Nurse Corps' impact on increasing registered nurse staffing in critical shortage areas and facilities, 2017 to 2022.

BACKGROUND: The registered nurse (RN) workforce experienced critical pre-pandemic and pandemic shortages of labor in some areas in the United States. People living in these health professional shortage areas (HPSAs) may have less access to health services. The Bureau of Health Workforce within the Health Resources and Services Administration administers Nurse Corps scholarship and loan repayment programs to increase healthcare access by increasing the supply and distribution of RNs, nurse practitioners, and nurse faculty to HPSAs. The American Rescue Plan Act of 2021 (ARPA) made available considerable new resources for the program. PURPOSE: This paper reports on Nurse Corps applications, awards, and distribution in 2 cohorts in the period 2017 to 2022 to assess the impact of receiving an additional $200 million appropriated in 2021. DISCUSSION: Additional funds through ARPA were associated with nearly threefold increases in the number of Nurse Corps awards. Program participants worked in a total of 1,316 counties (42% of all U.S. counties) in 2020 to 2022, a 76% increase from 749 counties in 2017 to 2019. CONCLUSION: Increased funding for scholarship and loan repayment can help to improve the distribution of nurse labor to a greater number of critical shortage areas in the United States.

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.

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.

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.

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.

Newly licensed registered nurses' perceptions of job difficulties, demands and control: individual and organizational predictors.

AIMS: To determine predictors of newly licensed registered nurses' perceptions of job difficulties, job demands and job control. BACKGROUND: In previous studies, new registered nurses describe their work environment as stressful, yet little is known about factors that influence these experiences. METHODS: We surveyed a random sample of newly licensed registered nurses in Florida. Dependent variables included indicators of job difficulty, job demand and job control. Independent variables included individual and organizational characteristics hypothesized to be related to the dependent variables. Logistic and ordinary least squares regressions were used to analyse survey data. RESULTS: Inadequate orientation, working the day shift, working a greater number of hours and caring for a higher number of patients were significantly related to a greater likelihood of perceptions of job difficulty and job demand. Less adequate orientation and a greater number of float shifts were related to a lower likelihood of perceptions of job control. CONCLUSIONS AND IMPLICATIONS: Adequacy of orientation, patient load, work hours, shift work and floating are priority items that need improvement in the work environment of newly licensed registered nurses. IMPLICATIONS FOR NURSING MANAGEMENT: The present study identified factors involved with newly licensed registered nurses' perceptions of job difficulties, job demands and job control which will help managers redesign work settings to retain new nurses.

Should I stay or should I go? Career change and labor force separation among registered nurses in the U.S.

Efforts to retain nurses within the profession are critical for resolving the global nursing shortage, but very little research explores the phenomenon of nursing workforce attrition in the U.S. This study is the first to simultaneously investigate the timing of attrition through survival analysis, the exit path taken (career change vs. labor force separation), and the major socioeconomic, family structure, and demographic variables predicting attrition in this country. Using nationally representative U.S. data from the 2004 National Sample Survey of Registered Nurses (N=29,472), we find that the rate of labor force separation is highest after the age of 60, a typical pattern for retirement. However, a non-trivial proportion of career change also occurs at older ages (50+ years old), and the rate of labor force separation begins to climb at relatively young ages (30-40 years old). Particularly strong predictors of early labor force separation include being married and providing care to dependents in the home (young children or elderly parents). Career change is predicted strongly by higher levels of education, male gender, and current enrollment in a non-nursing degree program. Having an Advanced Practice credential reduced the hazards of attrition for both exit paths. The results suggest a fruitful path for future research and a number of policy approaches to curbing nurse workforce attrition.

Toward standardization (part 1): assessment of state and national nursing workforce data sources.

Despite concerns expressed over the past 25 years, little progress has been made in improving the accuracy, availability, and timeliness of national data on the U.S. nursing workforce. In Part 1 of this two-part series, we review the current national data sources on nurse supply, demand, and education programs. We discuss the advantages that state-level data collection efforts enjoy in many states and propose that national data sets could be easily and cost-effectively built from state-level contributions-if states collected a standardized set of information. As part of a larger effort to standardize state-level data, from July to December 2008, we analyzed surveys and codebooks from 26 states collecting nurse workforce data. We present the results of this data assessment and conclude that data collection practices as of 2008 varied substantially from state to state. Creation and adoption of standardized minimum nursing workforce data sets is suggested to bring states into alignment.

Validating HRSA's nurse supply and demand models: a state-level perspective.

In addition to federal initiatives, solutions to the nursing shortage must also be devised at the state level. Understanding the timing and severity of the nursing shortage in a particular state is paramount to devising appropriate solutions In 2005, the Health Resources and Services Administration released new versions of the Nurse Supply Model and Nurse Demand Model designed to project the supply of RNs and demand for RNs, LPNs, and nurse aides in the United States through the year 2020. The process used by two state-level analysts to project nurse supply and demand in North Carolina using the HRSA models is described. The authors conclude that the models work well for state-level forecasting but that users should carefully assess the default data provided with the model against independent data sources specific to their states.

Evidence and root causes of an inadequate pipeline for nursing faculty.

The North Carolina Center for Nursing (NCCN) examined the projected supply of nursing faculty in the state of North Carolina. Coupled with a longitudinal educational mobility study of the state's registered nurses, the forecast shows that the growing faculty shortage is real and that its root cause is a growing shortfall in the pipeline of RNs prepared educationally to pursue graduate education and assume faculty roles.

Educational mobility of RNs in North Carolina: who will teach tomorrow's nurses? A report on the first study to longitudinally examine educational mobility among nurses.

OBJECTIVE: Affected by the current nursing shortage, schools of nursing cite a lack of qualified nursing faculty as a primary barrier to program expansion. We sought to identify patterns in how nurses' entry-level degrees and other individual characteristics correlated with the timing and achievement of subsequent advanced nursing education. METHODS: Using longitudinal analysis of data gathered as part of North Carolina's licensing renewal process, we studied the educational mobility of newly graduated RNs with a variety of entry degrees in this state. We followed one cohort of 3,384 new graduates who were licensed in 1984 (2,850 remained active and in the study at the 10-year point, and 2,418 remained active and in the study at the 20-year point) and another cohort of 5,341 new graduates who were licensed in 1994 (4,211 remained active and in the study at 10 years). Demographic data for a third cohort of 5,400 new graduates who were licensed in 2004 were included and considered along with data gathered by the National League for Nursing for nursing education research, to assist us in making comparisons between North Carolina and other states. RESULTS: Only 26% of the 2,418 members of the 1983-84 cohort at 20 years and 17% of the 4,211 members of the 1993-94 cohort at 10 years pursued higher degrees, and just 19% and 12% of the respective cohorts did so in nursing. More than 80% of all nurses in either cohort who attained a master's degree in nursing or a doctorate in any field began their nursing career with a bachelor's degree. Younger age at entry into nursing, male sex, and belonging to a racial or ethnic minority were associated with being more likely to pursue higher academic degrees. CONCLUSIONS: Based on our findings, we suggest that increasing the number of graduates with a bachelor of science in nursing degree, especially those who are men or members of a racial or ethnic minority, will have the most immediate effect on increasing the potential nursing faculty pool.

Which pasture is really greener? A research note on salary studies.

National and regional studies of nurse salaries often draw conclusions based solely on the dollar values reported. This brief research note shows the importance of including information about the cost of living into any comparative analysis of geographic differences in salaries.