Literature DB >> 28123753

Estimating the prevalence and incidence of type 2 diabetes using population level pharmacy claims data: a cross-sectional study.

Sarah-Jo Sinnott1, Sheena McHugh2, Helen Whelton3, Richard Layte4, Steve Barron5, Patricia M Kearney2.   

Abstract

OBJECTIVE: To estimate the prevalence and incidence of type 2 diabetes using a national pharmacy claims database. RESEARCH DESIGN AND METHODS: We used data from the Health Service Executive-Primary Care Reimbursement Service database in Ireland for this cross-sectional study. Prevalent cases of type 2 diabetes were individuals using an oral hypoglycemic agent, irrespective of insulin use, in 2012. Incident cases were individuals using an oral hypoglycemic agent in 2012 who had not used one in the past. Population level estimates were calculated and stratified by age and sex.
RESULTS: In 2012, there were 114 957 prevalent cases of type 2 diabetes giving a population prevalence of 2.51% (95% CI 2.49% to 2.52%). Among adults (≥15yrs), this was 3.16% (95% CI 3.15% to 3.18%). The highest prevalence was in those aged 70+ years (12.1%). 21 574 people developed type 2 diabetes in 2012 giving an overall incidence of 0.48% (95% CI 0.48% to 0.49%). In adults, this was 0.60% (95% CI 0.60% to 0.61%). Incidence rose with age to a maximum of 2.08% (95% CI 2.02% to 2.15%) in people aged 65-69 years. Men had a higher prevalence (2.96% vs 2.04%) and incidence (0.54% vs 0.41%) of type 2 diabetes than women.
CONCLUSIONS: Pharmacy claims data allow estimates of objectively defined type 2 diabetes at the population level using up-to-date data. These estimates can be generated quickly to inform health service planning or to evaluate the impact of population level interventions.

Entities:  

Keywords:  Administrative Data; Epidemiology; Incidence; Type 2

Year:  2017        PMID: 28123753      PMCID: PMC5253438          DOI: 10.1136/bmjdrc-2016-000288

Source DB:  PubMed          Journal:  BMJ Open Diabetes Res Care        ISSN: 2052-4897


Many estimates for the burden of diabetes come from cohort studies and surveys, which are often not population based. There are very few estimates of incidence of diabetes globally. We used a national administrative pharmacy claims database to calculate prevalence and incidence of type 2 diabetes. This method captures all treated cases of diabetes and thus avoids problems with sampling and generalizing to whole populations that arise from cohort and survey methods. Understanding the true burden of disease in our society is essential for the planning of health services, which in turn help achieve optimal outcomes in terms of diabetes-related morbidity and mortality. Using pharmacy claims data is a straightforward method of achieving up to date estimates.

Introduction

Diabetes mellitus is a leading cause of death globally, causing almost 4 million deaths in 2010.1 Morbidity arising from the disease is also substantial; the Global Burden of Disease study estimated a 30% increase in disability-adjusted life years for diabetes between 1990 and 2010 due to increasing prevalence of the disease and increased longevity of those living with diabetes.2 To reduce diabetes-related mortality and morbidity, access to appropriate healthcare services for people with diabetes is a necessity. For this to be successful, up-to-date population level estimates of disease burden are required to rationally plan and deliver the required health services. The Institute of Public Health (IPH) estimates and forecasts for the prevalence of diabetes (combined type 1 and type 2) are often cited and are a valuable resource.3 However, the most recent estimates from the IPH are based on a cross-sectional survey of adults in the Survey of Lifestyle, Attitudes and Nutrition (SLAN) which dates back to 2007.3 4 Other estimates of diabetes prevalence come from The Irish Longitudinal Study of Ageing (TILDA), which is limited to those over 50 years,5 6 the Mitchelstown cohort which was limited to adults aged 50–69 years in one rural area in the South of Ireland7 and the Central Statistics Office (CSO) Quarterly National Household Survey (QNHS) from 2010.8 These estimates are based on a variety of self-reported doctor diagnosis; self-reported diabetes medication usage; or a combination of self-report and HbA1c data; and all are limited to adult populations. Furthermore, there is a scarcity of data available on the incidence of diabetes in Ireland.9 In this study, we used national pharmacy claims data to estimate the prevalence and incidence of type 2 diabetes. These data provide an objective measure of treated diabetes in the total population to complement existing sample-based estimates.

