METHODS: We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey.
RESULTS: About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor.
CONCLUSIONS: Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data.
方法:我们使用了我们所有人(2017-2022年)的数据并在两个示例中获得了患病率估计值:医疗环境中的歧视(N=41.875)和粮食不安全(N=82.266)。使用已知的人口比例(年龄,性别,种族/民族,居住地区,家庭年收入,和房屋所有权)来自2020年全国健康访谈调查。
结果:大约37%的成年人在医疗环境中经历过歧视。大约20%没有看过医生的成年人报告说食物不安全,相比之下,14%的成年人经常看医生。
结论:使用raking进行校准具有成本效益,并且在分析所有我们的数据时可能会导致更精确的估计。