

gross health payments individual headcount ratio is:

8$699

=

∑

:; ∑ :

8$699

= 1 if xi < PL and is 0 otherwise, si is the household size and N is the

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Where ;

8$699 567

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Let xi be the equivalised total expenditure of household ith. Then, an estimate of the

given by replacing ;

8$699

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number of households in the sample. The net of health payments headcount

<= 567

is

with ;<= = 1 if xi – OOPPi < PL (and 0 otherwise). The

poverty headcount refers to the proportion of individuals living below the poverty line.

8$699

( ? − @ ), then the mean gap in currency units is:

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;

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Let the gross health payments individual-level poverty gap given by: > 8$699 = A 8$699 =

∑ : > 8$699 ∑ :

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The poverty gap expresses the mean expenditure shortfall of poor individuals from the poverty line in reference to the total population. The net of health payments poverty gap A <= is calculated by replacing >

8$699

with ><= = ;<= ( ? − (@ −

)).

To enable comparisons across countries with different poverty lines and currency units, the poverty gap can be normalised as: A 8$699 =

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A 8$699 ?

ACCEPTED MANUSCRIPT The post-payment normalised gap

A <= .

A <= can be defined by replacing A 8$699 with

The intensity of poverty for poor individuals alone can be estimated with the mean

positive poverty gap (MPG); the MPG can be normalised on the poverty line (NMPG)

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as well.

The poverty impact of OOPP is simply defined as the difference between the relevant pre–and post–payment measures, e.g. the impoverishing headcount is given by −

8$699 567 ,

<= and the impoverishing gap by / C = A567 − A567 .

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3.2.3.

<= 567

Vulnerability

8$699

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/B =

Furthermore, we explored the determinants of a) catastrophic and b) impoverishing (relative poverty) health payments employing binary logistic regression analysis. The study specifies a logistic regression model of the form:

(F) ) = GH + GI JI 1 − (F)

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ln (

Where P(y) is the probability of a household sustaining catastrophe due to OOPP, bo

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is a constant, X is a vector of k independent variables and bk is the vector of the coefficients. Analogously is specified the model with impoverishment as the

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dependent variable. Following previous studies (Abu-Zaineh et al., 2013; Buigut et al., 2015; Kronenberg & Barros, 2014; Özgen Narcı et al., 2015; Van Minh et al., 2013; Yardim et al., 2010), we assumed that the odds for each model were driven by a set of household characteristics, i.e. household size, region and urbanicity, insurance (social and private) status of household, socioeconomic group (equivalent total expenditure quintiles), presence of at least one senior (≥65 years), junior (children under 5) and unable to work member in the household, as well as some features of the household head, namely age, gender, education level and economic activity status. As 15

ACCEPTED MANUSCRIPT no health variables were available, incapacity to work was considered a proxy for disability (Kronenberg & Barros, 2014). These independent variables were identified in the literature, and no specific hypotheses regarding their effect were tested, hence, all explanatory variables were entered simultaneously into the Logit models.

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However, it was expected that factors associated with greater health needs (e.g. higher age, presence of seniors, children and disabled members) would increase the likelihood of households being exposed to financial risk, more so in the case of conditions

(Abegunde

&

Stanciole,

2008).

Furthermore,

higher

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chronic

socioeconomic status (e.g. consumption, education) and risk pooling mechanisms

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(social and private insurance, higher household size) could act protectively against financial risk in situations of ill-health, while the relationship between the socioeconomic determinants and health is also well-established (Marmot, 2007). The above model was calculated separately for 2008 and 2015 for both catastrophe and

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impoverishment. We tested the equality of the coefficients between 2008 and 2015 with the Wald test, after combining the estimates of the two years for each model with the command suest of STATA. Results are reported as odds ratios (ORs).

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Statistical significance was set at α = 0.05 for all of examinations, but other levels are

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reported as well. The statistical analysis was conducted with SPSS v.24 and STATA v.14.

4. Results 4.1. CHE

The descriptive statistics of the weighted data can be found in Table S3 in the online Appendix. Table 1 presents the evolution of expenditures across the period of 20082015. Equivalised total consumption demonstrates a constant downward trend up until 2014, while in 2015 a slight increase is observed, registering an overall reduction of 16

ACCEPTED MANUSCRIPT 35.7% in the period examined. OOPP are decreasing during the first years of the crisis, but from 2012 onwards a steady increase is observed (23.5% overall reduction). As far as the main components are concerned, medical products and inpatient care increased by 20.2% and 63%, respectively, while outpatient expenses showed a

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significant reduction of 62%. Pharmaceutical spending, which is the major driver of expenses in medical products, is gradually rising starting 2012. Households’ CTP registered a huge decline of 42.4%, reflecting the substantial downward course of

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total consumption. The aforementioned changes resulted in the rise of the average OOPP share of households’ CTP from 6.9% to 7.8%. Disaggregating these estimates

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by expenditure quintiles revealed that OOPP of the poorest households decreased by 13.8% during the crisis, which ensued from the reduction of outpatient (-44%) and medical products (-4.3%) expenses (see Table S4 in online appendix); inpatient payments rose significantly in relative terms (116.9%), however their relative

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importance was quite small in the poorest strata (15.5% of OOPP in 2015). All the other quintiles were associated with increases in OOPP, which appear to be higher in wealthier households (45% overall rise in the fifth quintile); these are driven by the

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upward trend primarily in inpatient outlays, which significantly expanded their share

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in all groups, and to a much lesser extent in medical products. An upsurge in the incidence of CHE is recorded for all thresholds in 2015 compared with 2008 (Tables 2-5). Α de-escalation was occurring until 2011, which was followed by a stronger opposite trend, while a halt is observed in 2015 for the 40% level. The increase in the incidence of CHE has been mainly absorbed by households in the middle expenditure quintiles at the lower thresholds, while the richest households are getting more exposure to financial risk at the higher thresholds (see Tables S5-8 in online Appendix) The poorest households are associated with a relative decrease for all

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ACCEPTED MANUSCRIPT thresholds since the onset of the crisis, though an ascending trend is apparent as of 2012. Nevertheless, the incidence of CHE was generally more concentrated in poorer households, as it is shown by the persistently negative values of CIs and the higher than one values of the quintile ratios, more so when applying the 10-30% thresholds.

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In general, inequality in the incidence of CHE fluctuated over time, however, it appears to be significantly decreasing in the recent years. Hence, whereas the estimates of the weighted headcounts, which incorporates the inequality impact, were

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higher than the unweighted measures, their overall increase was smaller during the crisis.

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Concerning the average intensity of CHE, mean overshoot was higher in 2015 in relation to 2008 at the 10-30% levels, while the estimate was found equal at the highest threshold. A downward trend was noted between 2009 and 2011, which was succeeded by a reverse course from 2012 onwards for all levels, albeit the break in the

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trend was delayed for a couple of years for the 40% threshold. The poorest households were again associated with an overall decrease, and the main bulk of the escalation was rested on the shoulders of the higher expenditure quintiles, (see Tables

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S5-8 in online Appendix). Economically disadvantaged households were typically

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associated with a higher mean overshoot in the first years of the crisis, which implies that they were not only more likely to exceed the CHE thresholds, but they also spent more above the threshold relative to the better-off. However, a considerable reduction in inequality is discernible over the course of the period, especially at the highest thresholds, as the signs have even been reversed, implying that overshoot is now burdening more the wealthier strata. This is mirrored in the lower estimates of the weighted overshoot in relation to the unweighted measure, which, consequently,

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ACCEPTED MANUSCRIPT registered a decrease between 2008 and 2015, with the exception of the lowest threshold. The abovementioned results of overshoot are driven by changes in the headcount as well as in the mean positive overshoot measure, which expresses the average

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overshoot only for households whose OOPP exceeded the pre-specified budget threshold, i.e. the intensity of financial risk. We notice an overall decrease in the mean values for almost all thresholds between 2008 and 2015. This was mainly the outcome

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of a large decline occurring in 2015, which followed a significant rise in the previous

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year. Inequality was also found to fluctuate, being quite small in most instances. Table 6 presents the evolution of FFCI between 2008 and 2015. The FFCI showed a gradual improvement up until 2012, while afterwards the trend was reversed indicating an aggravation of the inequality in the OOPP burden. In 2015, we again notice an increase in the index.

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4.2.Poverty and impoverishment

When using the subsistence poverty line, pre– and post–payment poverty headcounts

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were estimated at only 0.62% and 0.68% of the population, respectively, in 2015, recording a small gradual increase in absolute terms since 2008 (Table 7). The share

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of people impoverished due to OOPP was about the same in 2015 compared with 2008 (0.05% vs. 0.06%, respectively), but it varied in the interim period, probably exhibiting an upward trend from 2012 onwards. As the poverty line was quite low, the normalised gross and net health spending poverty gaps were negligible, which also applies to the poverty impact of OOPP. The normalised mean positive poverty gap, which measures the mean poverty gap only for individuals being below the poverty line (as a share of the poverty line), exhibited a considerable increase in 2015, albeit it varied significantly in the previous years, as was also the case with the respective 19

ACCEPTED MANUSCRIPT impact of OOPP. Finally, concerning the contributors of total poverty gap, we cannot discern a clear trend, as the magnitude fluctuated over time. As far as the relative poverty is concerned, 47.1% and 49% of the sample were living in poverty before and after OOPP, respectively, in 2015, exhibiting a gradual increase

RI PT

since the onset of the crisis, which accumulated to an overall growth of 240.92% and 230.9%, respectively, in the estimates. The share of people impoverished as a result of health spending decreased between 2010 and 2011, but a significant ascending trend

SC

is noticed from 2012 onwards. The overall impoverishment impact was 1.88% in 2015, i.e. it was larger by 90.5% in relation to the beginning of the crisis. The

M AN U

magnitude would have been even greater, but a significant share of the people pushed below the poverty line was matched by those leaving it, as a result of the lower postpayment poverty line that was applied. The normalised poverty gap gradually increased over time, and it reached 12.8% and 13.4% of the pre– and post-payment

TE D

poverty lines in 2015, recording a rise of 318.4% and 263.6%, respectively, since 2008. The respective impact of OOPP showed a downward trend between 2010 and 2012, which was reversed in the following years, resulting in a net 61.2% growth at

EP

the end of the period relative to its onset. The mean distance of poor people from the

AC C

poverty lines is gradually expanding between 2010 and 2014, while a small decline is observed in 2015, reaching the levels of 27.2% and 27.4% of the pre– and post– payment poverty lines, respectively, at the end of the period. On the other hand, the impact of health spending on the intensity of poverty fluctuated over time, though it appears to be increasing starting 2012. Finally, the overall poverty impact of health spending also appears to escalate as of 2012, as we can discern from the descending tendency in the contribution of pre-payment poverty gap to the overall poverty magnitude.

20

ACCEPTED MANUSCRIPT 4.3.Vulnerability Table 8 displays the results concerning the determinants of households incurring CHE at the 40% threshold (see Table S9 in online Appendix for the other levels). The region of household did not affect, ceteris paribus, the likelihood of CHE

RI PT

significantly in almost all cases. However, it is noteworthy that numerically the ORs were reversed in 2015, i.e. families residing in Attica had on average higher chances of catastrophe in relation to the other regions. On the other hand, more densely

SC

populated areas appear to be associated with lower financial risk, with the effect

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achieving statistical significance at the lower thresholds.

Larger households were less likely to sustain financial catastrophe due to health spending in both years, though it appears that the protective effect is losing weight in 2015, at the lower thresholds in particular, as it is rendered insignificant. Having an elderly in the household was associated with significantly higher likelihood of CHE in

TE D

2015, and the change in the effect established statistical significance; a similar result is observed concerning the age of the household head. The presence of juniors was also linked with higher risk of CHE, though it was found more moderate in 2015.

EP

Female-headed households had lower chances of financial risk with respect to men,

AC C

but the ORs were not significant. Higher education level of the breadwinner operates as an important protective mechanism against catastrophe, regardless the threshold and the year of the estimation. It appears that its shielding action was enhanced in 2015, though the change in its effect was not significant. Notably, concerning the economic activity of household heads, the ORs were reversed in 2015, as employed household heads were now associated with higher likelihood of financial risk compared with the other statuses. However, the coefficients (and their change) did not generally establish 21

ACCEPTED MANUSCRIPT statistical significance in 2015, with the exception of the pensioners at the highest threshold. Households of higher expenditure quintile were found significantly more likely to face catastrophe due to OOPP in 2015. Notably, the difference in the odds was

RI PT

increased from 2008 in most instances, at the lower thresholds in particular.

Social and private insurance didn’t affect significantly the likelihood of CHE. However, notably, all cases of CHE are observed in the partially (7 cases) and fully

SC

(77 cases) socially insured households at the 40% threshold, while just one case of

M AN U

catastrophe is found in the partially and fully privately insured households. Finally, having a disable member was associated with higher odds of catastrophe, with the impact being particularly pronounced at the lower thresholds.

Regarding impoverishment due to OOPP in relative poverty, households belonging to the lower-middle expenditure group, residing in rural areas or with an elderly member

TE D

were, ceteris paribus, significantly linked with higher likelihood of being impoverished in 2015, while richer households presented considerable lower

EP

probability (Table 9). It is also worth noting that household heads with postsecondary (non-tertiary) education level, older or unemployed were also facing lower

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odds of catastrophe. Comparing the changes in the effects between 2008 and 2015, only the reversal of the odds of lower education levels established statistical significance.

