The resurgence of mumps around the world occurs frequently in recent years. As the country with the largest number of cases in the world, the status of mumps epidemics in China is not yet clear. This study, taking the relatively serious epidemic province of Guangxi as the example, aimed to examine the spatiotemporal pattern and epidemiological characteristics of mumps, and provide a scientific basis for the effective control of this disease and formulation of related health policies. Geographic information system (GIS)-based spatiotemporal analyses, including spatial autocorrelation analysis, Kulldorff’s purely spatial and space-time scan statistics, were applied to detect the location and extent of mumps high-risk areas. Spatial empirical Bayesian (SEB) was performed to smoothen the rate for eliminating the instability of small-area data. A total of 208,470 cases were reported during 2005 and 2016 in Guangxi. Despite the fluctuations in 2006 and 2011, the overall mumps epidemic continued to decline. Bimodal seasonal distribution (mainly from April to July) were found and students aged 5–9 years were high-incidence groups. Though results of the global spatial autocorrelation based on the annual incidence largely varied, the spatial distribution of the average annual incidence of mumps was nonrandom with the significant Moran’s I. Spatial cluster analysis detected high-value clusters, mainly located in the western, northern and central parts of Guangxi. Spatiotemporal scan statistics identified almost the same high-risk areas, and the aggregation time was mainly concentrated in 2009–2012. The incidence of mumps in Guangxi exhibited spatial heterogeneity in 2005–2016. Several spatial and spatiotemporal clusters were identified in this study, which might assist the local government to develop targeted health strategies, allocate health resources reasonably and increase the efficiency of disease prevention.

The study aims to determine the spatial and temporal variation of a longevous region and explore the correlation between longevity and socioeconomic development. Population data at the township level were obtained from the last four population censuses (1982–2010). Five main lifespan indicators and the Human Development Index (HDI) were calculated. Getis-Ord G*, Gravity modeling, and Pearson’s r between lifespan indicators and HDI were applied. In this study, a stable longevous gathering area was discovered in Hechi during different periods. Under the influence of social and economic development, more longevous areas appeared. However, the effects of genetic and natural environmental factors on longevity were always dominant in this remote and mountainous city. Furthermore, longevity indicators lacked any significant correlation with life expectancy. No significant positive correlation was detected between lifespan indicators and HDI. Thus, we conclude that lifespan indicators can determine the spatial distribution and variation pattern of longevity from multiple dimensions. The geographical scope of longevity in Hechi City is gradually expanding, and significant spatial clustering was detected in southwestern, southern, and eastern parts of Hechi. This study also found that social economic development is likely to have a certain impact on new longevous areas, but their role on extreme longevity is not significant.

This study aims to determine the association between the apolipoprotein C-I polymorphism and the longevity and genetic variants in ApoC-I that can influence the serum lipid levels in Bama. ApoC-I genotypes were determined by Taqman single nucleotide polymorphism (SNP) genotyping assays in 178 long-lived inhabitants (longevity group aged from 90 to 110 years), 147 healthy controls (Control 1 group aged from 40 to 79 years old) from Bama County, and 190 healthy controls (Control 2 group aged from 40 to 79 years old) from Nandan County without a family history of longevity. Statistical analysis was conducted using SPSS 16.0. All genotype distributions of rs584007 and rs4420638 were consistent with the Hardy–Weinberg equilibrium (p > 0.05). Significant differences were observed in the frequencies of the three genotypes (GG, AG, and AA) among the longevity and the two control groups (χ² = 11.238, p = 0.024) for rs584007. No significant differences were observed in the frequencies of the three genotypes (GG, AG, and AA) among the longevity and the two control groups (χ² = 4.587, p = 0.318) for rs4420638. The levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein-cholesterol (HDL-c), and low-density lipoprotein-cholesterol (LDL-c) were not different among the three genotypes of rs584007 in the three groups. The levels of HDL-c for GG, AG, and AA were significantly different (the highest being in the longevity group), while the levels of TG for AA and AG genotypes (the lowest being in the longevity group) and the levels of LDL-c for AG were significantly different (p < 0.05) among the three groups for rs584007. The levels of TG and HDL-c were significantly different among the three rs4420638 genotypes in the longevity group. The levels of TC for GG, AG, and AA were significantly different in the Control 2 group, while the levels of TG and HDL-c for AA and AG genotypes were significantly different (p < 0.05) among the three groups for rs4420638. The level of HDL-c was highest in the longevity group for AA and AG genotypes, and the level of TG was highest in the Control 2 group for rs4420638. Serum lipid parameters were related to environmental factors, including age, gender, BMI, DBP, SBP, rs4420638, and rs584007. The ApoC-I polymorphism might be one of the genetic factors of longevity in Bama. The ApoC-I rs4420638 and rs584007 SNPs are associated with serum TG and HDL-c levels in the longevous population.