Estimating prevalence of diagnosed and undiagnosed diabetes

The approach to estimate prevalence of diagnosed and undiagnosed diabetes by population age (20–34, 35–44, 45–54, 55–59, 60–64, 65–70, and over 70), sex, insurance type (commercial, Medicare, Medicaid, and uninsured), and state is documented elsewhere, though detailed prevalence estimates by insurance type have not been previously published [3, 4, 10]. Using the 2012 American Community Survey (ACS, n = 2,375,715), we constructed a population file where each person residing in the community was matched with a similar person from the combined 2011 and 2012 Behavioral Risk Factor Surveillance System (BRFSS, n = 982,154) and each person residing in a nursing home was matched with a similar person from the 2004 National Nursing Home Survey (NNHS, n = 14,017). Detailed information on file construction is published elsewhere, but the ACS-BRFSS match criteria used state, age, sex, race/ethnicity, medical insurance status, and household income [3, 4]. The ACS-NNHS match criteria used age, sex, and race/ethnicity. The 2011 and 2012 BRFSS files were combined to increase sample size and assume little change in diabetes diagnosis rates between 2011 and 2012 controlling for patient demographics. The 2004 NNHS is the most recently available person-level file for the nursing home population, but the underlying rate of diagnosed diabetes was adjusted to reflect a 2011 national study that found diabetes prevalence was close to 33% among nursing home residents [11].

Diagnosed diabetes status (and hypertension and hyperlipidemia status) from BRFSS was determined based on previously having been told by a health professional that the respondent had the condition. Diabetes status from NNHS was determined by ICD-9 diagnosis code (250.xx); hypertension and hyperlipidemia status were determined by ICD 401.xx and 272.xx, respectively. Applying ACS sampling weights provides state-level estimates of adults with diagnosed diabetes and prevalence of conditions such as hypertension and hyperlipidemia among this population by demographic and insurance type.

To estimate prevalence of undiagnosed diabetes by state, age group, sex, and insurance type, we extrapolated national rates to states using the detailed state population characteristics available in the constructed ACS-BRFSS-NNHS file. We first analyzed national data in the combined 2009–2010 and 2011–2012 files of the National Health and Nutrition Examination Survey (NHANES) for adults who (a) had not previously been told by a health professional they had diabetes; (b) were not taking insulin; (c) were not pregnant; and (d) had laboratory results for hemoglobin A1c, fasting plasma glucose test, 2-h oral glucose tolerance test, or a combination of tests. We estimated a logistic regression where the dependent variable was diabetes (n = 649) as determined by laboratory results (see Additional file 1) exceeding thresholds for diabetes, versus no diabetes (n = 9,296). The explanatory variables were age group, sex, race/ethnicity, body weight category, insurance type, current smoker, year, and previous history or diagnosis of asthma, arthritis, heart attack, stroke, cancer, hypertension, hypercholesterolemia, and cardiovascular disease. We applied regression results (see Additional file 1) to the constructed ACS-BRFSS-NNHS 2012 population file to estimate the overall prevalence of undiagnosed diabetes by state.

Estimating prevalence of T2DM among the insured population

We analyzed 2011–2012 medical and pharmacy claims for commercially insured adults in OptumInsight’s de-identified Normative Health Information (dNHI) database) (n = 29,948,496), for the Medicare population using the 2011 Medicare Standard Analytical File 5% sample (n = 2,805,812), and for the Medicaid population using the 2008 Mini-Max file (n = 3,095,634). All analyses were done by state, age, and sex. The dNHI database consists of longitudinally linked and de-identified individual-level data from one of the nation’s largest private insurance plans. The Medicare extract contains medical and prescription claims. Mini-Max is a 5% sample of the Medicaid Analytic eXtract data-a set of person-level data files on service utilization and payments for more than 60 million Medicaid enrollees extracted from the Medicaid Statistical Information System. Patients analyzed in each of these databases were continuously enrolled in a fee-for-service coverage type plan with no more than one gap in enrollment of up to 45 days during the measurement year for the commercially insured population, and were enrolled for all 12 months for the Medicare and Medicaid populations.

