Monitoring the prevalence of chronic conditions: which data should we use?

Published: October 22, 2012
Category: Bibliography > Papers
Authors: Esnaola S, Grandes G, Mateos M, Nuno-Solinis R, Orueta JF, Vergara I
Countries: Spain
Language: null
Types: Care Management
Settings: Hospital

BMC Health Serv Res 12:365.

Osakidetza, Basque Health Service, C/ Alava n° 45, Vitoria-Gazteiz, Spain

BACKGROUND: Chronic diseases are an increasing threat to people’s health and to the sustainability of health organisations. Despite the need for routine monitoring systems to assess the impact of chronicity in the population and its evolution over time, currently no single source of information has been identified as suitable for this purpose. Our objective was to describe the prevalence of various chronic conditions estimated using routine data recorded by health professionals: diagnoses on hospital discharge abstracts, and primary care prescriptions and diagnoses.

METHODS: The ICD-9-CM codes for diagnoses and Anatomical Therapeutic Chemical (ATC) codes for prescriptions were collected for all patients in the Basque Country over 14 years of age (n=1,964,337) for a 12-month period. We employed a range of different inputs: hospital diagnoses, primary care diagnoses, primary care prescriptions and combinations thereof. Data were collapsed into the morbidity groups specified by the Johns Hopkins Adjusted Clinical Groups (ACGs) Case-Mix System. We estimated the prevalence of 12 chronic conditions, comparing the results obtained using the different data sources with each other and also with those of the Basque Health Interview Survey (ESCAV). Using the different combinations of inputs, Standardized Morbidity Ratios (SMRs) for the considered diseases were calculated for the list of patients of each general practitioner. The variances of the SMRs were used as a measure of the dispersion of the data and were compared using the Brown-Forsythe test.

RESULTS: The prevalences calculated using prescription data were higher than those obtained from diagnoses and those from the ESCAV, with two exceptions: malignant neoplasm and migraine. The variances of the SMRs obtained from the combination of all the data sources (hospital diagnoses, and primary care prescriptions and diagnoses) were significantly lower than those using only diagnoses.

CONCLUSIONS: The estimated prevalence of chronic diseases varies considerably depending of the source(s) of information used. Given that administrative databases compile data registered for other purposes, the estimations obtained must be considered with caution. In a context of increasingly widespread computerisation of patient medical records, the complementary use of a range of sources may be a feasible option for the routine monitoring of the prevalence of chronic diseases.

PMID: 23088761
PMCID: PMC3529101

High-Impact Chronic Conditions,Diagnostic Certainty,Practice Patterns Comparison,Spain,Adolescent,Adult,Aged,Cross-Sectional Studies,Gender,Health Surveys/statistics & numerical data,Medical Records,Middle Aged,Patient Discharge/statistics & numerical data,Population Surveillance/methods,Practice Patterns,Physicians’/statistics & numerical data,Prevalence,Primary Health Care/statistics & numerical data,Spain/epidemiology,Young Adult

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