5. Discussion

To the best of our knowledge, this is the first study exploring the progression of change in financial protection of Greek households against OOPP during the economic crisis. In general, the catastrophic character of OOPP was considerably exacerbated in terms of both incidence and average intensity (overshoot), and there is 22

ACCEPTED MANUSCRIPT indication that the impact will continue to aggravate. This was particularly evident in the lower thresholds, where the increase in the headcount was 31.8% and 22.8% for the 10% and 20% thresholds, respectively, in relative terms during the crisis, while at the higher levels the rise was more moderate. In 2015, 1.2% of the households in the

RI PT

sample were exposed to financial risk due to OOPP at the 40% threshold. This implies that about 110,000 individuals in the general population in Greece were living in households spending more than 40% of their CTP to OOPP, at the possible expense of

SC

other basic necessities. This growth was mainly driven by the new cost-shifting policies adopted from 2010 and later, as the contraction of households’ CTP has been

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slowing down following 2012 (see Figure 1 & Table 1). However, it appears that the additional burden has been distributed somewhat progressively, as it is indicated by the decreasing estimates of (relative) inequality indices, which have even been reversed at the highest CTP thresholds, implying a higher burden for the wealthier

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strata. Overall, it can be deduced that (both relative and absolute) inequalities in financial risk have been mitigated in the recent years. This is also reflected in the estimates of FFCI, which focuses on inequalities in the budget shares allocated to

EP

OOPP, as the decrease observed in the years 2013-2014 is succeeded by an

AC C

improvement in 2015.

Food poverty has increased significantly in relative terms since the onset of the crisis, but its incidence still remains at very low levels, indicating that the vast majority of people were able to cover their basic food needs. The intensity of food poverty fluctuated over time, and we cannot safely assume its overall trend, though a substantial increase was recorded at the end of the period. The findings concerning the anchored relative poverty are less ambiguous. The incidence of relative poverty recorded a significant growth of more than 230%, reaching the substantial levels of

23

ACCEPTED MANUSCRIPT 47.1% and 49% regarding the pre– and post–payment headcounts, respectively in 2015; the mean distance from the poverty line increased by about 20% in the same period. Furthermore, both the incidence and intensity were associated with a considerable rise following 2012, which probably reflects the changes in cost-sharing

RI PT

policies. The overall poverty impact of OOPP also appears to be exacerbating starting 2012. In 2015, 1.9% of the sample was impoverished due to OOPP, which corresponds to over 200,000 people, if we extrapolate to the total population in

SC

Greece.

The aforementioned results were driven by the significant increases in pharmaceutical

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and inpatient OOPP, while outpatient household expenses decreased considerably. Data from EOPYY showed that the average cost-sharing for pharmaceuticals rose from 13.3% in 2012 to 18% in 2013 (Siskou et al., 2014). Another study in private pharmacies in central Greece indicated an increase in the mean percentage rate of

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patient contributions by 157.8% and in the average patient charge per prescription by 65.2% between 2011 and 2014, despite price reductions in the same period (Gouvalas et al., 2016). Furthermore, current prescribing rules do not appear to effectively

EP

prompt physicians to prescribe generics, and at the same time Greek patients are also

AC C

sceptical concerning the use of generic drugs, though economic incentives do exist (Economou, 2015; Yfantopoulos et al., 2016). Owing to the diachronic lack of credible primary healthcare, the outpatient care in Greece is greatly depended on the private sector, often leading to supplier-induced demand (Siskou et al., 2008). Between 2009 and 2013, visits to public hospital outpatient departments and afternoon surgeries decreased by 8.2% and 26.4%, which could at least partly be attributed to the increases in user charges (Economou, 2015). Patients may decide to delay or refrain from necessary first-contact healthcare 24

ACCEPTED MANUSCRIPT utilisation due to the double financial burden of the economic crisis and co-payments. It is quite worrisome that the percentage of individuals reporting unmet medical examination needs due to barriers of access has risen from 5.4% to 12.3% between 2008 and 2015 (see Table S10 in online Appendix). Furthermore, demand of

RI PT

emergency services has increased in the recent years, which may be explained by the absence of any co-payments at the point of service, unlike outpatient care (Economou et al., 2014).

SC

Patient admissions to public hospitals have increased since the beginning of the crisis (Economou, 2015). However, the substantial shrinkage of human and material

M AN U

resources and quality deterioration may have forced some insured patients to the private healthcare sector, where private providers have incentives to raise their revenues, and patients face a significant informational disadvantage (Grigorakis et al., 2016). Moreover, EOPYY imposes to private hospitalisations of insured individuals a

TE D

statutory co–insurance rate of 30% or 50% (depending on their former social security carrier) on Greek DRG’s total cost (3054/18.11.2012, Gazette of the Government). The reimbursement policy does not encompass the productive factor labor, which

EP

results in high direct (formal and informal) fees paid by the health users at the point of

AC C

service (Grigorakis et al., 2016). The structural problems within the Greek health system, such as long queues, lack of information management systems, insufficient financial management and accounting processes, limited administrative capacity, lacking monitoring mechanisms, large number of physicians, and a pricing and remuneration policy unrelated to performance have fostered a pervasive culture of informal or under-the-table payments from patients to healthcare professionals in Greece (Economou, 2015). This phenomenon is primarily related to requesting preferential treatment, including bypassing waiting lists or ensuring better quality of

25

ACCEPTED MANUSCRIPT service and more attention from physicians. There are reports of increased informal payments in the recent years in the presence of expanded demand and scarcer resources in public hospitals, as public health servants may seek to offset lost revenues from the across-the-board salary cuts (Economou, 2015; European

RI PT

Commission, 2017).

It is difficult to compare findings in CHE in view of the extensive methodological heterogeneity. A study using the Greek HBS of 1998-1999 estimated that 2.44% of

SC

households assigned more than 40% of their CTP to healthcare payments, with 67% of them incurring OOPP (Economou et al., 2004), while Murray et al. (2003)

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calculated the incidence of CHE due to OOPP at 2.17% employing the same data. Furthermore, there is evidence of a significant increase in chronic patients sustaining CHE between 2012-2013, mainly owing to pharmaceutical OOPP (Skroumpelos et al., 2014), while Grigorakis et al. (2016) demonstrated the considerable catastrophic

TE D

impact of inpatient OOPP. Concerning peer countries, the incidence of CHE at 40% of CTP in Greece (0.8%) in 2010 was equal to that of Denmark’s, much lower than in Poland (8.8%), and higher than in Germany (0.4%) (Zawada et al., 2016). For the

EP

same year, the occurrence of CHE in Portugal was smaller than in Greece for all

AC C

thresholds (Quintal & Lopes, 2016), while a similar comparison favoured Turkey for the 20-40% levels (Özgen Narcı et al., 2015). Concerning vulnerability, it was demonstrated that factors affect differently the financial risk and impoverishment due to OOPP. In general, households of higher size, lower expenditure quintile, residing in urban areas, without disabled, elderly or very young children members, and with younger or retired, better-educated breadwinners were, ceteris paribus, significantly less vulnerable to CHE in 2015. Overall, the effects of some determinants strengthen (elderly, education level, 26

ACCEPTED MANUSCRIPT expenditure quintile, retirement status, household size) at higher thresholds, while others are weakened (disabled, children), which supports the need for exploration of multiple levels. The analysis of CHE is based on the assumption that OOPP are involuntary consumption not contributing to household welfare, which doesn’t hold

RI PT

perfectly (Moreno-Serra et al., 2013). Applying higher thresholds increases the likelihood that health spending exceeding this limit is indeed not discretionary, while it also attaches more weight to those households facing greater burden (Saksena et al.,

SC

2014). Moreover, CHE are driven by different items of OOPP as the threshold

Figure S4 in online appendix).

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changes, namely inpatient care increases its share of OOPP at higher levels (see

Regarding impoverishment due to OOPP, households belonging to the lower-middle expenditure quintile, located in rural regions and with elderly members were, ceteris paribus, facing higher risk, while wealthier families exhibited considerable lower

TE D

likelihood of financial hardship. The higher vulnerability of the lower-middle group compared with the poorest is reasonable as impoverishment examines the lower-tail of the expenditure distribution and many households were already living in poverty.

EP

Furthermore, it is also worth noting that the economic crisis induced a severe

AC C

downward economic mobility, especially for salaried employees of the middle class, along with significant reductions in social transfers as part of the economic adjustment programmes (Karanikolos et al., 2013). On the other hand, wealthier households being associated with a higher risk of CHE may be misleading concerning the actual situation in financial protection in Greece. This is because it may hide financial barriers to healthcare use for disadvantaged groups, as it was mentioned earlier, or not account for the coping strategies (e.g. selling assets or borrowing) temporarily increasing income and masking the present or long-term financial

27

ACCEPTED MANUSCRIPT hardship (Van Doorslaer et al., 2007). In this regard, there are studies advocating the use of progressive thresholds, which define catastrophe as a function of a household’s rank in the income distribution (Ataguba, 2012; Kwesiga et al., 2015). Providing universal coverage is a necessary, but not a sufficient condition to prevent

RI PT

catastrophic and impoverishing medical spending. Access to healthcare in Greece is largely dependent upon employment (Karanikolos & Kentikelenis, 2016). A large share of the Greek population is left without healthcare coverage during the crisis, as

SC

unemployment rates have skyrocketed (27.5% in 2013) and many individuals have discontinued contribution payments due to financial difficulties (Economou, 2015).

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Unemployment has been associated with increased health problems owing to psychological issues, unhealthy behaviour and reduced financial resources to support a healthy living and cope with ill-health (Karanikolos et al., 2013). Until the standardisation of the benefits package, the healthcare coverage of the uninsured was

TE D

different across funds and largely temporary. Only those with very low income holding a poverty booklet were entitled to continued free access to healthcare services (Economou et al., 2015). There are reports of health providers unofficially providing

EP

healthcare to uninsured patients in recent years, though not in a systematic manner

AC C

(European Commission & Economic Policy Committee, 2016), while some municipalities, NGOs and other unofficial networks of health professionals and volunteers provide primary health services free-of-charge, with their role growing with time (Economou, 2015; Karanikolos & Kentikelenis, 2016). The Greek government has adopted some measures to facilitate access to healthcare for uninsured individuals, including a health-voucher programme (September 2013October 2015) to grant access to primary healthcare, which was followed by the provision of universal coverage of primary care for all citizens (February 2014) and 28

ACCEPTED MANUSCRIPT entitlements to secondary health services (free of charge) as well as pharmaceuticals (same co-payments as the insured) for the uninsured and their families (June 2014). However, these measures were of limited success in terms of access, mainly due to high co-payments for medicines, stigmatizing procedures, bureaucratic barriers,

RI PT

insufficient public information and inadequate implementation by the providers (Economou et al., 2014; Kentikelenis, 2015). More recently (2016), a social bill was enacted addressing some of the shortcomings of the existing scheme, while it also

SC

expanded coverage to refugees and other vulnerable groups (European Commission & Economic Policy Committee, 2016). However, the effectiveness of these policies is

M AN U

yet to be determined.

Expanding insurance coverage does not automatically improve financial protection against ill-health, as the insured, once inside the system, are subject to costs from which their insurance fails to protect them (Wagstaff et al., 2009). Furthermore, the

TE D

underfunded and already overloaded system will be unable to respond to the additional strain arising from the health effects of the crisis. Evidence is already accruing concerning the deterioration of population health in Greece primarily in the

EP

form of worsening general health, increased psychological problems and suicide

AC C

attempts, while budget cuts in prevention and treatment programmes have led to the emergence of several public health issues (e.g. HIV outbreak and increase in the incidence of tuberculosis among drug users) (Chantzaras & Yfantopoulos, 2017; Karanikolos & Kentikelenis, 2016; Karanikolos et al., 2013; Kentikelenis et al., 2014). This can only induce further compromises in the quality of service at the expense of all beneficiaries, some of which will be compelled to shift to the private sector, increasing the likelihood of incurring CHE. In other words, this situation may promote a de facto two-tiered health system, where those with sufficient resources

29

ACCEPTED MANUSCRIPT will be able to afford private healthcare and meet their health needs, while economically weak individuals will access health services from a severely strained public system (Economou, 2015). The aforementioned situation is already evident in our estimates of inpatient out-of-

RI PT

pocket spending, as it has more than quadrupled in the richest expenditure quintile (from €368.31 to €1690.12 between 2008 and 2015) during the crisis, while in the

€33.55 to €72.78 see Table S4 in online appendix).

SC

poorest the increase is more moderate in both absolute and relative terms (from

M AN U

It is expected even in countries with advanced welfare states that households should contribute financially to their healthcare at least to some extent, as it’s a way of demand and cost restraining (Kronenberg & Barros, 2014; Yardim et al., 2010). It is the over-reliance on OOPP that puts at risk households’ standard of living, while it also restricts access to adequate healthcare for the poor (Van Doorslaer et al., 2007;

TE D

Wagstaff, 2007). OOPP is a quite regressive way to finance healthcare, as it increases the financial risk and barriers of healthcare access especially for the most vulnerable groups, such as lower socioeconomic status families, especially in the presence of

EP

senior, chronically ill and not employed members as well as undocumented migrants

AC C

and homeless people (Karanikolos & Kentikelenis, 2016). Some recent measures are certainly in the right direction and will enhance both the access and the resilience of economically disadvantaged households to the economic shocks of ill-health. However, typically, social legislation doesn’t take into consideration the significant financial effect of involuntary health payments. Hence, there are substantial numbers of people (hidden poor), in the lower-middle incomes particularly, barely failing to meet the preconditions of the respective provisions. Cost-sharing policies should, therefore, recognise the different social protection needs of households in the current 30

ACCEPTED MANUSCRIPT economic predicament. In this context, converting the employment contributionsbased social insurance scheme to a tax-based one, especially in the presence of persistently high unemployment rates, could be a more progressive financing strategy (Grigorakis et al., 2016).

