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.

scholarly journals Chronic Disease Prevalence. A perspective using Administrative Data from Australia

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Shaun Purkiss ◽  
Tessa Keegal ◽  
Dennis Wollersheim ◽  
Hassan Vally
Keyword(s):  
Chronic Disease ◽  
Administrative Data ◽  
Chronic Conditions ◽  
Census Data ◽  
Anatomical Therapeutic Chemical ◽  
Disease Prevalence ◽  
World Health ◽  
Good Representation ◽  
Prevalence Estimates ◽  
Chronic Disease Prevalence

BackgroundPharmaceutical administrative data can provide an alternative method to assess chronic disease prevalence. The data within prescription exchanges includes the specific nature of the medication dispensed which can be utilised for case definition by proxy of certain chronic diseases. ObjectivesThis study examines the potential of Australian administrative pharmaceutical data to define chronic disease and provide population prevalence estimates. The utility of allocated World Health Organization Anatomical Therapeutic Chemical (ATC) codes to the treatment supplied will be assessed and the validity of the results generated compared with other Australian sources of chronic disease prevalence. Methods23 chronic conditions were defined by ATC codes within an Australian (administrative) Pharmaceutical Benefits Scheme (PBS) dataset. This enabled calculation of chronic disease prevalences for the period 2003 to 2014 using Australian census data as denominator values. FindingsPrevalence estimates from PBS data when compared with questionnaire based studies demonstrated homogeneity overall (Mann-Whitney P>0.05). PBS prevalence estimates of diabetes, gout and asthma showed respective correlations of 0.999, 0.8385 and 0.58 to 0.82 with community surveys. In general, the prevalence of most chronic conditions rose. Cardiovascular disorders, iron deficiency treatment, HIV and prescription pain medication however demonstrated notable increases. Prevalence estimates were influenced by artefactual factors including new government regulation in 2012. For diabetes prevalence estimates this improved the correlation associated with community survey data. ConclusionsAustralian pharmaceutical administrative data have potential utility for chronic disease prevalence estimates. Advantages include low costs, speed of analysis, high power and good representation. We consider the technique offers a complimentary perspective of chronic disease prevalence providing new insights into population health.

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.

scholarly journals Mapping Chronic Disease Prevalence based on Medication Use and Socio-demographic variables: an Application of LASSO in healthcare in the Netherlands

2020 ◽  
Author(s):  
Koen Füssenich ◽  
Hendriek C. Boshuizen ◽  
Markus M.J. Nielen ◽  
Erik Buskens ◽  
Talitha L. Feenstra
Keyword(s):  
Chronic Disease ◽  
Hospital Admission ◽  
Medication Use ◽  
Model Performance ◽  
Disease Prevalence ◽  
Percentage Error ◽  
Admission Diagnosis ◽  
Regional Patterns ◽  
Prevalence Estimates ◽  
Chronic Disease Prevalence

Abstract Objectives 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 use a LASSO (Least Absolute Shrinkage and Selection) model on a wide set of variables including medication use to obtain small geographic area prevalence estimates for four common chronic diseases and 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=707021) 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 and hospital admission was available. A LASSO operator regression model for binary outcomes was used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on the average of individual predicted probabilities. Results Adding medication use data as a predictor substantially improves model performance. Estimates at the municipality level are best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst WPE for COPD, with 14.5%. Disease prevalence has 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 can be aggregated into any desired regional level and provide a useful tool to identify regional patterns and subsequently inform local policy.

Employment, chronic disease prevalence and medical care among the older population in India: an assessment of linkages

2014 ◽  
Vol 15 (2) ◽  
pp. 76-89
Author(s):  
Priyanka Namdevrao Yadav ◽  
Srinivas Goli ◽  
Arokiasamy Perianayagam ◽  
Ladumai Maikho Apollo Pou
Keyword(s):  
Chronic Disease ◽  
Medical Care ◽  
Chronic Diseases ◽  
Disease Prevalence ◽  
Older Population ◽  
Content Type ◽  
Multiple Classification Analysis ◽  
Multinomial Logit Regression ◽  
Multiple Classification ◽  
Chronic Disease Prevalence

