Concept: Diabetes - Measuring Prevalence

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Concept Description

Last Updated: 2015-12-14

Definition of Diabetes
    A chronic condition in which the pancreas no longer produces enough insulin (Type I Diabetes) or when cells stop responding to the insulin that is produced (Type II Diabetes), so that glucose in the blood cannot be absorbed into the cells of the body. See MedlinePlus® - Health Topics - Diabetes for more information.
Literature Review
    Administrative data has been widely used to ascertain cases of diabetes. The diabetes algorithms examined in previous research are primarily based on hospital (i.e., inpatient) and physician (i.e., out-patient) data, although some studies also used medication codes in prescription drug data to identify diabetes cases.

    Table 1 summarizes eight papers, published prior to 2006, that used administrative data to ascertain diabetes cases.
Manitoba Diabetes Algorithms
1. Young et al. (1991)
    In Young et al. (1991) , physician claims and hospital separations containing the ICD-9-CM code 250 for diabetes were used for case identification. At least one claim or separation containing a diabetes code over a period of five years was used to identify cases. This method was validated using the Manitoba Health Service Commission (MHSC) database.
2. Blanchard et al. (1996)

    In Blanchard et al. (1996) , a diabetes case was defined as at least two physician visits with a tariff prefix of '7' and a diabetic diagnosis code (ICD-9-CM code 250), or one hospital claim that contained a diabetic diagnosis code (ICD-9-CM code 250) in any field over a period of three years. This method was validated using the Manitoba diabetes database.
3. Robinson et al. (1997)
    In Robinson et al. (1997) , the following ICD-9-CM codes were used to define diabetes cases:
    • 250: Diabetes mellitus
    • 271.4 Renal diabetes
    • 648.0 Gestational diabetes
    • 648.8 Abnormal glucose tolerance in pregnancy
    • 790.2 Abnormal glucose tolerance

    Robinson used both hospital and physician data to identify diabetes cases, and investigated the effect of the change in the number of years of data and the number of required diagnoses on agreement between administrative data and Manitoba Heart Health Survey data. The respondents were 18 to 74 years of age.
4. Lix et al. (2006)
    In Lix et al. (2006) , the ICD-9-CM code 250 (diabetes mellitus) was used to identify cases from Manitoba's hospital and physician data. A single second-level Anatomical Therapeutic Chemical (ATC) code, A10 (drugs used in diabetes) was used to identify diabetes cases from Manitoba's prescription drug data. All of the Drug Identification Numbers (DINs) with this ATC code were selected from the MCHP Master Formulary.

    Eighteen diabetes algorithms were evaluated based on one, two, or three years of administrative data. All algorithms required at least one occurrence of a diagnostic code in hospital separations for an individual to be classified as a diabetes case. However, the algorithms varied in the number of occurrences of a physician claim or an ATC code in prescription drug data for an individual to be classified as a diabetes case. The algorithms were validated using the Canadian Community Health Survey (CCHS) cycle 1.1.
Validation Results
    Discussion of the validation results for diabetes can be found in Section 6.3 Validation Results of the deliverable.

    Table 25 in this section contains the estimates of agreement (к), sensitivity, specificity, Youden's Index, PPV (positive predictive value) and NPV (negative predictive value) for each of the 18 algorithms that were investigated for diabetes.
5. Lix et al. (2008)
    Lix et al. (2008) provided an update to the 2006 study with a report titled Defining and Validating Chronic Disease: An Administrative Data Approach. An Update with ICD-10-CA. The purpose of the 2008 report is to examine the validity of administrative data for monitoring the prevalence of chronic disease in Manitoba. Specific objectives are to:

    • Report relevant ICD-10-CA codes for ascertaining cases of chronic disease in administrative health data;
    • Evaluate the validity of multiple algorithms for identifying disease cases from Manitoba administrative data.

    The 2008 report uses the same methods and algorithms as described in the 2006 report, with the following modifications:

    • ICD-10-CA codes were used to define specific chronic diseases from hospital separation data, beginning April 1, 2004. This is due to a change in coding systems used in Manitoba hospitals. The same ICD-9-CM code (250 - diabetes mellitus) identified in the 2006 report was used to identify hospital cases prior to April 1, 2004.
    • data from the Canadian Community Health Survey (CCHS), cycle 3.1, collected from January 2005 to January 2006 were used to evaluate the validity of the administrative data. The cohort consisted of 5,099 adults 19+ years of age.
    • fourteen additional algorithms were investigated: six looking at either one or more OR two or more physician claims only over one, two or three years of data, and an additional eight algorithms over five years of data.