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As Thomson et al. (2015) point out, in the face of fiscal pressures in the health sector, policymakers may adopt strategies to enhance efficiency, to cut spending and restrict coverage, and to mobilise additional revenue. Initial policy responses in Greece were

SC

mainly based on easily implemented funding cuts and cost-shifting strategies, neglecting much needed, but politically troublesome to enforce, policy changes to

M AN U

improve the efficiency of the health system. The citizen/patient side and the equitable provision of high-quality health services was largely ignored (Economou et al., 2015). Focus on spending cuts may result in implicit or explicit rationing of healthcare resources, with adverse effects on healthcare access, financial protection and public

TE D

satisfaction, and even larger inefficiencies and an unintended health impact in the longer term (Thomson et al., 2015). The allocation of financial resources to the public healthcare system to improve the quality of provided services would certainly benefit

EP

the access of the worse-off groups in particular. However, caught between Scylla and

AC C

Charybdis, Greek government’s only viable policy path is to prioritise the enhancement of the health system’s efficiency, while also implementing strategies that insulate the most vulnerable groups from the financial risks of health shocks. Some general approaches to promote efficiency in the Greek setting could be the implementation of health technology assessment (HTA), addressing the fragmentation in pooling, purchasing and service delivery, cost-reducing substitution, reducing waste, improving efficiency and quality in service delivery, restricting induced demand and combating corruption (Thomson et al., 2015). The importance of HTA

31

ACCEPTED MANUSCRIPT should be highlighted, as its enactment may discard less cost-effective medical technologies, which will serve the financial protection of the most vulnerable groups in particular (Łuczak & García-Gómez, 2012); HTA of reimbursed drugs was instituted in Greece in mid-January 2018, however it is still not operational (May

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2018).

Moreover, the Greek DRG’s pricing and reimbursement system needs to be revised to reflect the higher international price levels, which may reduce the direct OOPP of

SC

health consumers (Grigorakis et al., 2016). This might lead to increased social security contributions, nevertheless, the enhanced risk pooling will serve the financial

M AN U

protection of the deprived population against health shocks. Finally, implementing policies that better encourage the prescription and dispense of generics will lessen the financial burden related to co-payments for citizens.

There were some limitations in our study. First, we used cross-sectional data, which

TE D

cannot detect the specific welfare changes following ill-health (Wagstaff, 2007). It is difficult to identify the coping mechanisms of the households incurring CHE, and whether medical expenses were financed through sacrificing current consumption or

EP

through drawing on savings, the sale of assets or credit, borrowing or through

AC C

transfers from social networks (Van Doorslaer et al., 2007). Second, households afflicted with health shocks that could not afford the treatment and did not proceed in the necessary purchase of medical care were not considered in the analysis (O'Donnell et al., 2008). Third, this approach doesn’t distinct between types of medical care purchased, namely whether it corresponded to actual health needs as well its quality (Van Doorslaer et al., 2007). Fourth, other significant determinants of catastrophic and impoverishing health payments were omitted from the risk analysis, with the most notable being the health status of households’ members. Nevertheless, the framework 32

ACCEPTED MANUSCRIPT of the WHO which we employed in this study determines financial fairness independently of the healthcare utilisation and health status of the individual (Murray et al., 2000). Fifth, as in all similar studies, it was assumed that reduced resources owing to OOPP has a direct negative impact on household’s welfare. However,

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medical spending may also increase household health stock and improve future productivity (Séne & Cissé, 2015). 6. Conclusions

SC

In summary, the catastrophic and impoverishing impact of OOPP appears to have

M AN U

been aggravated during the economic crisis, and there is indication that it will continue to exacerbate in the forthcoming years. This is induced by the simultaneous effect of households’ diminishing’ CTP and the increased OOPP burden, which ensued from the recently implemented reforms as part of the economic adjustment programmes. Our study also provided evidence that vulnerability to financial risk

TE D

(CHE) differs from that of financial hardship (impoverishment), and that policy interest regarding families with increased health needs and of lower socioeconomic status should go together with specific considerations for the hidden poor of the

EP

lower-middle economic class. Myopic budget cuts and cost-shifting rather than

AC C

focusing on health system’s efficiency and effectiveness can only worsen barriers of healthcare access and, presumably, morbidity in the Greek population. In turn, this will put more pressure on public healthcare finances in the long-term, not to mention the potential social cost in human lives. Effective social protection policies should promote risk-pooling instead of OOPP. Financial fairness is not only a desired outcome from a normative perspective, but it may also improve healthcare access and, consequently, health outcomes.

33

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Figure 1. Timeline of major reforms in the Greek health system, 2008-2015

Note: GDP, Gross Domestic Product Growth; PUHE, Total public health expenditure per capita at current prices; PRHE, Total private health expenditure per capita at current prices; Poverty, At-risk-of-poverty rate at 60 % of the national median equivalised disposable income (after social transfers); GINI, Gini index; Unempl., unemployment rate. Source of social indicators: Eurostat (2017)

41

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Financial protection of households against health shocks in Greece during the economic crisis

M AN U

Online supplementary Material

AC C

EP

TE D

This Appendix includes information referred to in the full version of the article

1

ACCEPTED MANUSCRIPT

UNIT

Financing schemes

2009

2010

2011

RI PT

Supplementary Table S1 Healthcare expenditure by financing scheme in Greece, 2009-2015

2012

2013

2009-2015 2014

2015

Absolute

Relative

change

change (%)

22,490.9

21,608.7

18,835.7

16,780.9

15,058.5

14,130.9

14,731.9

-7,759.0

-34.5

Total public health expenditure

15,412.2

14,920.9

12,425.3

11,082.8

9,302.9

8,194.7

8,704.5

-6,707.7

-43.5

General government

6,115.4

6,475.4

4,202.3

5,046.5

4,603.1

4,176.5

4,459.5

-1,655.9

-27.1

Social security funds

9,296.8

8,445.4

8,223.1

6,036.3

4,699.8

4,018.2

4,245.0

-5,051.8

-54.3

7,076.6

6,683.7

6,375.6

5,673.7

5,630.9

5,753.6

5,793.8

-1,282.8

-18.1

Private insurance

484.3

605.6

551.8

554.8

509.7

550.7

569.7

85.4

17.6

Household out-of-pocket payments

6,592.3

6,078.0

5,823.8

5,118.9

5,121.2

5,202.9

5,224.1

-1,368.2

-20.8

Rest of the world financing schemes (non-resident)

2.1

4.2

34.8

24.5

124.7

182.6

233.7

231.5

10,869.5

Euro per

Total health expenditure in current prices

2,024.9

1,943.0

1,696.2

1,519.3

1,373.3

1,297.3

1,361.4

-663.5

-32.8

inhabitant

Total public health expenditure

1,387.6

1,341.6

1,118.9

1,003.4

848.4

752.3

804.4

-583.2

-42.0

550.6

582.3

378.4

456.9

419.8

383.4

412.1

-138.5

-25.1

837.0

759.4

740.5

546.5

428.6

368.9

392.3

-444.7

-53.1

637.1

601.0

574.1

513.7

513.5

528.2

535.4

-101.7

-16.0

Social security funds Total private health expenditure

M AN U

TE D

AC C

General government

EP

Total private health expenditure

SC

Total health expenditure in current prices

Million euro

2

ACCEPTED MANUSCRIPT

43.6

54.5

49.7

50.2

46.5

50.6

52.6

9.0

20.7

Household out-of-pocket payments

593.5

546.5

524.4

463.5

467.0

477.7

482.8

-110.8

-18.7

Rest of the world financing schemes (non-resident)

0.2

0.4

3.1

2.2

11.4

16.8

21.6

21.4

11,263.2

Percentage

Total health expenditure in current prices

9.5

9.6

9.1

8.8

8.3

7.9

8.4

-1.1

-11.5

of gross

Total public health expenditure

6.5

6.6

6.0

5.8

5.2

4.6

5.0

-1.5

-23.7

2.0

2.6

2.6

2.4

2.5

0.0

-1.2

SC

RI PT

Private insurance

General government

2.6

2.9

product

Social security funds

3.9

3.7

4.0

3.2

2.6

2.3

2.4

-1.5

-38.1

3.0

3.0

3.1

3.0

3.1

3.2

3.3

0.3

10.4

Private insurance

0.2

0.3

0.3

0.3

0.3

0.3

0.3

0.1

60.0

Household out-of-pocket payments

2.8

2.7

2.8

2.7

2.8

2.9

3.0

0.2

6.8

0.0

0.0

0.0

0.0

0.1

0.1

0.1

0.1

-

Total public health expenditure

68.5

69.1

66.0

66.0

61.8

58.0

59.1

-9.4

-13.8

27.2

30.0

22.3

30.1

30.6

29.6

30.3

3.1

11.3

41.3

39.1

43.7

36.0

31.2

28.4

28.8

-12.5

-30.3

31.5

30.9

33.9

33.8

37.4

40.7

39.3

7.9

25.0

2.2

2.8

2.9

3.3

3.4

3.9

3.9

1.7

80.0

29.3

28.1

30.9

30.5

34.0

36.8

35.5

6.2

21.0

0.0

0.0

0.2

0.2

0.8

1.3

1.6

1.6

15,800.0

share of

General government

total

Social security funds

current

Total private health expenditure

health

Private insurance

expenditure

Household out-of-pocket payments

(CHE)

TE D

Rest of the world financing schemes (non-resident)

EP

Percentual

Total private health expenditure

AC C

(GDP)

M AN U

domestic

Rest of the world financing schemes (non-resident)

3

ACCEPTED MANUSCRIPT

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Source: Eurostat, (2017). Eurostat online database. http://ec.europa.eu/eurostat/web/lfs/data/database (accessed 08.07.2017).

Supplementary Table S2 Mean pharmaceutical expenditure as a component of total consumption, OOPP and medical products, 2008-2015

Total

Medical

consumption

OOPP

products

2008

19670.77

1085.66

248.52

2009

19274.71

1064.68

2010

16720.26

904.83

2011

14786.40

809.93

2012

13333.39

731.60

2013

12969.27

773.40

TE D

Pharmaceutical

2014

12609.93

805.36

2015

12649.60

Absolute change 2008-2015

-7021.17

Relative (%) change 2008-

-35.69

Share (%) of pharmaceutical expenditure in: Total

Total

Medical

consumption

OOPP

products

221.24

1.12

20.38

89.03

247.94

214.00

1.11

20.10

86.31

249.59

216.06

1.29

23.88

86.57

243.10

200.31

1.35

24.73

82.40

254.97

220.35

1.65

30.12

86.42

295.27

262.54

2.02

33.95

88.92

320.13

286.24

2.27

35.54

89.41

830.72

319.17

291.79

2.31

35.13

91.42

-254.94

70.66

70.55

1.18

14.75

2.40

-23.48

28.43

31.89

105.09

72.36

2.69

EP

M AN U

Total

AC C

Survey year

SC

Equivalised (€) expenditure

4

ACCEPTED MANUSCRIPT

Note: OOPP, out-of-pocket payments.

SC

Supplementary Table S3. Descriptive statistics of the sample (weighted), 2008-2015

RI PT

2015

Mean (SE)

M AN U

Variables

Categories 2008

2009

2010

2011

2012

2013

2014

2015

3454

3524

3512

3515

3577

3471

5888

6150

2.67 (0.02)

2.65 (0.02)

2.65 (0.02)

2.65 (0.03)

2.64 (0.03)

2.62 (0.04)

2.58 (0.03)

2.58 (0.03)

Attica

37.08 (0.90)

39.08 (1.95)

41.48 (2.02)

40.57 (2.20)

40.41 (2.15)

38.92 (2.03)

40.25 (1.01)

40.08 (1.52)

Northern Greece

31.97 (0.86)

31.29 (1.86)

30.39 (1.88)

31.42 (2.12)

31.55 (2.07)

30.74 (1.91)

30.31 (1.00)

30.71 (1.40)

Central Greece

21.22 (0.74)

20.35 (1.60)

19.16 (1.55)

18.38 (1.58)

19.06 (1.61)

20.07 (1.58)

19.67 (1.04)

19.57 (1.28)

Aegean islands,

9.73 (0.54)

9.28 (1.12)

8.96 (1.16)

9.63 (1.38)

8.98 (1.17)

10.27 (1.25)

9.77 (0.88)

9.65 (0.87)

Number of

Region

Crete

EP

Household size

AC C

(unweighted)

TE D

households

5

Urban

45.39 (0.93)

71.50 (1.66)

48.01 (2.12)

43.47 (2.26)

42.81 (2.20)

66.00 (1.92)

42.64 (1.54)

42.73 (1.61)

domain

Semi-urban

11.86 (0.60)

11.90 (1.20)

14.10 (1.53)

14.40 (1.71)

14.93 (1.75)

13.05 (1.40)

29.66 (1.60)

29.48 (1.50)

Rural

42.76 (0.91)

16.61 (1.28)

37.90 (1.98)

42.14 (2.26)

42.26 (2.19)

20.95 (1.60)

27.70 (1.39)

27.79 (1.43)

34.17 (0.84)

33.83 (0.92)

33.81 (0.95)

34.82 (1.09)

