Showing papers in "Health Services Research in 2011"

The Residential History File: Studying Nursing Home Residents' Long‐Term Care Histories*

Abstract: The adoption of prospective payment, first for hospitals in the early 1980s and then postacute settings a decade ago, created conflicting payment silos, with hospitals reducing length of stay and postacute providers accepting complex patients likely to be rehospitalized. These conflicting Medicare reimbursement incentives have been associated with high rates of transitions between providers because there are no consequences for maximizing reimbursements in this way. In particular, patients who are disabled or chronically or terminally ill, who are often served in nursing homes (NH) as their main long-term care provider have been subject to the consequences of the conflicting reimbursement incentives and have thus suffered from multiple transitions. In spite of the increasing recognition of the importance of care transitions among long-term care residents, most of the literature has concentrated on reporting total utilization per service type (mostly either inpatient or Medicare-paid skilled nursing facility [SNF] care) (Coleman et al. 2004; Mor et al. 2010;). Even the recent focus on geographic variation in Medicare costs has emphasized regional and hospital differences in the average intensity of inpatient use rather than the extent of variation in patients' experiences across the continuum of care within and between geographic areas (http://www.dartmouthatlas.org). Historically it has been difficult to assemble patient histories using existing claims data for those discharged from hospital to postacute settings because conflicting reimbursement incentives also translate to disparate reimbursement systems and therefore administrative data systems. Thus, composing transition histories using only Medicare claims does not provide information on long-term NH care (Burton et al. 1995; Brown et al. 1999; Cooper et al. 2000;). Alternatively, using only NH federally mandated regular assessment of all NH residents using the Minimum Data Set (MDS) resident assessment instrument data provides limited information about transitions outside of the NH (Coburn, Keith, and Bolda 2002). The absence of data on NH use may lead to misleading conclusions. Some studies of Medicare expenditures at the end of life found those to be lower for older than younger persons (Gornick, McMillan, and Lubitz 1993; Levinsky et al. 2001;). However, Roos, Montgomery, and Roos (1987), using a Canadian longitudinal data set that included data on both acute and long-term care utilization, found end-of-life public health care utilization expenditures did not decrease with age. The discrepancy between the United States and Canadian findings may be explained by the absence of NH stays in the U.S. research and highlight the need for accurate information on all utilization when studying expenditures and budgeting for care. The purpose of this paper is to describe the creation of a “residential history file” (RHF), using an algorithm that links Medicare claims and NH MDS assessments that results in a dataset (the RHF) which tracks the timing and location of health service use. Initially, we developed this method to track postacute care for patients hospitalized for hip fracture or stroke (Intrator et al. 2003). Subsequently we expanded the method to other applications ranging from studies of hospice use among Medicaid NH residents to tracking posthospitalization disposition of NH residents (Miller et al. 2004; Intrator et al. 2007;). In this paper we present the structure of the RHF algorithm and apply it to a cohort of all Medicare beneficiaries who were in one of 202 free-standing nonpediatric NHs at some time in 2006. We describe the resulting RHF, present an alternative RHF based only on MDS data, and conduct an illustrative analysis of a study of place of death. We then present results from comparisons identifying patients in NHs based on the RHF versus on Online Survey of Certification and Reporting (OSCAR) and on the place of service codes recorded on Medicare part B claims.

Mapping Physician Networks with Self‐Reported and Administrative Data

Abstract: Relationships between health care providers are essential to a functioning health care system. Physicians rely on their relationships with physician colleagues for patient referrals (Gonzalez and Rizzo 1991), clinical advice (Keating, Zaslavsky, and Ayanian 1998), and information about the latest clinical advances (Gabbay and le May 2004). Given their importance, understanding the nature of such relationships could yield valuable knowledge about the emergence of local practice patterns and the diffusion of health care practices. This understanding could in turn inform health policy decisions aimed at modifying physicians' behavior. Since every doctor has a range of interactions with an array of other doctors, physicians are embedded within a network of relationships, or ties, with their physician colleagues. Using tools from the emerging field of complex network analysis (Newman 2003), physician professional networks defined by such formal or informal relationships can be analyzed at a deeper level than previously possible. Some studies have already begun using these methods to analyze health care networks, addressing topics such as the exchange of clinical advice, the diffusion of pharmaceutical use, or organizational performance and cost-efficiency (Keating et al. 2007; Christakis and Fowler 2010; Iyengar, Van den Bulte, and Valente 2010). A major hurdle to studying physician networks is the lack of data on physician relationships which limits the scope of many studies. For instance, Keating and colleagues studied information seeking among primary care physicians (PCPs) about issues related to women's health, but they studied only 38 doctors whom they surveyed personally. Such de novo survey work requires laborious ascertainment of each relationship among pairs of doctors to map a relationship network, which is a formidable barrier to replication across multiple hospitals or practices. One potential way to identify physician relationships would be to use records regarding patients shared between physicians, as identified in administrative databases. Furthermore, using shared patients to define relationships is clinically intuitive. Physicians often email, phone, or “curbside” a colleague with specific clinical questions or cases, and these informal requests for information are formalized when the patient is actually referred for care (Keating, Zaslavsky, and Ayanian 1998). Thus, the presence of shared patients in administrative data—arising because of referral, patient self-selection, administrative rules (e.g., insurer policies regarding second opinions), or even chance—may represent an important source of information about physician relationships that could be useful for large-scale studies using the tools of network science. In this study, we sought to validate the use of patient sharing identified in administrative data as a source of information on physician networks. To do so, we first identified physicians connected via shared patients in Medicare data. We then surveyed physicians in a large physicians' organization and asked them about their referral and information-sharing relationships with other physicians and evaluated the correspondence between those relationships and patient sharing measured by Medicare claims.

Psychometric properties of the patient activation measure among multimorbid older adults.

Abstract: Objectives The Patient Activation Measure (PAM) quantifies the extent to which people are informed about and involved in their health care. Objectives were to determine the psychometric properties of PAM among multimorbid older adults and evaluate a theoretical, four-stage model of patient activation.

Perceptions of Hospital Safety Climate and Incidence of Readmission

Abstract: Preventable hospital readmission represents an increasingly prominent target in policy discussions aimed at reducing morbidity and cost in the U.S. health care system. Approximately one-fifth (19.6 percent) of Medicare fee-for-service beneficiaries will experience hospital readmission within 30 days of discharge (Jencks, Williams, and Coleman 2009). Up to three-quarters of these unplanned readmissions are potentially avoidable and are associated with an annual cost of U.S.$12 billion (MedPAC 2007). In response to these cost and quality implications, in July 2009 the Centers for Medicare and Medicaid Services (CMS) began reporting 30-day risk-standardized readmission rates as a measure of hospital quality (Keenan et al. 2008). This was likely a first step toward readmission rates becoming a standard indicator of inpatient and post-discharge quality of care and a metric for performance-based reimbursement (Epstein 2009). Reflecting a belief that hospital readmission is a function not only of patient morbidity but also of hospitals' management of safe transitions between the inpatient and the post-acute care setting, the Agency for Healthcare Research and Quality (AHRQ) and the National Quality Forum (NQF) have focused on the relationship between hospital readmission and patient safety (AHRQ 2009a, b; NQF 2009;). Worse performance on AHRQ's Patient Safety Indicators (PSIs) has been associated with higher readmission rates (Friedman et al. 2009). Modeled in part on Project RED (Re-Engineered Discharge) at Boston University Medical Center (Jack et al. 2009), NQF has designated evidence-based improvement in “discharge systems” as a requisite hospital practice in order to be considered “safe.” If the frequency of readmission reflects inadequate patient safety processes in the hospital, then hospitals with worse safety culture would be expected to exhibit higher levels of hospital readmission. To evaluate this supposition, we examined the relationship of a measure of hospital patient safety culture—hospital patient safety climate—and hospital readmission. We used data from a survey measuring safety climate in a national sample of hospitals and CMS risk-standardized measures of 30-day hospital readmission.

