scholarly journals A process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records

2021 ◽  
Author(s):  
Kenneth A. Scott ◽  
Sara Deakyne Davies ◽  
Rachel Zucker ◽  
Toan Ong ◽  
Emily McCormick Kraus ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Records ◽  
Disease Prevalence ◽  
Distributed Data ◽  
Health Records ◽  
Data Network ◽  
Prevalence Estimates ◽  
Electronic Health ◽  
Chronic Disease Prevalence

scholarly journals Mapping chronic disease prevalence based on medication use and socio-demographic variables: an application of LASSO on administrative data sources in healthcare in the Netherlands

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Koen Füssenich ◽  
Hendriek C. Boshuizen ◽  
Markus M. J. Nielen ◽  
Erik Buskens ◽  
Talitha L. Feenstra
Keyword(s):  
Chronic Disease ◽  
Hospital Admission ◽  
Medication Use ◽  
Disease Prevalence ◽  
Percentage Error ◽  
Economic Variables ◽  
Regional Patterns ◽  
Prevalence Estimates ◽  
Socio Economic Variables ◽  
Chronic Disease Prevalence

Abstract Background Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease. Methods Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant. Results Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age. Conclusion Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy.

Sensitivity of Clinical Pediatric Obesity Diagnosis Documented in Electronic Health Records

2020 ◽  
Vol 59 (14) ◽  
pp. 1274-1281
Author(s):  
Christine B. San Giovanni ◽  
Myla Ebeling ◽  
Robert A. Davis ◽  
C. Shaun Wagner ◽  
William T. Basco
Keyword(s):  
Body Mass Index ◽  
Logistic Regression ◽  
Chronic Disease ◽  
Electronic Health Records ◽  
Pediatric Obesity ◽  
Disease Diagnosis ◽  
Health Records ◽  
Academic Affiliation ◽  
Specific Factors ◽  
Electronic Health

Objective. This study tested the sensitivity of obesity diagnosis in electronic health records (EHRs) using body mass index (BMI) classification and identified variables associated with obesity diagnosis. Methods. Eligible children aged 2 to 18 years had a calculable BMI in 2017 and had at least 1 visit in 2016 and 2017. Sensitivity of clinical obesity diagnosis compared with children’s BMI percentile was calculated. Logistic regression was performed to determine variables associated with obesity diagnosis. Results. Analyses included 31 059 children with BMI at or above 95th percentile. Sensitivity of clinical obesity diagnosis was 35.81%. Clinical obesity diagnosis was more likely if the child had a well visit, had Medicaid insurance, was female, Hispanic or Black, had a chronic disease diagnosis, and saw a provider in a practice in an urban area or with academic affiliation. Conclusion. Sensitivity of clinical obesity diagnosis in EHR is low. Clinical obesity diagnosis is associated with nonmodifiable child-specific factors but also modifiable practice-specific factors.

scholarly journals CKD as a Model for Improving Chronic Disease Care through Electronic Health Records

2015 ◽  
Vol 10 (8) ◽  
pp. 1488-1499 ◽  
Author(s):  
Paul E. Drawz ◽  
Patrick Archdeacon ◽  
Clement J. McDonald ◽  
Neil R. Powe ◽  
Kimberly A. Smith ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Records ◽  
Health Records ◽  
Chronic Disease Care ◽  
Electronic Health

scholarly journals Open source electronic health records and chronic disease management

2014 ◽  
Vol 21 (e1) ◽  
pp. e50-e54 ◽  
Author(s):  
Jason C Goldwater ◽  
Nancy J Kwon ◽  
Ashley Nathanson ◽  
Alison E Muckle ◽  
Alexa Brown ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Electronic Health Records ◽  
Open Source ◽  
Chronic Disease Management ◽  
Health Records ◽  
Electronic Health

scholarly journals comparison of Australian chronic disease prevalence estimates using administrative pharmaceutical dispensing data with international and community survey data

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Shaun Francis Purkiss ◽  
Tessa Keegel ◽  
Hassan Vally ◽  
Dennis Wollersheim
Keyword(s):  
Chronic Disease ◽  
Chronic Diseases ◽  
Disease Prevalence ◽  
World Health ◽  
Community Surveys ◽  
Prevalence Estimates ◽  
Australian Community ◽  
Health Organization ◽  
Chronic Disease Prevalence ◽  
Dispensing Data

Introduction: Chronic disease (CD) is a leading cause of population mortality, illness and disability. Identification of CD using administrative data is increasingly used and may have utility in monitoring population health. Pharmaceutical administrative data using World Health Organization Anatomic Therapeutic Chemical Codification (ATC) assigned to prescribed medicines may offer an improved method to define persons with certain CD and enable the calculation of population prevalence.   Objective: To assess the feasibility of Australian Pharmaceutical Benefits Scheme (PBS) dispensing data to provide realistic measures of chronic disease prevalence using ATC codification and compare values with international data using similar ATC methodology and Australian community surveys.   Methods: Twenty-two chronic diseases were identified using World Health Organization (WHO) formulated ATC codes assigned to treatments received and recorded in a PBS database. Distinct treatment episodes prescribed to individuals were counted annually for prevalence estimates. Comparisons were then made with estimates from international studies using pharmaceutical data and published Australian community surveys.   Results: PBS prevalence estimates for a range of chronic diseases listed in European studies and Australian community surveys demonstrated good correlation (r > .83, p < .001). PBS estimates of the prevalence of diabetes, cardiovascular disease and hypertension, dyslipidemia, and respiratory disease with comparable Australian National Health Survey data by age groupings (>45 years) showed correlations of between (r = 0.82 - 0.99, p < .001) and a range of percentage difference of -15% to 77%. However, other conditions such as psychological disease and migraine showed greater disparity and correlated less well.   Conclusions: Although not without limitations, Australian administrative pharmaceutical dispensing data may provide an alternative perspective on population health and a useful resource to estimate the prevalence of a number of chronic diseases within the Australian population.

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