Lee David C, Long Judith A, Wall Stephen P, Carr Brendan G, Satchell Samantha N, Braithwaite R Scott, Elbel Brian
David C. Lee and Stephen P. Wall are with the Ronald O. Perelman Department of Emergency Medicine and R. Scott Braithwaite and Brian Elbel are with the Department of Population Health, New York University School of Medicine, New York, NY. Judith A. Long is with the Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA. Brendan G. Carr is with the Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA. Samantha N. Satchell is with the Milken Institute School of Public Health, George Washington University, Washington, DC.
Am J Public Health. 2015 Sep;105(9):e67-74. doi: 10.2105/AJPH.2015.302679. Epub 2015 Jul 16.
We sought to improve public health surveillance by using a geographic analysis of emergency department (ED) visits to determine local chronic disease prevalence.
Using an all-payer administrative database, we determined the proportion of unique ED patients with diabetes, hypertension, or asthma. We compared these rates to those determined by the New York City Community Health Survey. For diabetes prevalence, we also analyzed the fidelity of longitudinal estimates using logistic regression and determined disease burden within census tracts using geocoded addresses.
We identified 4.4 million unique New York City adults visiting an ED between 2009 and 2012. When we compared our emergency sample to survey data, rates of neighborhood diabetes, hypertension, and asthma prevalence were similar (correlation coefficient = 0.86, 0.88, and 0.77, respectively). In addition, our method demonstrated less year-to-year scatter and identified significant variation of disease burden within neighborhoods among census tracts.
Our method for determining chronic disease prevalence correlates with a validated health survey and may have higher reliability over time and greater granularity at a local level. Our findings can improve public health surveillance by identifying local variation of disease prevalence.
我们试图通过对急诊就诊情况进行地理分析来确定当地慢性病患病率,以改善公共卫生监测。
利用一个全支付方行政数据库,我们确定了患有糖尿病、高血压或哮喘的急诊患者的比例。我们将这些比率与纽约市社区健康调查所确定的比率进行了比较。对于糖尿病患病率,我们还使用逻辑回归分析了纵向估计的准确性,并利用地理编码地址确定了普查区的疾病负担。
我们识别出2009年至2012年间纽约市有440万成年人前往急诊就诊。当我们将急诊样本与调查数据进行比较时,社区糖尿病、高血压和哮喘患病率相似(相关系数分别为0.86、0.88和0.77)。此外,我们的方法显示出逐年波动较小,并识别出普查区之间社区内疾病负担的显著差异。
我们确定慢性病患病率的方法与经过验证的健康调查相关,随着时间推移可能具有更高的可靠性,并且在地方层面具有更高的精细度。我们的研究结果可以通过识别疾病患病率的局部差异来改善公共卫生监测。