Biro Suzanne, Williamson Tyler, Leggett Jannet Ann, Barber David, Morkem Rachael, Moore Kieran, Belanger Paul, Mosley Brian, Janssen Ian
Kingston, Frontenac, and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, ON, K7M 1V5, Canada.
Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.
BMC Med Inform Decis Mak. 2016 Mar 11;16:32. doi: 10.1186/s12911-016-0272-9.
Electronic medical records (EMRs) used in primary care contain a breadth of data that can be used in public health research. Patient data from EMRs could be linked with other data sources, such as a postal code linkage with Census data, to obtain additional information on environmental determinants of health. While promising, successful linkages between primary care EMRs with geographic measures is limited due to ethics review board concerns. This study tested the feasibility of extracting full postal code from primary care EMRs and linking this with area-level measures of the environment to demonstrate how such a linkage could be used to examine the determinants of disease. The association between obesity and area-level deprivation was used as an example to illustrate inequalities of obesity in adults.
The analysis included EMRs of 7153 patients aged 20 years and older who visited a single, primary care site in 2011. Extracted patient information included demographics (date of birth, sex, postal code) and weight status (height, weight). Information extraction and management procedures were designed to mitigate the risk of individual re-identification when extracting full postal code from source EMRs. Based on patients' postal codes, area-based deprivation indexes were created using the smallest area unit used in Canadian censuses. Descriptive statistics and socioeconomic disparity summary measures of linked census and adult patients were calculated.
The data extraction of full postal code met technological requirements for rendering health information extracted from local EMRs into anonymized data. The prevalence of obesity was 31.6 %. There was variation of obesity between deprivation quintiles; adults in the most deprived areas were 35 % more likely to be obese compared with adults in the least deprived areas (Chi-Square = 20.24(1), p < 0.0001). Maps depicting spatial representation of regional deprivation and obesity were created to highlight high risk areas.
An area based socio-economic measure was linked with EMR-derived objective measures of height and weight to show a positive association between area-level deprivation and obesity. The linked dataset demonstrates a promising model for assessing health disparities and ecological factors associated with the development of chronic diseases with far reaching implications for informing public health and primary health care interventions and services.
基层医疗中使用的电子病历（EMR）包含可用于公共卫生研究的广泛数据。电子病历中的患者数据可与其他数据源相链接，比如将邮政编码与人口普查数据相链接，以获取有关健康环境决定因素的更多信息。尽管前景广阔，但由于伦理审查委员会的担忧，基层医疗电子病历与地理测量之间的成功链接有限。本研究测试了从基层医疗电子病历中提取完整邮政编码并将其与区域环境测量相链接的可行性，以证明这种链接如何用于研究疾病的决定因素。以肥胖与区域贫困之间的关联为例，来说明成年人肥胖的不平等现象。
分析纳入了2011年在一个基层医疗点就诊的7153名20岁及以上患者的电子病历。提取的患者信息包括人口统计学信息（出生日期、性别、邮政编码）和体重状况（身高、体重）。信息提取和管理程序旨在降低从源电子病历中提取完整邮政编码时个人重新识别的风险。根据患者的邮政编码，使用加拿大人口普查中使用的最小区域单位创建基于区域的贫困指数。计算了链接的人口普查数据和成年患者的描述性统计数据以及社会经济差异汇总指标。
完整邮政编码的数据提取满足了将从本地电子病历中提取的健康信息转换为匿名数据的技术要求。肥胖患病率为31.6%。贫困五分位数之间存在肥胖差异；最贫困地区的成年人肥胖的可能性比最不贫困地区的成年人高35%（卡方检验=20.24(1)，p<0.0001）。创建了描绘区域贫困和肥胖空间表示的地图，以突出高风险区域。
基于区域的社会经济测量与电子病历得出的身高和体重客观测量相链接，显示出区域贫困与肥胖之间存在正相关。链接的数据集展示了一个有前景的模型，用于评估与慢性病发展相关的健康差异和生态因素，对为公共卫生和初级卫生保健干预及服务提供信息具有深远影响。
