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Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data

INTRODUCTION: The incidence and prevalence of diabetes within a population are important public health metrics. Pharmaceutical administrative data may offer a resource that can contribute to quantifying these measures using the recorded signals derived from the drugs used to treat people with diabet...

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Autores principales: Purkiss, Shaun, Keegel, Tessa, Vally, Hassan, Wollersheim, Dennis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121372/
https://www.ncbi.nlm.nih.gov/pubmed/34007898
http://dx.doi.org/10.23889/ijpds.v6i1.1398
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author Purkiss, Shaun
Keegel, Tessa
Vally, Hassan
Wollersheim, Dennis
author_facet Purkiss, Shaun
Keegel, Tessa
Vally, Hassan
Wollersheim, Dennis
author_sort Purkiss, Shaun
collection PubMed
description INTRODUCTION: The incidence and prevalence of diabetes within a population are important public health metrics. Pharmaceutical administrative data may offer a resource that can contribute to quantifying these measures using the recorded signals derived from the drugs used to treat people with diabetes. OBJECTIVE: To estimate the longitudinal incidence and prevalence of drug treated (DT) diabetes in Australia utilising an Australian Pharmaceutical Benefits Scheme (PBS) dataset and compare estimates with community survey data for all diabetes reported in the Australian National Health Survey (NHS). METHODS: Persons with DT diabetes were identified within the PBS dataset using assigned Anatomic Therapeutic Chemical codes for ‘Drugs used in diabetes’. Prevalent persons with DT diabetes were determined by a single annual treatment, and incident cases from the earliest treatment with diabetes medications. Counts were aggregated by age group and utilised Australian national census data as a denominator to calculate diabetes disease frequencies for the period 2004–14. Comparison of PBS prevalence data was made with NHS surveys over equivalent years. RESULTS: The age adjusted incidence of DT diabetes was 3.4/1000 in 2006 and increased to 3.8/1000 in 2011 and 5.1/1000 in 2014. Age adjusted prevalence of DT diabetes in Australia also rose from 26.7/1000 in 2006 to 32.1/1000 in 2011 and 42.1/1000 in 2014. DT diabetes prevalence estimates correlated with NHS estimates of self-reported diabetes prevalence across age groups and in 2014 was r = 0.987. However, PBS estimates of DT diabetes prevalence generally underestimated NHS values of self-reported diabetes in older age groups with mean percentage differences of –22% to –3%. In contrast, PBS data captured more younger persons with diabetes in comparison to NHS data. These differences were then used to adjust DT diabetes incidence rates to provide age specific estimates that could potentially reflect diabetes incidence estimates acquired by community survey. CONCLUSIONS: PBS data representing dispensed medications prescribed to persons with diabetes offers a perspective for the assessment of diabetes incidence and prevalence. PBS derived DT diabetes prevalence estimates correlate well with community survey estimates of self-reported diabetes, but underestimate NHS data in older age groups. Calibrated DT incidence estimates may potentially reflect community survey derived diabetes incidence estimates and may offer a method for longitudinal monitoring.
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spelling pubmed-81213722021-05-17 Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data Purkiss, Shaun Keegel, Tessa Vally, Hassan Wollersheim, Dennis Int J Popul Data Sci Population Data Science INTRODUCTION: The incidence and prevalence of diabetes within a population are important public health metrics. Pharmaceutical administrative data may offer a resource that can contribute to quantifying these measures using the recorded signals derived from the drugs used to treat people with diabetes. OBJECTIVE: To estimate the longitudinal incidence and prevalence of drug treated (DT) diabetes in Australia utilising an Australian Pharmaceutical Benefits Scheme (PBS) dataset and compare estimates with community survey data for all diabetes reported in the Australian National Health Survey (NHS). METHODS: Persons with DT diabetes were identified within the PBS dataset using assigned Anatomic Therapeutic Chemical codes for ‘Drugs used in diabetes’. Prevalent persons with DT diabetes were determined by a single annual treatment, and incident cases from the earliest treatment with diabetes medications. Counts were aggregated by age group and utilised Australian national census data as a denominator to calculate diabetes disease frequencies for the period 2004–14. Comparison of PBS prevalence data was made with NHS surveys over equivalent years. RESULTS: The age adjusted incidence of DT diabetes was 3.4/1000 in 2006 and increased to 3.8/1000 in 2011 and 5.1/1000 in 2014. Age adjusted prevalence of DT diabetes in Australia also rose from 26.7/1000 in 2006 to 32.1/1000 in 2011 and 42.1/1000 in 2014. DT diabetes prevalence estimates correlated with NHS estimates of self-reported diabetes prevalence across age groups and in 2014 was r = 0.987. However, PBS estimates of DT diabetes prevalence generally underestimated NHS values of self-reported diabetes in older age groups with mean percentage differences of –22% to –3%. In contrast, PBS data captured more younger persons with diabetes in comparison to NHS data. These differences were then used to adjust DT diabetes incidence rates to provide age specific estimates that could potentially reflect diabetes incidence estimates acquired by community survey. CONCLUSIONS: PBS data representing dispensed medications prescribed to persons with diabetes offers a perspective for the assessment of diabetes incidence and prevalence. PBS derived DT diabetes prevalence estimates correlate well with community survey estimates of self-reported diabetes, but underestimate NHS data in older age groups. Calibrated DT incidence estimates may potentially reflect community survey derived diabetes incidence estimates and may offer a method for longitudinal monitoring. Swansea University 2021-05-10 /pmc/articles/PMC8121372/ /pubmed/34007898 http://dx.doi.org/10.23889/ijpds.v6i1.1398 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Population Data Science
Purkiss, Shaun
Keegel, Tessa
Vally, Hassan
Wollersheim, Dennis
Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data
title Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data
title_full Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data
title_fullStr Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data
title_full_unstemmed Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data
title_short Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data
title_sort estimates of drug treated diabetes incidence and prevalence using australian administrative pharmaceutical data
topic Population Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121372/
https://www.ncbi.nlm.nih.gov/pubmed/34007898
http://dx.doi.org/10.23889/ijpds.v6i1.1398
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