Key Points
Question
Were there any changes in population-level achievement of diabetes treatment targets in the United States from 2005 to 2016?
Findings
In this population-based study of 2488 individuals, approximately 1 in 4 adults with diagnosed diabetes achieved the composite goal in the United States. No significant improvement in any of the individual targets was observed between 2005 and 2016, and gaps in achieving diabetes care targets, particularly among young (18-44 years), female, and nonwhite adults, persisted during the study period.
Meaning
It appears that advances in diabetes care over the past decade have not translated into meaningful improvement in population-level treatment outcomes.
Abstract
Importance
Treatment advances in diabetes can meaningfully improve outcomes only if they effectively reach the populations at risk.
Objectives
To evaluate whether the cascade of US diabetes care, defined as diabetes diagnosis, linkage to care, and achievement of individual and combined treatment targets, improved from 2005 to 2016 and to investigate potential disparities in US diabetes care.
Design, Setting, and Participants
Nationally representative, serial cross-sectional studies included in the 2005-2016 National Health and Nutrition Examination Survey were evaluated. Data on nonpregnant US adults (age ≥18 years) with diabetes who had reported fasting for 9 or more hours (n = 1742 diagnosed and 746 undiagnosed) were included. Data analysis was performed from August 1, 2018, to May 10, 2019.
Exposures
Time period (2005-2008, 2009-2012, and 2013-2016), age, sex, race/ethnicity, health insurance, and educational level incorporated into logistic regression models predicting odds of target achievement.
Main Outcomes and Measures
Proportion of participants overall and stratified by age, sex, and race/ethnicity who were linked to diabetes care and met glycemic (hemoglobin A1c <7.0%-8.5%, depending on age and complications), blood pressure (<140/90 mm Hg), cholesterol level (low-density lipoprotein cholesterol <100 mg/dL), and smoking abstinence targets and a composite of all targets.
Results
In 2013-2016, of 1742 US adults with diagnosed diabetes, 94% (95% CI, 92%-96%) were linked to diabetes care; 64% (95% CI, 58%-69%) met hemoglobin A1c level, 70% (95% CI, 64%-75%) met blood pressure level, and 57% (95% CI, 51%-62%) met cholesterol level targets; 85% were nonsmokers (95% CI, 82%-88%); and 23% (95% CI, 17%-29%) achieved the composite goal. Results were similar in 2005-2008 (composite 23%) and in 2009-2012 (composite 25%). There was no significant improvement in diagnosis or target achievement during the study period. Compared with middle-aged adults (45-64 years) with diagnosed diabetes, older patients (≥65 years) had higher odds (adjusted odds ratio [aOR], 1.70; 95% CI, 1.17-2.48) and younger adults (18-44 years) had lower odds (aOR, 0.53; 95% CI, 0.29-0.97) of meeting the composite target. Women had lower odds of achieving the composite target than men (aOR, 0.60; 95% CI, 0.45-0.80). Non-Hispanic black individuals vs non-Hispanic white individuals had lower odds of achieving the composite target (aOR, 0.57; 95% CI, 0.39-0.83). Having health insurance was the strongest predictor of linkage to diabetes care (aOR, 3.96; 95% CI, 2.34-6.69).
Conclusions and Relevance
It appears that the diabetes care cascade in the United States has not significantly improved between 2005 and 2016. This study’s findings suggest that gaps in diabetes care that were present in 2005, particularly among younger adults (18-44 years), women, and nonwhite individuals, persist.
This population-based study examines the changes in care from 2005 to 2016 for patients with diabetes in the United States.
Introduction
Diabetes is a major US public health burden, affecting 30.2 million adults, with an expected prevalence of 54 million by 2030.1,2 Diabetes accounted for $237 billion in direct medical costs in 2017.3
There has been general consensus regarding several major goals of diabetes treatment for over 2 decades: achieving hemoglobin A1c (HbA1c) levels, blood pressure (BP), and low-density lipoprotein cholesterol (LDL-C) level treatment targets and promoting smoking cessation. Although numeric treatment targets have shifted slightly over the past decade, the overall approach has not.4,5 Previous studies reported that population achievement of diabetes treatment goals (the ABCs of diabetes: HbA1c level [A], BP [B], and LDL-C level [C]) had improved from the late 1990s to 2010 in the United States.6 To date, it is not known whether there has been continued progress.
Examining each stage along the diabetes care cascade may shed light on the population-level steps required to improve diabetes care. We define the care cascade as diagnosis, linkage to care, achievement of individual treatment targets, and a composite of all individual targets.7,8,9
In a prior study, Ali et al9 presented a cascade of diabetes care in the United States in 2007-2012 and reported that diabetes care was suboptimal in that period. We build on their work by (1) using newer data to illustrate the cascade of diabetes care in the United States in 2013-2016, (2) providing a more detailed examination of whether US diabetes care improved from 2005 to 2016, and (3) evaluating potential demographic disparities in the cascade of US diabetes care over this period.
Methods
Analytic Overview
We analyzed the National Health and Nutrition Examination Survey (NHANES) cross-sectional data from 2005 to 2016.10 To increase statistical reliability of our estimates, we combined NHANES’ 2-year cycles to construct three 4-year intervals: 2005-2008, 2009-2012, and 2013-2016. Data analysis was performed from August 1, 2018, to May 10, 2019. We created a cascade of diabetes care in each period, then investigated whether care variables have improved over time. We further assessed the potential age, sex, and racial/ethnic disparities in US diabetes care. NHANES protocols were approved by the National Center for Health Statistics research ethics review board. With use of publicly available data, this study was considered exempt from review and thus no patient written informed consent was needed.
NHANES Data
NHANES used stratified, multistage, probability cluster sampling techniques to obtain samples that are representative of the US, noninstitutionalized civilian population.10 Data were collected through household interviews and clinical examinations at a mobile examination center (morning and afternoon appointments). The average response rate for the NHANES cycles between 2005 and 2016 was 74% (range, 61%-80%). Study design, survey instruments, and laboratory methodologies are described elsewhere.10
Participants
During the study period (2005-2016), 58 660 participants completed the interview and clinical examination. We included only participants from the morning sessions who were aged 18 years or older, not pregnant, had reported fasting for 9 hours or more, and had results of HbA1c and fasting plasma glucose (FPG) levels available. Restricting the cohort to participants examined in the morning session was required for assessment of FPG and LDL-C levels. The base case cohort was restricted to adults with diagnosed diabetes; outcomes including individuals with undiagnosed diabetes are reported in eTable 4 and eFigure 6 in the Supplement.