Quality and Cost Analysis of Nurse Staffing, Discharge Preparation, and Postdischarge Utilization

Abstract: Readmission and emergency department (ED) use within the first 30 days following hospital discharge represent adverse, potentially avoidable, and costly outcomes of hospitalization (Friedman and Basu 2004; Goldfield et al. 2008; Jencks, Williams, and Coleman 2009). The aims of this study were to determine the following: (1) the direct effect of nursing unit staffing structure onpostdischarge utilization of readmissions and ED visits within 30 days; (2) the indirect effect through its influence on the quality of discharge teaching and patient's perception of readiness for discharge; and (3) the cost-benefit of adjustments to unit-level nurse staffing on postdischarge utilization.

Does information matter? Competition, quality, and the impact of nursing home report cards.

Abstract: It is well known that the lack of available consumer information about product quality can lead to poor market outcomes (Akerlof 1970). If the quality of a good is difficult to assess, consumers and sellers may have difficulty agreeing on a price. Furthermore, if consumers have difficulty assessing quality, then it diminishes the incentive for firms to invest in improving quality. Asymmetric information about quality is present in health care markets (Arrow 1963), and the lack of quality information in the nursing home industry is thought to be particularly acute (Hirth 1999; Chou 2002). Although nursing home care is fairly nontechnical in nature, monitoring of care can often be difficult, and the learning period may be nontrivial relative to the length of stay in some instances. The patient is often neither the decision maker nor able to easily evaluate quality or communicate concerns to family members and staff. Since the late 1980s, there has been increasing interest in providing useful information to consumers regarding the quality of care across the medical care sector. This interest has given rise to several public and private provider health plan report cards, including nursing home report card initiatives. However, the overall welfare implications of these report card efforts are unclear. The issuance of report cards may change the incentives of a nursing home to invest in quality, but it is uncertain what the net impact of the change in incentives will be on the quality of care. On the one hand, report cards may empower consumers to make more informed choices and increase quality competition among providers. However, report cards may also increase market power on the part of providers, which may ultimately decrease quality of care (Miller 2006). Failure to account for the underlying market structure in constructing quality policy initiatives may doom these initiatives to irrelevancy. This paper examines the introduction of nursing home report cards by the Centers for Medicare and Medicaid Services (CMS) on nursing home quality and the demand for nursing home care.

How have state policies to expand dependent coverage affected the health insurance status of young adults

Abstract: Among the U.S. population, young adults are more likely to lack health insurance than any other age group, with potentially serious consequences for their health and financial well-being. In 2008, 28.6 percent of persons ages 18–24 and 26.5 percent of those between 25 and 34 lacked coverage. By comparison, less than a fifth of persons ages 35–64 and less than a tenth of children under age 18 were uninsured (DeNavas-Walt, Proctor, and Smith 2009). The relatively high uninsured rate of young adults has important implications for their access to health care, protection against the financial consequences of illness, and may indirectly impact their future health and health care needs. For example, uninsured young adults are three to four times more likely than their insured peers to delay or forgo medical care due to costs and two to four times less likely to see a medical provider, have a usual source of care, or fill a prescription due to cost (Callahan and Cooper 2005; Nicholson et al. 2009;). Additionally, uninsured young adults are twice as likely as those with coverage to have trouble paying medical bills and to have medical debt (Nicholson et al. 2009). Lack of coverage may also compromise young adults' ability to address their frequently observed obesity and alcohol and tobacco use that lead to health and economic problems in adulthood (Merluzzi and Nairn 1999). Several reasons help to explain why young adults lack coverage. Completion of high school or college frequently results in loss of eligibility as a dependent on a parent's health plan. Young adults who fail to obtain postsecondary education or high-skilled vocational training may lack the human capital necessary for jobs that provide health insurance. Such transitions lead to sharp increases in young adult uninsured rates: 38 percent of high school graduates who did not go to college were without coverage for some subsequent period, and after turning age 19, uninsured rates increase to nearly 29 percent for young adults ages 19–29, up from 11 percent for children 18 and under (Nicholson et al. 2009). State efforts to improve access to affordable coverage through regulation of small group and individual health insurance markets may have unintended consequences for young adults. In states that tightly constrain premium variation based on individual health status, young adults may face premiums that fail to reflect their actuarial risk and together with their relatively low incomes, may make coverage economically unattractive. Apart from affordability, some young adults may have a low demand for coverage due to their relatively good health, attitudes toward risk taking, and lack of information regarding the health and financial consequences of going without coverage. In response to the significant disparity in coverage for young adults relative to other age groups, by 2008, 21 states had implemented legislation requiring private insurers to expand dependent coverage. As shown in Table 1, Utah was the first state to do so in 1995, permitting adults through age 25 to enroll in a parent's plan.1 Between 2003 and 2006, six other states followed suit, including New Jersey, which implemented the most expansive dependent eligibility (through age 29 with an expansion through age 30 beginning January 2009). In 2007 through January 2008, 14 additional states implemented expansion policies. In addition, as of mid-August 2009, 8 other states had enacted adult dependent coverage expansions that were implemented after January 2008 or not yet implemented (National Conference of State Legislatures 2009). Table 1 States Implementing Adult Dependent Coverage Expansions by January 2008 Among enacting states, requirements for eligibility vary on the basis of age limits, marital status, residence with parents, transitions from prior insurance, among other factors. In all cases, state laws do not apply to self-funded employer benefit programs due to their exemption from state regulation under a provision of the 1974 Employee Retirement Income Security Act (ERISA). Such an exemption will likely limit the reach of the expansion legislation because many large employers offer self-funded health benefits.2 Of the 21 states implementing expansions through January 2008 (Table 1), 19 increased eligibility of nonstudents an average of 5.3 additional years, and 14 increased eligibility for full-time students by an average of 3.5 years. In this paper, we address the question of whether state implementation of expanded dependent coverage has been effective in increasing coverage among young adults. We do so through an econometric analysis of the relationship between the implementation of this policy and its impact on young adults' health insurance status. We address the issue of policy endogeneity, consider the expansion's effect on different groups of young adults, examine the timing of the legislation's impact, and consider how the distribution of young adult coverage might change were all states to implement the expansions. For our most expansive sample, young adults ages 19–29, we find that state expansion legislation had a small impact on their insurance status, increasing coverage as a dependent on employer-sponsored insurance (ESI) by 1.52 percentage points (an 8.5 percent increase in such coverage over the 17.9 percent of targeted young adults in this group with dependent coverage in the preintervention period). For young adults ages 19–25 who live with their parents, ESI dependent coverage increased by 3.84 percentage points (an 11.9 percent increase over the 32.4 percent of targeted young adults with dependent coverage in the preimplementation period). In all cases, we also find that the increase in dependent coverage was largely offset by a reduction of coverage as an ESI policyholder. We find no impact on young adult uninsured rates, suggesting that the expansion legislation may have had an unintended consequence of reducing young adult coverage as an ESI policyholder.

