The Geography of Obesity: Predicting Obesity Rates in California Based on Access to Health Care

Health outcomes vary on a small scale; therefore, national level statistics inaccurately represent the true health of U.S. citizens. In order to get a better idea of why such variation exists, this paper looks at how geography affects county level obesity rates in California. County level obesity rates are predicted using an OLS regression that includes not only health predictors and socioeconomic variables, but also variables representing distance to health clinics and the emphasis on primary care in each county. The results ultimately show that distance to health facilities is significant in predicting obesity rates, and that more urbanized areas, with higher densities of clinics and doctors, are likely to have the lowest obesity rates.

健康结局存在小幅波动，因此国家级统计数据无法准确反映美国公民的真实健康状况。为探究此类差异的成因，本文考察了地理因素对加利福尼亚州各县肥胖率的影响。研究采用普通最小二乘（OLS）回归模型对县级肥胖率进行预测，该模型纳入了健康预测因子、社会经济变量，以及表征各县至医疗诊所的距离与初级医疗重视程度的变量。研究结果最终表明，至医疗设施的距离对肥胖率预测具有统计学显著性；且诊所与医师密度更高的城市化区域，其肥胖率普遍更低。