There are few studies estimating the loneliness of the Hakka elderly in China. This study aims to examine the loneliness status and related factors among the Hakka elderly in Fujian, China. The short-form UCLA Loneliness Scale (ULS-8) was used to assess the loneliness of the Hakka elderly. Factors associated with loneliness were classified as individual indicators, behavioral indicators, interpersonal indicators, and social indicators according to the health ecological model (HEM). Hierarchical linear regression models were established to identify the main factors that were most predictive of loneliness. A sample of 1,262 Hakka elderly people was included in this study. Females (β = 0.631, P = 0.012 ), those with ≥2 chronic diseases (β = 1.340, P < 0.001 ), those who were currently living in rural areas (β = 4.863, P < 0.001 ) or suburban areas (β = 2.027, P < 0.001 ), those with parents both died (β = 0.886, P = 0.001 ), and those with the Urban Employees Basic Medical Insurance (UEBMI; β = 0.852, P = 0.030 ) obtained a higher score of ULS-8. Those exercised regularly (β = −2.494, P < 0.001 ), those had leisure activities (β = −1.937, P < 0.001 ), those ate healthy (β = −1.270, P < 0.001 ), and those with better self-rated financial status and higher education level received a lower score of ULS-8. There are differences in loneliness among different Hakka elderly population subgroups, and healthy behaviors and lifestyles may reduce the loneliness of the Hakka elderly. Relevant interventions should be implemented in a targeted manner, focusing on susceptible populations. This is most evident among those who were female, living in rural areas, with parents both died, with lower education, and with multiple chronic diseases.

There is limited evidence regarding the factors correlated with dietary diversity (DD) and dietary pattern (DP) in rural residents of China. This study aims to identify the DD and DP of rural residents and their association with socio-demographic factors. A cross-sectional survey was conducted in Pingnan, China. The Food Frequency Questionnaire (FFQ) was applied to evaluate dietary intake. Latent class analysis (LCA) was used to identify patterns of six food varieties, including vegetables–fruits, red meat, aquatic products, eggs, milk, and beans–nuts. Generalized linear models and multiple logistic regression models were used to determine factors associated with the DD and DP. Three DPs were detected by LCA, namely “healthy” DP (47.94%), “traditional” DP (33.94%), and “meat/animal protein” DP (18.11%). Females exhibited lower DD (β = −0.23, p = 0.003) and were more likely to adhere to “traditional” DP (OR = 1.46, p = 0.039) and “meat/animal protein” DP (OR = 2.02, p < 0.001). Higher educational levels and annual household income (AHI) were positively associated with higher DD (p < 0.05) and less likely to have “traditional” DP and “meat/animal protein” DP (p < 0.05). Non-obese people exhibited higher DD (β = 0.15, p = 0.020) and were less likely to have “meat/animal protein” DP (OR = 0.59, p = 0.001). Our study reveals that females, those with lower educational levels and AHI, and obese people are more likely to have a lower DD and are more likely to adhere to “traditional” DP and “meat/animal protein” DP. The local, regional, and even national performance of specific diet-related health promotion measures and interventions must target these vulnerable populations to improve a healthier DD and DP.

IntroductionTo explore gender differences in the relationship between loneliness and health-related behavioral risk factors (BRFs) among the Hakka elderly.MethodsLoneliness was measured by the UCLA Loneliness Scale Short-form (ULS-8). Seven BRFs were examined. Mann–Whitney U, Kruskal-Wallis, and post hoc tests were conducted to compare the differences in ULS-8 scores among the Hakka elderly with different BRFs. Generalized linear regression models were employed to examine the associations of specific BRF and its number with the ULS-8 scores among the Hakka elderly in male, female, and total samples.ResultsPhysical inactivity (B = 1.96, p &lt; 0.001), insufficient leisure activities participation (B = 1.44, p &lt; 0.001), unhealthy dietary behavior (B = 1.02, p &lt; 0.001), and irregular sleep (B = 2.45, p &lt; 0.001) were positively correlated with the ULS-8 scores, whereas drinking (B = −0.71, p &lt; 0.01) was negatively associated with the ULS-8 scores in the total sample. In males, insufficient leisure activities participation (B = 2.35, p &lt; 0.001), unhealthy dietary behavior (B = 1.39, p &lt; 0.001), and irregular sleep (B = 2.07, p &lt; 0.001) were positively associated with the ULS-8 scores. In females, physical inactivity (B = 2.69, p &lt; 0.001) and irregular sleep (B = 2.91, p &lt; 0.001) was positively correlated with the scores of ULS-8, while drinking (B = −0.98, p &lt; 0.05) was negatively associated with the ULS-8 scores. More BRFs were significantly related to greater loneliness (p &lt; 0.001).ConclusionThere are gender differences in the relationship between loneliness and BRFs among the Hakka elderly, and individuals with more BRFs were more likely to feel loneliness. Therefore, the co-occurrence of multiple BRFs requires more attention, and integrated behavioral intervention strategies should be adopted to reduce the loneliness of the elderly.

Previous studies on the correlation between life expectancy and GDP per capita showed some variation. This study describes the trends of life expectancy and GDP per capita in 13 main countries along the Belt and Road from 1990 to 2020, and analyzes the relationship between male life expectancy, female life expectancy, or total life expectancy and GDP per capita in these countries using linear regression models. The results show that life expectancy and GDP per capita both increase gradually with increasing years. There is an immediate correlation between life expectancy and GDP per capita, and male life expectancy, female life expectancy, and total life expectancy are positively correlated with GDP per capita. This study confirms the positive correlation between life expectancy and GDP per capita, and these results will be the starting point for the public to pay attention to the health status of people in the Belt and Road countries.