Performance Characteristics of a Methodology to Quantify Adverse Events over Time in Hospitalized Patients

Abstract: Despite extensive documentation of risk to hospitalized patients (Kohn, Corrigan, and Donaldson 2000; Leape and Berwick 2005; Joint Commission on the Accreditation of Healthcare Organizations 2009; Ornstein 2007;) and substantial efforts to improve in-hospital patient safety (Milstein et al. 2000; Berwick et al. 2006; Catalano 2006; McCannon, Hackbarth, and Griffin 2007; Vemula, Robyn Assaf, and Al-Assaf 2007; Jha et al. 2008), progress in this area has been slow (Leape and Berwick 2005; Vincent et al. 2008;). Indeed, recent studies suggest that adverse events due to hospital care remain common (Rozich, Haraden, and Resar 2003; Resar et al. 2006; Sharek et al. 2006; Griffin and Classen 2008; Takata et al. 2008;). Assessment of the impact of large-scale patient safety initiatives requires generally accepted, rigorous, standardized, and practical measures of adverse events (Leape and Berwick 2005; Vincent et al. 2008;). Such measures are also necessary for individual hospitals to assess their own adverse event rates, as well as the results of their improvement efforts. A number of approaches to measuring adverse event rates have been used historically, including voluntary reports (“incident reports”), mining of administrative databases, and the two-stage review process used in the Harvard Medical Practice Study (Brennan, Localio, and Laird 1989; Brennan et al. 1991; Thomas et al. 2002; Sharek and Classen 2006;). Each of these methods has limitations. The “trigger tool” approach to measuring adverse event rates, by guiding chart reviewers to specific times and events during a patient's hospitalization more likely to contain an adverse event, appears to provide a more efficient and focused variation on retrospective chart reviews and may overcome many of these limitations (Rozich, Haraden, and Resar 2003; Resar et al. 2006; Sharek and Classen 2006; Sharek et al. 2006; Griffin and Classen 2008; Takata et al. 2008; Office of the Inspector General March 2010;). Abnormal laboratory results, prescriptions for antidote medications, and other medical record-based “hints” can all serve to “trigger” the suggestion that an adverse event might have occurred, and that a more thorough review of the medical record is indicated. The Institute for Healthcare Improvement's (IHI's) Global Trigger Tool (GTT) is a single, comprehensive, operationalized, and well-described tool (Table 1; Griffin and Resar 2007) currently used by a number of hospitals in the United States and abroad (Classen et al. 2008) for executive and medical leadership to determine the level of safety in their organization and if improvement has occurred over time. We have performed the current study to provide further data on the performance characteristics, practicality, and generalizability of the GTT to evaluate its potential for tracking adverse events rates at institutional, as well as regional and national levels. This study adds to the present trigger tool literature by tracking adverse events over a 6-year time span, involving a large random sample of eligible patients hospitalized in North Carolina, and comparing hospital-based versus externally hired chart review teams. Table 1 Triggers Included in the Institute for Healthcare Improvement Global Trigger Tool The current report presents the results of a study of the GTT in a representative stratified random sample of 10 hospitals in North Carolina. This study had two principal aims: (1) to determine the performance characteristics (including reliability, sensitivity, and specificity) of the GTT and (2) to compare the medical record abstraction performance of internal hospital-selected reviewers with similarly trained external reviewers selected and supervised by a contract research organization (CRO).

Staffing Ratios and Quality: An Analysis of Minimum Direct Care Staffing Requirements for Nursing Homes

Abstract: Objective To study the impact of minimum direct care staffing (MDCS) requirements on nurse staffing levels, nurse skill mix, and quality.

Adverse drug events in U.S. adult ambulatory medical care.

Abstract: Ensuring patient safety is a major public health challenge. According to the Institute of Medicine (IOM), in the United States, as many as 98,000 deaths per year are attributable to preventable adverse events that occur in the hospital setting, with annual costs (lost income, disability, and health care costs) of between U.S.$17 billion and U.S.$29 billion(IOM 2000). Because the patient safety movement originated in and has focused on acute care settings (IOM 2000), less is known about safety outside the hospital setting (Wachter 2006; Sarkar et al. 2009). Adverse drug events (ADEs), defined as injuries resulting from a medication taken for medical intervention (Bates et al. 1995; Gurwitz et al. 2003; Bourgeois et al. 2009), constitute an important aspect of patient safety. Not all ADEs are preventable or can be considered medical errors; nevertheless, detection and prevention of ADEs is central to improving safety. Several studies have reported high rates of ADEs among specific populations, such as elderly patients (Gurwitz et al. 2003) and those with chronic diseases (Zhang et al. 2007), but important gaps in our current understanding of ambulatory ADEs remain. Apart from studies focused exclusively on emergency departments (EDs) (Budnitz et al. 2006, 2007), national population estimates for ADEs in the United States are lacking. Accordingly, we analyzed data from the National Center for Health Statistics (NCHS) to describe the frequency and distribution of ambulatory ADEs among U.S. adults, to estimate age-specific rates for ADE visits. In addition, we explored which medication classes are most commonly reported in ADE visits, and whether demographic and clinical characteristics were associated with ADE visits.

Does Higher Quality of Diabetes Management in Family Practice Reduce Unplanned Hospital Admissions

Abstract: Objective. To investigate the association between indicators of quality of diabetic management in English family practices and emergency hospital admissions for short-term complications of diabetes. Study Setting. A total of 8,223 English family practices from 2001/2002 to 2006/2007. Study Design. Multiple regression analyses of associations between admissions and proportions of practice diabetic patients with good (glycated hemoglobin [HbA1c] ≤7.4 percent) and moderate (7.4 percent

Getting Physicians to Respond: The Impact of Incentive Type and Timing on Physician Survey Response Rates

Abstract: Survey research is a critical tool in health services research necessary to assess clinical practice patterns that influence the implementation of evidence-based care in an era of comparative effectiveness research. However, survey research, especially among physicians, suffers from lackluster participation relative to their nonphysician counterparts, with response rates to surveys of the former about 10 percentage points lower than surveys of the latter, on average (Asch, Jedrziewski, and Christakis 1997). Although emerging evidence indicates that response rates are poorly correlated with response bias (Groves 2006; Groves and Peytcheva 2008;), in our experience few general medical journals are willing to publish physician surveys with response rates below 50 percent. A recent survey of scientific journal editors showed that approximately 90 percent believe response rate is at least somewhat important in publication decision making (Carley-Baxter et al. 2009). In addition, virtually none of the surveyed editors indicated they have changed their response rate standards in the past 10 years, despite widespread evidence of decreasing response rates to both general population surveys (Steeh et al. 2001; de Leeuw and de Heer 2002; Curtin, Presser, and Singer 2005; Berk, Schur, and Feldman 2007;) and surveys of physicians (Cummings, Savitz, and Konrad 2001; Cull et al. 2005;) over that timeframe. Commonly, survey researchers face the difficult decision of whether to send an additional wave of surveys in hopes of surpassing a key response rate threshold for the general medical literature such as 50 percent or 60 percent. Knowing how to do so judiciously remains an important challenge. Optimizing physician response rates without adversely influencing response bias also remains a key concern among health services researchers. Physician participation in surveys has been shown to be effectively increased through the use of incentives, especially when the incentive is monetary and offered in advance of completing a survey (prepaid) versus being offered contingent on completion of a survey (promised) (Asch, Christakis, and Ubel 1998; Kellerman and Herold 2001; VanGeest, Johnson, and Welch 2007; Flanigan, McFarlane, and Cook 2008;). However, the evidence supporting prepayment over promised monetary incentives is undermined by the lack of controlled experiments testing the two approaches among physicians. As such, the literature can only be construed as suggestive on this matter. Moreover, the extant literature offers little guidance on whether the effectiveness of prepaid monetary incentives varies by the method of its delivery (i.e., cash versus check). Finally, from a research subject protection perspective, immediate incentives (check or cash) that do not require the participant to disclose a tax identification number such as a social security number (SSN) allow participants to better protect their personal privacy and maintain confidentiality—the primary risks associated with survey research. Nevertheless, many institutions require tax identification numbers as a condition of dispensing funds to research participants. This can severely limit the mode and timing of remuneration—each of which have significant implications for response rates. In an era of identity theft, it is important to determine the best ways to both optimize response rates by offering remuneration to research participants who receive small, one-time survey incentives while protecting their privacy. To better understand the effects of payment timing (prepaid versus promised), form of payment (cash versus check), and having to provide an SSN on response rate, response bias, and item nonresponse, we embedded a randomized incentive experiment crossing several of these factors within the third-wave mailing to a nationally representative sample of U.S. physicians. We hypothesized that immediate incentives offered without the provision of an SSN would increase response rates beyond the rates from promised incentives, that cash is more effective than check as an immediate incentive, and that promised incentives not requiring provision of an SSN would yield higher response rates than those requiring it.

