XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Cost Effectiveness in Health and Medicine.

We estimate the relative influence of trade versus technology on wages in a "large-country" setting, where technological change affects product prices. Trade is measured by the foreign outsourcing of intermediate inputs, while technological change is measured by expenditures on high-technology capital such as computers. The estimation procedure we develop, which modifies the conventional "price regression," is able to distinguish whether product price changes are due to factor-biased versus sector-biased technology shifts. In our base specification we find that computers explain about 35 percent of the increase in the relative wage of nonproduction workers, while outsourcing explains 15 percent; both of these effects are higher in other specifications.

https://cid.econ.ucdavis.edu/Papers/pdf/tfp13.pdf

The Impact of Outsourcing and High-Technology Capital on Wages: Estimates For the United States, 1979–1990

Ensuring timely access to health care is a major policy concern in the United States. Delayed access has been found to increase the use of emergency rooms for nonurgent conditions (Shesser et al. 1991; Cunningham et al. 1995) and to decrease patient satisfaction (United States Government Accounting Office [U.S. GAO] 1993; VanDeusen Lukas et al. 2004). Most importantly, delayed access to health care is assumed to negatively affect health outcomes due to delays in diagnosis and treatment (Kenagy, Berwick, and Shore 1999; Institute of Medicine 2004). For these reasons, the IOM's Committee on the Quality of Health Care in America (2004) chose reducing delays for health care as one of six aims to improve the quality of America's health care system. However, despite the declared importance of ensuring timely access to care, little research has actually examined the empirical association between waiting for outpatient care and health outcomes.

The lack of empirical research on wait times and health outcomes is likely due to a lack of data on wait times. A commonly used definition of wait times for medical care is the time between when an appointment is requested and when the appointment is scheduled (U.S. GAO 2001; Center for Medicare and Medicaid Services [CMS] 2004). Estimates of how long individuals wait for medical appointments are largely based on self-reported survey data (e.g., Community Tracking Study [CTS], Medicare Current Beneficiary Survey) where patients are asked how long they had to wait for their most recent physician or outpatient clinic appointment (CMS 2004; Center for Studying Health Systems Change 2005). A few studies of specific specialties (e.g., dermatology) have measured wait times by contacting providers at a point in time and asking when the next available appointment for a new or established patient could be scheduled (Resneck, Pletcher, and Lozano 2004), but in general, health care systems have not systematically collected data on how long individuals wait for appointments.

The Veterans Affairs (VA) health care system is an important exception because it has invested substantial resources and effort into monitoring and decreasing wait times. In response to reports of long wait times, the VA implemented a policy that by 1998, 90 percent of veterans seeking care would be seen in both primary and specialty care within 30 days of the appointment request (U.S. GAO 2001). To help medical centers meet this goal, the VA began automatically extracting data from its outpatient scheduling system so that wait times could be easily monitored.

The VA has also implemented and evaluated policies to decrease wait times. In 2000, the VA began a nationwide initiative to implement the advanced access model for certain appointment types (e.g., primary care) (U.S. GAO 2001; VanDeusen Lukas et al. 2004). The advanced access model changes how appointments are scheduled by evaluating the supply of appointment availability relative to demand for appointments and adjusting scheduling procedures so that demand is met as soon as it is requested. Clinics work down the backlog of patients waiting for appointments and then attempt to see patients the same day appointments are requested. As all physicians have open slots in their schedule, patients see their regular physician, eliminating recheck appointments that result when a patient goes to a different clinician because of a long wait to see their regular clinician (Murray and Berwick 2003). In the VA, greater implementation of the advanced access model was significantly associated with decreased wait times in primary care, urology, and orthopedics but not in eye care, audiology, or cardiology, probably because the initiative was implemented later in the three latter types of appointments (VanDeusen Lukas et al. 2004). A limited number of case studies suggest this model can be implemented in a variety of health care settings outside of the VA. It has been found to decrease wait times in several different types of primary care settings, such as community health centers, outpatient departments of large urban teaching hospitals, and small fee-for-service practices (Murray et al. 2003; Gordon and Chin 2004).

This article makes use of the VA waiting time data and tests the assumption that long waits for appointments have a negative effect on health outcomes. Specifically, we examine the association between waiting for outpatient care and mortality.

Delayed Access to Health Care and Mortality

Uninsured persons with disability confront substantial barriers to health care services.

