State-level metabolic comorbidity prevalence and control among adults age 50-plus with diabetes: estimates from electronic health records and survey data in five states.

BACKGROUND: Although treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically. METHODS: We analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts. RESULTS: The estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states. CONCLUSIONS: Despite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care. TRIAL REGISTRATION: Not applicable.

Use and Impact of Type 2 Diabetes Prevention Interventions.

INTRODUCTION: RCTs have found that type 2 diabetes can be prevented among high-risk individuals by metformin medication and evidence-based lifestyle change programs. The purpose of this study is to estimate the use of interventions to prevent type 2 diabetes in real-world clinical practice settings and determine the impact on diabetes-related clinical outcomes. METHODS: The analysis performed in 2020 used 2010‒2018 electronic health record data from 69,434 patients aged ≥18 years at high risk for type 2 diabetes in 2 health systems. The use and impact of prescribed metformin, lifestyle change program, bariatric surgery, and combinations of the 3 were examined. A subanalysis was performed to examine uptake and retention among patients referred to the National Diabetes Prevention Program. RESULTS: Mean HbA1c values declined from before to after intervention for patients who were prescribed metformin (-0.067%; p<0.001) or had bariatric surgery (-0.318%; p<0.001). Among patients referred to the National Diabetes Prevention Program lifestyle change program, the type 2 diabetes postintervention incidence proportion was 14.0% for nonattendees, 12.8% for some attendance, and 7.5% for those who attended ≥4 sessions (p<0.001). Among referred patients to the National Diabetes Prevention Program lifestyle change program, uptake was low (13% for 1‒3 sessions, 15% for ≥4 sessions), especially among males and Hispanic patients. CONCLUSIONS: Findings suggest that metformin and bariatric surgery may improve HbA1c levels and that participation in the National Diabetes Prevention Program may reduce type 2 diabetes incidence. Efforts to increase the use of these interventions may have positive impacts on diabetes-related health outcomes.

The Effect of National Family Caregiver Support Program Services on Caregiver Burden.

Extant evidence on the effectiveness of caregiver programs in alleviating caregiver burden is mixed, underscoring the need for further investigations. This study evaluated the effect of the National Family Caregiver Support Program (NFCSP) educational services and respite care on caregiver burden. We used survey data from caregivers assigned to program (n = 491) or comparison (n = 417) group based on their reported use of NFCSP services. Adjusted difference-in-differences (DiD) analysis found an increase in mean burden scores for both groups from baseline to 6 or 12 months. Among program caregivers receiving ≥4 hr of NFCSP respite care per week (n = 307) and matched comparisons (n = 370), burden scores decreased slightly for program caregivers (-0.095 points), but increased for comparison caregivers (+0.145 points). The DiD (0.239 points) was not statistically significant. More research is needed to determine the minimum amount of respite care needed to positively impact caregiver burden.

Identifying optimal survey-based algorithms to distinguish diabetes type among adults with diabetes.

OBJECTIVES: Surveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, we linked survey data collected from adult patients with diabetes to a gold standard diabetes type. RESEARCH DESIGN AND METHODS: We collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. We tested 34 survey classification algorithms utilizing information on respondents' report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of EHR data and endocrinologist review of selected cases. RESULTS: Algorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as "other diabetes type" all respondents who reported "other," and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations. CONCLUSIONS: The major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.

Predictors of Unmet Need Among Informal Caregivers.

OBJECTIVES: This study investigates the relationship of caregiver demographics, caregiving intensity, caregiver support use, and aspects of the caregiving situation to a self-reported measure of unmet need among U.S. informal caregivers of older adults living at home with various conditions. METHODS: Response data from 1,558 caregiver participants interviewed by telephone during the December 2016 baseline period of the Outcome Evaluation of the National Family Caregiver Support Program were used. Caregivers who responded "Definitely No" to the question "Are you receiving all the help you need?" were classified as reporting unmet need. Logistic regression was used to find significant factors associated with unmet need among the full sample and among caregivers tiered by three levels of burden. RESULTS: Unmet need was reported by 22% of the caregivers. In a fully adjusted model, unmet need was predicted by higher levels of caregiving intensity, non-White race of the caregiver, and the caregiver not feeling appreciated by their care recipient. Other predictors associated with unmet need were no use of caregiver educational services, fewer respite hours, not living in a rural area, and caregiver having an education past high school. DISCUSSION: Caregivers who do not feel appreciated by their care recipient and non-White caregivers should be identified as potential targets for intervention to address unmet need, especially if they are also reporting higher levels of caregiver burden. Understanding the factors associated with self-reported unmet need can assist caregiver support programs in measuring and addressing the needs of informal caregivers to support their continued caregiving.

Patient Safety Culture, Health Information Technology Implementation, and Medical Office Problems That Could Lead to Diagnostic Error.

BACKGROUND: Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment or by leading to unnecessary or harmful treatment. OBJECTIVES: The aim of the study was to investigate the relationship between patient safety culture, health information technology (IT) implementation, and the frequency of problems that could lead to diagnostic errors in the medical office setting, such as unavailable test results, unavailable medical records, or unpursued abnormal results. METHODS: We used survey data from 925 medical offices nationwide that voluntarily submitted results to the 2012 Agency for Healthcare Research and Quality Medical Office Surveys on Patient Safety Culture database. At the office level, we ran a multivariate regression model to estimate the effect of culture on problem frequency while controlling for office-reported implementation levels of health IT, office characteristics such as the number of locations, and survey characteristics such as the percent of respondents that were physicians. RESULTS: The most frequent problem was "results from a lab or imaging test were not available when needed"; across 925 offices, the average was 15% reporting that it happened daily or weekly. Higher overall culture scores were significantly associated with fewer occurrences of each problem assessed. Compared with offices with completed health IT implementation, offices in the process of health IT implementation had higher frequency of problems. CONCLUSIONS: This study offers insight into how patient safety culture and health IT implementation in medical offices can influence the frequency of breakdowns in processes of care, thereby identifying potential vulnerabilities that can increase diagnostic errors.