Cross-Border Utilization of Health Care: Evidence from a Population-Based Study in South Texas

Abstract: Access to health care is particularly challenging for US residents living along the US–Mexico border, a vast area extending from San Diego, California, to Brownsville, Texas A significant barrier to health care access lies in the economic deprivation to which the border area has long been exposed, as indicated by exceedingly high rates of poverty and uninsurance About 47 percent of the residents in the 32 border counties in Texas lived below 150 percent of the federal poverty line in 2000, compared with the US national average of 21 percent An even more alarming gap exists in health insurance coverage, with Texas border counties having an estimated uninsurance rate of 42 percent in 2002, compared with the national average of 15 percent (US Department of Health and Human Services 2007) In light of these economic, financial, and health care access barriers, a sizable proportion of border residents resort to Mexico to meet their health care needs—with much more affordable prescription medications as well as services from dentists and doctors Results from two congressional reports on drug price differences between the United States and Mexico showed that the average drug price in the United States ranged from 95 to 102 percent higher than in Mexico (US House of Representatives 1998, 1999) This substantial price gap has motivated many US border residents to go to Mexico to buy prescription medications without a prescription, trusting the free medical advice routinely offered at Mexican pharmacies (Rivera, Ortiz, and Cardenas 2009) Clearly, these patterns of cross-border health care utilization reflect major unmet needs on the US side of the border It would be difficult to address these unmet needs without understanding the contributing factors to cross-border utilization of Mexican care by US border residents Previous studies have documented the utilization of health care services in Mexico by US border residents (Macias and Morales 2001; Seid et al 2003; Escobedo and Cardenas 2006; Fernandez and Amastae 2006; Bastida, Brown, and Pagan 2007, 2008; Rivera, Ortiz, and Cardenas 2009; Wallace, Mendez-Luck, and Castaneda 2009) A consistent finding from these studies is that lack of health insurance coverage is one of the most significant predictors of cross-border utilization of health care services Because of data limitations, however, extant research is primarily based on small, nonrandom, local samples that target specific health care services (Bastida, Brown, and Pagan 2008), making it difficult to generalize findings beyond the selected study areas or population groups In this study, we seek to contribute to the literature on the utilization of Mexican health care services by US border residents by analyzing data from a population-based, random sample survey that covers 32 border counties in Texas Specifically, our study has two aims: (1) to assess the use of different types of health care services in Mexico by Texas border residents; (2) to identify the contributing factors to utilizing specific health care services in Mexico, including medication purchases, doctor and dentist visits, and inpatient care by Texas border residents Besides health insurance status, we also analyze the impact of demographics, education, income, cultural affinity with Mexico, self-rated health status, and the perceived quality of health care received in the United States This multivariate approach allows us to single out the most significant predictors of cross-border utilization of health care services among a range of theoretically relevant factors

The unintended consequence of diabetes mellitus pay-for-performance (P4P) program in Taiwan: are patients with more comorbidities or more severe conditions likely to be excluded from the P4P program?

Abstract: Pay-for-performance (P4P) programs that reward providers based on outcome-based performance measures, or other “external” incentives that are not determined solely by provider behavior, can produce unintended consequences (Epstein, Lee, and Hamel 2004). This is important especially when performance measures do not include risk adjustments to account for patient comorbidity or the severity of patients' conditions (Shen 2003). Providers who treat patients with more severe conditions may worry about unfair penalization because these patients are likely to cause a drop in providers' performance scores (Werner and Asch 2005). Incentives sometimes exist, therefore, to reap greater rewards by inappropriately excluding patients with more severe conditions. The potential for gaming the system using such adverse selection is problematic whenever providers are allowed to select patients for their P4P programs. Of course, P4P programs could exclude patients appropriately in a number of circumstances. Programs might, for example, benefit from systematically excluding patients with characteristics that make them inappropriate for the measurement tools being used, or who require unique treatments (British Medical Association [BMA] 2009; Centre for Studies in Social Sciences 2009;). However, this kind of exclusion (active exclusions) such as “exception reporting” may function also as acts of adverse selection. One study showed little evidence of adverse selection, with hospitals reporting low rates of patient exclusion in P4P programs (Doran et al. 2008). Other studies reached the opposite conclusion, showing that adverse selection does indeed pose a significant problem (Doran et al. 2006; Sigfrid et al. 2006; Gravelle, Sutton, and Ma 2010;). These latter three U.K. studies had a different context than our study generally. For instance, providers in the United Kingdom were not allowed to select the patients included in the P4P program, and these studies focused mostly on the exclusion rate for each hospital, not patient comorbidities or complications (Ryan 2009). A key objective in this paper is to evaluate the exclusion from P4P programs for diabetes mellitus (DM) of patients who have comorbidities or severe conditions. In this study, we hypothesize that providers are likely to exclude older patients and patients with high comorbidities or more severe conditions from P4P programs.

Identifying Chronic Conditions in Medicare Claims Data: Evaluating the Chronic Condition Data Warehouse Algorithm

Abstract: As the population ages and the treatment and management of chronic conditions such as heart disease, cancer, and diabetes has improved, the number of older people with one or more chronic conditions has increased (Vogeli et al. 2007). In 2005, among persons 65 and older, 91.5 percent had at least one chronic condition, and 76.6 percent had at least two chronic conditions. About 59 percent of all medical care expenses for persons age 65 and older were for treatment of chronic conditions (Machlin, Cohen, and Beauregard 2008). A significant body of research has used administrative databases to assess chronic conditions, but there are limitations in how well these data can identify a range of conditions, especially comorbid conditions (Taylor, Fillenbaum, and Ezell 2002; Rector et al. 2004; Kern et al. 2006; Klabunde, Harlan, and Warren 2006; Harrold et al. 2007; Ostbye et al. 2008). In support of the goals of Section 723 of the 2003 Medicare Prescription Drug, Improvement, and Modernization Act, the Center for Medicare and Medicaid Services (CMS) created the Chronic Condition Data Warehouse (CCW), consisting of CMS Medicare beneficiary data linked by a unique ID across multiple Medicare data sources. As part of the CCW, beneficiaries with chronic conditions are identified through a predefined algorithm based on particular diagnosis and procedure codes found on certain types of claims within a specified reference period. The ability to easily identify beneficiaries with particular chronic conditions in Medicare claims data has great potential to facilitate and expand research. The CCW algorithms were developed based on prior research using Medicare claims data to identify various chronic conditions (Katz et al. 1997; Herbert et al. 1999; Taylor, Fillenbaum, and Ezell 2002; Losina et al. 2003; Foley et al. 2005). Yet, to date, it is unknown how well the CCW algorithm identifies preexisting chronic conditions, which may have been first diagnosed years before the date of the claims records. The aim of this paper is to examine the strengths and limitations of using CMS's CCW algorithm with Medicare claims data to identify chronic conditions in older persons. Records from the NHANES I Epidemiologic Follow-up Study (NHEFS), including data from questionnaires, physical examinations, medical facility records, and death certificates, have been linked to Medicare claims records. We selected five conditions common among older persons: diabetes, ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), dementia, and arthritis. We compared diagnoses for these five conditions derived from the two data sources (NHEFS and Medicare claims using the CCW algorithm). Specifically, we explored (a) the number of years of Medicare claims history necessary to find a preexisting condition and (b) the proportion of preexisting versus newly diagnosed conditions identified by the Medicare claims using the CCW algorithm.

