Can we use the pharmacy data to estimate the prevalence of chronic conditions? a comparison of multiple data sources

<p>Abstract</p> <p>Background</p> <p>The estimate of the prevalence of the most common chronic conditions (CCs) is calculated using direct methods such as prevalence surveys but also indirect methods using health administrative databases.</p> <p>The aim of this study is to provide estimates prevalence of CCs in Lazio region of Italy (including Rome), using the drug prescription's database and to compare these estimates with those obtained using other health administrative databases.</p> <p>Methods</p> <p>Prevalence of CCs was estimated using pharmacy data (PD) using the Anathomical Therapeutic Chemical Classification System (ATC).</p> <p>Prevalences estimate were compared with those estimated by hospital information system (HIS) using list of ICD9-CM diagnosis coding, registry of exempt patients from health care cost for pathology (REP) and national health survey performed by the Italian bureau of census (ISTAT).</p> <p>Results</p> <p>From the PD we identified 20 CCs. About one fourth of the population received a drug for treating a cardiovascular disease, 9% for treating a rheumatologic conditions.</p> <p>The estimated prevalences using the PD were usually higher that those obtained with one of the other sources. Regarding the comparison with the ISTAT survey there was a good agreement for cardiovascular disease, diabetes and thyroid disorder whereas for rheumatologic conditions, chronic respiratory illnesses, migraine and Alzheimer's disease, the prevalence estimates were lower than those estimated by ISTAT survey. Estimates of prevalences derived by the HIS and by the REP were usually lower than those of the PD (but malignancies, chronic renal diseases).</p> <p>Conclusion</p> <p>Our study showed that PD can be used to provide reliable prevalence estimates of several CCs in the general population.</p

Prevalence and costs of multimorbidity by deprivation levels in the Basque Country: A population based study using health administrative databases

Background: Multimorbidity is a major challenge for healthcare systems. However, currently, its magnitude and impact in healthcare expenditures is still mostly unknown. Objective: To present an overview of the prevalence and costs of multimorbidity by socioeconomic levels in the whole Basque population. Methods: We develop a cross-sectional analysis that includes all the inhabitants of the Basque Country (N = 2,262,698). We utilize data from primary health care electronic medical records, hospital admissions, and outpatient care databases, corresponding to a 4 year period. Multimorbidity was defined as the presence of two or more chronic diseases out of a list of 52 of the most important and common chronic conditions given in the literature. We also use socioeconomic and demographic variables such as age, sex, individual healthcare cost, and deprivation level. Predicted adjusted costs were obtained by log-gamma regression models. Results: Multimorbidity of chronic diseases was found among 23.61\% of the total Basque population and among 66.13\% of those older than 65 years. Multimorbid patients account for 63.55\% of total healthcare expenditures. Prevalence of multimorbidity is higher in the most deprived areas for all age and sex groups. The annual cost of healthcare per patient generated for any chronic disease depends on the number of coexisting comorbidities, and varies from 637 (sic) for the first pathology in average to 1,657 (sic) for the ninth one. Conclusion: Multimorbidity is very common for the Basque population and its prevalence rises in age, and unfavourable socioeconomic environment. The costs of care for chronic patients with several conditions cannot be described as the sum of their individual pathologies in average. They usually increase dramatically according to the number of comorbidities. Given the ageing population, multimorbidity and its consequences should be taken into account in healthcare policy, the organization of care and medical research

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p