Methods

Health system

In Ireland, access to and reimbursement for diabetes medicines occur via two publicly funded community drug schemes. The first is the General Medical Services (GMS) scheme; the main public health insurance program providing primary and secondary healthcare free at the point of access to ∼40% of the Irish population on a means-tested basis.10 Medicines are included under this scheme but are subject to a copayment (€2.50 currently). The second drug scheme is the long-term illness (LTI) scheme. The LTI provides free access to condition-related medicines for individuals diagnosed with any of 16 chronic illnesses including diabetes. LTI coverage is independent of income.

Data

Pharmacists dispensing medicines to all patients (adults and children) on the GMS and the LTI scheme are reimbursed by the government via the Health Service Executive-Primary Care Reimbursement Service (HSE-PCRS). We used dispensing data from the HSE-PCRS database from July 2011 to December 2012. Data were available for the drug dispensed (classified by WHO Anatomical Therapeutic Chemical (WHO ATC) code), date dispensed, quantity and strength, in addition to patient age and sex. Population denominator data for the year 2012 were population estimates derived by the CSO based on the 2011 census.11

Definitions

Type 2 diabetes was classified as using any strength or quantity of an oral hypoglycemic agent (WHO ATC A10B), irrespective of age or insulin use. The different agents included in this study, stratified by age and sex, are given in table 1.
Table 1

Types of medicines used in 2012

Medicine groupBiguanidesSulfonylureasThiazolidinedionesDPP-4OtherTotal
A10BAA10BBA10BGA10BHA10BC, A10BF, A10BXn (%)
WHO ATC coden (%)n (%)n (%)n (%)n (%)
Total907 125 (51.9)527, 886 (30.2)25 658 (1.5)126 345 (7.2)60 069 (3.4)1 747 755 (100.0)
<15 years1547 (50.0)914 (29.5)50 (1.6)142 (4.6)139 (4.5)3096 (0.18)
15–24 years2959 (69.7)675 (15.9)57 (1.3)178 (4.2)212(5.0)4247 (0.24)
25–34 years12 537 (67.6)3020 (16.3)247 (1.3)734(4.0)1088 (5.9)18 554 (1.06)
35–4448 558 (59.3)17 542 (21.4)1243 (1.5)4414 (5.4)4787 (5.8)81 960 (4.7)
45–54 years125 732 (54.0)57 833 (24.8)3553 (1.5)14 760 (6.3)14 168 (6.1)232 955 (13.3)
55–64 years236 515 (52.6)122 554 (27.3)7493 (1.7)31 253 (6.9)20 675 (4.6)449 432 (25.7)
65–69 years140 569 (52.0)79 897 (29.6)4372 (1.6)20 725 (7.7)7936 (2.9)270 106 (15.5)
70+ years333 853 (49.3)242 256 (35.8)8435 (1.3)53 435 (7.9)10 600 (1.6)677 401 (38.8)
Women368 707 (53.2)204 465 (29.5)9450 (1.4)49 794 (7.2)25 412 (3.7)692 565 (39.6)
Men536 766 (51.0)322 465 (30.7)16 073 (1.5)76 275 (7.3)34 545 (3.3)1 051 910 (60.2)

Numbers are numbers of prescriptions in 2012.

Other includes sulfonamides, α glucosidase inhibitors and ‘other’ agents as defined by WHO ATC dictionary.

DPP-4, dipeptidyl peptidase-4 inhibitors.

Types of medicines used in 2012 Numbers are numbers of prescriptions in 2012. Other includes sulfonamides, α glucosidase inhibitors and ‘other’ agents as defined by WHO ATC dictionary. DPP-4, dipeptidyl peptidase-4 inhibitors.

Calculation of incidence and prevalence

To estimate the prevalence of type 2 diabetes, we used dispensing data for 2012. We counted the number of people in the database who met our definition of type 2 diabetes and used this as the numerator. The total population count published by the CSO was the denominator.11 To establish the annual incidence for type 2 diabetes in 2012, we used data from July 2011 to December 2012. An individual's first occurrence in 2012 meeting the definition of type 2 diabetes was referred to as the index date. A 6 month look-back period was used to rule out prior use before the index date. If no prior use of an oral hypoglycemic agent occurred in the look-back period, then the individual was an incident user of oral hypoglycemic medicines and thus an incident case. This count was used as the numerator, while the denominator was the total population count published by the CSO minus the number of prevalent of cases.11 We carried out subgroup analyses by age group (<15 years, ≥15 years, 15–24 years, 25–34 years, 35–44 years, 45–54 years, 55–64 years, 65–69 years and >70+ years) and sex.