Health systems' responsiveness and its characteristics: a cross-country comparative analysis.

Abstract: This paper investigates the influence of aggregate country-level characteristics on health system responsiveness, using data on 62 countries present in the World Health Survey. While evidence exists on variations in reported levels of health system responsiveness across countries, the literature is sparse on the determinants of responsiveness, particularly of system wide characteristics (World Health Report, 2000). We attempt to bridge this gap in the literature by considering simultaneously several plausible country-level characteristics as potential determinants of health system responsiveness. These characteristics refer to the way health care systems are organised and funded, the socio-demographic traits of the populations served and the economic, cultural and institutional characteristics of countries. We pay particular attention to the role of health care expenditures per capita while controlling for potential confounding factors. Data on responsiveness and socio-demographic characteristics of respondents are taken from the World Health Survey, a survey launched by the World Health Organization in 2001. Information on the country-level characteristics are obtained from the United Nations Development Program (UNDP), the World Value Survey and the Polity IV Project database. The empirical analysis is performed by adopting a two step procedure. First, we increase the crosscountry comparability of the data by adjusting for variation in the way survey respondents rate an objective level of responsiveness using the hierarchical ordered probit (hopit) model. Secondly, we investigate the influence of health spending per capita and other country characteristics on the adjusted country-level measures of responsiveness. Our results suggest that the most relevant determinants of responsiveness appear to be health expenditure per capita, health care expenditure in the public sector and population levels of education.

The Accuracy of Present‐on‐Admission Reporting in Administrative Data

Abstract: Objective To test the accuracy of reporting present-on-admission (POA) and to assess whether POA reporting accuracy differs by hospital characteristics.

Public attitudes about health information technology, and its relationship to health care quality, costs, and privacy.

Abstract: The United States spends more on health care than any other country and has one of the fastest growth rates in health spending among developed countries (Organization for Economic Co-Operation and Development 2008). Yet the United States performs below other countries on measures such as life expectancy, access to care, and demographic disparities (Kaiser Family Foundation 2007). There is also evidence that preventable errors lead to high costs and poor health care outcomes in the United States (Kohn, Corrigan, and Donaldson 2000; Fernandopulle et al. 2003; Zhan and Miller 2003; Encinosa and Hellinger 2008; Kumar and Steinebach 2008;). The Institute of Medicine has identified the use of health information technology (health IT) as a measure to help improve health care system performance and quality of care (Committee on Quality of Health Care in America, Institute of Medicine 2001). The health care system still operates primarily with paper-based records and lags behind many other industries in adoption of IT (Hillestad et al. 2005; Robert Wood Johnson Foundation 2006; Blumenthal and Glaser 2007; Shields et al. 2007; DesRoches et al. 2008; Furukawa et al. 2008; Jha et al. 2009;). In 2004, President Bush set a goal of assuring that most Americans have electronic medical records (EMRs) within the next 10 years, and he announced several new initiatives, including doubling funding for demonstration projects on health IT, using the federal government to foster the adoption of health IT, and creating the position of National Coordinator for Health Information Technology (George W. Bush White House Archives 2004; Office of the National Coordinator 2008). More recently, President Obama set the goal of achieving EMRs for all Americans within 5 years (Childs, Chang, and Grayson 2009) and set aside more than U.S.$20 billion (Congressional Budget Office 2009) in direct and indirect supports for health IT adoption as part of the American Recovery and Reconstruction Act of 2009 (U.S. Congress 2009). Although health IT has been demonstrated to improve medical care under certain conditions, there is no consensus on how to achieve these benefits across the U.S. health care system as a whole (Chaudhry et al. 2006; Blumenthal and Glaser 2007;). Existing research on the slow adoption rate of health IT notes the high cost of adoption; technological change leading to system obsolescence, accreditation, and standardization issues; concerns about integration with administration systems; providers' productivity during implementation; identifying systems that meet organizational needs; and privacy and security concerns (Valdes et al. 2004; Hillestad et al. 2005; Robert Wood Johnson Foundation 2006; Shields et al. 2007; DesRoches et al. 2008; Furukawa et al. 2008; Blumenthal 2009; Jha et al. 2009;). Most research has examined clinicians' and health care organizations' decisions and attitudes about health IT. There are only a handful of studies on consumer attitudes about health IT despite substantial evidence that consumer attitudes toward a technology are a very important factor in its adoption and success (Venkatesh 2000). An environmental scan to identify surveys on consumers' use and opinions on electronic health records (EHRs) and personal health record (PHRs) found few that were rigorous, noting that response rates and question development methods were generally unreported (Donelan and Miralles 2008). Previous studies of consumers' perceptions of health IT studies have generally shown the public to be relatively unknowledgeable of health IT, but they have also yielded contradictory results. In a 2006 study, only 4 percent of respondents had doctors who used any form of health IT and less than one-third of Americans had heard of the federal government's efforts to create a nationwide system of EMRs (Harris Poll 2007). This study showed strong public support for the entire range of currently available health IT, and a 2006 survey showed that Americans overwhelmingly want to have electronic copies of their medical records. However, another study conducted at the same time found that only 34 percent of respondents believed that an EMR would improve the quality of health care they receive; some 24 percent did not believe an EMR would help improve quality; and 42 percent were undecided or needed more information (Robeznieks 2006). Most recently, a study conducted among members of a large, staff model managed care organization making use of health IT shows that patients agree that health IT can improve the efficiency of care delivery (Chen et al. 2009). Prior surveys also reveal important consumer concerns over the potential exposure of their private medical information (Connecting for Health 2003; Harris Poll 2007;). While some studies show that the majority of consumers trust in hospitals', public health agencies', and providers' treatment of their privacy (Harris Poll 2005), these studies indicated that the public is wary of the ability of the institutions to manage their personal data (Goodwin et al. 2002). A 2006 web survey found that individuals believe the expected benefits of EMRs to patients and society were offset by the risks to privacy (Harris Poll 2007). Although useful, prior studies on consumer attitudes toward health IT have important limitations. First, most of these studies are 3–6 years old, and given the rapid developments in this field, the public's awareness of and attitudes toward health IT are likely to be changing rapidly as well. In addition, as noted above, these studies focused on a small group of specific questions or were conducted with a very select group of respondents (e.g., closed panels of members from a single health plan). Finally, these studies used web panels, omnibus surveys, and polls with relatively small sample sizes, with the accompanying methodological problems such as nonrandom sampling, biased selection, and nonresponse bias, and limited statistical power. These methodological limitations are a possible explanation for the studies' often contradictory results. In this paper, we present findings from a very recent, comprehensive, and methodologically rigorous survey of public attitudes toward health IT and EMRs in particular. The current study uses a large sample size (1,015), is based on probability-based random-digit-dial (RDD) sampling, was conducted using a stand-alone interview (and not a larger omnibus survey on many topics), and with rigorous callback rules and in-depth interviewer training, unlike many polls. While these approaches do not eliminate all potential sources of bias (e.g., from nonresponse), they do ensure that this study provides timely and rigorous results that are generalizable to the U.S. general population. We also explore a number of topics that have not been previously investigated.