Definitions of Outcomes
We defined the following outcomes to create a cascade of care for each period. Patients were considered to have diabetes if they responded yes to the question, “other than during pregnancy, have you ever been told by a doctor/health professional that you have diabetes/sugar diabetes?” or if they had either an HbA1c level of 6.5% or higher (to convert to proportion of total hemoglobin, multiply by 0.01) or FPG level of 126 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0555).5 No distinction was made between type 1 and type 2 diabetes.
Diagnosed diabetes was defined by a yes response to the question, “other than during pregnancy, have you ever been told by a doctor/health professional that you have diabetes/sugar diabetes?” Patients were labeled as having undiagnosed diabetes if they responded no to this question but had either an HbA1c level of 6.5% or higher or FPG level of 126 mg/dL or higher.
Patients with diagnosed diabetes were considered to be linked to diabetes care if they reported that they (1) saw a physician or other health care professional for diabetes in the past year, (2) were taking “diabetic pills” to lower blood glucose levels, (3) were taking insulin, or (4) saw a diabetes specialist in the past 2 years. Those who did not meet any of these criteria were considered to be not linked to diabetes care.
Glycemic control was considered an HbA1c level less than or equal to the target level, applied to all 3 time periods in the analysis. For each patient, we defined a liberal personalized HbA1c level target between 7.0% and 8.5%, depending on the patient’s age and presence of complications (cardiovascular disease [CVD] history, nephropathy, and retinopathy) per the 2018 American Diabetes Association guidelines.5 For patients younger than 65 years, the target HbA1c level was 7.0% in the absence of reported complications or 8.0% if diabetes complications were reported. For patients 65 years or older, the target HbA1c level was 7.5% in patients without complications or 8.5% in those with complications.
We used the mean of all BP measurements (3-4 readings) to estimate each patient’s systolic and diastolic BP. Blood pressure control was defined as a systolic/diastolic BP level less than 140/90 mm Hg per the 2018 American Diabetes Association guidelines.5 We also estimated the proportion of patients who met lower BP targets (eFigure 2 in the Supplement).11
Cholesterol control was defined as having an LDL-C level less than 100 mg/dL (to convert to millimoles per liter, multiply by 0.0259). We also estimated the proportion of patients who (1) reported taking statins (ie, general use of statins among US adults with diagnosed diabetes); (2) were eligible for CVD primary prevention (ie, age ≥40 years and no CVD history), reported taking statins, and had an LDL-C level less than 100 mg/dL; and (3) were eligible for CVD secondary prevention (ie, had a CVD history), reported taking statins, and had an LDL-C level less than 70 mg/dL (eFigure 1 in the Supplement).5,12
Participants were defined as current nonsmokers if they had serum cotinine levels of 10 ng/mL or lower (to convert to nanomoles per liter, multiply by 5.675) or responded no to either, “have you smoked at least 100 cigarettes in life?” or “do you now smoke cigarettes?” Participants were defined as current smokers if they had serum cotinine levels greater than 10 ng/mL or provided a positive response to the question, “do you now smoke cigarettes?”13
We further defined combination outcomes using the outcomes combined BC control (BP and LDL-C control); combined ABC control (BC and glycemic control); and combined ABCN control or composite control (ABC and not smoking).
Statistical Analysis
We conducted the statistical analysis using the fasting sample weights provided in the NHANES data to adjust for complex survey designs, missing data, and nonresponse. We tested for differences in cohort characteristics across the 3 periods (2005-2008, 2009-2012, and 2013-2016) using the adjusted Wald F test.
To develop a cascade of diabetes care for each period, we calculated the proportions and 95% CIs of patients with diagnosed diabetes who were linked to diabetes care; met the target HbA1c, BP, and LDL-C level thresholds; and were nonsmokers. To do so, we used the svyciprop function with likelihood method in the R survey package, version 3.33-2 (R Foundation), which uses a scaled χ2 distribution for the log likelihood from a binomial distribution and is known to produce accurate 95% CIs for proportions, especially near 0 and 1.14,15,16
To assess whether linkage and achievement of treatment goals improved over time and investigate disparities in diabetes care, we developed a logistic regression model and adjusted for period, demographics (age, sex, and race/ethnicity), as well as educational level and health insurance coverage as a proxy for socioeconomic status. We adjusted for socioeconomic status because it can influence access to care and medication and, thus, achievement of care goals. We also developed a more complex model that further adjusts for diabetes severity and complications since disease severity may influence achievement of diabetes care goals (eTable 1 in the Supplement). Moreover, we calculated the proportion of patients in distinct clinically relevant ranges of HbA1c level, BP, LDL-C level, and body mass index in each period (eFigure 2 in the Supplement).
To determine which variables had a significant effect on linkage and treatment target achievement, we calculated a 2-tailed, unpaired P value for each categorical variable, using the Wald test for the hypothesis that all coefficients associated with the term were 0. We used the adjusted odds ratio (aOR) and 95% CI to assess the association between the individual levels of each categorical variable and achieving the diabetes care goals in each model. A significance level of .05 was used in all statistical analyses.
Additional analyses assessing the cascade of care for different populations are reported. These include (1) all adults with diagnosed and undiagnosed diabetes (eTable 2 and eFigure 3 in the Supplement), (2) only adults with diagnosed diabetes and linked to diabetes care (eTable 3 and eFigure 5 in the Supplement), and (3) only adults with undiagnosed diabetes (eTable 4 and eFigure 6 in the Supplement).
Results
Changes in Adults With Diagnosed Diabetes
Over the 3 study periods, a total of 1742 NHANES participants had diagnosed diabetes; an additional 746 participants had undiagnosed diabetes (eTable 4 and eFigure 6 in the Supplement). Missing data in the sample ranged from 0% (demographic variables) to 7.2% (LDL-C level in 2009-2012).
There was no statistically significant difference across the 3 periods regarding most demographic and clinical variables, income, time since diabetes diagnosis, CVD history, or glucose-lowering medication use in the base case cohort. Educational level, insurance coverage, and urinary albumin-creatinine ratio improved in the last period compared with the first period (Table 1). For example, the mean (SE) proportion of participants with an educational level of college graduate or above was 12.8% (2.2%) in 2005-2008, 19.7% (2.2%) in 2009-2012, and 21.1% (2.8%) in 2013-2016.