We identified patients with diabetes if the patient had at least one emergency department visit or hospitalization or two separate ambulatory visits with a diabetes diagnosis (ICD-9 of 250.xx) submitted during the year, or if the patient used insulin or other diabetes-related medications. Sample inclusion and exclusion criteria and the algorithm for distinguishing whether a patient had type 1 or type 2 diabetes are described in Additional file 1. Patients with a diagnosis of gestational diabetes were excluded. This analysis assumes that within strata defined by age group and sex, the proportion of diabetes cases that are T2DM remained relatively constant between 2012 and 2008 for the Medicaid population and between 2012 and 2011 for the Medicare population.

Estimating the proportion of T2DM patients receiving medication and exams

Using pharmacy claims from the dNHI, Medicaid Mini-MAX data, and Medicare Part D files for each population strata, we calculated the percentage of patients with claims for insulin, non-insulin injectables, or oral antidiabetic agents. We calculated the percentage of patients with at least one claim for anti-hypertensive medications, statins, and angiotensin-converting enzyme (ACE) inhibitors/angiotensin II receptor blockers (ARB). Using procedure codes (see Additional file 1) we calculated the percentage of patients in each population stratum with indication of at least one cholesterol screening test, urine albumin test, or retinal eye exam during the year. These medications and exams are measures recommended by the HEDIS Comprehensive Diabetes Care 2012, and HEDIS is the source of the drug codes and procedure codes used.

Estimating the characteristics and medical expenditures of treated T2DM patients by controlled status

Whether people have their diabetes under control is generally determined by hemoglobin A1c levels – with A1c < 7% often considered tight control, A1c > 9% considered uncontrolled, and recommended individual patient targets as high as 8.5% depending on a patient’s circumstances [1]. Lab results with A1c values were available only for a subset of the commercially insured patients and unavailable for Medicare and Medicaid beneficiaries. Therefore, to identify patients with uncontrolled diabetes, we used ICD-9 diagnosis codes of 250.x2 and 250.x3 in any claim during the year. For discussion purposes, we refer to “uncontrolled” or “poorly controlled” status for anyone with an ICD-9 code indicating uncontrolled diabetes at some point during the year, and use the term “controlled” to define patients with no indication of uncontrolled diabetes. Use of one claim for uncontrolled could overestimate the number of patients with uncontrolled diabetes; however, use of a diagnosis code rather than A1c results could also miss some patients with uncontrolled diabetes, so there are potentially errors in both directions. Later we discuss the limitations of using ICD-9 versus A1c to define uncontrolled status.

We used primary ICD-9 diagnosis codes (see Additional file 1) in medical claims to identify the presence of eight categories of diabetes complications: neurological symptoms, peripheral vascular disease, cardiovascular disease, renal complications, endocrine/metabolic complications, ophthalmic complications, other complications, and orthopedic problems. Using medical claims to indicate comorbidity presence could undercount prevalence of complications. Diabetes is one of multiple risk factors for these complications, and we report total medical expenditures by category because estimating the proportion of complications attributable to diabetes is beyond the scope of this study. We calculated the total annual medical expenditures and pharmacy expenditures for all medical conditions, with all costs inflated to 2012 dollars using the medical and pharmacy cost components of the Consumer Price Index.

Diabetes prevalence

We estimate that close to 25.3 million insured adults (age 20 or older) in the US had diabetes in 2012 (Fig. 1, Table 1). This includes over 18.3 million adults with diagnosed diabetes and 6.9 million undiagnosed. Of the insured, diagnosed population, approximately 16.9 million (about 92%) had T2DM, and 1.45 million had type 1 diabetes. The percent T2DM ranged from 87.1% for the commercially insured population to 97.6% for the Medicare population, and across the insured population ranged from approximately 88% in Utah to 95% in New Mexico (see Additional file 2 for all state-level estimates). The proportion of undiagnosed diabetes cases that are T2DM is unknown. Applying age-specific percent T2DM rates to the undiagnosed diabetes population suggests 6.3 million of the 6.9 million insured adults with undiagnosed diabetes probably have T2DM, though this is likely a lower bound for the proportion of T2DM.