The Impact of Medicare Part D on Hospitalization Rates

Abstract: The Medicare Part D program, launched in 2006, increased the share of Medicare beneficiaries with prescription drug coverage from 59 to 89 percent (authors' calculation). This expansion of benefits recognizes that prescription drugs are an indispensable component of care management, particularly for chronic disease. The evidence suggests that, even in a narrow time window, better management of certain conditions with prescription drugs can reduce the likelihood of adverse events like hospitalizations and the costs associated with them (Goldman, Joyce, and Zheng 2007; Stuart, Doshi, and Terza 2009;). Thus, because use of prescription medications is related to the generosity of coverage (Goldman et al. 2007), we would expect that the increase in drug coverage resulting from Part D will increase adherence to important medications and lead to improved health and fewer hospitalizations.1 Evidence from national data supports the first portion of this argument, that Part D increased prescription drug use (Yin et al. 2008; Schneeweiss et al. 2009;). Other research examining the experience of a single insurer suggests that extra spending on medications was offset by reduced spending on other medical services (Zhang et al. 2009). Presumably, a reduction in hospitalizations was a significant component of this offset. The effects could be greatest on admissions for ambulatory care sensitive conditions (Weissman, Gatsonis, and Epstein 1992; Bindman et al. 1995;). Examination of hospitalization is also important because it can help assess the clinical impact of Part D. Specifically, because Part D did not affect incentives for hospitalization, any changes in hospitalizations related to Part D-induced changes in drug coverage are likely due to changes in underlying health status. By assessing the impact of Part D on hospitalizations, we can gain insight about the effects of this policy change on health more generally. Existing research has not examined the impact of Part D on hospitalization (or any markers of health outcomes) directly. In part, this is because linked data on drug coverage, drug utilization, and outcomes are not available. We surmount this problem by conducting an area level study. Our analytic strategy uses the fact that drug coverage was more prevalent in some geographic areas than in others before Part D. Thus, some states were more affected by Part D than others. We assess whether the states most affected by Part D had greater changes in admissions for diagnoses potentially amenable to drug coverage, compared with states less affected by Part D. We control for unobserved state trends by examining admission rate changes in the same states over the same study period for individuals aged 60–64, who for the most part did not see their drug coverage change with the introduction of Part D. This study design is analogous to an intention to treat analysis and therefore addresses the issue of nonrandom selection into Part D plans by examining market-level effects.

Health Behaviors and Utilization among Users of Complementary and Alternative Medicine for Treatment versus Health Promotion

Abstract: Complementary and alternative medicine (CAM) is a diverse array of health services (such as acupuncture, massage therapy, and chiropractic care), natural products, and self-care therapies that is used by a substantial number of Americans. Nearly 4 out of 10 U.S. adults report recently using CAM (Barnes, Bloom, and Nahin 2008) and in 2007 alone, U.S.$34 billion was spent out of pocket on CAM services and products (Nahin et al. 2009). Nationally representative studies suggest that people who use CAM in general are more likely to be middle-aged, female, affluent, and of poorer health status (Eisenberg et al. 1993, 1998; Astin 1998; Bishop and Lewith 2008), and CAM utilization is associated with a holistic and natural orientation to health (Astin 1998). Qualitative studies suggest reasons for CAM use vary, but generally it is used to complement medical care as a modality to treat illness or for general health and wellness purposes (Astin 1998; Bishop, Yardley, and Lewith 2008). This implies there may be two distinct types of CAM User: those who use it to treat an illness and those who use it to promote health. Typically studies on CAM do not distinguish those who use it to treat an illness from those who use it for health promotion. Assuming that the substantial use of CAM will continue to grow, potential impacts of these two different CAM uses could have profound public health and economic consequences. For instance, differences among CAM User types in the use of conventional health care services could either consume or liberate scarce medical resources. Furthermore, a deeper understanding of these two different types of CAM use would inform future policy decisions regarding CAM's potential involvement in national health care reform efforts. In this study, we sought to estimate the national use of CAM to treat illness versus for health promotion and examine these different CAM Users' sociodemographic characteristics, health behaviors, and use of conventional health services.

Following the Money: Factors Associated with the Cost of Treating High-Cost Medicare Beneficiaries

Abstract: Objective. To identify factors associated with the cost of treating high-cost Medicare beneficiaries. Data Sources. A national sample of 1.6 million elderly, Medicare beneficiaries linked to 2004–2005 Community Tracking Study Physician Survey respondents and local market data from secondary sources. Study Design. Using 12 months of claims data from 2005 to 2006, the sample was divided into predicted high-cost (top quartile) and lower cost beneficiaries using a risk-adjustment model. For each group, total annual standardized costs of care were regressed on beneficiary, usual source of care physician, practice, and market characteristics. Principal Findings. Among high-cost beneficiaries, health was the predominant predictor of costs, with most physician and practice and many market factors (including provider supply) insignificant or weakly related to cost. Beneficiaries whose usual physician was a medical specialist or reported inadequate office visit time, medical specialist supply, provider for-profit status, care fragmentation, and Medicare fees were associated with higher costs. Conclusions. Health reform policies currently envisioned to improve care and lower costs may have small effects on high-cost patients who consume most resources. Instead, developing interventions tailored to improve care and lowering cost for specific types of complex and costly patients may hold greater potential for “bending the cost curve.”

Enhancing Response Rates in Physician Surveys: The Limited Utility of Electronic Options

Abstract: Gathering quality data on physician practices and patterns of care is crucial to enhancing efficiency in health care delivery while continuing to improve patient outcomes. One of the more common approaches to gathering such data has been survey research in the form of mail questionnaires. This approach has significant advantages over other modes of data collection: reasonably accurate contact information is readily available; survey delivery and return are relatively inexpensive; and no technologically advanced skills or capabilities are required for the researchers or the respondents. The only significant disadvantage to mail questionnaires involves the unwillingness of some physicians to participate, resulting in low response rates, which in turn raise issues of nonresponse bias and problems of generalizability. This article describes an attempt to boost response rates in a survey of primary care physicians in Alabama by offering a range of electronic options for survey completion.

Patient engagement in health care.