Table 1. Characteristics of US Nonpregnant Adults With Diagnosed Diabetes in 2005-2016.
| Characteristic | % (SE) | P Valuea | ||
|---|---|---|---|---|
| 2005-2008 (n = 505) | 2009-2012 (n = 603) | 2013-2016 (n = 634) | ||
| Age, y | ||||
| 18-44 | 14.8 (2.4) | 10.9 (2.0) | 12.8 (1.4) | .82 |
| 45-64 | 46.7 (2.5) | 46.0 (2.6) | 48.2 (3.0) | |
| ≥65 | 38.5 (2.6) | 43.1 (2.9) | 39.0 (2.2) | |
| Age, mean (SE), y | 58.9 (0.9) | 60.6 (0.8) | 60.1 (0.4) | .28 |
| Women | 54.9 (3.0) | 50.0 (2.8) | 47.2 (3.1) | .19 |
| Race/ethnicity | ||||
| Non-Hispanic white | 63.3 (4.2) | 58.1 (3.7) | 62.0 (3.3) | .61 |
| Non-Hispanic black | 16.8 (2.8) | 15.7 (2.7) | 15.0 (1.7) | |
| Hispanic | 13.8 (2.1) | 15.7 (2.9) | 15.5 (2.3) | |
| Other | 6.1 (1.5) | 10.5 (2.0) | 7.5 (1.1) | |
| Educational level | ||||
| <High school | 30.7 (2.1) | 28.1 (3.3) | 21.4 (2.3) | .04b |
| High school graduate | 28.0 (2.6) | 23.8 (3.1) | 21.9 (2.4) | |
| Some college | 28.5 (3.2) | 28.4 (2.5) | 35.5 (3.2) | |
| ≥College graduate | 12.8 (2.2) | 19.7 (2.2) | 21.1 (2.8) | |
| Annual household income, $ | ||||
| <20 000 | 23.4 (2.1) | 23.6 (2.7) | 16.7 (2.7) | .57 |
| 20 000-74 999 | 54.1 (2.9) | 59.6 (4.6) | 55.3 (3.7) | |
| ≥75 000 | 22.5 (2.5) | 16.8 (3.1) | 28.0 (4.0) | |
| No health insurance | 11.0 (1.5) | 13.3 (1.9) | 8.2 (1.2) | .05b |
| BMI | ||||
| <25.0 | 14.8 (2.0) | 13.1 (1.3) | 11.4 (1.8) | .05 |
| 25.0-29.9 | 24.2 (2.6) | 24.9 (2.5) | 31.6 (2.1) | |
| ≥30.0 | 61.0 (2.7) | 62.0 (2.8) | 56.9 (2.4) | |
| Time since diabetes diagnosis, yc | ||||
| 0-5 | 30.8 (2.9) | 33.4 (2.4) | 26.1 (2.4) | .11 |
| 5-15 | 43.5 (3.0) | 38.7 (2.7) | 42.5 (2.3) | |
| >15 | 25.7 (2.3) | 27.9 (1.9) | 31.4 (2.0) | |
| History of CVD | ||||
| MI | 13.1 (1.5) | 9.9 (1.7) | 11.0 (1.8) | .53 |
| Stroke | 13.1 (2.1) | 7.4 (0.8) | 9.2 (1.6) | |
| CHF | 11.0 (1.7) | 10.3 (1.6) | 8.7 (1.0) | |
| Angina | 8.3 (1.4) | 9.8 (1.7) | 5.4 (1.5) | |
| Use of OADsd | 73.6 (3.1) | 76.7 (3.3) | 73.1 (2.2) | .64 |
| Use of insulind | 29.7 (2.7) | 26.8 (2.2) | 28.3 (2.7) | .73 |
| HbA1c, mean (SE) | 7.3 (0.1) | 7.3 (0.1) | 7.5 (0.1) | .17 |
| BP, mean (SE), mm Hg | ||||
| Systolic | 131.3 (1.1) | 129.7 (0.9) | 130.3 (1.1) | .57 |
| Diastolic | 68.3 (1.1) | 67.5 (0.8) | 70.1 (0.6) | .08 |
| Cholesterol, mean (SE), mg/dL | ||||
| Total | 185.0 (2.6) | 179.5 (2.2) | 177.1 (2.3) | .06 |
| HDL | 50.0 (1.0) | 47.5 (0.8) | 48.2 (0.7) | .13 |
| LDL | 100.4 (1.8) | 100.0 (1.7) | 99.1 (1.6) | .85 |
| Triglycerides | 183.9 (13.4) | 163.8 (9.1) | 154.7 (10.0) | .17 |
| Serum creatinine, mean (SE), mg/dL | 0.99 (0.03) | 1.0 (0.05) | 1.0 (0.04) | .72 |
| Albuminuriae | ||||
| Normoalbuminuria | 63.5 (2.6) | 75.5 (2.0) | 70.8 (2.9) | .01b |
| Microalbuminuria | 26.5 (3.0) | 17.8 (1.6) | 21.0 (2.5) | |
| Macroalbuminuria | 10.0 (1.9) | 6.7 (1.3) | 8.2 (1.5) | |
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); BP, blood pressure; CHF, congestive heart failure; CVD, cardiovascular disease; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MI, myocardial infarction; OAD, oral antidiabetic drug.
SI conversion factors: To convert HbA1c to proportion of total hemoglobin, multiply by 0.01; HDL, LDL, and total cholesterol to millimoles per liter, multiply by 0.0259; serum creatinine to micromoles per liter, multiply by 88.4; and triglycerides to millimoles per liter, multiply by 0.0113.
Values were calculated using the adjusted Wald F test.
Statistically significant (P < .05, before rounding).
Data on diabetes duration were missing for 29.0% to 31.8% of the participants.
Data were based on self-report.
Normoalbuminuria, microalbuminuria, and macroalbuminuria were defined as having a urinary albumin-creatinine ratio of less than 30, 30 to 300, and greater than 300 mg per gram, respectively.
The Diabetes Care Cascade
In 2013-2016, 94% (95% CI, 92%-96%) of US adults with diagnosed diabetes were linked to diabetes care. In the same period, 64% (95% CI, 58%-69%) of all adults with diagnosed diabetes met the individualized HbA1c target levels, 70% (95% CI, 64%-75%) achieved BP control, 57% (95% CI, 51%-62%) met the LDL-C target level, and 85% (95% CI, 82%-88%) were nonsmokers. However, only 41% (95% CI, 35%-47%) achieved BC control, 25% (95% CI, 20%-31%) met ABC targets, and 23% (95% CI, 17%-29%) met the combined ABCN target (Figure 1). Results were similar in 2005-2008 (composite control 23%) and 2009-2012 (composite control 25%).
