Income and wealth as determinants of voluntary private health insurance: empirical evidence in Spain, 2008-2014.

BACKGROUND: Few studies have quantitatively estimated the income elasticity of demand of voluntary private health insurance (VPHI) in countries with a universal National Health Service. Most studies to date have uses cross-sectional data. METHODS: In this paper we used a longitudinal database from the Bank of Spain to analyse the financial behaviour of approximately six thousand families per wave. We used three waves (2008, 2011 and 2014). We estimated income and wealth semi-elasticities of VPHI in Spain considering personal and family characteristics (age, sex, level of health, education, composition of the household), i.e. changes in the probability of buying VPHI as result of 1% change in income or wealth. We estimated cross-sectional models for each wave and longitudinal models for families remaining for at least two waves, taking account of possible selection bias due to attrition. RESULTS: Cross-sectional models suggest that the income effect on the probability of buying a VPHI increased from 2008 to 2014. The positive impact was observed for, wealth. In 2008 a 1% increase in income is associated with an increase in the probability of having VPHI of 0.064 [95%-CI: 0.023; 0.104] - on the probability scale (0.1) - whereas in 2014, this effect is of 0.116 [95%-CI, 0.094; 0.139]. In 2011 and 2014 the wealth effect is not significant at 5%. The estimation of the longitudinal model leads to different results where both, income and wealth are associated with non- significant results. CONCLUSION: Our three main conclusions are: 1) Cross-sectional estimates of semi-elasticities of VPHI might be biased upwards; 2) Wealth is alongside income are economic determinants, of the decision to buy VPHI in high-income countries; 3) The effects of income and wealth on the probability of buying VHPI are neither linear nor log-linear. There are no significant differences among 60% of the most disadvantaged families, while the families of the two upper wealth quintiles show clearly differentiated behaviour with a higher probability of insurance.

Differences in healthcare utilisation between users and non-users of homeopathic products in Spain: Results from three waves of the National Health Survey (2011-2017).

OBJECTIVE: To compare the differences in the use of healthcare services: visits to the doctor and hospitalisation, performance of routine tests, and preventive influenza vaccination, between users and non-users of homeopathic products. METHODS: We used the microdata for adults over 15 years old from three waves of the Spanish National Health Survey, corresponding to the years 2011, 2014 and 2017. We proposed a comparative design of a quasi-experimental type, considering as the treatment group the respondents who said that they had used homeopathic products in the past two weeks; and another group, for control, comprising respondents who said that they had not used this type of products, but only conventional medicines, with observable characteristics similar to those of the treatment group. We used a model for rare events logistics regression (relogit) to estimate the probability of using homeopathy. From the propensity score and a vector of control variables, we used techniques of genetic matching to match individuals from the treatment group with similar individuals belonging to the control group. RESULTS: There are no statistically significant differences between users and non-users of homeopathy in visits to the general practitioner (P>|z| 0.387), to the specialist (P>|z| 0.52), in hospitalisations (P>|z| 0.592) or in the use of emergency services (P>|z| 0.109). Nor were there any statistically significant differences in the performance of routine tests, except for the faecal occult blood test, which is more prevalent in users of homeopathic products. 20.9% of users of homeopathy had done this test compared with 15.3% of non-users (P>|z| 0.022). There are also significant differences in vaccination against influenza with 12.6% of homeopathy users stating that they had been vaccinated in the last influenza campaign, against 21.0% of non-users (P>|z| <0.001). The health conditions which homeopathy users reported were constipation (OR: 1.65 CI: 1.16-2.36), malignant tumour (OR: 1.60 CI: 1.09-2.36) osteoporosis (OR: 1.49 CI: 1.05-2.10), varicose veins (OR: 1.35 CI: 1.05-1.74) and allergy (OR: 1.35 CI: 1.06-1.72). CONCLUSIONS: Differences in the use of healthcare resources between users and non-users of homeopathic products have not been found to be statistically significant in Spain. It has been shown that most homeopathic products are used as a complement to treatment with conventional medicine. Nevertheless, our results highlight some warning signs which should raise the attention of healthcare authorities. The use of these therapies in patients with malignant tumours and the rejection of vaccines are warning signs of a possible health hazard in the long term.

Pinocchio testing in the forensic analysis of waiting lists: using public waiting list data from Finland and Spain for testing Newcomb-Benford's Law.

OBJECTIVE: Newcomb-Benford's Law (NBL) proposes a regular distribution for first digits, second digits and digit combinations applicable to many different naturally occurring sources of data. Testing deviations from NBL is used in many datasets as a screening tool for identifying data trustworthiness problems. This study aims to compare public available waiting lists (WL) data from Finland and Spain for testing NBL as an instrument to flag up potential manipulation in WLs. DESIGN: Analysis of the frequency of Finnish and Spanish WLs first digits to determine if their distribution is similar to the pattern documented by NBL. Deviations from the expected first digit frequency were analysed using Pearson's χ2, mean absolute deviation and Kuiper tests. SETTING/PARTICIPANTS: Publicly available WL data from Finland and Spain, two countries with universal health insurance and National Health Systems but characterised by different levels of transparency and good governance standards. MAIN OUTCOME MEASURES: Adjustment of the observed distribution of the numbers reported in Finnish and Spanish WL data to the expected distribution according to NBL. RESULTS: WL data reported by the Finnish health system fits first digit NBL according to all statistical tests used (p=0.6519 in χ2 test). For Spanish data, this hypothesis was rejected in all tests (p<0.0001 in χ2 test). CONCLUSIONS: Testing deviations from NBL distribution can be a useful tool to identify problems with WL data trustworthiness and signalling the need for further testing.