    The following ICD-10-CA codes were used to define diabetes in administrative hospital separation data from April 1, 2004 to March 31, 2006:

    • E10 - Insulin-dependent diabetes mellitus
    • E11 - Non-insulin-dependent diabetes mellitus
    • E12 - Malnutrition-related diabetes mellitus
    • E13 - Other specified diabetes mellitus
    • E14 - Unspecified diabetes mellitus
Validation Results
    Discussion of the validation results for diabetes can be found in Chapter 6 of the full report, available here: Chapter 6: Diabetes

    Table 6-2 in this section contains the estimates of agreement (к), sensitivity, specificity, Youden's Index, PPV (positive predictive value) and NPV (negative predictive value) for each of the 32 algorithms that were investigated for diabetes.
6. Brownell et al. (2008) and Brownell et al. (2012)
    In Brownell et al. (2008) and Brownell et al. (2012), diabetes definitions for children were based on using three years of hospital discharge, physician visit and/or prescription data. In 2008, Brownell's definition for children included those aged 5 to 19 years. In 2012, the definition for children included those aged 6 to 19 years. Diabetes was defined by at least one of the following conditions:

    • one or more hospitalizations with a diabetes diagnosis (ICD-9-CM: 250 or ICD-10-CA: E10-E14); OR
    • two or more physician visits with a diabetes diagnosis (ICD-9-CM: 250); OR
    • two or more prescriptions for a diabetes medication.

    In Brownell et al. (2012), the Diabetes medication list is based on the latest ATC codes and Drug Identification Numbers (DINs) available for diabetes.
7. Fransoo et al. (2009), Martens et al. (2010), Raymond et al. (2011), Fransoo et al. (2011), Chartier et al. (2012), Katz et al. (2013), Smith et al. (2013), Fransoo et al. (2013), Katz et al. (2014), Martens et al. (2015) and Chartier et al. (2015)

    In the following deliverables:


      • one or more hospitalizations with a diagnosis of diabetes: ICD-9-CM code 250 or ICD-10-CA codes E10-E14; OR
      • two or more physician visits with a diagnosis of diabetes: prefix=7 and ICD-9-CM code 250, OR
      • one or more prescriptions for medications to treat diabetes (see individual project medication lists below).
        ++NOTE: In Martens et al. (2015), this was restricted to survey respondents aged 12 and older in the three years prior to their survey date.
        ^^NOTE: In Chartier et al. (2015), diabetes was investigated for children (age 0-17 years old) and adults (age 18 years and older)

    List of drugs / medications used to treat diabetes (with ATC codes) - from individual MCHP reports:


    **NOTE:
    In the Obesity and The Cost of Smoking deliverables, for participants of the Manitoba Heart Health Survey (MHHS) who were surveyed in 1989-1990, there is no prescription data available. The Drug Program Information Network (DPIN) data is only available from 1995 onwards. Therefore, in these research projects, the definition of diabetes used for MHHS participants is:

    • one or more hospitalizations in three years (or within three years prior to the survey date) with a diagnosis of diabetes: ICD-9-CM code 250 or ICD-10-CA codes E10-E14,
    • two or more physician visits in three years (or within three years prior to the survey date) with a diagnosis of diabetes: ICD-9-CM code 250

    ++NOTE:

    In The Cost of Smoking deliverable, in addition to calculating diabetes prevalence from administrative data, they investigated self-reported diabetes from the survey data. In the MHHS respondents were asked, "Have you been told by a doctor that you have diabetes?" Possible responses include "yes", "no" or "not sure". In the NPHS and CCHS respondents were asked, "Do you have diabetes?" Possible responses include "yes", "no" or "don't know."

    The weighted crude self-reported prevalence of diabetes was calculated for survey respondents aged 12 and older as the percentage of respondents who answered "yes" out of all respondents who gave a valid answer. Respondents who answered "don't know" or "not sure" or those with missing or invalid data were excluded from the prevalence calculation.
Further Information
8. Martens et al. (2010)
    In Health Inequities in Manitoba: Is the Socioeconomic Gap in Health Widening or Narrowing Over Time? (Martens et al. (2010)), they defined diabetes using the following definition: residents age 19 or older within a three-year period, with either:
    • one or more hospitalizations with a diagnosis of diabetes: ICD-9-CM code 250 or ICD-10-CA codes E10-E14, or
    • two or more physician visits with a diagnosis of diabetes: ICD-9-CM code 250

    This study used the same ICD codes as the definition above, but because prescription medication data was not available for the entire time period of this study, prescription medications were not included in the calculation of diabetes prevalence. The definition also excludes gestational diabetes.
9. Finlayson et al. (2010)
    • one or more hospitalizations OR one or more physician visits OR one or more prescriptions in a two-year period for those aged 19+, or
    • one or more hospitalizations OR two or more physician visits in a two-year period for those aged 19+, or
    • one or more hospitalizations OR two or more physician visits OR one or more prescriptions over a three year period for those aged 19+.