Figure 1. Cascade of Diabetes Care in the United States for 2005-2016.
The cascade of diabetes care (also known as the diabetes care continuum) for US nonpregnant adults with diagnosed diabetes in 2005-2008, 2009-2012, and 2013-2016. The cascade of care illustrates the population-level steps of linkage to care, meeting individual treatment targets, and attaining the combined targets of care, and helps reveal the gaps in diabetes care. Glycemic control was defined as hemoglobin A1c (HbA1c) level less than or equal to an individualized target. Blood pressure (BP) control was defined as systolic/diastolic BP less than 140/90 mm Hg. Cholesterol control was defined as low-density lipoprotein cholesterol level less than 100 mg/dL (to convert to millimoles per liter, multiply by 0.0259). BC control refers to BP and cholesterol level control; ABC control refers to HbA1c, BP, and cholesterol level control; and ABCN control was defined as HbA1c, BP, and cholesterol level control and not smoking tobacco. Error bars indicate 95% CI.
The LDL-C level was consistently the least likely target to be achieved across all periods, a finding that was associated with low statin use (eFigure 1 in the Supplement). Time period had no statistically significant association with linkage to diabetes care, meeting individual treatment targets, and the composite (Table 2). The distributions of HbA1c level, BP, LDL-C level, and BMI, an important diabetes care variable, were stable across the 3 periods (eFigure 2 in the Supplement). The mean (SE) BMI (calculated as weight in kilograms divided by height in meters squared) did not significantly change across the 3 periods (2005-2008: 32.6 [0.4], 2009-2012: 33.0 [0.4], 2013-2016: 32.5 [0.4]; P = .72). The Hosmer-Lemeshow goodness-of-fit test for the null hypothesis that the model fits the data well resulted in P = .08 (linkage to diabetes care), P = .16 (glycemic control), P = .01 (BP control), P = .66 (cholesterol level control), P = .33 (nonsmoking), P = .43 (combined ABC control), and P = .24 (combined ABCN control).
Table 2. Multivariable Logistic Regression Results Assessing Potential Demographic and Socioeconomic Status Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diagnosed Diabetes in 2005-2016.
| Characteristic | Adjusted Odds Ratio (95% CI)a | ||||||
|---|---|---|---|---|---|---|---|
| Linkage to Diabetes Care | Control | Nonsmoking | Combined Control | ||||
| Glycemic | BP | Cholesterol Level | ABCb | ABCNc | |||
| Period | |||||||
| 2005-2008 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| 2009-2012 | 0.71 (0.36-1.43) | 0.72 (0.48-1.07) | 1.42 (1.01-2.02) | 0.87 (0.62-1.22) | 1.34 (0.92-1.94) | 0.92 (0.62-1.36) | 1.03 (0.69-1.53) |
| 2013-2016 | 0.62 (0.34-1.15) | 0.63 (0.44-0.91) | 0.97 (0.68-1.39) | 0.91 (0.65-1.26) | 1.37 (0.95-1.98) | 0.74 (0.49-1.11) | 0.87 (0.56-1.36) |
| P value | .38 | .07 | .07 | .73 | .17 | .31 | .68 |
| Age group, y | |||||||
| 18-44 | 0.34 (0.18-0.67)d | 0.79 (0.51-1.22) | 1.40 (0.81-2.43) | 0.75 (0.45-1.26) | 0.76 (0.50-1.16) | 0.81 (0.46-1.41) | 0.53 (0.29-0.97)d |
| 45-64 | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| ≥65 | 0.85 (0.45-1.61) | 3.75 (2.77-5.06)d | 0.37 (0.25-0.55)d | 1.70 (1.25-2.30)d | 3.50 (2.38-5.15)d | 1.54 (1.05-2.25)d | 1.70 (1.17-2.48)d |
| P value | .01 | <.001 | <.001 | .001 | <.001 | .03 | .001 |
| Sex | |||||||
| Male | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Female | 0.53 (0.30-0.94)d | 1.25 (0.95-1.63) | 1.04 (0.80-1.36) | 0.66 (0.53-0.82)d | 1.20 (0.85-1.69) | 0.59 (0.46-0.76)d | 0.60 (0.45-0.80)d |
| P value | .03 | .11 | .74 | <.001 | .30 | <.001 | .001 |
| Race/ethnicity | |||||||
| Non-Hispanic white | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Non-Hispanic black | 1.30 (0.69-2.43) | 0.88 (0.62-1.23) | 0.42 (0.30-0.60)d | 0.62 (0.45-0.85)d | 0.77 (0.51-1.16) | 0.60 (0.42-0.84)d | 0.57 (0.39-0.83)d |
| Hispanic | 0.43 (0.24-0.77)d | 0.66 (0.46-0.96) | 0.83 (0.56-1.21) | 0.60 (0.43-0.84)d | 1.13 (0.74-1.71) | 0.60 (0.42-0.85)d | 0.69 (0.48-1.00) |
| P value | .01 | .25 | .001 | .001 | .59 | .02 | .05 |
| Health insurance | |||||||
| No | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| Yes | 3.96 (2.34-6.69)d | 1.61 (1.10-2.36)d | 1.35 (0.86-2.14) | 1.32 (0.95-1.84) | 1.26 (0.81-1.94) | 1.41 (0.85-2.34) | 1.60 (0.93-2.75) |
| P value | <.001 | .02 | .20 | .10 | .31 | .18 | .09 |
| Education | |||||||
| <High school | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] | 1 [Reference] |
| High-school graduate | 0.72 (0.35-1.49) | 0.82 (0.54-1.25) | 0.75 (0.53-1.06) | 1.06 (0.71-1.60) | 1.23 (0.75-2.02) | 1.04 (0.68-1.59) | 0.95 (0.61-1.49) |
| Some college | 0.51 (0.28-0.96) | 1.01 (0.65-1.54) | 0.89 (0.61-1.32) | 1.33 (0.95-1.87) | 0.98 (0.62-1.53) | 1.11 (0.76-1.61) | 1.22 (0.80-1.87) |
| ≥College | 0.66 (0.28-1.60) | 0.99 (0.60-1.66) | 1.16 (0.78-1.72) | 1.45 (0.95-2.22) | 2.48 (1.31-4.67)d | 1.21 (0.87-1.70) | 1.47 (1.00-2.14) |
| P value | .37 | .76 | .24 | .31 | .05 | .82 | .27 |
Abbreviation: BP, blood pressure.