Results

In 2012, 1 655 013 people accessed a prescription on the GMS scheme and were available in our data set. The mean age was 42.9 years (SD 25.9), and the population was 54.4% women. On the LTI scheme, 68 996 people accessed at least one prescription in 2012. The mean age was 48.4 years (SD 25.2), and the population was 38% women.

Type 2 diabetes mellitus

In 2012, 114 957 people were classified as prevalent type 2 diabetes cases, leading to a prevalence of 2.51% (95% CI 2.49% to 2.52%) in the total population. After excluding those aged <15 years, an adult population prevalence of 3.16% (95% CI 3.15% to 3.18%) was obtained (table 2). Figure 1 demonstrates how the prevalence increased with age; 55–64 years (6.50%), 65–69 years (10.75%) and 70+ years (12.10%). Men had a higher prevalence of type 2 diabetes than women at 2.96% (95% CI 2.94 to 2.98) vs 2.04% (95% CI 2.02% to 2.06%) (χ2 test for homogeneity p<0.0001).
Table 2

Prevalence and incidence of type 2 diabetes

GMSLTITotalPopulation (CSO)Estimate (%)95% CI
Prevalence estimates
 Total population81 17733 780114 9574 585 0002.512.49 to 2.52
 Total population ≥15 years80 61832 987113 6053 590 6003.163.15 to 3.18
 <15 years72111981919994 8000.190.18 to 0.20
 15–24 years594128722553 5000.130.12 to 0.14
 25–34 years17027652467733 5000.340.32 to 0.35
 35–44 years430930817390700 0001.061.03 to 1.08
 45–54 years8964808817 052586 3002.912.87 to 2.95
 55–64 years16 46613 93830 404468 0006.506.43 to 6.57
 65–69 years12 396712019 516181 50010.7510.61 to 10.90
 70+ years41 098339744 495367 80012.1011.99 to 12.20
 Women36 96810 23147 1992 315 8002.042.02 to 2.06
 Men43 70723 51067 2172 269 6002.962.94 to 2.98
Incidence estimates
 Total population15 788578621 5744 470 0430.480.48 to 0.49
 Total population ≥15 years15 353567921 0323 476 9950.600.60 to 0.61
 <15 years213234447992 8810.050.04 to 0.05
 15–24 years40555460552 6780.080.08 to 0.09
 25–34 years9362931229731 0330.170.16 to 0.18
 35–44 years15848192403692 6100.350.33 to 0.36
 45–54 years245915233982569 2480.700.68 to 0.72
 55–64 years348520265511437 5961.261.23 to 1.29
 65–69 years24069713377161 9842.082.02 to 2.15
 70+ years55854135998323 3051.861.81 to 1.9
 Women7242195992012 268 6010.410.40 to 0.41
 Men8165381511 9802 202 3830.540.53 to 0.55
Figure 1

Prevalence and incidence of type 2 diabetes.

Prevalence and incidence of type 2 diabetes Prevalence and incidence of type 2 diabetes. In the same year, 21 574 people developed type 2 diabetes giving an incidence of 0.48% (95% CI 0.48% to 0.49%). This was estimated at 0.60% (95% CI 0.60% to 0.61%) in the population aged ≥15 years. The incidence of type 2 diabetes increased with age, reaching its highest level of 2.08% (95% CI 2.02 to 2.15) in people aged 65–69 years. Men had a higher incidence of type 2 diabetes (0.54%) than women (0.41%) (table 2 and figure 1).