Purpose – The purpose of this paper is to examine the linkages of employment, chronic disease prevalence and medical care of the older population in India. Design/methodology/approach – This study used the India Human Development Survey data for the analysis. Bivariate, multinomial logit regression and multiple classification analysis are used as methods for the study. Findings – The findings suggest a bi-directional relationship between employment and chronic diseases: the older population who are engaged in regular paid work has lower likelihood to the risk of chronic diseases compared to those who are not working. Conversely, the older population with chronic diseases may be unable to work in regular paid jobs. The greater proportions of not-working older population with savings and retirement pensions are more likely to seek modern treatment for the chronic diseases. Overall, the results foster that employment determines and is determined by chronic disease prevalence among the older population in India. Originality/value – This paper for the first time presents evidence on the linkages of employment, chronic disease prevalence and medical care of the older population in India by using a unique and comprehensive data source.

scholarly journals Incidence of drug-treated chronic diseases using administrative pharmaceutical data

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Shaun Purkiss ◽  
Tessa Keegel ◽  
Hassan Vally ◽  
Dennis Wollersheim
Keyword(s):  
Chronic Disease ◽  
Chronic Diseases ◽  
Survey Data ◽  
Census Data ◽  
Respiratory Illness ◽  
Self Report ◽  
World Health ◽  
Population Census ◽  
Incidence Data ◽  
Anatomic Therapeutic Chemical

Background Pharmaceutical data can be used to identify the presence of drug-treated chronic diseases (CD) in individuals using assigned World Health Organization Anatomic Therapeutic Chemical (ATC) classifications of medicines prescribed. ATC codes define treatment domains and provides a method to case define CD that has previously been used to estimate CD prevalence within populations. Main Aim We determined selected CD incidence from an administrative pharmaceutical dataset, and compared them with published CD incidence results. Approach An Australian Pharmaceutical Benefits Scheme (PBS) database covering the period 2003-14 was used for this study. The earliest prescriptions exchanged by individuals for an ATC defined CD were identified and the annual count recorded. These values were combined with Australian population census data to calculate the annual incidence of ATC defined CD. Australian PBS derived incidence estimates (PDI) were compared with published Australian and world incidence data. Results The PDI of 16 chronic diseases were compared with incidence estimates using self-report surveys from the literature. Mean percentage differences between PDI estimates varied greatly when compared to survey data (mean 33% (SD ±79%). Diabetes (-29%), gout (4%), glaucoma (69%) and tuberculosis (14%) showed closer associations. In contrast, PDI estimates (n/1000/year) showed particularly high incidence levels as compared with self-report data for dyspepsia (16.9 v 4.5), dyslipidaemia (11.6 v 5.6) and respiratory illness (17.6 v 2.6). Conclusion Incidence estimates of drug treated chronic disease can be obtained using pharmaceutical data and may be a useful source for a number of conditions. Some PDI differ considerably from survey data. The interpretation of PDI requires context on how a particular CD presents. Accuracy and relevance are likely to depend upon how drug treatments relate to the initial management of the chronic disease.

Gestione delle patologie cardiovascolari croniche in ambiente ambulatoriale: primi dati di un ambulatorio Hub della regione Lombardia

2020 ◽  
pp. 281-288
Author(s):  
Marcella Longo ◽  
Cristiana Valerio
Keyword(s):  
Chronic Diseases ◽  
High Volume ◽  
World Health ◽  
Chronic Illness Care ◽  
Chronic Patients ◽  
The World ◽  
Coronary Syndromes ◽  
Difficult Challenge ◽  
Health Organization

Chronic diseases are the main cause of death and hospitalizations in the world. In 2005 World Health Organization estimated that over 60% of all annual deaths were due to chronic diseases, even with a high neconomic impact. For these reasons chronic illness care is one of the most difficult challenge for the health service: the management of chronic patients needs a different set-ting, as compared with the “hospital – based model” used for acute conditions. In this work, we described the first data of a Hub cardiology out-patient clinic of Azienda Socio Sanitaria Milano Nord, of Lombardia region. Between August 1, 2015 and August 31, 2016, we evaluated 2956 clinical examinations and 4364 instrumental tests. The five main diagnoses were: hypertension (25%), diabetes (17%), chronic coronary syndromes (12%), atrial fibrillation (14%), chronic heart failure (4%). Our results show the high volume of activities of cardiology service and demonstrate the important role of territorial cardiology for chronic cardiovascular disease management.

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