An epidemiological features and risk factors study of osteoporosis among residents aged 40 and over in four counties of Hunan,2018
Objective This research focuses on understanding the epidemiology,prevalence,and associated risks of osteoporosis and low bone mass among individuals aged 40 and over in four counties of Hunan Province.It aims to bolster the efforts in bone health management and osteoporosis prevention in the region.Methods A multi-stage stratified random sampling was conducted for this epidemiological study,encompassing questionnaire survey,bone mass measurement and physical measurement such as body weight and height measurements.Rate,constituent ratio and mean were used for statistical despcrition of the data.Rao-Scott x2test was conducted for rate comparisons,and Survey logistic regression model was used for risk factors analysis.All the statistic procedures mentioned above were conducted by SAS 9.4 software.Results A total of 1 597 subjects were finally investigated,including 704 males(44.08％)and 893 females(55.92％).The overall prevalence of osteoporosis in the cohort was 15.37％(95％CI:12.08-18.65),increasing with age(Rao-Scott x2=131.00,P＜0.001)and higher in females than males(26.95％:4.55％,Rao-Scott x2=54.34,P＜0.001).Low bone mass was prevalent in 53.33％of the population(95％CI:49.61-57.04,P＜0.001),while low bone mass was 54.28％prevalent in the studied males and 52.01％in females,and no statistically signif-icant gender differences was found(Rao-Scott x2=0.63,P=0.420).Risk factors for osteoporosis in males included a BMI＜18.5 kg/m2(OR=6.52,95％CI:1.51-28.12,P=0.010).While in females,they risk factors included menopause before 45(OR=2.47,95％CI:1.53-3.90,P＜0.001),history of fracture(OR=2.76,95％CI:1.62-4.65,P＜0.001),BMI＜18.5 kg/m2(OR=17.67,95％Cl:2.54-122.87,P=0.004),and being aged≥60 years(OR=11.35,95％CI:3.62-35.56,P＜0.001).Conclusions Osteoporosis prevalence is notably high in Hunan Province,particularly among females.The study underscores the significant potential risk for osteoporosis in both genders.
NETL
NSTL
万方数据