35.26 (1.04)

36.51 (1.05)

37.20 (0.82)

40.47 (0.86)

12.37 (0.62)

13.86 (0.72)

13.33 (0.73)

13.76 (0.87)

14.48 (0.94)

12.24 (0.70)

13.11 (0.62)

8.91 (0.54)

4.66 (0.39)

3.69 (0.36)

3.09 (0.32)

3.31 (0.36)

3.09 (0.33)

4.00 (0.38)

3.65 (0.31)

3.58 (0.31)

39.06 (0.62)

37.89 (0.68)

37.03 (0.69)

34.72 (0.74)

32.28 (0.70)

32.88 (0.79)

31.53 (0.58)

31.45 (0.58)

3.06 (0.21)

4.73 (0.29)

7.10 (0.37)

10.13 (0.45)

10.83 (0.52)

9.86 (0.37)

9.60 (0.33)

Households with at least one child under 5 Households with at

unable to work Share of employed

Share of unemployed per household Share of retired per household Share of non-

3.84 (0.28)

AC C

per household

TE D

least one member

M AN U

least one elderly

SC

Households with at

RI PT

Population density

EP

ACCEPTED MANUSCRIPT

23.76 (0.60)

24.71 (0.72)

23.78 (0.72)

24.76 (0.79)

25.83 (0.80)

25.43 (0.77)

27.58 (0.64)

27.97 (0.63)

34.12 (0.58)

33.55 (0.62)

34.46 (0.62)

33.42 (0.67)

31.76 (0.61)

30.86 (0.70)

31.03 (0.60)

30.98 (0.54)

6

ACCEPTED MANUSCRIPT

per household

RI PT

economically active

None insured

1.38 (0.22)

1.01 (0.18)

2.47 (0.36)

2.52 (0.34)

4.33 (0.52)

6.33 (0.60)

5.42 (0.41)

4.31 (0.33)

household

Partially insured

7.08 (0.51)

5.52 (0.48)

6.41 (0.52)

8.12 (0.62)

9.99 (0.69)

11.25 (0.75)

10.78 (0.52)

9.82 (0.50)

Fully insured

91.54 (0.54)

93.46 (0.52)

91.11 (0.63)

89.36 (0.69)

85.68 (0.86)

82.42 (0.87)

83.80 (0.66)

85.87 (0.59)

Private insurance per

None insured

90.68 (0.56)

91.17 (0.69)

93.10 (0.61)

94.71 (0.54)

94.60 (0.62)

95.95 (0.49)

97.82 (0.27)

95.92 (0.33)

household

Partially insured

5.54 (0.45)

4.78 (0.50)

3.64 (0.44)

1.96 (0.30)

2.83 (0.47)

2.00 (0.33)

0.95 (0.17)

1.27 (0.17)

Fully insured

3.78 (0.36)

4.05 (0.42)

3.26 (0.38)

3.33 (0.45)

2.57 (0.36)

2.05 (0.33)

1.23 (0.19)

2.80 (0.26)

53.13 (0.31)

54.07 (0.38)

54.08 (0.39)

54.57 (0.42)

54.65 (0.39)

55.20 (0.39)

55.70 (0.32)

57.19 (0.31)

TE D

head (in years)

M AN U

Age of household

SC

Social insurance per

Male

74.75 (0.80)

76.26 (0.77)

75.83 (0.78)

74.96 (0.94)

73.02 (0.99)

70.80 (1.04)

69.28 (0.81)

69.85 (0.79)

head

Female

25.25 (0.80)

23.74 (0.77)

24.17 (0.78)

25.04 (0.94)

26.98 (0.99)

29.20 (1.04)

30.72 (0.81)

30.15 (0.79)

Level of studies

No formal

11.70 (0.55)

11.57 (0.64)

12.37 (0.73)

11.34 (0.71)

12.89 (0.77)

12.73 (0.80)

11.06 (0.57)

11.23 (0.60)

completed by the

education or

household head

below ISCED 1

24.00 (0.91)

26.64 (1.04)

23.37 (1.01)

24.75 (1.00)

23.15 (0.77)

22.14 (0.69)

education (ISCED

AC C

Primary

EP

Sex of household

26.38 (0.79)

28.37 (0.97)

1)

7

ACCEPTED MANUSCRIPT

37.87 (1.01)

42.11 (1.06)

38.75 (1.12)

4.52 (0.41)

3.82 (0.39)

4.35 (0.42)

4.73 (0.46)

19.05 (0.75)

18.36 (0.90)

17.17 (0.89)

Employed

52.60 (0.92)

52.03 (1.01)

Unemployed

2.61 (0.32)

Retired

32.27 (0.83)

Non-economically

12.52 (0.61)

education (ISCED 2 &3) Post-secondary

4)

active

Note: SE, standard error.

5.61 (0.48)

5.29 (0.35)

4.96 (0.33)

19.22 (0.99)

21.29 (0.74)

22.73 (0.80)

51.39 (1.04)

49.13 (1.17)

45.56 (1.16)

42.76 (1.07)

43.72 (0.88)

43.17 (0.90)

3.63 (0.41)

4.54 (0.47)

6.16 (0.52)

9.18 (0.67)

10.69 (0.71)

8.38 (0.46)

8.57 (0.44)

33.59 (0.92)

32.66 (0.97)

33.94 (1.04)

34.92 (1.06)

34.26 (1.00)

36.08 (0.82)

36.77 (0.82)

10.75 (0.61)

11.40 (0.59)

10.77 (0.64)

10.34 (0.63)

12.29 (0.68)

11.82 (0.63)

11.50 (0.54)

TE D

EP

AC C

head

38.94 (0.83)

16.50 (0.99)

5 & 6)

activity of household

39.22 (0.88)

18.54 (0.96)

education (ISCED

Main economic

M AN U

education (ISCED

Tertiary

4.72 (0.47)

37.70 (1.06)

SC

non-tertiary

42.52 (1.19)

RI PT

38.36 (0.91)

Secondary

8

ACCEPTED MANUSCRIPT

Main

expenditure quintiles

components

2008

2009

2010

2011

2012

SC

Household equivalised

RI PT

Supplementary Table S4. OOPP and components per expenditure quintile, 2008-2015

2013

Change 2008-2015 2014

2015

Relative Absolute (%)

Mean equivalised household expenditure (€)

3

312.16

225.05

248.02

274.48

266.96

-11.84

-4.25

Outpatient

233.27

287.36

201.92

143.92

118.44

139.83

127.61

130.74

-102.53

-43.95

Inpatient

33.55

33.73

31.62

34.78

33.00

62.18

64.31

72.78

39.22

116.90

Total

545.62

633.25

458.58

377.72

354.13

450.03

466.40

470.48

-75.14

-13.77

Medical products

215.28

242.60

223.53

201.94

236.21

300.54

338.94

339.30

124.02

57.61

Outpatient

420.17

396.49

345.14

239.17

292.05

260.24

288.72

246.67

-173.50

-41.29

Inpatient

73.27

111.29

48.79

60.52

113.31

146.33

159.13

182.11

108.84

148.56

Total

708.72

Medical products

235.13

Outpatient Inpatient Total

199.01

202.70

TE D

M AN U

278.80

EP

2

Medical products

750.38

617.46

501.63

641.58

707.11

786.79

768.08

59.36

8.38

222.90

222.37

230.11

273.63

338.83

339.86

385.20

150.07

63.83

AC C

1

637.42

537.61

524.55

473.83

429.95

356.48

355.26

415.52

-221.90

-34.81

121.30

129.94

133.36

241.63

182.84

288.03

283.92

381.49

260.19

214.50

993.85

890.45

880.28

945.56

886.42

983.34

979.04

1182.21

188.36

18.95

9

ACCEPTED MANUSCRIPT

251.74

225.75

238.37

302.73

349.68

372.12

385.34

123.22

47.01

Outpatient

739.18

777.39

591.99

577.03

479.31

522.74

523.47

502.20

-236.98

-32.06

Inpatient

202.40

191.14

281.51

235.49

417.48

438.07

555.98

529.32

326.92

161.52

Total

1203.70

1220.26

1099.26

1050.89

1199.52

1310.50

1451.56

1416.86

213.16

17.71

Medical products

336.82

270.07

418.77

521.27

462.46

398.16

499.61

489.51

152.70

45.34

Outpatient

1271.34

1162.68

1193.88

1495.78

1045.24

1011.46

901.92

687.04

-584.29

-45.96

Inpatient

368.31

508.06

579.59

977.46

1257.57

1284.74

1406.97

1690.12

1321.80

358.88

Total

1976.47

1940.81

2192.24

2994.51

2765.27

2694.36

2808.49

2866.68

890.21

45.04

52.69

57.24

55.11

58.85

56.74

5.64

11.05

RI PT

262.11

SC

5

Medical products

M AN U

4

Share (%) in total OOPP 49.30

Outpatient

42.75

45.38

44.03

38.10

33.44

31.07

27.36

27.79

-14.96

-35.00

Inpatient

6.15

5.33

6.89

9.21

9.32

13.82

13.79

15.47

9.32

151.55

Medical products

30.38

32.33

36.20

40.26

36.82

42.50

43.08

44.18

13.80

45.43

Outpatient

59.29

52.84

55.90

47.68

45.52

36.80

36.70

32.11

-27.17

-45.83

Inpatient 3

Medical products Outpatient Inpatient

49.07

TE D

51.10

EP

2

Medical products

AC C

1

10.34

14.83

7.90

12.06

17.66

20.69

20.23

23.71

13.37

129.35

23.66

25.03

25.26

24.34

30.87

34.46

34.71

32.58

8.92

37.72

64.14

60.37

59.59

50.11

48.50

36.25

36.29

35.15

-28.99

-45.20

12.21

14.59

15.15

25.55

20.63

29.29

29.00

32.27

20.06

164.39

10

ACCEPTED MANUSCRIPT

20.63

20.54

22.68

25.24

26.68

25.64

27.20

5.42

24.89

Outpatient

61.41

63.71

53.85

54.91

39.96

39.89

36.06

35.44

-25.96

-42.28

Inpatient

16.81

15.66

25.61

22.41

34.80

33.43

38.30

37.36

20.54

122.18

Medical products

17.04

13.92

19.10

17.41

16.72

14.78

17.79

17.08

0.03

0.20

Outpatient

64.32

59.91

54.46

49.95

37.80

37.54

32.11

23.97

-40.36

-62.74

Inpatient

18.63

26.18

26.44

32.64

45.48

47.68

50.10

58.96

40.32

216.38

RI PT

21.78

M AN U

5

Medical products

SC

4

EP

TE D

Note: The equivalised expenditure quintiles are anchored to the 2008 expenditure distribution. OOPP, out-of-pocket payments.

2008

AC C

Supplementary Table S5. Changes in the distribution of incidence and intensity of CHE at 10% threshold of CTP, 2008-2015

2009

2010

Change 2008-2015 2011

2012

2013

2014

2015 Absolute

11

Relative (%)

ACCEPTED MANUSCRIPT

28.77

23.43

21.66

(1.77)

(1.72)

(1.55)

(1.30)

(1.24)

21.39

23.78

20.00

13.63

21.03

(1.62)

(1.68)

(1.54)

(1.35)

(1.77)

22.11

17.62

19.49

22.16

19.44

(1.70)

(1.64)

(1.59)

(2.03)

19.37

21.75

17.24

(1.67)

(1.85)

(1.88)

16.80

14.23

19.64

(1.55)

(1.53)

(2.13)

Test for linear trend

<0.01

<0.01

Total

22.77

Richest quintile

(0.75) Overshoot

(1.29)

(1.01)

(1.04)

23.20

24.63

26.18

(1.37)

(1.42)

22.53

23.17

28.88

(2.22)

(2.61)

(2.02)

(1.75)

15.79

20.48

21.73

28.02

21.77

(2.25)

(2.68)

(3.05)

(2.50)

(2.33)

29.62

26.90

24.86

32.47

24.12

(3.42)

(3.67)

(4.23)

(2.99)

(2.74)

<0.01

<0.01

0.48

0.08

0.02

0.01

TE D

4

29.79

EP

3

28.23

SC

38.34

(1.80)

M AN U

34.15

2

28.07

AC C

Poorest quintile

RI PT

Headcount

23.71

22.22

20.45

21.48

25.59

27.06

27.95

(0.82)

(0.82)

(0.87)

(0.88)

(0.93)

(0.73)

(0.75)

12

-4.36*

-12.77

4.79*

22.39

6.77*

30.62

2.40

12.39

7.32*

43.57

5.18*

22.75

Poorest quintile

3.32 (0.26)

4.07 (0.28)

2.53 (0.19)

1.94 (0.15)

1.85 (0.14)

2.52 (0.16)

2

2.09 (0.24)

2.32 (0.23)

1.56 (0.17)

0.96 (0.12)

1.66 (0.17)

3

1.90 (0.20)

1.73 (0.23)

1.60 (0.18)

1.92 (0.25)

1.48 (0.21)

4

1.66 (0.21)

1.54 (0.18)

1.33 (0.21)

1.14 (0.21)

1.69 (0.36)

RI PT

Richest quintile

1.41 (0.20)

1.18 (0.20)

1.68 (0.31)

2.92 (0.54)

1.90 (0.38)

Test for linear trend

<0.01

<0.01

<0.01

<0.01

0.66

Total

2.08 (0.10)

2.24 (0.11)

1.86 (0.10)

1.69 (0.10)

9.73 (0.60)

10.60

8.79 (0.49)