Discussion

This cross-sectional study estimated the prevalence and incidence of type 2 diabetes using population level data from a national pharmacy claims database. The overall prevalence in the adult population was 3.16%. The incidence of type 2 diabetes was 6 cases per 1000 adult people in 2012. Existing prevalence estimates pertaining to the general adult population range from 3% in the Quarterly National Household survey to 3.5% in those aged ≥18 years using SLAN survey data.4 8 Our estimate of 3.16% is thus comparable to previous figures. In addition, our age stratified estimates for people aged ≥50 years are similar to those based on TILDA using self-report of doctor diagnosis and HbA1c measures.5 6 However, our estimates may underestimate the true burden of diabetes given that we have excluded type 1 diabetes and we also could not account for lifestyle-treated diabetes or undiagnosed diabetes. Despite this, the true prevalence rate of diabetes in Ireland is likely lower than that in the USA which was recently estimated at 8.3% in the adult population.12 In England, diagnosed diabetes in the population aged ≥16 years is estimated at 5.6% from Health Survey for England data.13 The only other estimate of incidence of type 2 diabetes in Ireland is 2 cases per 1000, in contrast to the 6 cases per 1000 we found in this study.9 An American study using the National Health Interview Survey found an incidence rate of 7.1/1,000 people aged ≥20 years in 2012, indicating that Irish incidence rates are below those in North America.12 A recent Danish study calculated incidence rates for every year of age.14 While it is difficult to compare single age estimates with estimates for age categories, our estimates appear comparable to those in the Danish study, albeit somewhat higher in the older age groups.14 Making international comparisons is helpful to aid in understanding the plausibility of our estimates, however differences do exist between populations for demographic and methodological reasons.15 The study is limited by lack of information on undiagnosed diabetes and lifestyle-treated diabetes. Other data sources, for example the Mitchelstown Cohort and SLAN survey, provide information on undiagnosed diabetes, which can be used in tandem with our results.4 7 Unpublished data from the Mitchelstown Cohort study of over 2000 adults aged 50–69 years reveal that ∼7% of those with self-reported diabetes are treated with diet only.16 Although these data are not nationally representative, they provide some context on the magnitude of underestimation. Furthermore, we relied on diagnosed individuals adhering to their treatment regimens, their dispensed medications thus appearing in the pharmacy claims database. We did not anticipate non-adherence to be a major problem given that medicines are free on the LTI scheme and subject to a small copayment on the GMS scheme (€0.50 per item in 2012).17 The study is strengthened by the objective and reliable nature of the data.18 Additionally, because diabetes medicines are generally provided only through the GMS and LTI drug schemes, data on those with diagnosed and treated diabetes should be nationally complete in this database offering population level data for all ages, including children, in contrast to previous surveys and cohort studies which are limited to adults. Unfortunately, the database does not have access to diagnosis codes, thus we made the assumption that all oral hypoglycemic agents were being used to treat diabetes. A notable exception is the use of metformin for polycystic ovarian syndrome (PCOS). However, as PCOS effects only a small proportion of women of reproductive age, ∼8%, and only some of these will be treated, any effect on our estimates is likely to be small. Further, many women with PCOS will have diabetes, and we will have intended to include them in our estimates.19 Metformin is also used in pre-diabetes, which due to lack of diagnosis codes, we have not been able to separate from our estimates. We know from our prior research that the prevalence of pre-diabetes in those aged ≥45 years is 20%.20 From the most recent audit of diabetes management in General Practice, we know that ∼90% of those with pre-diabetes are treated with dietary intervention (unpublished).21 Thus, any bias contributed to our results from including those with metformin-treated pre-diabetes is likely to be inconsequential. Our study has demonstrated the utility of routinely collected administrative claims data in calculating measures of disease burden, including incidence. The method is straightforward, and while we used data from 2012 for this study, more current data would allow estimating disease burden for the most recently completed calendar year along with establishing longitudinal trends. This approach affords advantages in real-time monitoring of disease burden and thus presents a key resource for evaluating public health interventions to reduce the prevalence and incidence of type 2 diabetes, thus informing health policy and health service planning. To address acknowledged weaknesses in using these data, estimates for prevalence and incidence should be considered in combination with cross-sectional and cohort study results to account for undiagnosed and lifestyle-treated cases.
  16 in total

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2.  The prevalence of Type 2 diabetes and related complications in a nationally representative sample of adults aged 50 and over in the Republic of Ireland.

Authors:  M L Tracey; S M McHugh; C M Buckley; R J Canavan; A P Fitzgerald; P M Kearney
Journal:  Diabet Med       Date:  2015-08-16       Impact factor: 4.359

3.  Relative accuracy and availability of an Irish National Database of dispensed medication as a source of medication history information: observational study and retrospective record analysis.