National metrics	Insured				Uninsured
	Total	Commercial	Medicarea	Medicaidb
Total with diabetes	25,260,000	11,400,000	11,430,000	2,430,000	4,950,000
Diagnosed	18,340,000	8,290,000	8,280,000	1,770,000	3,740,000
Percent diabetes cases type 2	92.1%	87.1%	97.6%	89.8%	82.0%c
Adults with diagnosed type 2 diabetes	16,890,000	7,220,000	8,080,000	1,590,000	3,440,000
Undiagnosed	6,920,000	3,110,000	3,150,000	660,000	1,210,000
Percent of type 2 diabetes patients undiagnosedd	27%	27%	28%	27%	23%

Note: ^aDiabetes population estimates include only adults age 65 and older-including Medicaid/Medicare dual-eligible patients and veterans receiving care through the Veterans Health Administration

^bDiabetes population estimates include only adults under age 65 who participate in a government-sponsored insurance program-including Medicaid/Medicare dual-eligible patients; veterans receiving care through the Veterans’ Health Administration; and beneficiaries enrolled in other federal or state-subsidized medical insurance plans (e.g., Indian Health Service)

^cThe percent type 2 was estimated using age-specific proportions of type 2 among the insured population

^dUndiagnosed cases in each age group are assumed to have the same proportion type 2 as diagnosed cases in each age group

California (with 12% of the nation’s adult population) has an estimated 3.4 million people with diabetes (11% of the national total) including over 1 million with undiagnosed diabetes. Estimates of undiagnosed diabetes prevalence are based on the presence of risk factors among the population not diagnosed with diabetes. Alaska had the highest estimated proportion of diabetes cases that are undiagnosed, at 38%, whereas West Virginia (a state with high prevalence of diagnosed diabetes) had the lowest estimated proportion (20%) of cases undiagnosed.

Among those with diagnosed diabetes, 14% nationwide were without medical insurance in 2012 (pre Affordable Care Act), with this percentage ranging from a high of 21% in Louisiana to lows in the 5–6% range for Hawaii, Massachusetts, and Washington, DC.

Exams and medication use

Prescription claims for patients with diagnosed T2DM indicate 77% received medication for diabetes (73% for Medicare, 75% for Medicaid, and 82% for commercially insured). Rates ranged from 70% treated with medication in New Jersey to 82% in Utah. Among Medicare patients, 76% of the age 65–74 population had claims for diabetes medication versus 71% of the population age 75 and older. Overall, 46% had claims for metformin, 23% had claims for insulin, and 29% had claims for sulfonylureas, with smaller percentages of patients having claims for other diabetes single therapy or combination medications. Rates varied by state even within each insurance type. While 90% of commercially insured people with diagnosed T2DM in California received medication, the percentages were substantially lower in Northeastern states including Maine (74%), New Jersey (76%), Massachusetts (77%), and Rhode Island and New Hampshire (78%). There was even greater state variation in percentage receiving medication within the Medicaid population-ranging from 84% in Connecticut to 44% in Arizona.

Diagnosis codes and medication claims suggest 80% of diagnosed T2DM adults had hypertension (controlled or uncontrolled, ranging from 91% for Medicare to 61% for Medicaid), and 76% had hyperlipidemia (ranging from 83% for Medicare to 43% for Medicaid). Medication claims for these diagnosed T2DM patients indicate 63% received ACE inhibitors/ARBs, 47% received other anti-hypertensive medications (e.g., diuretics), and 58% received statins.

Poorly controlled diabetes, sequelae, and costs

Total annual health care expenditures for the treated population with controlled diabetes averaged $17,500 per patient (ranging from $13,160 for commercially insured patients to $21,280 for Medicare patients). This includes both diabetes- and non-diabetes-related expenditures. In comparison, annual health care expenditures averaged $22,410 for the uncontrolled population (ranging from $19,730 for commercially insured patients to $24,440 for Medicare patients). Adjusting the age-sex distribution of the controlled population to match demographics of the uncontrolled population suggests that patients with uncontrolled diabetes have higher annual health care expenditures averaging $4,860 per T2DM patient-including $4,150 in higher medical expenditures and $710 in higher prescription expenditures (with higher prescription expenditures associated primarily with higher costs for insulin). Average expenditure differences between controlled and uncontrolled were highest for the commercially insured population ($6,680), followed by the Medicaid population ($4,360) and Medicare population ($3,430). Average difference in medical costs between controlled and uncontrolled patients was highest for Nevada ($7,800) and Nebraska ($7,530), while New Mexico ($2,080) and California ($2,410) had the smallest excess costs (Additional file 2).