The odds of meeting a care target with the specific level of the categorical variable compared with a reference level. The Hosmer-Lemeshow goodness-of-fit test for the null hypothesis that the model fits the data well resulted in P = .08 (linkage to diabetes care), P = .16 (glycemic control), P = .01 (BP control), P = .66 (cholesterol level control), P = .33 (nonsmoking), P = .43 (combined ABC control), and P = .24 (combined ABCN control).
Combined ABC control refers to meeting the personalized hemoglobin A1c, blood pressure (systolic/diastolic <140/90 mm Hg), and cholesterol (low-density lipoprotein <100 mg/dL [to convert to millimoles per liter, multiply by 0.0259]) level targets.
Combined ABCN control (composite control) refers to both meeting the ABC targets and not smoking tobacco.
Statistically significant.
In 2013-2016, of all US adults with diabetes, 74% (95% CI, 70%-78%) were aware of their diagnosis, 70% (95% CI, 65%-74%) were linked to diabetes care, and 20% (95% CI, 16%-25%) met the composite target (eFigure 3 in the Supplement).
Disparities in Care
Age
Compared with middle-aged (45-64 years) adults with diabetes, older patients (≥65 years) had lower odds of achieving BP control (aOR, 0.37; 95% CI, 0.25-0.55) but higher odds of meeting glycemic (aOR, 3.75; 95% CI, 2.77-5.06), LDL-C level (aOR, 1.70; 95% CI, 1.25-2.30), nonsmoking (aOR, 3.50; 95% CI, 2.38-5.15), and combined ABCN (aOR, 1.70; 95% CI, 1.17-2.48) targets. Younger adults (18-44 years) with diabetes had lower odds of being linked to diabetes care (aOR, 0.34; 95% CI, 0.18-0.67) or achieving ABCN control (aOR, 0.53; 95% CI, 0.29-0.97) compared with middle-aged patients (Table 2). For example, in 2013-2016, 30% of older adults with diabetes achieved the combined ABCN targets, whereas only 20% of middle-aged adults and 12% of young adults with diabetes achieved the combined ABCN goals (Figure 2).
Figure 2. Cascade of Diabetes Care in the United States for 2005-2016 by Age.
Cascade of diabetes care for US nonpregnant adults with diagnosed diabetes by age, shown as diagnosed diabetes in the base cohort linked to diabetes care and glycemic control (A), blood pressure (BP) and cholesterol control in the nonsmoking cohort (B), and combined control (C). Glycemic control was defined as HbA1c less than or equal to an individualized target. Blood pressure control was defined as systolic/diastolic BP less than 140/90 mm Hg. Cholesterol control was defined as low-density lipoprotein cholesterol level less than 100 mg/dL (to convert to millimoles per liter, multiply by 0.0259). BC control refers to BP and cholesterol level control; ABC control refers to HbA1c, BP, and cholesterol level control; and ABCN control was defined as HbA1c, BP, and cholesterol level control and not smoking tobacco. Error bars indicate 95% CI.
Sex
Women had lower odds of achieving the combined ABCN control (aOR, 0.60; 95% CI, 0.45-0.80) than men, mainly owing to lower odds of LDL-C level control (aOR, 0.66; 95% CI, 0.53-0.82). Female sex was also associated with lower odds of linkage to care (aOR, 0.53; 95% CI, 0.30-0.94) (Table 2; eFigure 4 in the Supplement).
Race/Ethnicity
Reporting non-Hispanic black race was associated with significantly lower odds of achieving BP (aOR, 0.42; 95% CI, 0.30-0.60), LDL-C level (aOR, 0.62; 95% CI, 0.45-0.85), and combined ABCN (aOR, 0.57; 95% CI, 0.39-0.83) targets compared with reporting non-Hispanic white race (Table 2). Reporting Hispanic ethnicity vs non-Hispanic white race was associated with lower odds of linkage to care (aOR, 0.43; 95% CI, 0.24-0.77), LDL-C level (aOR, 0.60; 95% CI, 0.43-0.84), and combined ABC control (aOR, 0.60; 95% CI, 0.42-0.85) targets (Table 2). For example, in 2013-2016, 25% of US adults with diabetes who reported their race as non-Hispanic white achieved the composite, whereas only 14% of those reporting non-Hispanic black race and 18% of those reporting Hispanic ethnicity achieved the composite (Figure 3).
Figure 3. Cascade of Diabetes Care in the United States for 2005-2016 by Race/Ethnicity.
Cascade of diabetes care for US nonpregnant adults with diagnosed diabetes by race/ethnicity, shown as diagnosed diabetes in the base cohort linked to diabetes care and glycemic control (A), blood pressure (BP) and cholesterol control in the nonsmoking cohort (B), and combined control (C). Glycemic control was defined as hemoglobin A1c (HbA1c) level less than or equal to an individualized target. Blood pressure (BP) control was defined as systolic/diastolic BP less than 140/90 mm Hg. Cholesterol control was defined as low-density lipoprotein cholesterol level less than 100 mg/dL (to convert to millimoles per liter, multiply by 0.0259). BC control refers to BP and cholesterol level control; ABC control refers to HbA1c, BP, and cholesterol level control; and ABCN control was defined as HbA1c, BP, and cholesterol level control and not smoking tobacco. Error bars indicate 95% CI.
Other Correlates of the Care Cascade
Adults with diabetes in the United States who had health insurance coverage had higher odds of being linked to diabetes care (aOR, 3.96; 95% CI, 2.34-6.69) and meeting glycemic targets (aOR, 1.61; 95% CI, 1.10-2.36) (Table 2). Most disparities in care persisted in the more complex multivariable logistic regression model (eTable 1 in the Supplement). When analyses were restricted to patients who were linked to diabetes care, age (P = .001) and sex (P = .001) disparities in achieving combined ABCN control persisted, but racial/ethnic disparities did not reach statistical significance (P = .07) (eTable 3 in the Supplement).
Discussion
The cascade of diabetes care revealed persistent gaps and disparities between 2005 and 2016. Similar to results of a prior study,9 our investigation noted that, in the most recent period (2013-2016), less than 1 in 4 US adults with diagnosed diabetes met the composite glycemic, blood pressure, cholesterol, and nonsmoking target (ABCN). In addition, we found that none of the US diabetes care variables improved from 2005 to 2016.