Authors:  T Grimes; M Fitzsimons; M Galvin; T Delaney
Journal:  J Clin Pharm Ther       Date:  2013-01-27       Impact factor: 2.512

4.  Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980-2012.

Authors:  Linda S Geiss; Jing Wang; Yiling J Cheng; Theodore J Thompson; Lawrence Barker; Yanfeng Li; Ann L Albright; Edward W Gregg
Journal:  JAMA       Date:  2014-09-24       Impact factor: 56.272

5.  Mortality attributable to diabetes: estimates for the year 2010.

Authors:  Gojka Roglic; Nigel Unwin
Journal:  Diabetes Res Clin Pract       Date:  2009-11-14       Impact factor: 5.602

Review 6.  Epidemiology of diabetes and complications among adults in the Republic of Ireland 1998-2015: a systematic review and meta-analysis.

Authors:  Marsha L Tracey; Michael Gilmartin; Kate O'Neill; Anthony P Fitzgerald; Sheena M McHugh; Claire M Buckley; Ronan J Canavan; Patricia M Kearney
Journal:  BMC Public Health       Date:  2016-02-09       Impact factor: 3.295

7.  Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

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Roy Burstein; Bianca Calabria; Benjamin Campbell; Charles E Canter; Hélène Carabin; Jonathan Carapetis; Loreto Carmona; Claudia Cella; Fiona Charlson; Honglei Chen; Andrew Tai-Ann Cheng; David Chou; Sumeet S Chugh; Luc E Coffeng; Steven D Colan; Samantha Colquhoun; K Ellicott Colson; John Condon; Myles D Connor; Leslie T Cooper; Matthew Corriere; Monica Cortinovis; Karen Courville de Vaccaro; William Couser; Benjamin C Cowie; Michael H Criqui; Marita Cross; Kaustubh C Dabhadkar; Manu Dahiya; Nabila Dahodwala; James Damsere-Derry; Goodarz Danaei; Adrian Davis; Diego De Leo; Louisa Degenhardt; Robert Dellavalle; Allyne Delossantos; Julie Denenberg; Sarah Derrett; Don C Des Jarlais; Samath D Dharmaratne; Mukesh Dherani; Cesar Diaz-Torne; Helen Dolk; E Ray Dorsey; Tim Driscoll; Herbert Duber; Beth Ebel; Karen Edmond; Alexis Elbaz; Suad Eltahir Ali; Holly Erskine; Patricia J Erwin; Patricia Espindola; Stalin E Ewoigbokhan; Farshad Farzadfar; Valery Feigin; David T Felson; Alize Ferrari; Cleusa P Ferri; 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Ratilal Lalloo; Laura L Laslett; Tim Lathlean; Janet L Leasher; Yong Yi Lee; James Leigh; Daphna Levinson; Stephen S Lim; Elizabeth Limb; John Kent Lin; Michael Lipnick; Steven E Lipshultz; Wei Liu; Maria Loane; Summer Lockett Ohno; Ronan Lyons; Jacqueline Mabweijano; Michael F MacIntyre; Reza Malekzadeh; Leslie Mallinger; Sivabalan Manivannan; Wagner Marcenes; Lyn March; David J Margolis; Guy B Marks; Robin Marks; Akira Matsumori; Richard Matzopoulos; Bongani M Mayosi; John H McAnulty; Mary M McDermott; Neil McGill; John McGrath; Maria Elena Medina-Mora; Michele Meltzer; George A Mensah; Tony R Merriman; Ana-Claire Meyer; Valeria Miglioli; Matthew Miller; Ted R Miller; Philip B Mitchell; Charles Mock; Ana Olga Mocumbi; Terrie E Moffitt; Ali A Mokdad; Lorenzo Monasta; Marcella Montico; Maziar Moradi-Lakeh; Andrew Moran; Lidia Morawska; Rintaro Mori; Michele E Murdoch; Michael K Mwaniki; Kovin Naidoo; M Nathan Nair; Luigi Naldi; K M Venkat Narayan; Paul K Nelson; Robert G Nelson; Michael C Nevitt; 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Journal:  PLoS One       Date:  2013-10-16       Impact factor: 3.240

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Journal:  PLoS One       Date:  2013-11-25       Impact factor: 3.240

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