    In all cases, the events were coded with an ICD code representing diabetes or a dispensed prescription for treating diabetes.
10 . Heaman et al. (2012) - Maternal Diabetes

    In Heaman et al. (2012), a woman was considered to have maternal diabetes if she had:

    • one or more hospitalizations with diagnosis code 250 (ICD-9-CM) or E10-14 (ICD-10-CA) in any diagnosis field over three years of data; OR
    • two or more physician claims with diagnosis code 250 over three years of data; OR
    • one or more hospitalizations with a gestational diabetes code in the gestation period (ICD-9-CM: 648.8, ICD-10-CA: O24); OR
    • one or more prescriptions for diabetic drugs:
      • Insulins and Analogues (A10A);
      • Blood Glucose Lowering Drugs excluding Insulin (A10BA02, A10BB01, A10BB02, A10BB03, A10BB09, A10BB12, A10BB31, A10BD03, A10BF01, A10BG02, A10BG03, A10BX02, A10BX03) over three years of data.
Calculating Population-based Prevalence Rates
    The population registered with Manitoba Health Service Commission (MHSC) was used by Young et al. (1991) to derive numerator and denominator data for calculating prevalence estimates for a 5-year period from 1980 to 1984. The first 3 years data was discarded and the mean number of incident cases in the total Manitoba population from 1983 to 1984 was calculated. (Note: Young et al. (1991) estimates were based on the population 25 years of age and older.)

    A single date approach was used by Blanchard et al. (1996). The annual incidence rate was calculated using the mid-year population based on the Manitoba Health population registry. The annual period prevalence was estimated by adding all new incident cases within a year to all the incidence cases from previous years who has neither died nor left the province prior to the beginning of the year (according to the population registry). (Note: Blanchard et al. (1996) estimates were based on the population 25 years of age and older.)

    The population registry was used by Lix et al. (2006) to define population cohorts to derive numerator and denominator data for calculating crude provincial prevalence estimates for each algorithm from 1998/99 to 2002/03. (Note: Lix et al. (2006) estimates are based on the population 19 years of age and older.)

    Crude provincial prevalence estimates for the 32 diabetes algorithms in Lix et al. (2008) are reported in Table 4 . These estimates are based on the population 19 years of age and older. Discussion of the prevalence rates for diabetes can be found in Chapter 6 of the full report, available through a link from the Lix et al. (2008) reference.
Cautions / Limitations
  • Measures of diabetes combine Type I and Type II diabetes, as physician claims data do not allow separate identification because they only record three digit ICD-9-CM codes.
  • Gestational diabetes has a separate diagnosis code and is not typically included in our definition of diabetes. However, in some cases it may be included if gestational diabetes was not properly coded. For more information on gestational diabetes, please see the Diabetes in Pregnacy - Differentiating Between Maternal Pre-Gestational Diabetes and Gestational Diabetes concept.
  • Medication lists should be reviewed prior to any new research to ensure that the drugs included in the list are relevant to the time period being studied in the research project.

Related concepts 

Related terms 

References 

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  • Martens P, Brownell M, Au W, MacWiliam L, Prior H, Schultz J, Guenette W, Elliott L, Buchan S, Anderson M, Caetano P, Metge C, Santos R, Serwonka K. Health Inequities in Manitoba: Is the Socioeconomic Gap in Health Widening or Narrowing Over Time? Winnipeg, MB: Manitoba Centre for Health Policy, 2010. [Report] [Summary] [Supplements/Data extras] [Errata] (View)
  • Martens P, Nickel N, Forget E, Lix L, Turner D, Prior H, Walld R, Soodeen RA, Rajotte L, Ekuma O. The Cost of Smoking: A Manitoba Study. Winnipeg, MB: Manitoba Centre for Health Policy, 2015. [Report] [Summary] [Errata] (View)
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  • Rector TS, Wickstrom SL, Shah M, Thomas Greenlee N, Rheault P, Rogowski J, Freedman V, Adams J, Escarce JJ. Specificity and sensitivity of claims-based algorithms for identifying members of medicare plus choice health plans that have chronic medical conditions. Health Services Research 2004;39(6):1839-1857. [Abstract] (View)
  • Robinson JR, Young TK, Roos LL, Gelskey DE. Estimating the burden of disease: Comparing administrative data and self-reports. Med Care 1997;35(9):932-947. [Abstract] (View)
  • Smith M, Finlayson G, Martens P, Dunn J, Prior H, Taylor C, Soodeen RA, Burchill C, Guenette W, Hinds A. Social Housing in Manitoba. Part II: Social Housing and Health in Manitoba: A First Look. Winnipeg, MB: Manitoba Centre for Health Policy, 2013. [Report] [Summary] (View)
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  • Wilson C, Susan L, Lynch A, Saria R, Peterson D. Patients with diagnosed diabetes mellitus can be accurately identified in an Indian Health Service patient registration database. Public Health 2001;29(1):55-62. [Abstract] (View)
  • Young TK, Roos NP, Hammarstrand KM. Estimated burden of diabetes mellitus in Manitoba according to health insurance claims: a pilot study. CMAJ 1991;144(3):318-324. [Abstract] (View)

Keywords 

  • chronic disease
  • Health Measures
  • Insulin
  • obesity
  • physician claims
  • Validation


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