Searching for longevity hotspots in Denmark

Geographical distinctions of longevity indicators and their correlation with climatic factors in the area where most Chinese Yao are distributed

Longevity research is a hot topic in the health field. Considerable research focuses on longevity phenomenon in Bama Yao Autonomous County, which has a typical karst landform and is located in Southwest China. This study aims to illustrate the spatial feature of longevity indicators in other Yao areas, to analyze the correlation between climatic factors and longevity indicators, and to provide new clues and targets for further longevity studies. We collect and integrate population, climate, and terrain data into a spatial database. The main analysis methods include spatial autocorrelation, high/low clustering, and multiscale geographically weighted regression (MGWR). Two longevity clusters are identified in Guijiang River Basin (longevity index (LI%): 2.49 ± 0.63) and Liujiang River Basin (LI%: 2.13 ± 0.60). The spatial distribution of longevity indicators is autocorrelative (Moran's I = 0.652, p < 0.001) and clustered significantly (Z score = 4.268, p < 0.001). MGWR shows that the atmospheric pressure significantly affects the spatial distribution of LI% (estimate value (EV) =  - 0.566, p = 0.012), centenarity index (CI%) (EV =  - 0.425, p = 0.007), UC (EV =  - 0.502, p = 0.006), and CH (EV =  - 0.497, p = 0.007). Rainfall significantly affects the spatial distribution of LI% (EV = 0.300, p = 0.003) and CI% (EV =  - 0.191, p = 0.016). The spatial distribution of the main longevity indicators shows significant heterogeneity and autocorrelation, and they cluster in the Guijiang River and Liujiang River basins. Atmospheric pressure and rainfall may contribute to the longevity phenomenon through complex mechanisms. The longevity phenomenon in the Yao nationality in Guijiang River Basin requires further study to improve our understanding of the health effect of meteorological, environmental, and social conditions on longevity.

Does Lolland-Falster make people sick, or do sick people move to Lolland-Falster? An example of selective migration and mortality in Denmark, 1968-2017

INTRODUCTION

Lolland-Falster is a rural area in south-eastern Denmark that scores unfavourable in health surveys and has the lowest life expectancy in the country. To determine the origin of poor health in Lolland-Falster, we investigated impact on mortality of long-term population movements.

METHODS

We used data from the Danish Central Population Register 1968-2017 to track movements in and out of Lolland-Falster. This enabled us to calculate mortality based on tenure of residence. Poisson regression adjusted for sex, 5-year age-groups, and calendar year; separately for men and women; and ages <30, 30-64 and ≥ 65 years; was reported as mortality rate ratios (MRR) with 95% confidence intervals (95% CI).

RESULTS

Until 1988, mortality in Lolland-Falster was fairly similar to that in the rest of Denmark. Hereafter, mortality rates drifted apart. In 2008-2017, MRR of the total Lolland-Falster population was 1.21 (95% CI: 1.19-1.23). In each 10-year calendar period, people recently in-migrating constituted about one fourth of the population. MRRs of the in-migrating population increased over time from 1.17 (95% CI: 1.08-1.26) in 1968-1977, to 1.82 (95% CI: 1.75-1.89) in 2008-2017. Persons aged 30-64 constituted the largest in-migrating group and had highest excess mortality, MRR 2.34 (95% CI: 2.19-2.50) in 2008-2017.

CONCLUSION

Long-term selective in-migration of vulnerable persons was behind the gradual build-up of the currently high mortality in Lolland-Falster compared to the rest of Denmark. In particular, people of working age in-migrating to Lolland-Falster contributed to this disparity.