EP

ACCEPTED MANUSCRIPT

Poorest quintile

(0.52) 9.77 (0.85)

3

8.60 (0.69)

4

8.57 (0.83)

9.76 (0.70)

7.82 (0.58)

AC C

2

2.77 (0.14)

-0.55*

-16.57

2.47 (0.20)

2.32 (0.17)

0.23

11.00

1.93 (0.34)

1.99 (0.23)

2.74 (0.28)

0.84*

44.21

1.78 (0.31)

2.21 (0.29)

2.50 (0.39)

0.84*

50.60

1.93 (0.42)

2.28 (0.35)

2.85 (0.44)

1.44*

102.13

0.18

0.04

0.27

1.75 (0.10)

2.28 (0.11)

2.56 (0.10)

2.65 (0.10)

0.57*

27.40

8.28 (0.43)

8.55 (0.42)

8.97 (0.38)

9.95 (0.35)

9.28 (0.29)

-0.45

-4.62

7.05 (0.64)

7.89 (0.62)

9.52 (0.69)

10.05

8.84 (0.45)

-0.93

-9.52

M AN U

SC

2.21 (0.22)

TE D

Mean positive overshoot

2.81 (0.15)

(0.63)

9.83 (0.91)

8.19 (0.70)

8.66 (0.84)

7.64 (0.78)

8.57 (0.94)

8.59 (0.72)

9.48 (0.74)

0.88

10.23

7.09 (0.55)

7.72 (0.81)

7.24 (0.95)

8.27 (1.44)

8.20 (0.99)

7.89 (0.78)

11.48

2.91

33.96

13

ACCEPTED MANUSCRIPT

Richest quintile

8.41 (0.94)

8.28 (1.19)

8.55 (1.25)

9.86 (1.62)

7.06 (1.14)

0.53

0.00

0.68

0.31

0.67

Total

9.13 (0.34)

9.45 (0.33)

8.35 (0.31)

8.25 (0.38)

8.14 (0.30)

7.77 (1.14)

0.70

SC

Test for linear trend

RI PT

(1.47) 11.81

3.40*

40.43

0.34

3.72

(1.33)

0.01

0.11

9.47 (0.27)

9.47 (0.25)

M AN U

8.92 (0.29)

7.01 (0.98)

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test following a fractional probit regression for overshoot and F-test for concentration index. Linear trend between expenditure quintiles was tested with F-tests following a

TE D

logistic regression for headcounts and a fractional probit regression for overshoot. CTP, capacity to pay; CHE, catastrophic health expenditure.

Headcount Poorest quintile

2009

2010

Change 2008-2015 2011

2012

2013

2014

2015

AC C

2008

EP

Supplementary Table S6. Changes in the distribution of incidence and intensity of CHE at 20% threshold of CTP, 2008-2015

12.88

15.44

8.76 (0.81)

6.88 (0.69)

14

7.03 (0.69)

9.76 (0.78)

10.34

10.79

Absolute

Relative (%)

-2.09

-16.23

(1.21)

(1.23)

2

6.89 (0.96)

8.25 (1.06)

5.65 (0.83)

3.42 (0.58)

5.56 (0.81)

3

6.46 (0.96)

5.86 (0.92)

5.25 (0.83)

6.89 (1.22)

6.13 (1.16)

4

7.00 (1.06)

5.62 (0.90)

4.77 (1.15)

4.16 (1.21)

3.88 (1.17)

RI PT

ACCEPTED MANUSCRIPT

(0.64)

Richest quintile

4.71 (0.84)

4.65 (0.85)

5.34 (1.19)

8.31 (1.88)

4.24 (1.42)

(0.66)

8.90 (0.85)

8.86 (0.81)

1.97

28.59

7.36 (1.87)

6.34 (0.98)

9.60 (1.22)

3.14*

48.61

4.94 (1.32)

7.06 (1.30)

8.25 (1.39)

1.25

17.86

6.45 (2.12)

6.28 (1.38)

10.51

5.80*

123.14

2.41*

31.75

M AN U

SC

9.42 (1.20)

(1.74)

<0.01

<0.01

<0.01

<0.01

0.17

0.08

0.01

0.27

Total

7.59 (0.46)

8.24 (0.47)

6.38 (0.42)

5.84 (0.44)

6.11 (0.44)

8.83 (0.59)

9.07 (0.45)

10.00

1.22 (0.16)

1.52 (0.17)

2

0.83 (0.15)

0.80 (0.13)

3

0.62 (0.12)

4

0.58 (0.12)

(0.47)

0.82 (0.10)

0.57 (0.08)

0.54 (0.07)

0.78 (0.09)

0.99 (0.08)

0.88 (0.07)

-0.34*

-27.87

0.42 (0.08)

0.22 (0.05)

0.51 (0.09)

0.74 (0.11)

1.00 (0.13)

0.76 (0.09)

-0.07

-8.43

AC C

Poorest quintile

EP

Overshoot

TE D

Test for linear trend

0.66 (0.14)

0.48 (0.10)

0.60 (0.14)

0.37 (0.09)

0.54 (0.16)

0.63 (0.13)

1.03 (0.17)

0.41*

66.13

0.37 (0.08)

0.34 (0.09)

0.30 (0.10)

0.57 (0.25)

0.45 (0.16)

0.59 (0.17)

1.19 (0.27)

0.61*

105.17

15

Richest quintile

0.49 (0.13)

0.46 (0.13)

0.64 (0.20)

1.25 (0.38)

0.58 (0.24)

0.51 (0.18)

Test for linear trend

<0.01

<0.01

0.00

<0.01

0.69

Total

0.75 (0.06)

0.79 (0.06)

0.58 (0.05)

0.52 (0.06)

0.52 (0.05)

RI PT

ACCEPTED MANUSCRIPT

Poorest quintile

9.48 (0.92)

9.83 (0.80)

9.35 (0.78)

8.25 (0.69)

7.70 (0.61)

2

12.00

9.75 (1.01)

7.45 (1.05)

6.46 (1.24)

3

9.62 (1.27)

11.20

9.20 (1.18)

10.35 (1.93)

Test for linear trend

0.33

Total

9.86 (0.57)

6.50 (1.10)

9.99 (1.89)

7.13 (1.23)

EP

Richest quintile

8.33 (1.25)

11.95

7.31 (2.26)

6.04 (0.98)

14.79

0.31

0.70 (0.06)

0.91 (0.06)

0.91 (0.06)

0.16

21.33

9.60 (0.52)

8.17 (0.42)

-1.31

-13.82

11.21

8.60 (0.74)

-3.40*

-28.33

10.74

1.12

11.64

6.11*

73.35

0.46

4.44

-0.75

-7.61

8.02 (0.53) 7.83 (0.82)

7.39 (1.21)

(1.03) 9.93 (1.51)

9.08 (2.01)

8.37 (1.97)

(4.39) 13.71

(2.40)

(3.24)

(3.58)

0.12

0.21

0.07

0.01

9.59 (0.53)

9.03 (0.53)

8.86 (0.69)

8.46 (0.55)

16

132.65

(1.23)

15.09

AC C

4

TE D

(1.53)

8.76 (1.38)

0.65*

0.13

M AN U

(1.31)

9.18 (0.97)

1.14 (0.29)

0.35

SC

Mean positive overshoot

0.80 (0.24)

14.44 (1.87)

7.90 (1.68)

12.72

10.81

(2.95)

(2.19)

0.96

0.47

0.00

7.94 (0.39)

10.03

9.11 (0.37)

ACCEPTED MANUSCRIPT

(0.43)

RI PT

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test following a fractional probit regression for overshoot and F-test for concentration index. Linear trend between expenditure quintiles was tested with F-tests following a

M AN U

SC

logistic regression for headcounts and a fractional probit regression for overshoot. CTP, capacity to pay; CHE, catastrophic health expenditure.

2009

2010

2011

Change 2008-2015 2012

2013

2014

2015 Absolute

Relative (%)

EP

2008

TE D

Supplementary Table S7. Changes in the distribution of incidence and intensity of CHE at 30% threshold of CTP, 2008-2015

Poorest quintile

4.73 (0.73)

2

3.62 (0.73)

3

2.37 (0.56)

AC C

Headcount 5.71 (0.73)

3.15 (0.50)

1.95 (0.35)

2.03 (0.35)

2.91 (0.49)

3.72 (0.37)

3.47 (0.38)

-1.26

-26.64

3.53 (0.65)

1.37 (0.39)

0.66 (0.24)

1.93 (0.44)

3.02 (0.63)

3.69 (0.57)

2.58 (0.42)

-1.04

-28.73

2.55 (0.63)

1.85 (0.52)

2.52 (0.78)

0.75 (0.32)

1.80 (0.58)

1.56 (0.48)

4.21 (0.82)

1.84*

77.64

17

2.31 (0.59)

1.06 (0.34)

1.28 (0.53)

0.64 (0.29)

2.35 (0.91)

2.39 (0.97)

Richest quintile

1.81 (0.52)

1.54 (0.50)

2.65 (0.97)

3.70 (1.13)

1.48 (0.72)

Test for linear trend

0.01

<0.01

0.04

0.00

0.25

Total

2.97 (0.28)

2.99 (0.28)

2.18 (0.26)

1.73 (0.25)

1.82 (0.23)

Poorest quintile

0.44 (0.09)

0.53 (0.10)

0.29 (0.06)

0.14 (0.03)

0.14 (0.03)

2

0.32 (0.08)

0.25 (0.07)

0.12 (0.04)

0.07 (0.03)

3

0.22 (0.06)

0.26 (0.08)

0.13 (0.05)

0.18 (0.07)

4

0.20 (0.06)

0.10 (0.05)

0.06 (0.03)

Richest quintile

0.19 (0.08)

0.17 (0.07)

0.28 (0.12)

Test for linear trend

0.12

<0.01

Total

0.27 (0.03)

0.27 (0.04)

9.30 (1.32)

Poorest quintile

4.86 (1.17)

2.55*

110.39

1.45

80.11

2.20 (0.81)

3.26 (0.95)

0.36

0.01

0.16

2.66 (0.30)

3.20 (0.25)

3.46 (0.27)

0.49

16.50

0.19 (0.03)

0.36 (0.04)

0.24 (0.03)

-0.20*

-45.45

0.13 (0.04)

0.18 (0.05)

0.44 (0.08)

0.24 (0.05)

-0.08

-25.00

0.06 (0.03)

0.16 (0.06)

0.24 (0.08)

0.41 (0.10)

0.19

86.36

0.11 (0.07)

0.27 (0.18)

0.15 (0.07)

0.24 (0.11)

0.57 (0.16)

0.37*

185.00

0.68 (0.27)

0.25 (0.15)

0.11 (0.07)

0.38 (0.17)

0.56 (0.21)

0.37

194.74

<0.01

0.21

0.91

0.48

0.01

0.19 (0.03)

0.17 (0.03)

0.15 (0.03)

0.17 (0.02)

0.35 (0.03)

0.30 (0.03)

0.03

11.11

9.20 (1.22)

9.36 (1.11)

7.12 (1.06)

6.92 (0.91)

6.51 (0.67)

9.60 (0.80)

6.98 (0.64)

-2.32

-24.95

SC

1.12 (0.65)

EP

TE D

M AN U

Overshoot

Mean positive overshoot

1.56 (0.57)

AC C

4

RI PT

ACCEPTED MANUSCRIPT

0.01

18

ACCEPTED MANUSCRIPT

2

8.97 (1.52)

7.16 (1.38)

8.63 (1.84)

9.96 (2.32)

6.86 (1.18)

5.95 (0.84)

11.83

9.19 (1.29)

0.22

2.45

9.78 (1.67)

0.37

3.93

15.56

11.65

3.16

37.22

(4.79)

(1.53)

17.47

17.04

6.48

61.36

(5.84)

(4.47)

-0.47

-5.07

3

9.41 (1.71)

10.09

7.30 (1.75)

7.24 (2.06)

7.73 (1.66)

9.10 (2.79)

4.97 (1.37)

17.19

11.43

(7.00)

(5.37)

6.07 (1.94)

11.32

10.41

18.29

17.05

(3.24)

(2.62)

(3.09)

(3.94)

(5.55)

Test for linear trend

0.98

0.61

0.22

0.01

0.18

0.48

<0.01

<0.01

Total

9.27 (0.79)

9.05 (0.78)

8.75 (0.83)

8.02 (0.98)

6.59 (0.53)

11.03

8.80 (0.56)

TE D

10.56

9.92 (1.10)

9.38 (2.37)

(0.67)

EP

Richest quintile

8.49 (1.74)

15.49

(2.91)

M AN U

4

8.71 (2.29)

SC

(2.15)

RI PT

(1.21)

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test following a

AC C

fractional probit regression for overshoot and F-test for concentration index. Linear trend between expenditure quintiles was tested with F-tests following a logistic regression for headcounts and a fractional probit regression for overshoot. CTP, capacity to pay; CHE, catastrophic health expenditure.