National health reform is expected to increase how long individuals have to wait between requests for appointments and when their appointment is scheduled. The increase in demand for care due to more widespread insurance will result in longer waits if there is not also a concomitant increase in supply of healthcare services. Long waits for healthcare are hypothesized to compromise health because less frequent outpatient visits result in delays in diagnosis and treatment. Research testing this hypothesis is scarce due to a paucity of data on how long individuals wait for healthcare in the United States. The main exception is the Veterans Health Administration (VA) that has been routinely collecting data on how long veterans wait for outpatient care for over a decade. This narrative review summarizes the results of studies using VA wait time data to answer two main questions: 1) How much do longer wait times decrease healthcare utilization and 2) Do longer wait times cause poorer health outcomes? Longer VA wait times lead to small, yet statistically significant decreases in utilization and are related to poorer health in elderly and vulnerable veteran populations. Both long-term outcomes (e.g. mortality, preventable hospitalizations) and intermediate outcomes such as hemoglobin A1C levels are worse for veterans who seek care at facilities with longer waits compared to veterans who visit facilities with shorter waits. Further research is needed on the mechanisms connecting longer wait times and poorer outcomes including identifying patient sub-populations whose risks are most sensitive to delayed access to care. If wait times increase for the general patient population with the implementation of national reform as expected, U.S. healthcare policymakers and clinicians will need to consider policies and interventions that minimize potential harms for all patients.

What Are the Consequences of Waiting for Health Care in the Veteran Population

Importance Primary care services based at home have the potential to reduce the likelihood of hospitalization among older adults with multiple chronic diseases. Objective To characterize the association between enrollment in Home-Based Primary Care (HBPC), a national home care program operated by the US Department of Veterans Affairs (VA), and hospitalizations owing to an ambulatory care–sensitive condition among older veterans with diabetes mellitus. Design and Setting Retrospective cohort study. Patients admitted to VA and non-VA hospitals were followed up from January 1, 2006, through December 31, 2010. Participants Veterans 67 years or older who were fee-for-service Medicare beneficiaries, were diagnosed as having diabetes mellitus and at least 1 other chronic disease, and had at least 1 admission to a VA or non-VA hospital in 2005 or 2006. Exposures Enrollment in HBPC, defined as a minimum of 2 HBPC encounters during the study period. Main Outcomes and Measures Admission to VA and non-VA hospitals owing to an ambulatory care–sensitive condition, as measured by the Agency for Healthcare Research and Quality’s Prevention Quality Indicators in VA medical records and Medicare claims. Outcomes were analyzed using distance from the veteran’s residence to a VA facility that provides HBPC as an instrumental variable. Results Among 56 608 veterans, 1978 enrolled in HBPC. These patients were older (mean age, 79.1 vs 77.1 years) and had more chronic diseases (eg, 59.2% vs 53.5% had congestive heart failure). Multivariable predictors for HBPC enrollment included paralysis (odds ratio [OR], 2.11; 95% CI, 1.63-2.74), depression (OR, 1.99; 95% CI, 1.70-2.34), congestive heart failure (OR, 1.36; 95% CI, 1.17-1.58), and distance from the nearest HBPC-providing VA facility (OR, 0.59; 95% CI, 0.50-0.70 for >10-30 vs Conclusions and Relevance Home-Based Primary Care is associated with a decreased probability of ambulatory care–sensitive condition hospitalization among elderly veterans with diabetes mellitus. In accountable care models, HBPC may have an important role in the management of older adults with multiple chronic diseases.

https://jamanetwork.com/journals/jamainternalmedicine/articlepdf/1904760/ioi140089.pdf

Home-based primary care and the risk of ambulatory care-sensitive condition hospitalization among older veterans with diabetes mellitus.

Objectives
To demonstrate how falsification tests can be used to evaluate instrumental variables methods applicable to a wide variety of comparative effectiveness research questions.

Study Design
Brief conceptual review of instrumental variables and falsification testing principles and techniques accompanied by an empirical application. Sample STATA code related to the empirical application is provided in the Appendix.

Empirical Application
Comparative long-term risks of sulfonylureas and thiazolidinediones for management of type 2 diabetes. Outcomes include mortality and hospitalization for an ambulatory care–sensitive condition. Prescribing pattern variations are used as instrumental variables.

Conclusions
Falsification testing is an easily computed and powerful way to evaluate the validity of the key assumption underlying instrumental variables analysis. If falsification tests are used, instrumental variables techniques can help answer a multitude of important clinical questions.

/pdf/falsification-testing-of-instrumental-variables-methods-for-1fbsw9ceez.pdf

Steven D. Pizer

Papers

Delayed Access to Health Care and Mortality

Uninsured persons with disability confront substantial barriers to health care services.

What Are the Consequences of Waiting for Health Care in the Veteran Population

Home-based primary care and the risk of ambulatory care-sensitive condition hospitalization among older veterans with diabetes mellitus.

Falsification Testing of Instrumental Variables Methods for Comparative Effectiveness Research