Study strengths and limitations

This study analyzed medical claims for large samples of commercially insured, Medicare, and Medicaid populations to provide insight on treatment patterns, prevalence of diabetes-related complications, and medical and prescription medicine expenditures. The use of medical claims provides a comparison to diabetes management statistics based on self-reported surveys. Study limitations include the following:

Analysis of medical claims was restricted to a fee-for-service population as medical claims are often incomplete for patients in capitated managed care plans. It is unclear how this might bias study results. Medicare patients with poorer health might choose a fee-for-service plan over a managed care plan because it gives them greater access to physicians not in a managed care network, but this is unlikely to affect diabetes detection, treatment, and control. Medicaid patients often have less ability to select whether they are in a managed care or fee-for-service type arrangement (with plan type often determined by state policy).

Uncontrolled status was determined by diagnosis code and not independent lab results. While laboratory results are available for a subset of the commercially insured T2DM patients, Medicare and Medicaid files do not contain laboratory results. To create a consistent definition of diabetes control across insurance types, we identified patients with controlled diabetes as those who were not diagnosed with uncontrolled diabetes (ICD-9 code 250.×2 or 250.×3) during the study year. For the subset of commercially insured T2DM patients for which we had both ICD-9 and A1c information, we performed additional correlation analyses to assert the strength of the association between the two measures of diabetes control. We found a statistically significant and positive correlation between ICD-9- and A1c-based case identification measures (Spearman rank correlation coefficients of 0.22 [P < 0.001] for A1c > 9% as uncontrolled). However, the low correlation suggests that ICD-9 indications of uncontrolled diabetes, as currently coded, are a weak proxy for actual A1c results. Chi-square statistics were also significant. Patients who are sicker tend to have more touch points with the health care system and thus generate more medical claims, so there is a higher likelihood that they might be categorized as having uncontrolled diabetes. This could bias high the estimated expenditure impact associated with uncontrolled diabetes.

Medical claims data were unavailable for care provided through the Veterans Health Administration, Indian Health Services, and other providers. Estimates for the number of adults age 65 or older with diabetes are counted under Medicare-including veterans receiving care through the VHA. Estimates for the number of adults under age 65 with diabetes who receive health care through a government-sponsored program are counted under Medicaid-including Medicaid dual-eligible patients, veterans receiving care through the VHA, and other government-sponsored plans such as IHS.

The OptumInsight dNHI claims data, while nationally representative of the privately insured patient population, might not be representative for some states where United Healthcare and other insurers represented in dNHI have smaller market share. Consequently, for this analysis we estimated outcomes using dNHI data in each state by age and gender and then weighted outcomes based on the age and gender distribution of privately insured people in each state in our constructed state population file. States with small sample size in the dNHI are Alaska, Hawaii, Montana, South Dakota, Vermont, and Wyoming. When the sample was less than n = 30 patients for a particular age-gender strata, we used national outcomes rather than state outcomes for that demographic group. Hence, for these states the metrics reported in this paper for the commercially insured population are biased toward the national average.

The analysis relied in part on self-reported data collected through surveys (American Community Survey, Behavioral Risk Factor Surveillance System, and National Health and Nutrition Examination Survey). Self-reported household income, body weight, smoking status, and other patient characteristics can be unreliable.

For some data sources we combined data from multiple years between 2008 and 2012 to increase sample size. Combining data across years makes the implicit assumption that diagnosis rates and the proportion of diagnosed diabetes cases that are T2DM remained relatively constant over this period.

These data limitations suggest possible areas for improvement. For example, a centralized source for patient information-such as all-payer claims databases that some states are developing-will facilitate comparing diabetes-related outcomes across geographic locations and payers, thus facilitating the identification of populations doing well (to identify possible best practices) and identifying populations doing poorly (to identify needs). This study used ICD-9 diagnosis codes and self-reported data in surveys, but such sources can be unreliable. Increased availability of data from electronic health records could provide more reliable data for future benchmarking.