Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Implementation of data management and effect on chronic disease coding in a primary care organisation: A parallel cohort observational study.

BACKGROUND: Consistent and standardized coding for chronic conditions is associated with better care; however, coding may currently be limited in electronic medical records (EMRs) used in Canadian primary care.Objectives To implement data management activities in a community-based primary care organisation and to evaluate the effects on coding for chronic conditions. METHODS: Fifty-nine family physicians in Toronto, Ontario, belonging to a single primary care organisation, participated in the study. The organisation implemented a central analytical data repository containing their EMR data extracted, cleaned, standardized and returned by the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a large validated primary care EMR-based database. They used reporting software provided by CPCSSN to identify selected chronic conditions and standardized codes were then added back to the EMR. We studied four chronic conditions (diabetes, hypertension, chronic obstructive pulmonary disease and dementia). We compared changes in coding over six months for physicians in the organisation with changes for 315 primary care physicians participating in CPCSSN across Canada. RESULTS: Chronic disease coding within the organisation increased significantly more than in other primary care sites. The adjusted difference in the increase of coding was 7.7% (95% confidence interval 7.1%-8.2%, p < 0.01). The use of standard codes, consisting of the most common diagnostic codes for each condition in the CPCSSN database, increased by 8.9% more (95% CI 8.3%-9.5%, p < 0.01). CONCLUSIONS: Data management activities were associated with an increase in standardized coding for chronic conditions. Exploring requirements to scale and spread this approach in Canadian primary care organisations may be worthwhile.