BMC Public Health 2005, 5:65 http://www.biomedcentral.com/1471-2458/5/65 Table 1: Characteristics of socio-demographic indicators among municipalities in Japan (N = 3361). Indicator Population (thousands) Population density (per square kilometer) Proportion of elderly (%) a Percentage of households of a single elderly (%)a Percentage of nuclear households (%) Divorce rate (per 1000) Per capita income (thousand yen) Unemployment rate (%) a65 years or over chain Monte Carlo (MCMC) method [26-31]. The levels of hierarchy were municipalities for the lower level and secondary medical care zones (SMCZs) for the higher level. The details of calculation of Bayesian SCR are described in the additional file [see Additional file 1]. The range of SCR was decreased from crude SCR of 24.8 to 178.0 (S.D. = 18.3) to Bayesian SCR of 46.8 to 161.3 (S.D. = 16.5). The regression formula obtained from prefectural data and used for estimation of municipal HE65 was HE65 = 4.772 + 0.706 × LE65 – 1.776 × SCR for men and HE65 = 12.024 + 0.453 × LE65 – 4.576 × SCR for women. Statisti- cal analysis was conducted using SPSS 11.0 for linear regression analysis and MLwiN 1.10 for Bayesian hierar- chical Poisson regression. Geographic mapping of HE was conducted using ArcGIS 8.3, with the Japanese map (geo- graphic coordinate system: GRS 1980) obtained from ESRI Japan http://www.esrij.com. Analysis of relation to socio-demographic indicators The socio-demographic indicators used in this study were population, population density, proportion of elderly (aged 65 years or over), percentage of households of a single elderly person (aged 65 years or over), percentage of nuclear households, divorce rate, per capita income, and unemployment rate. The data were drawn from the database based on the population census and other national surveys of 1999 and 2000 [32]. Since crude divorce rate showed a large difference in variance within the three categories of municipalities according to the population size mentioned later, Bayesian method with hierarchical Poisson regression was applied to predict the value adjusted for the statistical fluctuation due to small population size, as well as SCR. A summary of the socio- demographic indicators is shown in Table 1. The relation of HE65 and LE65 to socio-demographic indicators was examined using correlation coefficient (Spearman's) for total municipalities. Then, we examined Mean ± S.D. 37.8 ± 77.3 928.9 ± 2257.6 23.7 ± 7.3 8.0 ± 4.2 54.4 ± 8.7 1.7 ± 0.4 1225 ± 299 3.88 ± 1.64 (range) (0.2 – 1024.1) (1.5 – 19854.1) (7.6 – 50.6) (0.7 – 31.6) (20.2 – 78.8) (0.9 – 4.0) (419 – 3926) (0.0 – 18.1) the correlation according to the population size of munic- ipalities, in which municipalities were divided into three categories according to population size: less than 10,000 (small, N = 1554), 10,000 to 100,000 (medium-size, N = 1469), and more than 100,000 (large, N = 338). Statistical analysis was conducted using SPSS 11.0. Results Estimated health expectancy Table 2 shows LE65 and estimated HE65. HE65 in men (years) ranged from 13.13 to 17.39 with a mean (S.D.) of 15.53 (0.52), and HE65 in women (years) ranged from 14.84 to 20.53 with a mean (S.D.) of 18.01 (0.77). For both men and women, large municipalities showed sig- nificantly (p < 0.001) lower HE65 than small and medium-size municipalities. HE65 for the overall Japa- nese population, which was estimated using the regres- sion formula, was 15.39 for men and 17.62 for women, while HE65 estimated by the Sullivan method with sex- and age-specific long-term care prevalence was 15.40 for men and 17.62 for women, and LE65 in 2000 was 17.56 for men and 22.46 for women [22]. The geographic mapping of HE65 in men is shown in Fig- ure 1. The central part of the mainland showed accumula- tion of municipalities with longer HE65, while the northern part of the mainland showed accumulation of municipalities with shorter HE65. Metropolitan areas, especially Osaka, showed relatively shorter HE65. Other regions such as Hokkaido, Kyushu and Shikoku showed a miscellaneous pattern of municipalities with shorter and longer HE65. The geographical pattern in women, shown in Figure 2, was similar to that in men, and the shorter HE65 in the metropolitan areas was more remarkable. The relation to socio-demographic indicators Table 3 shows the correlation coefficients of LE65 and HE65 with socio-demographic indicators. As the number of samples was extremely large, most of the correlation coefficients showed statistical significance (p < 0.001). Page 5 of 9 (page number not for citation purposes)