Abstract: People’s interactions with health care are now widely acknowledged to be a central focus of health services research. In the past several decades the research community has made great strides in developing and testing frameworks and influences on numerous aspects of individuals’ engagement at multiple points in the increasingly complicated matter of seeking and using health care services. Individuals are expected to decide whether and when to seek care, which plans and providers meet their needs, how to manage their health, and how to cope with sometimes conflicting advice from providers and friends and family, all amplified by advances in communications and information technology. To evaluate these increased responsibilities and expectations, researchers have used an array of methods and designs, drawing on economics, psychology, sociology, and other fields to enhance our understanding of how individuals participate at these and other decision points. Moreover, publication of the Institute of Medicine’s landmark report Crossing the Quality Chasm in 2001 formally articulated patient-centered care as an essential dimension of high-quality care, a clear focus of new models of delivering care such as primary care patient-centered medical homes (PCMH) and accountable care organizations (ACO) (Committee on Quality of Health Care in America, Institute of Medicine 2001). At the same time, while the use of researcher-developed tools to assess patient experiences of care (e.g., CAHPS) is now considered routine, and numerous provisions of the Patient Protection and Affordable Care Act reinforce patient-centered care as pivotal to achieving high-quality, affordable care, it is also clear that individuals’ tasks are increasingly complex. A recent report on patient engagement in health care describes an Engagement Behavior Framework and multiple behaviors that individuals must master to benefit optimally from their care (Center for Advancing Health 2010). Drawing on previous research, this report represents a precursor to estimating the scale and complexity of challenges confronting individual patients and caregivers, from navigation to managing increasingly sophisticated medical technology at home, often magnified by limited health literacy or past experiences. r Health Research and Educational Trust DOI: 10.1111/j.1475-6773.2011.01254.x

The Cost‐Effectiveness of Three Screening Alternatives for People with Diabetes with No or Early Diabetic Retinopathy

Abstract: Diabetic retinopathy (DR)—a complication involving the retinal microvasculature resulting in damage to the retina from ischema, neovascularization, hemorrhage, and edema—develops in virtually all people with diabetes mellitus and can progress to more advanced stages threatening permanent visual impairment and blindness (American Academy of Ophthalmology Retina Panel 2008). In 2004, DR was the estimated cause of 195,000 prevalent cases of visual impairment and blindness in the United States (The Eye Diseases Prevalence Research Group 2004). Screening to increase detection of DR progression and timely treatment can reduce the risk of permanent visual impairment. Based on clinical and cost-effectiveness evidence from the 1990s, the American Academy of Ophthalmology (AAO), the American Optometric Association, and the American Diabetes Association currently all recommend annual dilated evaluations by an eye-care professional (annual eye evaluations) for all patients with diabetes (Dasbach et al. 1991; Javitt et al. 1994; Javitt 1995; Javitt and Aiello 1996; Fodera 1999; Fong et al. 2003; American Academy of Ophthalmology Retina Panel 2008). This recommendation is likely most cost-effective for patients with advanced DR or elevated hemoglobin A1c (HbA1c) levels, but annual evaluation may be costly for those with no DR or with microaneurysms (MA) only. For lower-risk patients with diabetes, Vijan, Hofer, and Hayward (2000) found that less frequent evaluations provide nearly equivalent benefits as annual ones at lower costs (Vijan, Hofer, and Hayward 2000). In 2006, nearly 9.1 million Americans had type 2 diabetes with limited or no signs of DR (National Center for Health Statistics 2005–2006). After accounting for the rate of compliance with current screening recommendations and the costs of evaluation by an eye-care professional, a change from annual evaluations to biennial evaluations could save approximately U.S.$200 million in health expenditures annually with limited risks to health. However, Vijan, Hofer, and Hayward (2000) did not account for noncompliance with recommendations or for the benefits of detecting other ocular disorders. These limitations may have influenced their results because only 64 percent of people with diabetes aged 30 or older comply with current recommendations and because people with diabetes are at higher risk for incident glaucoma, cataract, and possibly vision-threatening age-related macular degeneration (AMD) than people without diabetes (Klein et al. 1995; Bonovas, Peponis, and Filioussi 2004; Clemons et al. 2005; National Center for Health Statistics 2005–2006). Furthermore, advocates for annual evaluation argue that the annual recommendation is easier to communicate, creating less risk of noncompliance. Over the past decade, retinal digital photography (telemedicine) has emerged as a lower cost alternative to annual evaluation by an eye-care professional. Telemedicine uses digital retinal photography to enable screening in nonophthalmologic settings. Images are electronically transferred to a grading center for evaluation, and patients with evidence of mild to severe DR are referred to an eye-care professional for a full evaluation. Telemedicine has shown better sensitivity (98 percent) and specificity (86 percent) in detecting DR than ophthalmoscopy (Moss et al. 1985; Ahmed et al. 2006) and usually costs less from both the health care and societal perspectives than dilated eye examinations because of lower provider reimbursements and lower patient productivity losses from time lost to treatment. Because 82 percent of people with diabetes visit a primary care provider annually, telemedicine could also potentially increase the annual probability of screening for DR compared with clinical eye evaluations (National Center for Health Statistics 2005–2006). Unfortunately, telemedicine currently has a limited ability to detect prevalent eye conditions other than AMD, such as cataract, glaucoma, or uncorrected refractive error (URE, a presenting acuity of 20/40 or worse that can be easily corrected with glasses or contact lenses). The much greater ability of clinical eye evaluation to detect these conditions may result in either annual or biennial evaluation being more cost-effective than telemedicine. We designed this study to provide additional information regarding the most cost-effective screening alternatives for people with diabetes who are at low risk of progression when accounting for imperfect compliance with screening recommendations and the ability of eye evaluation to detect other common visual disorders. We estimated the cost-effectiveness of four possible methods of managing this patient population: patient self-referral following visual symptoms, annual eye evaluation, biennial eye evaluation, and annual telemedicine screening in primary care settings. Our results can be used to evaluate the management practice that is most likely to be cost-effective at different societal valuations of the gains from medical therapy and to inform what additional research may be required to update recommendations.

The impact of Medicare Part D on out-of-pocket costs for prescription drugs, medication utilization, health resource utilization, and preference-based health utility.

Abstract: The implementation of Medicare Part D on January 1, 2006 provided a voluntary outpatient prescription drug benefit to 43 million Medicare beneficiaries for the first time since Medicare's inception (Doherty 2004). Following the implementation of Part D, Medicare's portion of national prescription drug spending increased from 2 percent (2005) to 18 percent (2006) (Kaiser Family Foundation 2008). Around 50–60 percent of Medicare beneficiaries without prior prescription drug coverage had Part D drug coverage in 2006 (Levy and Weir 2009). The estimated federal cost of Part D from 2007 through 2016 is U.S.$768 billion dollars (Kaiser Family Foundation 2006). Studies have found that Part D increased Medicare beneficiaries' prescription utilization and decreased their out-of-pocket costs (Lichtenberg and Sun 2007; Simoni-Wastila et al. 2008; Yin et al. 2008;). However, it is less clear whether the program has led to reductions, or offsets, in nonprescription utilization of health care services. Khan, Kaestner, and Lin (2008) used data from the 1992–2000 Medicare Current Beneficiary Survey and found that prescription drug insurance did not appear to reduce beneficiary's hospitalizations. In contrast, Hsu et al. (2006) analyzed Medicare Part C claims data from Kaiser Permanente–Northern California and concluded that increases in medication coverage resulted in reduced hospitalizations and lower health care expenditures. Similarly, Zhang et al. (2009) analyzed claims data from a Medicare Advantage plan for a Pennsylvania insurer 2 years before and 2 years after the implementation of Part D and found that the increased spending on prescription drugs was offset by lower nondrug medical spending among groups with limited or no previous drug coverage. We sought to study the overall policy impact of Part D on non-low-income Medicare beneficiaries using detailed health care utilization and expenditure data from a large, nationally representative sample of Medicare beneficiaries. This study hypothesized that Part D eligibility would be associated with an increase in Medicare beneficiaries' medication utilization and a reduction in their out-of-pocket costs for prescription drugs, emergency department use, and hospitalization rates (Gellad et al. 2006; Tjia and Schwartz 2006;) as well as improvement in Medicare beneficiaries' overall health measured by preference-based health utility.