19

ACCEPTED MANUSCRIPT

2008

2009

2010

2011

2012

1.59 (0.42)

1.95 (0.45)

1.18 (0.27)

0.54 (0.21)

2

1.10 (0.40)

0.83 (0.31)

0.51 (0.22)

0.36 (0.18)

0.54 (0.23)

3

1.06 (0.36)

1.08 (0.39)

0.37 (0.22)

0.26 (0.19)

4

0.64 (0.32)

0.32 (0.20)

0.14 (0.14)

0.25 (0.18)

Richest quintile

0.76 (0.33)

0.92 (0.38)

1.47 (0.76)

Test for linear trend

0.42

0.03

0.05

Total

1.03 (0.16)

1.05 (0.17)

Poorest quintile

0.14 (0.05)

2

0.11 (0.04)

0.57 (0.16)

2014

Change 2008-2015 2015 Absolute

Relative (%)

1.28 (0.19)

0.88 (0.16)

-0.71

-44.65

0.52 (0.23)

1.77 (0.37)

0.93 (0.24)

-0.17

-15.45

0.21 (0.16)

0.66 (0.34)

1.17 (0.41)

1.44 (0.45)

0.38

35.85

0.81 (0.58)

0.33 (0.33)

1.09 (0.46)

3.11 (1.02)

2.47*

385.94

2.57 (1.00)

0.84 (0.55)

0.84 (0.60)

0.86 (0.43)

2.01 (0.71)

1.25

164.47

<0.01

0.60

0.95

0.54

0.01

0.59 (0.14)

0.49 (0.11)

0.57 (0.11)

1.34 (0.15)

1.19 (0.15)

0.16

15.53

TE D

EP

AC C

Overshoot

0.43 (0.14)

M AN U

Poorest quintile

0.78 (0.15)

2013

SC

Headcount

RI PT

Supplementary Table S8. Changes in the distribution of incidence and intensity of CHE at 40% threshold of CTP, 2008-2015

0.18 (0.06)

0.08 (0.03)

0.02 (0.01)

0.03 (0.01)

0.03 (0.01)

0.12 (0.02)

0.05 (0.01)

-0.09*

-64.29

0.06 (0.04)

0.04 (0.02)

0.02 (0.01)

0.02 (0.01)

0.02 (0.01)

0.16 (0.04)

0.07 (0.03)

-0.04

-36.36

20

3

0.07 (0.03)

0.08 (0.05)

0.03 (0.02)

0.05 (0.04)

0.01 (0.01)

0.05 (0.03)

4

0.06 (0.03)

0.03 (0.03)

0.01 (0.01)

0.07 (0.05)

0.13 (0.12)

Richest quintile

0.08 (0.05)

0.07 (0.04)

0.11 (0.06)

0.38 (0.18)

0.14 (0.10)

Test for linear trend

0.59

0.14

0.03

<0.01

0.04

RI PT

ACCEPTED MANUSCRIPT

Total

0.09 (0.02)

0.09 (0.02)

0.06 (0.01)

0.06 (0.02)

0.04 (0.01)

Poorest quintile

8.67 (2.04)

9.44 (2.01)

7.13 (1.61)

4.50 (1.51)

7.24 (1.62)

2

9.87 (2.71)

7.58 (3.86)

7.32 (1.86)

5.08 (1.80)

3

6.55 (2.12)

7.51 (3.76)

8.94 (2.14)

18.72

10.33 (4.89)

EP

9.87 (4.72)

3.66 (0.00)

AC C

Richest quintile

8.80 (3.42)

7.82 (2.16)

7.60 (4.26)

100.00

0.13

216.67

0.02 (0.01)

0.24 (0.12)

0.29 (0.15)

0.21

262.50

0.78

0.73

<0.01

0.03 (0.01)

0.14 (0.02)

0.09 (0.02)

0.00

0.00

5.05 (1.01)

9.47 (0.97)

5.97 (0.89)

-2.70

-31.14

4.29 (1.47)

4.29 (0.89)

8.96 (1.34)

7.74 (1.95)

-2.13

-21.58

3.70 (2.30)

6.87 (1.76)

9.87 (2.67)

10.04

3.49

53.28

6.06 (1.34)

-2.74

-31.14

27.26

14.25

3.92

37.95

(5.39)

(5.68)

SC

0.19 (0.07)

(2.61)

26.45

16.27

(3.29)

(6.82)

14.90

16.24

(3.78)

(4.15)

21

0.07

0.12 (0.07)

(3.17)

4

0.14 (0.06)

0.01 (0.01)

M AN U

TE D

Mean positive overshoot

0.12 (0.05)

4.09 (0.00)

11.25 (4.17)

1.87 (1.11)

Test for linear trend

0.90

0.97

0.00

<0.01

0.01

0.38

Total

8.75 (1.29)

8.51 (1.36)

7.35 (1.40)

10.67

8.65 (1.88)

RI PT

ACCEPTED MANUSCRIPT

(1.98)

4.83 (0.70)

0.19 10.13

7.68 (1.03)

-1.07

-12.23

(0.85)

SC

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test following a fractional probit regression for overshoot and F-test for concentration index. Linear trend between expenditure quintiles was tested with F-tests following a

AC C

EP

TE D

M AN U

logistic regression for headcounts and a fractional probit regression for overshoot. CTP, capacity to pay; CHE, catastrophic health expenditure.

22

ACCEPTED MANUSCRIPT Supplementary Figure S1. Evolution of headcount of CHE for various thresholds of

M AN U

SC

RI PT

households’ CTP, 2008-2015

Note: Brackets represent 95% confidence intervals. P-value refers to the mean

TE D

difference between 2008 and 2015, which was tested with x2 test. CTP, capacity to

AC C

EP

pay; CHE, catastrophic health expenditure.

23

ACCEPTED MANUSCRIPT Supplementary Figure S2. Evolution of overshoot of CHE for various thresholds of

M AN U

SC

RI PT

households’ CTP, 2008-2015

Note: Brackets represent 95% confidence intervals. P-value refers to the mean

TE D

difference between 2008 and 2015, which was tested with t-test following a fractional

AC C

EP

probit regression. CTP, capacity to pay; CHE, catastrophic health expenditure.

24

ACCEPTED MANUSCRIPT

Supplementary Figure S3. Evolution of mean positive overshoot of CHE for various

M AN U

SC

RI PT

thresholds of households’ CTP, 2008-2015

TE D

Note: Brackets represent 95% confidence intervals. P-value refers to the mean difference between 2008 and 2015, which was tested with t-test following a fractional

AC C

EP

probit regression. CTP, capacity to pay; CHE, catastrophic health expenditure.

25

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

Supplementary Figure S4. Evolution of components’ share of OOPP for households with catastrophe, 2008-2015

Note: OOPP, out-of-pocket payments.

26

ACCEPTED MANUSCRIPT

OR (SE) 2008

OR (SE)

Wald test of equality

2015

of

2008

2008

2015

equality ORs p-value

1.116 (0.143)

0.934 (0.098)

0.282

1.109 (0.217)

0.887 (0.132)

0.362

0.908 (0.289)

0.680 (0.165)

0.470

1.144 (0.168)

0.765**

0.044

1.037 (0.225)

0.805 (0.149)

0.376

1.613 (0.507)

0.720 (0.224)

0.068

0.899 (0.171)

0.092

1.812 (0.667)

0.868 (0.253)

0.117

1.222 (0.174)

0.853

1.442 (0.481)

1.292 (0.302)

0.789

(0.103)

Crete

(0.250)

Urbanicity (Urban as ref.) 0.892 (0.140)

0.781* (0.103)

0.002

1.510* (0.365)

EP

1.516**

AC C

Aegean islands,

TE D

Greece

Semi-urban

of

Wald test of

SC M AN U

ref.)

Central Greece

equality

OR (SE)

ORs p-value

Region (Attica as

Northern

Wald test of

2015

ORs p-value

RI PT

Supplementary Table S9. Determinants of CHE of households at 10%, 20% and 30% thresholds, 2008 vs. 2015

1.351***

0.026

1.283 (0.286)

27

ACCEPTED MANUSCRIPT

Rural

0.988 (0.119)

1.627***

0.003

0.894 (0.160)

(0.188) 0.881**

(0.230)

0.934 (0.042)

0.395

0.701***

Elderly

in

1.348* (0.208)

household (no as

(0.060) 2.388***

M AN U

(0.045) 0.004

1.276 (0.338)

(0.294)

0.009

2.299***

0.073

0.865 (0.247)

3.085***

2.498***

household (no as

(0.532)

(0.456)

0.989 (0.120)

1.102 (0.102)

0.400

4.691***

2.974***

TE D

Children under 5 in

(1.463)

0.477

AC C

head (males as ref,)

EP

ref.)

Age of household

1.019***

1.029***

head

(0.005)

(0.004)

0.121

0.733* (0.130)

0.045

0.663***

0.957 (0.106)

0.039

3.543***

0.016

(0.092) 0.952 (0.408)

(1.182)

0.252

(0.734)

0.92 (0.113)

1.858** (0.470)

(0.447)

ref.)

Sex of household

0.925 (0.059)

0.051

SC

Household size

1.429**

RI PT

(0.135)

0.292

6.343***

2.776**

0.277

(3.651)

(1.377)

0.562**

0.893 (0.173)

0.168

1.013 (0.010)

0.109

(0.154)

1.032***

1.024***

(0.007)

(0.006)

28

0.373

1.039*** (0.012)

ACCEPTED MANUSCRIPT

Education

of

RI PT

household head (no formal education as

0.823* (0.092)

0.389

0.735* (0.131)

education

(0.092)

Secondary

0.686**

0.625***

0.638

0.75 (0.158)

education

(0.103)

(0.083)

Post-secondary

1.165 (0.314)

0.86 (0.181)

0.376

Tertiary

0.606***

0.497***

0.424

education

(0.117)

(0.076)

1.46 (0.478)

1.133 (0.180)

0.643* (0.165)

1.001 (0.206)

0.178

0.319

0.8 (0.244)

0.508**

0.285

0.681 (0.332)

EP

0.673 (0.208)

(0.149)

0.868 (0.234)

0.663

0.471 (0.356)

0.626 (0.281)

0.747

0.317***

0.053

0.5 (0.245)

0.301***

0.450

(0.074)

(0.138)

AC C

activity

of household head (Employed as ref.) Unemployed

0.577***

0.700

(0.091)

non-tertiary

Economic

0.799* (0.101)

M AN U

0.710***

TE D

Primary

SC

ref.)

0.486

0.402 (0.335)

29

0.858 (0.263)

0.393

a

1.095 (0.614)

0.871

ACCEPTED MANUSCRIPT

Retired

1.522***

0.93 (0.116)

0.014

1.49 (0.391)

0.818 (0.156)

1.058 (0.203)

0.842 (0.130)

0.355

1.016 (0.307)

0.739**

1.106 (0.102)

0.013

1.462***

0.006

RI PT

Non-

TE D

(0.236)

0.832 (0.185)

active

of

household (poorest as ref.)

0.887 (0.124)

Richest

0.828 (0.124)

0.735* (0.117)

AC C

(0.168) 4

0.012

1.702 (0.886)

0.711 (0.323)

0.206

(1.329)

1.229 (0.199)

1.789***

0.763 (0.151)

1.055 (0.130)

0.163

1.26 (0.364)

1.011 (0.205)

0.532

0.873 (0.183)

1.337* (0.229)

0.115

1.035 (0.333)

2.030***

0.102

EP

(0.098) 3

0.739 (0.280)

M AN U

Expenditure

2

0.593

3.119***

SC

economically

quintiles

0.065

0.073

<0.001

(0.521)

1.114 (0.259)

1.539* (0.339)

0.312

1.273 (0.438)

3.344***

0.038

(1.046) 0.758 (0.196)

30

2.663***

<0.001

1.151 (0.476)

2.966***

0.086

ACCEPTED MANUSCRIPT

insurance

status of household (none

insured

as

ref.) 0.959 (0.527)

1.047 (0.271)

0.885

0.718 (0.527)

0.623 (0.264)

0.867

0.252 (0.314)

0.633 (0.488)

0.529

1.045 (0.537)

1.089 (0.248)

0.941

0.633 (0.400)

0.861 (0.282)

0.666

0.308 (0.306)

0.632 (0.372)

0.533

1.379 (0.314)

0.754 (0.298)

0.186

0.721 (0.331)

0.850 (0.510)

0.827

0.519 (0.537)

0.567 (0.594)

0.952

1.043 (0.311)

1.149 (0.242)

0.672 (0.307)

0.848 (0.270)

0.676

0.518 (0.573)

0.493 (0.265)

0.967

M AN U

Partially insured Fully insured insurance

TE D

Private

status of household insured

as

Partially insured Fully insured

AC C

ref.)

EP

(none

(1.079)

SC

Social

(0.601)

RI PT

(0.344)

0.791

31

ACCEPTED MANUSCRIPT

2.941***

(0.391)

(0.596)

0.128***

0.050***

(0.081) No of observations

3454

Model F-statistic

F(26,

in

household (no as

0.084

1.253 (0.435)

2.527*** (0.549)

ref.)

3428)

1.231 (0.510)

0.719

0.014***

0.016***

0.931

(0.018)

(0.034)

(0.015)

(0.019)

(0.011)

6150

3454

6150

3376

6150

F(26,

SC

0.031***

3842)

0.688

1.572 (0.846)

0.044***

F(26,

0.197

M AN U

Constant

0.087

RI PT

1.745**

Disable

3428)

F(26,

3842)

F(25,

3351)

F(26,

3842)

=24.54,

=7.28,

=9.48,

=6.94,

=6.30,

p-value<0.01

p-value<0.01

p-value<0.01

p-value<0.01

p-value<0.01

p-value<0.01

Pseudo-R2

0.087

0.142

0.106

0.103

0.131

0.101

Hosmer-Lemeshow

x2(8)=8.07,

x2(8)=9.82,

x2(8)=5.84,

x2(8)=4.25,

x2(8)=2.90,

x2(8)=5.82,

goodness-of-fit test

p-value=0.427

p-value=0.278

p-value=0.666

p-value=0.834

p-value=0.941

p-value=0.668

EP

AC C

Joint tests of ORs

TE D

=9.58,

<0.01

0.016

Note: a All observations with no CHE. * P-value<0.1; ** p-value<0.05; *** p-value<0.01. OR, odds ratio; SE, standard error; CHE, catastrophic health expenditure.