State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

Many statisticians and policy researchers are interested in using data generated through the normal delivery of health care services, rather than carefully designed and implemented population-representative surveys, to estimate disease prevalence. These larger databases allow for the estimation of smaller geographies, for example, states, at potentially lower expense. However, these health care records frequently do not cover all of the population of interest and may not collect some covariates that are important for accurate estimation. In a recent paper, the authors have described how to adjust for the incomplete coverage of administrative claims data and electronic health records at the state or local level. This article illustrates how to adjust and combine multiple data sets, namely, national surveys, state-level surveys, claims data, and electronic health record data, to improve estimates of diabetes and prediabetes prevalence, along with the estimates of the method's accuracy. We demonstrate and validate the method using data from three jurisdictions (Alabama, California, and New York City). This method can be applied more generally to other areas and other data sources.

Promising Practices for Improving Hospital Patient Safety Culture.

BACKGROUND: Patient safety culture has a positive influence on the effectiveness of patient safety and quality improvement interventions. A study was conducted to gain knowledge about promising best practices used by hospitals to improve patient safety culture hospitalwide. METHODS: Agency for Healthcare Research and Quality (AHRQ) Surveys on Patient Safety Culture™ (SOPS) Hospital Survey longitudinal results from 536 hospitals that submitted data to the Hospital SOPS database from 2007 to 2014 were analyzed. Composite-level and aggregate improvement was measured, resulting in the identification of "top-improving," large hospitals (400 + beds). Semistructured interviews were conducted with one to three interviewees (for example, Vice President of Clinical Quality, Patient Safety Officer, Chief Medical Officer) from six top-improving hospitals. The transcripts of the interviews were analyzed to identify common themes and best practices among the hospitals. RESULTS: The mean change in the all-composite percent positive culture score was a 1.7 percentage point increase. The six hospitals interviewed had an average increase of 8.6 percentage points (range, 6.5-10.6) in their culture score. The three most common practices for improving culture as described by the hospital quality leaders from the six hospitals were (1) goal setting and strong action planning for quality improvement, (2) implementation of well-known patient safety initiatives and programs, and (3) rigorous survey administration methods. CONCLUSION: Among six large hospitals that improved their hospitalwide culture score, the common best practices were the implementation of routine culture measurement with a wide dissemination of results, strong action planning for improvement that includes leadership support and involvement from all staff levels, and multifaceted patient safety programs and education.

Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence.

States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases.

Hospital-Level Factors Related to 30-Day Readmission Rates.

This study investigates the relationship between inpatient quality of care as measured by the Agency for Healthcare Research and Quality (AHRQ) patient safety indicator (PSI) composite and all-cause, hospital-wide, 30-day readmission rates. Discharge data from 4 statewide databases were analyzed. Linear, repeated-measures regressions were performed to predict hospital-level 30-day readmission rates. The mean readmission rate was 12.9%, and the mean PSI composite ratio was 0.95 among 524 hospitals with 2592 observations. In the hospital-level analysis, the risk-adjusted AHRQ PSI composite was not significantly associated with hospital 30-day readmission rate after controlling for hospital-level characteristics, patient case mix, and sociodemographics. Inpatient quality of care appears to have less influence on hospital readmission rates than do clinical and socioeconomic factors. However, these results suggest that a patient safety composite measure that includes postdischarge complications would provide more information to assist hospitals and communities in understanding the association between quality of care and readmission rates.

The NC Quality Center: empowering excellence in health care.

The NC Quality Center is transforming health care quality and patient safety in North Carolina by providing leadership, direction, and a vision to ensure that North Carolina delivers the best health care possible.

The influence of physicians' guideline compliance on patients' statin adherence: a retrospective cohort study.

BACKGROUND: National cholesterol management guidelines recommend regular follow-up of patients and annual lipid evaluations to promote adherence to statin therapy. OBJECTIVE: This study examined the relationship between primary care physicians' (PCPs') compliance with primary care guidelines and patients' adherence to statin therapy. METHODS: A retrospective cohort study was conducted among statin users aged > or = 50 years who had an assigned PCP at a Veterans Affairs Medical Center. The dependent variable was statin adherence by patients over 24 months. Computerized pharmacy, laboratory, and medical records were used to measure PCPs' compliance with 4 recommendations in national cholesterol management guidelines: (1) lipid-lowering drug (LLD) initiation; (2) 8-week follow-up visit after an initial LLD prescription; (3) 6- or 12-month follow-up visit for established LLD users; and (4) annual lipid evaluation. Multilevel, multivariable regression models were used to estimate the effects of PCPs' guideline compliance on patients' adherence while controlling for patients' demographic characteristics, comorbid conditions, and pharmacotherapy factors. RESULTS: The sample included 82 PCPs caring for 4707 patients. The mean statin adherence rate was 83.9%. An increase in the annual lipid evaluation rate resulted in an increase in patients' adherence (P = 0.037). Black race and higher statin dose negatively influenced patients' adherence (both, P < 0.001). The effects of PCPs' compliance rates were not homogeneous across race. Specifically, the 8-week follow-up visit rate after initial LLD prescription was significantly associated with improved statin adherence among the black subpopulation only. CONCLUSIONS: Patients' adherence to statin therapy was influenced by their PCPs' compliance with cholesterol management guidelines. Efforts should be made to align PCPs' practice with published guidelines for optimal statin therapy, especially for vulnerable subpopulations of patients.