Although, compared with hypertension and hyperglycemia, hyperlipidemia may be relatively easier to treat, the LDL-C level target was the least likely of the 3 to be met. Despite a modest increase in statin use over the 3 study periods, only 6 in 10 US adults with diagnosed diabetes were taking a statin in 2013-2016. Lack of access to care may be an important modifiable factor. Cholesterol level control and achievement of the composite goals were better among adults who were linked to diabetes care compared with those who were not linked or diagnosed (eFigure 5 and eFigure 6 in the Supplement).
In general, having health insurance was the strongest indicator of diagnosis, linkage, and achievement of the composite for all patients combined (eTable 2 in the Supplement), although it did not reach statistical significance for meeting the composite when only adults with diagnosed diabetes were included. Health insurance coverage increased in the United States after 2014 because of the Affordable Care Act, which could have influenced the cascade of care in the third period (2013-2016). Prior observational studies have shown improved diabetes diagnosis and earlier treatment under the Affordable Care Act,17,18,19 but we did not observe national changes over time in the proportion of target achievement despite the introduction of the Affordable Care Act.
Certain populations, such as younger age (18-44 years), female sex, and nonwhite adults with diabetes, demonstrated persistent disparities in meeting diabetes care goals over the study period. Given that diabetes morbidity and mortality are mainly due to complications that accumulate over time,20 interventions to promote early control of risk factors for young adults with diabetes should be further expanded.21,22 Also, access to care and sex disparities in treatment of cardiac risk factors may have contributed to the sex disparities.23,24 Moreover, while there may be some underlying physiologic differences in the prevalence of hypertension and insulin deficiency that correlate with race/ethnicity,1,25 to date, access to and effectiveness of health care remain major factors that have not been adequately addressed on a population level despite numerous innovative interventions targeting these populations.26,27,28,29,30
Despite notable advances in diabetes drug development (eg, US Food and Drug Administration approval of dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 agonists to improve glycemic control early in the study period),31,32,33 multiple, well-publicized guidelines for BP and cholesterol management, and many attempts to create new care models for diabetes,34,35,36 the diabetes care cascade has not significantly improved since the early 2000s. NHANES data indicate that these developments are not effectively reaching the population at large, which could be in part associated with the high drug costs. In addition, barriers to access of care remain an important consideration: racial/ethnic disparities did not reach statistical significance in the cohort linked to care.
Continued attention to the diabetes care cascade may help to monitor the progress and motivate patients and health care professionals to work toward achieving treatment targets and improving population-based outcomes. For example, in Minnesota, continuous monitoring of health care quality and clinic-level performance through annual reports has contributed to sustained improvements in meeting care targets. Although in 2004 only 11.9% of patients with known diabetes in Minnesota achieved optimal diabetes care, defined similarly in that study and our analysis, this rate increased to 44.7% in 2017—more than twice the estimated national value.37,38
Limitations
Our findings are limited by the study design. First, we used cross-sectional survey and examination data from noninstitutionalized civilian adults to approximate the diabetes care cascade for the US resident population. Also, since we used cross-sectional data, the results are snapshots of diabetes care during the survey periods. Second, on par with previous research, we used the most recent guidelines to define diabetes and risk factor control and applied those definitions retroactively, albeit consistently, across all survey periods.6,9 Nevertheless, supplemental analyses illustrating the distribution of HbA1c levels, BP, and LDL-C levels over time, as well as the proportion of patients who achieved lipid level control for primary and secondary prevention of CVD, showed little change over time. In addition, although the American Diabetes Association began recommending the use of HbA1c levels for diabetes diagnosis in 2010,39 we used both HbA1c and FPG criteria retroactively across all periods. Third, we did not assess clinical outcomes in this study. Fourth, we created logistic regression models to predict various outcomes and used aORs to assess the association between different variables and achievement of diabetes care targets. The aOR is considered a close approximation to the relative risk when the prevalence of the risk is low but tends to exaggerate the magnitude of the outcome otherwise. Because the magnitude of the effect sizes in this study is not large, the qualitative judgments and conclusions should be valid.40 In addition, although we did not differentiate between type 1 and type 2 diabetes, treatment targets are similar for both.
Conclusions
Despite major advances in drug discovery and movement to develop innovative diabetes care delivery models over the past 2 decades, the diabetes care cascade did not appear to improve for US adults with diabetes between 2005 and 2016. More-frequent diabetes screening, expanded access to care and health insurance, and interventions to improve patients’ adherence to medication and to reduce clinical inertia must remain strategies to improve diabetes outcomes in the United States.41,42,43,44 In addition, the results of this study suggest that recent advances in diabetes treatment have not effectively reached the populations at risk and may indicate an immediate need for better approaches to diabetes care delivery, including a continued focus on reaching underserved populations with persistent disparities in care.
eMethods. Additional Analyses on the Cascade of Diabetes Care
eResults. Additional Analyses on the Cascade of Diabetes Care
eTable 1. Diagnosed Diabetes: Multivariable Logistic Regression Results Assessing Potential Demographic, Socioeconomic, and Disease Severity Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diagnosed Diabetes in 2005-2016
eTable 2. Diagnosed and Undiagnosed Diabetes: Multivariable Logistic Regression Results Assessing Potential Demographic and Socioeconomic Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diagnosed and Undiagnosed Diabetes in 2005-2016
eTable 3. Diagnosis and Linkage to Diabetes Care: Multivariable Logistic Regression Results Assessing Potential Demographic and Socioeconomic Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diabetes in 2005-2016 Who Were Diagnosed and Linked to Diabetes Care
eTable 4. Undiagnosed diabetes: Multivariable Logistic Regression Results Assessing Potential Demographic and Socioeconomic Status Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diabetes in 2005-2016 Who Were Undiagnosed
eFigure 1. Statin Use: Proportion of US Adults With Diagnosed Diabetes Who Were Taking Statins for Primary and Secondary Prevention of Cardiovascular Disease and Achieved the Recommended Targets, 2005-2016
eFigure 2. Risk Factor Control: Distribution of Hba1c, Blood Pressure, LDL Cholesterol, and Body Mass Index Among US Nonpregnant Adults With Diagnosed Diabetes, 2005-2016
eFigure 3. Cascade of Diabetes Care: Diagnosed and Undiagnosed Diabetes, 2005
eFigure 4. Cascade of Diabetes Care by Sex: Diagnosed Diabetes, 2005-2016
eFigure 5. Cascade of Diabetes Care: Diagnosed Diabetes Linked to Care, 2005-2016
eFigure 6. Cascade of Diabetes Care: Undiagnosed Diabetes, 2005-2016
References
- 1.Centers for Disease Control and Prevention National diabetes statistics report, 2017. http://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Accessed January 28, 2019.
