Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Atlanta, Georgia.
4770 Buford Highway NE, Mailstop F-76, Atlanta GA, 30341-3717. Email:
Prev Chronic Dis. 2019 Apr 11;16:E45. doi: 10.5888/pcd16.180530.
Burden of disease is often defined by using epidemiologic measures. However, there may be latent aspects of disease burden that are not factored into these types of estimates. This study quantified environmental burden of disease by using population health indicators and exploratory factor analysis at the county level across the United States.
Ninety-nine variables drawn from public use data sets from 2010 to 2016 were used to create a multifactor index - the burden index. We applied principal components analysis with promax rotation to allow the factors to correlate. Correlation coefficients for each factor and the outcome of interest, age-adjusted cancer death rate, were calculated. We used both unadjusted and adjusted linear regression techniques.
The final additive county-level index included 9 factors that explained 68.3% of the variance in the counties and county equivalents. The burden index had a moderate association with the age-adjusted cancer death rates (r =.48, P <.001), and adjusted linear regression with all 9 factors explained 34% of the variance in the age-adjusted cancer death rate. Results were mapped, and the geographic distribution of both the burden index and age-adjusted cancer mortality were assessed. There are distinct geospatial patterns for both.
Results from this study show potential areas of need, as well as the importance of including environmental variables in the study of cancer etiology. Future studies can aim to validate these findings by quantifying burden as it relates to overall cancer mortality by using epidemiologic measures, along with other confirmatory statistical methods.
疾病负担通常通过使用流行病学指标来定义。然而，这些类型的估计可能没有考虑到疾病负担的潜在方面。本研究通过使用人口健康指标和探索性因素分析在美国各县水平上量化了环境疾病负担。
从 2010 年至 2016 年的公共使用数据集，共选取了 99 个变量，用以创建一个多因素指数——负担指数。我们应用主成分分析与 promax 旋转，以允许各因素相关。计算了各因素与感兴趣的结果（年龄调整后的癌症死亡率）之间的相关系数。我们使用了未经调整和调整后的线性回归技术。
最终的县级加性指数包括 9 个因素，解释了各县和县级单位方差的 68.3%。负担指数与年龄调整后的癌症死亡率呈中度相关（r =.48，P <.001），并且使用所有 9 个因素的调整线性回归解释了年龄调整后的癌症死亡率方差的 34%。结果进行了映射，并评估了负担指数和年龄调整后的癌症死亡率的地理分布。两者都存在明显的地理空间模式。
本研究结果表明存在潜在的需求领域，以及在癌症病因学研究中纳入环境变量的重要性。未来的研究可以通过使用流行病学指标以及其他确认性统计方法，定量衡量与整体癌症死亡率相关的负担，从而验证这些发现。