32

<0.01

ACCEPTED MANUSCRIPT Supplementary Table S10 Self-reported unmet needs for medical examination, 2008-2015 2008

2009

2010

2011

2012

2013

2014

2015

5.4

5.5

5.5

7.5

8.0

9.0

10.9

12.3

First quintile

8.7

11.2

9.2

11.6

11.7

14.9

17.3

18.7

Second quintile

7.1

7.4

6.7

9.8

9.7

11.4

16.7

15.6

Third quintile

6.1

4.6

5.9

7.6

9.1

9.7

13.5

12.6

Fourth quintile

3.4

2.7

3.3

4.8

4.7

7.8

6.0

10.6

Fifth quintile

1.8

1.7

2.1

3.6

4.8

3.3

2.7

3.1

4.7

5.6

8.6

7.5

9.5

11.4

Total By income

1.1

3.9

M AN U

status Employed

6.1

7.4

8.8

Unemployed

10.8

11.7

15.7

15.6

persons Retired persons

9.3

Other inactive

5.8

9.3

8.9

11.7

10.8

11.2

12.5

15.6

7.3

6.4

6.9

8.1

9.7

11.7

11.5

EP

persons

TE D

persons

SC 1.0

By employment

RI PT

quintile

Note: Unmet need due to too expensive/too far/long waiting lists. Source: Eurostat, (2017).

AC C

Eurostat online database. http://ec.europa.eu/eurostat/web/lfs/data/database (accessed 08.07.2017).

33

ACCEPTED MANUSCRIPT Acknowledgements

AC C

EP

TE D

M AN U

SC

RI PT

The authors would like to thank two anonymous reviewers for their helpful and constructive comments that greatly contributed to improving the final version of the paper

ACCEPTED MANUSCRIPT

RI PT

Table 1. Changes in mean household consumption, health financing and CTP, 2008-2015 Equivalised household expenditure (€) Year

Medical products

Total

Outpatient

consumption

Pharmaceutical

M AN U

Total

CTP (€)

SC

Total OOPP

Inpatient

Per household

Share (%) of health expenditure to CTP

19670.77

1085.66

265.65

221.24

660.25

159.76

29656.48

6.93%

2009

19274.71

1064.68

261.51

214.00

615.59

187.58

28707.75

7.21%

2010

16720.26

904.83

251.45

216.06

487.00

166.37

24557.85

6.51%

2011

14786.40

809.93

237.50

200.31

391.11

181.32

21341.60

6.15%

2012

13333.39

731.60

246.54

220.35

295.48

189.57

18947.77

6.26%

2013

12969.27

773.40

287.96

262.54

274.93

210.50

17608.40

7.22%

2014

12609.93

805.36

316.62

286.24

263.99

224.75

17250.32

7.70%

2015

12649.60

830.72

319.17

291.79

251.17

260.38

17094.66

7.77%

Absolute change

-7021.17*

-254.94*

53.52*

70.55*

-409.08*

100.62*

-12561.82*

0.84%*

AC C

EP

TE D

2008

ACCEPTED MANUSCRIPT

Relative change

-35.69

-23.48

20.15

31.89

2008-2015 (%)

-61.96

RI PT

2008-2015 62.98

-42.36

12.12

SC

Note: All expenditures have been deflated to 2015 prices. OOPP, out-of-pocket payments; CTP, capacity to pay. * P-value<0.05 based on a t-

AC C

EP

TE D

M AN U

test of the mean difference between 2008 and 2015

ACCEPTED MANUSCRIPT

2009

2010

2011

2012

Headcount 23.71

22.22

20.45

21.48

(0.75)

(0.82)

(0.82)

(0.87)

Quintile ratio

2.03

2.69

1.46

CI

-0.138

-0.194

Weighted headcount

25.91

Total

2014

2008-2015 2015

Absolute

Relative

change

change (%)

5.18*

22.75

25.59

27.06

27.95

(0.88)

(0.93)

(0.73)

(0.75)

0.79

0.81

1.13

0.87

1.24

-0.80

-39.24

-0.104

-0.028

-0.007

-0.070

-0.012

-0.036

-0.18*

-73.73

28.32

24.53

21.02

21.64

27.38

27.39

28.96

3.05

11.77

2.08 (0.10)

2.24 (0.11)

1.86 (0.10)

1.69 (0.10)

1.75 (0.10)

2.28 (0.11)

2.56 (0.10)

2.65 (0.10)

0.57*

27.40

Quintile ratio

2.35

3.45

1.51

0.66

0.97

1.31

1.23

0.97

-1.38

-58.72

CI

-0.174

-0.269

-0.129

-0.016

-0.043

-0.074

-0.070

-0.014

0.16*

-91.73

Weighted overshoot

2.44

9.13 (0.34)

AC C

Total

EP

Overshoot

Mean positive overshoot

M AN U

22.77

TE D

Total

2013

SC

2008

RI PT

Table 2. Changes in the distribution of incidence and intensity of CHE at 10% threshold of CTP, 2008-2015

2.84

2.10

1.72

1.82

2.45

2.74

2.69

0.25

10.09

9.45 (0.33)

8.35 (0.31)

8.25 (0.38)

8.14 (0.30)

8.92 (0.29)

9.47 (0.27)

9.47 (0.25)

0.34

3.72

ACCEPTED MANUSCRIPT

1.16

1.28

1.03

0.84

1.21

1.15

1.42

0.79

-0.37

-32.08

CI

-0.036

-0.074

-0.029

0.007

-0.034

-0.003

-0.057

0.021

0.06*

-158.84

9.46

10.15

8.59

8.19

8.42

8.95

10.01

9.27

-0.19

-2.01

Weighted

mean

positive

overshoot

RI PT

Quintile ratio

SC

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test

AC C

EP

TE D

M AN U

following a fractional probit regression for overshoot and F-test for concentration index. CHE, catastrophic health expenditure.

ACCEPTED MANUSCRIPT

2009

2010

2011

2012

Headcount 7.59 (0.46)

8.24 (0.47)

6.38 (0.42)

5.84 (0.44)

6.11 (0.44)

8.83 (0.59)

M AN U

Total

2013

SC

2008

RI PT

Table 3. Changes in the distribution of incidence and intensity of CHE at 20% threshold of CTP, 2008-2015

2014

9.07 (0.45)

2008-2015 2015

10.00

Absolute

Relative

change

change (%)

2.41*

31.75

(0.47)

2.73

3.32

1.64

0.83

1.66

1.51

1.65

1.03

-1.71

-62.46

CI

-0.184

-0.274

-0.152

-0.046

-0.113

-0.076

-0.099

-0.043

0.14*

-76.83

Weighted headcount

8.98

10.49

7.35

6.11

6.80

9.50

9.97

10.43

1.44

16.04

Total

0.75 (0.06)

0.79 (0.06)

0.58 (0.05)

0.52 (0.06)

0.52 (0.05)

0.70 (0.06)

0.91 (0.06)

0.91 (0.06)

0.16

21.33

Quintile ratio

2.49

3.30

1.28

0.46

0.93

1.53

1.24

0.77

-1.72

-69.00

CI

-0.196

-0.297

-0.124

0.045

-0.039

-0.083

-0.094

0.047

0.24*

-124.17

Weighted overshoot

0.90

TE D

Quintile ratio

Total

9.86 (0.57)

AC C

Mean positive overshoot

EP

Overshoot

1.02

0.65

0.50

0.54

0.76

1.00

0.87

-0.03

-3.37

9.59 (0.53)

9.03 (0.53)

8.86 (0.69)

8.46 (0.55)

7.94 (0.39)

10.03

9.11 (0.37)

-0.75

-7.61

(0.43)

ACCEPTED MANUSCRIPT

0.92

0.98

0.78

0.55

0.56

1.02

0.75

0.76

-0.16

-17.49

CI

-0.013

-0.027

0.019

0.083

0.069

-0.008

0.003

0.088

0.10*

-794.38

9.98

9.85

8.85

8.12

7.88

8.01

10.00

8.31

-1.67

-16.76

Weighted

mean

positive

overshoot

RI PT

Quintile ratio

SC

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test

AC C

EP

TE D

M AN U

following a fractional probit regression for overshoot and F-test for concentration index. CHE, catastrophic health expenditure.

ACCEPTED MANUSCRIPT

2009

2010

2011

2012

2013

SC

2008

RI PT

Table 4. Changes in the distribution of incidence and intensity of CHE at 30% threshold of CTP, 2008-2015

Headcount

2014

2008-2015 2015

Absolute

Relative

change

change (%)

2.97 (0.28)

2.99 (0.28)

2.18 (0.26)

1.73 (0.25)

1.82 (0.23)

2.66 (0.30)

3.20 (0.25)

3.46 (0.27)

0.49

16.50

Quintile ratio

2.61

3.71

1.19

0.53

1.37

2.60

1.69

1.06

-1.55

-59.27

CI

-0.209

-0.325

-0.103

0.007

-0.094

-0.105

-0.144

0.035

0.24*

-116.93

Weighted headcount

3.59

3.96

2.40

1.72

1.99

2.94

3.66

3.34

-0.25

-7.01

Total

0.27 (0.03)

0.27 (0.04)

0.19 (0.03)

0.17 (0.03)

0.15 (0.03)

0.17 (0.02)

0.35 (0.03)

0.30 (0.03)

0.03

11.11

Quintile ratio

2.32

3.12

1.04

0.21

0.56

1.73

0.95

0.43

-1.89

-81.49

CI

-0.192

-0.291

-0.115

0.233

0.061

-0.061

-0.067

0.196

0.39*

-202.04

Weighted overshoot

0.32

0.35

0.21

0.13

0.14

0.18

0.37

0.24

-0.08

-25.00

9.92 (1.10)

8.02 (0.98)

6.59 (0.53)

11.03

8.80 (0.56)

-0.47

-5.07

Total

9.27 (0.79)

TE D

AC C

Mean positive overshoot

EP

Overshoot

9.05 (0.78)

M AN U

Total

8.75 (0.83)

(0.67)

ACCEPTED MANUSCRIPT

0.88

0.81

0.90

0.39

0.41

0.69

0.55

0.41

-0.47

-53.49

CI

0.010

0.022

-0.010

0.213

0.140

0.043

0.069

0.159

0.15

1419.39

9.17

8.85

8.84

7.81

6.90

6.31

10.26

7.40

-1.77

-19.32

Weighted

mean

positive

overshoot

RI PT

Quintile ratio

SC

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test

AC C

EP

TE D

M AN U

following a fractional probit regression for overshoot and F-test for concentration index. CHE, catastrophic health expenditure.

ACCEPTED MANUSCRIPT

2009

2010

2011

2012

2013

SC

2008

RI PT

Table 5. Changes in the distribution of incidence and intensity of CHE at 40% threshold of CTP, 2008-2015

Headcount

2014

2008-2015 2015

Absolute

Relative

change

change (%)

1.03 (0.16)

1.05 (0.17)

0.78 (0.15)

0.59 (0.14)

0.49 (0.11)

0.57 (0.11)

1.34 (0.15)

1.19 (0.15)

0.16

15.53

Quintile ratio

2.09

2.12

0.80

0.21

0.51

0.68

1.49

0.44

-1.65

-79.07

CI

-0.156

-0.233

-0.091

0.188

0.058

0.043

-0.057

0.232

0.39*

-248.28

Weighted headcount

1.19

1.30

0.85

0.48

0.46

0.55

1.42

0.91

-0.28

-23.22

Total

0.09 (0.02)

0.09 (0.02)

0.06 (0.01)

0.06 (0.02)

0.04 (0.01)

0.03 (0.01)

0.14 (0.02)

0.09 (0.02)

0.00

0.00

Quintile ratio

1.75

2.57

0.73

0.05

0.21

1.50

0.50

0.17

-1.58

-90.15

CI

-0.168

-0.262

-0.046

0.541

0.263

0.021

0.015

0.365

0.53

-317.04

Weighted overshoot

0.11

0.11

0.06

0.03

0.03

0.03

0.14

0.06

-0.05

-45.64

10.67

8.65 (1.88)

4.83 (0.70)

10.13

7.68 (1.03)

-1.07

-12.23

Total

8.75 (1.29)

TE D

AC C

Mean positive overshoot

EP

Overshoot

8.51 (1.36)

M AN U

Total

7.35 (1.40)

(1.98)

(0.85)

ACCEPTED MANUSCRIPT

0.84

1.21

0.94

0.30

0.45

2.70

0.35

0.42

-0.42

-50.08

CI

-0.021

-0.038

0.078

0.297

0.184

-0.007

0.076

0.134

0.15

-737.49

8.93

8.83

6.78

7.50

7.06

4.86

9.36

6.65

-2.28

-25.52

Weighted

mean

positive

overshoot

RI PT

Quintile ratio

SC

Note: Standard errors in parentheses.* P-value<0.05; mean difference between 2008 and 2015 was tested with x2 test for headcount, t-test

AC C

EP

TE D

M AN U

following a fractional probit regression for overshoot and F-test for concentration index. CHE, catastrophic health expenditure.