- 2.Rowley WR, Bezold C, Arikan Y, Byrne E, Krohe S. Diabetes 2030: insights from yesterday, today, and future trends. Popul Health Manag. 2017;20(1):6-12. doi: 10.1089/pop.2015.0181 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.American Diabetes Association Economic costs of diabetes in the US in 2017. Diabetes Care. 2018;41(5):917-928. doi: 10.2337/dci18-0007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.American Diabetes Association Standards of medical care in diabetes. Diabetes Care. 2005;28(suppl 1):S4-S36. doi: 10.2337/diacare.28.suppl_1.S4 [DOI] [PubMed] [Google Scholar]
- 5.American Diabetes Association Standards of medical care in diabetes—2018. January 2018; https://diabetesed.net/wp-content/uploads/2017/12/2018-ADA-Standards-of-Care.pdf. Accessed January 28, 2019.
- 6.Ali MK, Bullard KM, Saaddine JB, Cowie CC, Imperatore G, Gregg EW. Achievement of goals in US diabetes care, 1999-2010. N Engl J Med. 2013;368(17):1613-1624. doi: 10.1056/NEJMsa1213829 [DOI] [PubMed] [Google Scholar]
- 7.Kay ES, Batey DS, Mugavero MJ. The HIV treatment cascade and care continuum: updates, goals, and recommendations for the future. AIDS Res Ther. 2016;13:35. doi: 10.1186/s12981-016-0120-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Giordano TP. The HIV treatment cascade—a new tool in HIV prevention. JAMA Intern Med. 2015;175(4):596-597. doi: 10.1001/jamainternmed.2014.8199 [DOI] [PubMed] [Google Scholar]
- 9.Ali MK, Bullard KM, Gregg EW, Del Rio C. A cascade of care for diabetes in the United States: visualizing the gaps. Ann Intern Med. 2014;161(10):681-689. doi: 10.7326/M14-0019 [DOI] [PubMed] [Google Scholar]
- 10.Centers for Disease Control and Prevention National Center for Health Statistics. National Health and Nutrition Examination Survey. https://www.cdc.gov/nchs/nhanes/index.htm. Accessed January 28, 2019.
- 11.Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71(6):e13-e115. [DOI] [PubMed] [Google Scholar]
- 12.Nayor M, Vasan RS. Recent update to the US cholesterol treatment guidelines: a comparison with international guidelines. Circulation. 2016;133(18):1795-1806. doi: 10.1161/CIRCULATIONAHA.116.021407 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hukkanen J, Jacob P III, Benowitz NL. Metabolism and disposition kinetics of nicotine. Pharmacol Rev. 2005;57(1):79-115. doi: 10.1124/pr.57.1.3 [DOI] [PubMed] [Google Scholar]
- 14.Rao JN, Scott AJ. On chi-squared tests for multiway contingency tables with cell proportions estimated from survey data. Ann Stat. 1984;12(1):46-60. doi: 10.1214/aos/1176346391 [DOI] [Google Scholar]
- 15.Lumley T. Survey: analysis of complex survey samples. 2018; R package version 3.35. https://cran.r-project.org/web/packages/survey/survey.pdf. Published April 27, 2019. Accessed January 25, 2019.
- 16.Lumley T. Analysis of complex survey samples. J Stat Softw. 2004;9(1):1-19. [Google Scholar]
- 17.Kaufman HW, Chen Z, Fonseca VA, McPhaul MJ. Surge in newly identified diabetes among Medicaid patients in 2014 within Medicaid expansion states under the Affordable Care Act. Diabetes Care. 2015;38(5):833-837. doi: 10.2337/dc14-2334 [DOI] [PubMed] [Google Scholar]
- 18.McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Health of previously uninsured adults after acquiring Medicare coverage. JAMA. 2007;298(24):2886-2894. doi: 10.1001/jama.298.24.2886 [DOI] [PubMed] [Google Scholar]
- 19.Casagrande SS, McEwen LN, Herman WH. Changes in health insurance coverage under the Affordable Care Act: a national sample of US adults with diabetes, 2009 and 2016. Diabetes Care. 2018;41(5):956-962. doi: 10.2337/dc17-2524 [DOI] [PubMed] [Google Scholar]
- 20.Nathan DM. Long-term complications of diabetes mellitus. N Engl J Med. 1993;328(23):1676-1685. doi: 10.1056/NEJM199306103282306 [DOI] [PubMed] [Google Scholar]
- 21.RISE Consortium Metabolic contrasts between youth and adults with impaired glucose tolerance or recently diagnosed type 2 diabetes: I—observations using the hyperglycemic clamp. Diabetes Care. 2018;41(8):1696-1706. doi: 10.2337/dc18-0244 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.RISE Consortium Metabolic contrasts between youth and adults with impaired glucose tolerance or recently diagnosed type 2 diabetes: II—observations using the oral glucose tolerance test. Diabetes Care. 2018;41(8):1707-1716. doi: 10.2337/dc18-0243 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Wexler DJ, Grant RW, Meigs JB, Nathan DM, Cagliero E. Sex disparities in treatment of cardiac risk factors in patients with type 2 diabetes. Diabetes Care. 2005;28(3):514-520. doi: 10.2337/diacare.28.3.514 [DOI] [PubMed] [Google Scholar]
- 24.Ferrara A, Mangione CM, Kim C, et al. ; Translating Research Into Action for Diabetes Study Group . Sex disparities in control and treatment of modifiable cardiovascular disease risk factors among patients with diabetes: Translating Research Into Action for Diabetes (TRIAD) Study. Diabetes Care. 2008;31(1):69-74. doi: 10.2337/dc07-1244 [DOI] [PubMed] [Google Scholar]
