Statistical Trends Peculiar to Diabetes in Adults

Subject: Endocrinology
Pages: 3
Words: 897
Reading time:
4 min


Diabetes is still common in the U.S. population aged 18 and older and remains the eighth leading cause of death. Regarding prevalence trends, the percentage of adult U.S. citizens with type I/type II diabetes diagnosis has grown from 9% to 12% between 1999 and 2016 (Centers for Disease Control and Prevention [CDC], 2021b). Next, in terms of incidence, as of 2018, the number of new cases among U.S. adults was 1.5 million or 6.9 cases per 1000 adult citizens (CDC, 2021b). The national trend is, however, decreasing; in 2009, diabetes incidence per 1000 adults exceeded 9 cases (CDC, 2021b). In 2016, the national incidence rate for adults was lower than 7, and the 2018 result was 6.9 (CDC, 2021b). The data on the percentage of diabetes cases to expire from the condition are not available, but diabetes mortality for adults has varied between 27 and 31 cases per 1000 people in recent years (CDC, 2021b; Gregg et al., 2018). Finally, the statistical result to be used as a benchmark for comparison is 6.9 new diabetes cases per 1000 adults.

The abovementioned sources do not adequately explain the possible reasons for the trends’ existence. However, hypothetically, increases in prevalence and decreases in incidence could be explained by reductions in mortality rates and the decreased incidence of life-threatening complications in adults diagnosed with the disorder. An increased proportion of asymptomatic diabetes, better prediabetes screening methods, or improvements in diabetes prevention literacy could also explain reductions in incidence.

Quality Measures

For assessment purposes, the 2020 HEDIS Comprehensive Diabetes Care quality measures from five types of healthcare plans could be used. The first measure is the percentage of patients (18-75 years) with type I and II diabetes who have poorly controlled diabetes (HbA1c values exceeding 9%) (CDC, 2021a; National Committee for Quality Assurance [NCQA], 2020). In 2020, it varied between 45.4% for Medicaid HMOs and 23.4% for Medicare PPOs (NCQA, 2020). Another measure is the percentage of patients in the abovementioned group with optimal blood pressure control (BP levels not exceeding 140/90mmHg); it ranged from 65% for Medicare PPOs to 49% for commercial PPOs (NCQA, 2020). The third measure is the percentage of patients undergoing eye exams, ranging from 46.9% for commercial PPOs to 68.5% for Medicare PPOs (NCQA, 2020).

The data come from health plans and individual healthcare organizations (Aetna Better Health, 2020). Health plans’ representatives contact the NCQA’s data collection team and gain access to the Interactive Data Submission System (NCQA, n.d.). The data are collected for each measurement year for commercial HMOs, commercial PPOs, Medicaid HMOs, Medicare HMOs, and Medicare PPOs.

The factors influencing the quality measures above might include data accuracy and the peculiarities of patient profiles. Concerning accuracy, data errors are possible, and the number of excluded medical records is manifested in entities’ individual reports (NCQA, 2021). However, the very existence of errors reduces the sample size, which might run counter to generalizability. For patient characteristics, quality-related outcomes for entities serving the populations with high obesity prevalence and higher proportions of older adults might be lower since BMI and age factor into hypertension (Wang et al., 2021). Finally, patients’ self-care behaviors can also influence these quality estimates.

Compatibility of Data

The eye exam, BP control, and HbA1c control data from five sources, including Medicaid HMOs, Medicare HMOs/PPOs, and commercial HMOs/PPOs, possess optimal compatibility. This can be ensured by looking at the data’s characteristics. Specifically, the 2020 results for each source are represented as clear percent values instead of ranges, relate only to type I and II diabetes patients, and consider patients in the same age group (18-75) (NCQA, 2020). From an outside researcher’s perspective, challenges to standardization might include no access to information on the total number of cases from which each percentage for each plan type has been derived. Moreover, in the reporting guidelines for entities, the use of both administrative and hybrid data collection methods is allowed, but the final results for 2020 are not separated by method (NCQA, 2021; NCQA, 2020). These details would further improve an understanding of the results’ heterogeneity.

Effects of Information Quality on the HIE

HIEs and national databases serve different purposes, which explains the dissimilar effects of incomplete/inaccurate data submitted to them. An electronic HIE offers services for the safe transfer of patient information electronically through query-based, directed, and consumer-mediated exchange (Menachemi et al., 2018). In contrast, national databases store anonymized patient records for public health research purposes. The submission of erroneous information to HIEs can result in patient care mistakes. For instance, ER physicians using query-based data exchange could make incorrect clinical decisions after accessing the patient’s misleading medical records. Regarding national databases, flawed health-related information can compromise the assessment of diseases’ nationwide prevalence or prognoses, resulting in poor healthcare resource planning and gaps in medical knowledge. Therefore, information quality is crucial to effective decision-making at both individual and state levels.


In summary, since diabetes remains a widespread contributor to disabling health deficiencies and mortality rates, exploring the quality of care given to adults with this disorder is a crucial task. Multiple quality measures, including the proportions of adult diabetes patients receiving eye examinations, having BP levels within the normal range, and having poorly controlled HbA1c levels, could assist physician groups in care quality analysis endeavors. The accuracy and completeness of patient and quality-related data have essential implications for individual safety and large-scale health research.


Aetna Better Health. (2020). Comprehensive diabetes care (CDC): HEDIS measurement year 2020 & 2021 measures. Web.

Centers for Disease Control and Prevention. (2021a). Linking to quality measures. Web.

Centers for Disease Control and Prevention. (2021b). National and state diabetes trends. Web.

Gregg, E. W., Cheng, Y. J., Srinivasan, M., Lin, J., Geiss, L. S., Albright, A. L., & Imperatore, G. (2018). Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: An epidemiological analysis of linked national survey and vital statistics data. The Lancet, 391(10138), 2430-2440. Web.

Menachemi, N., Rahurkar, S., Harle, C. A., & Vest, J. R. (2018). The benefits of health information exchange: An updated systematic review. Journal of the American Medical Informatics Association, 25(9), 1259-1265. Web.

National Committee for Quality Assurance. (2020). Comprehensive diabetes care (CDC). Web.

National Committee for Quality Assurance. (2021). Proposed retirement for HEDIS®1 MY 2022: Comprehensive diabetes care (CDC)—HbA1c testing. Proposed changes to existing measure for HEDIS MY 2022: Comprehensive diabetes care (CDC). Web.

National Committee for Quality Assurance. (n.d.). HEDIS data submission. Web.

Wang, J., Li, J., Wen, C., Liu, Y., & Ma, H. (2021). Predictors of poor glycemic control among type 2 diabetes mellitus patients treated with antidiabetic medications: A cross-sectional study in China. Medicine, 100(43), 1-5. Web.