ACCEPTED MANUSCRIPT

2008

0.915

2009

0.913

2010

0.923

2011

0.925

2012

0.928

2013

0.925

2014

0.911

2015

0.916

SC

FFCI

M AN U

Survey year

RI PT

Table 6. Changes in FFCI in OOPP, 2008-2015

AC C

EP

TE D

Note: FFCI, Financial Fairness Contribution index

ACCEPTED MANUSCRIPT

2009

2010

2011

Poverty headcount

Normalised poverty gap NGgross

2013

2014

2015

(0.01)

(0.02)

(0.01)

0.06

0.04

(0.04)

(0.03)

0.05

0.02

(0.03)

(0.02)

0.09

0.12

0.08

0.40

0.62

(0.05)

(0.04)

(0.04)

(0.28)

(0.41)

TE D

0.01

0.03

0.09

0.13

0.15

0.44

0.68

(0.02)

(0.05)

(0.05)

(0.06)

(0.28)

(0.41)

0.02

0.00

0.01

0.07

0.04

0.06

(0.01)

(0.00)

(0.01)

(0.04)

(0.02)

(0.05)

EP

PIH

0.02

AC C

Hnet

0.01

M AN U

Subsistence poverty

Hgross

2012

2008-2015

SC

2008

RI PT

Table 7. Changes in the poverty impact of OOPP, 2008-2015

0.00

0.00

0.00

0.01

0.02

0.01

0.02

0.13

(0.00)

(0.00)

(0.00)

(0.00)

(0.01)

(0.00)

(0.02)

(0.09)

Absolute

Relative

change

change (%)

0.62*

8182.58

0.62*

994.07

0.00

6.16

0.13

26698.35

ACCEPTED MANUSCRIPT

0.00

0.00

0.01

0.03

0.01

0.03

0.15

(0.00)

(0.00)

(0.00)

(0.01)

(0.01)

(0.00)

(0.02)

(0.11)

0.01

0.00

0.00

0.00

0.00

0.00

0.01

0.02

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

(0.02)

6.47

9.13

9.99

(0.00)

(0.00)

(0.00)

9.10

8.81

9.83

(3.00)

(3.97)

(3.26)

2.64

-0.32

Population being poor pre-payment

8.58

59.24

Poor population becoming poorer post-

19.80

71.62

PING

PINMPG

0.02

324.52

14.44

223.32

13.25

145.59

6.81

6.13

20.91

(1.57)

(6.56)

(1.25)

(4.36)

(6.96)

11.50

19.76

6.70

6.95

22.36

(2.77)

(5.94)

(0.73)

(4.29)

(9.08)

2.90

1.84

-0.11

0.82

1.45

-1.19

-45.08

27.23

74.80

82.28

55.48

80.16

71.58

834.27

8.58

29.83

0.00

25.20

14.49

6.56

0.00

-19.80

-100.00

19.80

10.93

72.77

0.00

3.23

37.95

19.84

-51.78

-72.30

71.62

-0.16

EP

AC C

Non-poor population becoming poor post-

2584.83

17.92

Contributors to total poverty gap (%)

payment

0.15

8.60

TE D

NMPGnet

M AN U

Normalised mean positive gap NMPGgross

RI PT

0.01

SC

NGnet

ACCEPTED MANUSCRIPT

Relative poverty Poverty headcount

PIH

24.13

29.93

40.27

(0.65)

(0.85)

(1.12)

(1.30)

(1.47)

14.82

16.72

24.77

(0.67)

(0.91)

(1.12)

0.99

1.64

0.64

(0.25)

(0.38)

(0.35)

3.06

3.11

(0.17)

(0.22)

NGnet

PING

AC C

NGgross

46.54

47.15

(1.69)

(1.32)

(1.28)

29.94

40.78

46.67

47.69

49.03

(1.31)

(1.47)

(1.66)

(1.35)

(1.25)

0.01

0.51

1.35

1.16

1.88

(0.32)

(0.30)

(0.43)

(0.36)

(0.41)

7.18

10.96

11.93

12.90

12.81

(0.29)

(0.38)

(0.50)

(0.64)

(0.54)

(0.58)

5.40

EP

Normalised poverty gap

45.32

SC

15.08

M AN U

Hnet

13.83

TE D

Hgross

RI PT

payment

3.44

3.59

5.66

7.27

11.05

12.46

13.42

13.42

(0.18)

(0.23)

(0.30)

(0.38)

(0.50)

(0.66)

(0.53)

(0.57)

0.38

0.48

0.26

0.09

0.09

0.54

0.52

0.61

33.32*

240.94

34.22*

230.92

0.89*

90.48

9.74*

318.14

9.98*

289.93

0.23

61.15

ACCEPTED MANUSCRIPT

(0.05)

(0.06)

(0.05)

(0.06)

22.15

20.63

22.37

24.00

27.22

(0.78)

(0.84)

(0.70)

(0.64)

(0.65)

23.22

21.45

22.84

24.29

27.11

(0.75)

(0.80)

(0.68)

(0.65)

1.07

0.83

0.47

People leaving poverty

0.50

0.64

People staying in poverty

13.33

14.45

People entering poverty

1.49

2.28

Normalised mean positive gap

PINMPG

27.16

(0.81)

(0.64)

(0.76)

26.71

28.14

27.36

(0.65)

(0.83)

(0.61)

(0.74)

0.29

-0.11

0.39

0.43

0.94

1.26

1.04

1.02

23.18

28.67

39.24

1.58

1.27

AC C

EP

TE D

Population dynamics post-payment (%)

Contributors to total poverty gap (%)

(0.09)

27.71

5.01

22.64

4.14

17.83

0.20

-0.87

-81.31

1.44

1.13

0.62

124.54

44.30

45.09

46.03

32.70

245.32

1.55

2.37

2.60

3.00

1.52

101.95

SC

NMPGnet

(0.07)

26.32

M AN U

NMPGgross

(0.10)

RI PT

(0.05)

Population being poor pre-payment

75.49

72.50

81.11

85.33

87.62

84.02

85.25

84.31

8.83

11.69

Poor population becoming poorer post-

20.28

22.69

16.98

13.47

11.50

14.52

13.20

13.66

-6.62

-32.65

payment

ACCEPTED MANUSCRIPT

4.23

4.81

1.90

1.20

0.88

payment

1.47

1.55

RI PT

Non-poor population becoming poor post-

2.03

-2.20

-52.07

Note: Standard errors in parentheses. OOPP, out-of-pocket payments. * P-value<0.05; mean difference between 2008 and 2015 was tested with

AC C

EP

TE D

M AN U

SC

x2 test for poverty headcount and t-test following a linear regression for poverty gap.

ACCEPTED MANUSCRIPT Table 8. Determinants of CHE of households at 40% threshold, 2008 vs. 2015 OR (SE)

Wald test of equality of ORs p-value

Northern Greece

1.628 (0.853)

0.679 (0.235)

0.164

Central Greece

1.770 (0.955)

0.610 (0.245)

0.114

Aegean islands, Crete

1.515 (0.956)

SC

Region (Attica as ref.)

2015

RI PT

2008

0.831 (0.374)

0.439

M AN U

Urbanicity (Urban as ref.) 2.172 (1.226)

1.618 (0.512)

0.649

Rural

2.257* (1.101)

1.415 (0.518)

0.444

Household size

0.516*** (0.098)

0.788** (0.078)

0.049

Elderly in household (no as ref.)

0.611 (0.347)

3.661*** (1.552)

0.012

24.075*** (17.982)

1.917 (1.704)

0.029

0.478* (0.193)

0.624 (0.215)

0.615

1.037** (0.019)

1.024* (0.014)

0.556

Primary education

0.413** (0.166)

1.308 (0.494)

0.037

Secondary education

0.473 (0.216)

0.484 (0.259)

0.974

Post-secondary non-tertiary

0.751 (0.832)

0.824 (0.614)

0.945

Tertiary education

0.682 (0.512)

0.141*** (0.099)

0.125

TE D

Semi-urban

Children under 5 in household (no as ref.)

EP

Sex of household head (males as ref,) Age of household head

AC C

Education of household head (no formal education as ref.)

Economic activity of household head

ACCEPTED MANUSCRIPT

a

0.371 (0.321)

0.252

Retired

9.465*** (5.514)

0.313*** (0.137)

<0.001

Non-economically active

3.368* (2.362)

0.362 (0.233)

0.019

2

1.488 (0.719)

1.381 (0.446)

0.898

3

1.836 (0.835)

2.671*** (1.011)

0.526

4

1.53 (0.868)

8.675*** (3.433)

0.012

Richest

2.064 (1.209)

8.450*** (4.043)

0.062

0.657 (0.972)

1.836 (0.901)

0.510

0.289 (0.364)

b

0.325

Partially insured

a

a

Fully insured

a

0.273 (0.286)

0.216

Disable in household (no as ref.)

1.091 (0.959)

1.612 (0.960)

0.713

Constant

0.002*** (0.003)

0.004*** (0.004)

0.740

No of observations

3091

5835

Model F-statistic

F(23, 3068)=5.02,

F(24, 3844)=7.86,

p-value<0.01

p-value<0.01

0.137

0.106

RI PT

Unemployed

SC

(Employed as ref.)

Expenditure quintiles of household

Social insurance status of household (none insured as ref.)

Fully insured

TE D

Partially insured

M AN U

(poorest as ref.)

Private insurance status of household

AC C

EP

(none insured as ref.)

Pseudo-R2

ACCEPTED MANUSCRIPT Hosmer-Lemeshow

goodness-of-fit

test

x2(8)=12.699,

x2(8)=8.52,

p-value=0.123

p-value=0.384

Joint tests of ORs

<0.01

RI PT

Note: a All observations with no CHE; b all observations with CHE. * P-value<0.1; * p-value<0.05; *** p-value<0.01. OR, odds ratio; SE, standard error; CHE,

AC C

EP

TE D

M AN U

SC

catastrophic health expenditure.

ACCEPTED MANUSCRIPT Table 9. Determinants of impoverishment due to OOPP of households (relative poverty), 2008 vs. 2015 OR (SE)

Wald test of equality of ORs p-value

1.371 (0.576)

0.963 (0.203)

0.452

Central Greece

1.488 (0.635)

0.705 (0.233)

0.166

Aegean islands, Crete

1.535 (0.762)

0.982 (0.273)

0.433

M AN U

Northern Greece

SC

Region (Attica as ref.)

2015

RI PT

2008

Urbanicity (Urban as ref.) Semi-urban

1.272 (0.570)

1.196 (0.262)

0.901

Rural

0.813 (0.302)

1.851** (0.459)

0.065

0.631** (0.138)

0.960 (0.086)

0.077

TE D

Household size Elderly in household (no as ref.)

1.350 (0.744)

3.810*** (1.251)

0.106

Children under 5 in household (no as

4.510** (3.253)

1.502 (0.701)

0.200

Sex of household head (males as ref,)

0.479** (0.161)

0.955 (0.191)

0.078

Age of household head

1.013 (0.013)

1.014* (0.008)

0.938

Primary education

0.542* (0.174)

1.357 (0.284)

0.017

Secondary education

0.429* (0.191)

1.417 (0.364)

0.020

Post-secondary non-tertiary

0.921 (0.782)

2.899** (1.253)

0.229

Tertiary education

0.344 (0.358)

1.146 (0.456)

0.280

AC C

EP

ref.)

Education of household head (no formal education as ref.)

ACCEPTED MANUSCRIPT Economic activity of household head (Employed as ref.) 0.421 (0.460)

2.006* (0.833)

0.182

Retired

1.29 (0.692)

0.779 (0.261)

0.425

Non-economically active

1.818 (1.030)

1.093 (0.416)

0.456

2

1.405 (0.410)

1.551** (0.276)

0.771

3

0.092*** (0.059)

0.216*** (0.094)

0.272

4

0.037*** (0.039)

0.056*** (0.058)

Richest

a

a

RI PT

Unemployed

Expenditure quintiles of household

(none insured as ref.)

0.782

1.823 (2.393)

1.821 (1.166)

1.000

1.785 (1.832)

2.150 (1.186)

0.873

0.302 (0.316)

0.727 (0.766)

0.554

a

1.021 (0.763)

0.978

Disable in household (no as ref.)

1.059 (0.686)

0.915 (0.411)

0.854

Constant

0.030*** (0.042)

0.003*** (0.002)

0.120

No of observations

2761

5833

Model F-statistic

F(24, 2737)=3.33,

F(25, 3843)=6.07,

p-value<0.01

p-value<0.01

Fully insured

TE D

Partially insured

M AN U

Social insurance status of household

SC

(poorest as ref.)

EP

Private insurance status of household (none insured as ref.)

AC C

Partially insured Fully insured

ACCEPTED MANUSCRIPT Pseudo-R2 Hosmer-Lemeshow

goodness-of-fit

test

0.158

0.099

x2(8)=7.19,

x2(8)=8.36,

p-value=0.516

p-value=0.399

0.208

RI PT

Joint tests of ORs

Note: a All observations were not impoverished. * P-value<0.1; ** p-value<0.05; ***

AC C

EP

TE D

M AN U

SC

p-value<0.01. OR, odds ratio; SE, standard error; OOPP, out-of-pocket payments.

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT Research highlights

AC C

EP

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M AN U

SC

• • • •

Both incidence and overshoot of catastrophic out-of-pocket payments (OOPP) have increased considerably The additional catastrophic burden has been distributed progressively Both incidence and intensity of relative poverty have increased significantly 1.9% of households were impoverished due to OOPP in 2015 The catastrophic and poverty impact of OOPP is rising since 2012

RI PT

•