- 25.Linder BL, Fradkin JE, Rodgers GP. The TODAY study: an NIH perspective on its implications for research. Diabetes Care. 2013;36(6):1775-1776. doi: 10.2337/dc13-0707 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Egginton JS, Ridgeway JL, Shah ND, et al. Care management for type 2 diabetes in the United States: a systematic review and meta-analysis. BMC Health Serv Res. 2012;12:72. doi: 10.1186/1472-6963-12-72 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Palmas W, March D, Darakjy S, et al. Community health worker interventions to improve glycemic control in people with diabetes: a systematic review and meta-analysis. J Gen Intern Med. 2015;30(7):1004-1012. doi: 10.1007/s11606-015-3247-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Gary TL, Batts-Turner M, Yeh HC, et al. The effects of a nurse case manager and a community health worker team on diabetic control, emergency department visits, and hospitalizations among urban African Americans with type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med. 2009;169(19):1788-1794. doi: 10.1001/archinternmed.2009.338 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Egede LE, Williams JS, Voronca DC, Gebregziabher M, Lynch CP. Telephone-delivered behavioral skills intervention for African American adults with type 2 diabetes: a randomized controlled trial. J Gen Intern Med. 2017;32(7):775-782. doi: 10.1007/s11606-017-4023-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Fortmann AL, Gallo LC, Garcia MI, et al. Dulce Digital: an mHealth SMS-based intervention improves glycemic control in Hispanics with type 2 diabetes. Diabetes Care. 2017;40(10):1349-1355. doi: 10.2337/dc17-0230 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Drucker DJ, Habener JF, Holst JJ. Discovery, characterization, and clinical development of the glucagon-like peptides. J Clin Invest. 2017;127(12):4217-4227. doi: 10.1172/JCI97233 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Pathak R, Bridgeman MB. Dipeptidyl peptidase-4 (DPP-4) inhibitors in the management of diabetes. P T. 2010;35(9):509-513. [PMC free article] [PubMed] [Google Scholar]
- 33.Cahn A, Cernea S, Raz I. An update on DPP-4 inhibitors in the management of type 2 diabetes. Expert Opin Emerg Drugs. 2016;21(4):409-419. doi: 10.1080/14728214.2016.1257608 [DOI] [PubMed] [Google Scholar]
- 34.Shojania KG, Ranji SR, McDonald KM, et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA. 2006;296(4):427-440. doi: 10.1001/jama.296.4.427 [DOI] [PubMed] [Google Scholar]
- 35.Wubben DP, Vivian EM. Effects of pharmacist outpatient interventions on adults with diabetes mellitus: a systematic review. Pharmacotherapy. 2008;28(4):421-436. doi: 10.1592/phco.28.4.421 [DOI] [PubMed] [Google Scholar]
- 36.Deters MA, Laven A, Castejon A, et al. Effective interventions for diabetes patients by community pharmacists: a meta-analysis of pharmaceutical care components. Ann Pharmacother. 2018;52(2):198-211. doi: 10.1177/1060028017733272 [DOI] [PubMed] [Google Scholar]
- 37.MN Community Measurement. 2017 Health care quality report. http://mncm.org/wp-content/uploads/2018/06/2017-Health-Care-Quality-Report-Final-3-30-2018.pdf. Accessed January 28, 2019.
- 38.MN Community Measurement. 2004 Health care quality report. http://mncm.org/wp-content/uploads/2013/04/2004.pdf. Accessed January 28, 2019.
- 39.American Diabetes Association Standards of medical care in diabetes—2010. Diabetes Care. 2010;33(suppl 1):S11-S61. doi: 10.2337/dc10-S011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Davies HT, Crombie IK, Tavakoli M. When can odds ratios mislead? BMJ. 1998;316(7136):989-991. doi: 10.1136/bmj.316.7136.989 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Farmer AJ, Rodgers LR, Lonergan M, et al. Adherence to oral glucose-lowering therapies and associations with 1-year HbA1c: a retrospective cohort analysis in a large primary care database. Diabetes Care. 2016;39(2):258-263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Khunti K, Wolden ML, Thorsted BL, Andersen M, Davies MJ. Clinical inertia in people with type 2 diabetes: a retrospective cohort study of more than 80,000 people. Diabetes Care. 2013;36(11):3411-3417. doi: 10.2337/dc13-0331 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Schmittdiel JA, Uratsu CS, Karter AJ, et al. Why don’t diabetes patients achieve recommended risk factor targets? poor adherence versus lack of treatment intensification. J Gen Intern Med. 2008;23(5):588-594. doi: 10.1007/s11606-008-0554-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Renders CM, Valk GD, Griffin S, Wagner EH, Eijk JT, Assendelft WJ. Interventions to improve the management of diabetes mellitus in primary care, outpatient and community settings. Cochrane Database Syst Rev. 2001;(1):CD001481. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods. Additional Analyses on the Cascade of Diabetes Care
eResults. Additional Analyses on the Cascade of Diabetes Care
eTable 1. Diagnosed Diabetes: Multivariable Logistic Regression Results Assessing Potential Demographic, Socioeconomic, and Disease Severity Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diagnosed Diabetes in 2005-2016
eTable 2. Diagnosed and Undiagnosed Diabetes: Multivariable Logistic Regression Results Assessing Potential Demographic and Socioeconomic Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diagnosed and Undiagnosed Diabetes in 2005-2016
eTable 3. Diagnosis and Linkage to Diabetes Care: Multivariable Logistic Regression Results Assessing Potential Demographic and Socioeconomic Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diabetes in 2005-2016 Who Were Diagnosed and Linked to Diabetes Care
eTable 4. Undiagnosed diabetes: Multivariable Logistic Regression Results Assessing Potential Demographic and Socioeconomic Status Factors Influencing Achievement of Diabetes Care Targets for US Nonpregnant Adults With Diabetes in 2005-2016 Who Were Undiagnosed
eFigure 1. Statin Use: Proportion of US Adults With Diagnosed Diabetes Who Were Taking Statins for Primary and Secondary Prevention of Cardiovascular Disease and Achieved the Recommended Targets, 2005-2016
eFigure 2. Risk Factor Control: Distribution of Hba1c, Blood Pressure, LDL Cholesterol, and Body Mass Index Among US Nonpregnant Adults With Diagnosed Diabetes, 2005-2016
eFigure 3. Cascade of Diabetes Care: Diagnosed and Undiagnosed Diabetes, 2005
eFigure 4. Cascade of Diabetes Care by Sex: Diagnosed Diabetes, 2005-2016
eFigure 5. Cascade of Diabetes Care: Diagnosed Diabetes Linked to Care, 2005-2016
eFigure 6. Cascade of Diabetes Care: Undiagnosed Diabetes, 2005-2016