Mohammad Abu-Zaineh

Density of dental practitioners and access to dental care for the elderly: A multilevel analysis with a view on socio-economic inequality

OBJECTIVES To examine the relations between density of dental practitioners (DDP) and socio-economic and demographic factors shown to affect access to dental care for the elderly. METHODS Data are taken from a cross-sectional survey - 2008 Disability Healthcare - Household section Survey (HSM). HSM is a representative random sample of French people living in their own domiciles. Our study focuses on the 9233 individuals aged 60 years and above. Multilevel models are employed to disentangle the relations between the determinants of dental care utilisation and DDP. Statistical analyses are conducted using SAS 9.2 and HLM 6. RESULTS Low-income and lack of complementary health insurance are associated with higher odds of not having visited a dentist, revealing a high unequal access to dental care. By using multilevel modelling, DDP appears to be a significant factor to access to dental services. When considering the intricate relations between income gradient and DDP, the latter lessens the income-related inequality to access dental services. CONCLUSION DDP seems favouring a more equitable access to dental care, mitigating under-caring of the poorest. This point is to be added in the debate about density of healthcare suppliers.

Effect of development assistance on domestic health expenditures

www.thelancet.com Vol 376 August 21, 2010 591 Although we welcome the attempt by Chunling Lu and colleagues to test the hypothesis that development assistance for health might induce a reduction of domestic health expenditures, caution is needed when translating results of this single econometric study into policy recommendations. Uncertainty associated with incomplete cases remains an issue of primary concern for time-series cross-sectional analysis since it can intrinsically bias the estimates. Lu and colleagues acknowledge that the Wooldridge test of zero correlation between the error terms within countries points to the presence of autocorrelation. They suggest that it might be explained by their own computation of the government health expenditure as source (GHE-S), by subtracting the development assistance for health disbursed to government from the government health expenditure as agent (GHE-A), but imputation concerning the missing values might also be a source of such autocorrelation. To take into account the autocorrelation within countries, Lu and colleagues use the Arellano-Bover/ Blundel-Bond linear generalised method of moments, and fi nd that there is no signifi cant evidence of serial correlation in the fi rstdiff erenced errors at order 2. However, nothing is mentioned about the possibility of having a model with moving average errors. Data used by Lu and colleagues de facto introduce an additional source of autocorrelation since aid programmes devoted to health might diff er according to region-specifi c and country-specifi c prevailing diseases. Given that the study focuses on the eff ect of development assistance for health on GHE-S, and that recent increases in such assistance have been mainly driven by funding for disease-targeted programmes, a test for spatial autocorrelation in the context of a fi xedeff ects panel data model would have strengthened the robustness of their results.