Lottery-based versus fixed incentives to increase clinicians' response to surveys.

Abstract: Because low survey response rates increase the potential for nonresponse bias (Schweitzer and Asch 1995; Halpern and Asch 2003), researchers have explored a variety of methods to increase clinicians' response to both postal and web-based surveys. Nuances of the survey technique, such as manipulating the style of the outgoing envelopes (Asch and Christakis 1994) or return postage (Choi, Pack, and Purdham 1990), have produced some success, but financial incentives have generally proven most effective in inducing response (Fox, Crask, and Kim 1988; Asch, Jedrziewski, and Christakis 1997; Cummings, Savitz, and Konrad 2001). To date, most comparative effectiveness studies of financial incentives have focused on their size. For example, in a randomized trial of U.S.$2 versus U.S.$5 prepaid (i.e., unconditional) incentives, the U.S.$5 incentive increased response from 46 to 61 percent (Asch, Christakis, and Ubel 1998). Subsequent studies showed that larger prepaid payments may be more effective still, as U.S.$10 was superior to U.S.$5 in inducing response (Halpern et al. 2002) and U.S.$50 was superior to U.S.$20 (Keating et al. 2008). However, providing fixed payments of increasing size is expensive and likely yields diminishing marginal returns (Fox, Crask, and Kim 1988). By contrast, insights from the field of behavioral economics suggest that the ways in which similarly sized incentives are structured may alter their effects substantially without influencing their costs (Volpp et al. 2009; Haisley et al. in press). This may be particularly important in the domain of inducing response in web-based surveys, in which it is logistically difficult to incorporate prepaid incentives. Because investigators are increasingly turning to web-based surveys given their convenience, speed, and reduced fixed costs per response (such as mailing costs and data entry), improved methods for inducing response are needed. Although payments can be provided conditionally in web-based surveys—that is, provided after receiving a completed questionnaire—studies have shown that prepaid incentives are superior to such conditional incentives (Weiss, Friedman, and Shoemaker 1985; Berry and Kanouse 1987; Leung et al. 2004), likely because money in hand incites duties of reciprocity (Warriner et al. 1996; Fehr and Falk 2002). For these reasons and perhaps others, lottery-based incentives appear to offer an attractive means of improving response, particularly in web-based surveys. Lottery-based incentives are administratively simple, potentially inexpensive, offer the enticement of a chance at a large payout, and may take advantage of peoples' tendencies to overestimate small probabilities in the face of large potential rewards (Kahneman and Tversky 1979). Despite these theoretical advantages, previous studies on lottery incentives have yielded mixed results. Response rates among members of the public (Kalantar and Talley 1999; Goritz and Wolff 2007) and trauma patients (Harris et al. 2008) generally have not improved with lottery tickets or entries into drawings for gift cards. Among clinicians, available studies suggest that lottery-based incentives produce modest increases in response rate compared with no financial incentives (Baron, De Wals, and Milord 2001; Leung et al. 2002; Robertson, Walkom, and McGettigan 2005) and that lotteries are inferior to unconditional fixed payments (Leung et al. 2002; Ulrich et al. 2005). However, interpreting these findings is challenging for two reasons: first, none of these research teams informed respondents of their probability of winning the lotteries if they responded, and fixed-payment comparators, when used, were not actuarially equivalent to the lotteries. Thus, it is uncertain whether the observed effects are due to differences in the expected value of the different incentives, or to the different psychological impacts of guaranteed versus chance payments. Second, although lotteries generated lower response rates than prepaid fixed payments, it is unclear whether this effect was due to the differential influence of a guaranteed versus chance (lottery-based) payment or to the fact that the guaranteed payments in these studies were all provided unconditionally, whereas lottery entry was conditional upon response. Prior studies have also failed to evaluate the effectiveness of different types of lotteries. For example, because people tend overestimate probabilities near zero (Kahneman and Tversky 1979), lotteries with low probabilities of winning large prizes may be more effective than actuarially equivalent lotteries with somewhat higher probabilities of winning smaller prizes. To address these limitations, we conducted three randomized trials comparing lottery-based incentives to both unconditional and conditional fixed payments as well as to no financial incentive. We had three central hypotheses (1) that relatively affluent clinicians would be swayed more by a small chance for a large lottery payout than by an actuarially equivalent fixed payment because they would tend to be risk-seeking for gains; (2) that low-probability high-payout lotteries would be more effective than higher-probability lower-payout lotteries due to tendancies to pay more attention to the magnitudes of rewards and to overestimate small probabilities; and (3) that lottery-based incentives would compare more favorably versus conditional fixed payments (those promised upon response) than versus unconditional (prepaid) fixed payments because in the latter case, the possibility of a large payment would have to be sufficiently compelling to overcome the induced duties of reciprocity.

The Effect of the MassHealth Hospital Pay‐for‐Performance Program on Quality

Abstract: Pay-for-performance (P4P) in health care has been widely advanced as a means to improve the value of care. The majority of commercial HMOs now use P4P (Rosenthal et al. 2006), most state Medicaid programs use P4P (Kuhmerker and Hartman 2007), and Medicare has launched a number of P4P demonstrations (IOM 2006). Under the Patient Protection and Affordable Care Act (PPACA 2010), hospital P4P is scheduled for nationwide implementation in 2013 as part of Medicare's Value-Based Purchasing Program. However, questions remain about the effectiveness of hospital-based P4P. As described in a recent systematic review (Mehrotra et al. 2009), only three hospital-based P4P programs have been evaluated. Much of the published research draws on experience of the Premier Quality Incentive Demonstration (PHQID), a nationwide P4P demonstration implemented jointly by Medicare and Premier Inc. in 2003. While early studies found evidence that the PHQID was effective (Grossbart 2006; Lindenauer et al. 2007;), subsequent analysis has found limited evidence of an effect of the PQHID on process quality (Glickman et al. 2007) and casts doubt on the effect of the PHQID on quality and cost outcomes (Glickman et al. 2007; Bhattacharya et al. 2009; Ryan 2009a;). This study tests the early impact of the hospital P4P program recently implemented by the Massachusetts Medicaid program (also known as MassHealth).

The Relationship between Organizational Climate and Quality of Chronic Disease Management

Abstract: Objective. To test the utility of a two-dimensional model of organizational climate for explaining variation in diabetes care between primary care clinics. Data Sources/Study Setting. Secondary data were obtained from 223 primary care clinics in the Department of Veterans Affairs health care system. Study Design. Organizational climate was defined using the dimensions of task and relational climate. The association between primary care organizational climate and diabetes processes and intermediate outcomes were estimated for 4,539 patients in a cross-sectional study. Data Collection/Extraction Methods. All data were collected from administrative datasets. The climate data were drawn from the 2007 VA All Employee Survey, and the outcomes data were collected as part of the VA External Peer Review Program. Climate data were aggregated to the facility level of analysis and merged with patient-level data. Principal Findings. Relational climate was related to an increased likelihood of diabetes care process adherence, with significant but small effects for adherence to intermediate outcomes. Task climate was generally not shown to be related to adherence. Conclusions. The role of relational climate in predicting the quality of chronic care was supported. Future research should examine the mediators and moderators of relational climate and further investigate task climate.

The Impacts of State Health Reform Initiatives on Adults in New York and Massachusetts

Abstract: The 2010 national health reform legislation—The Patient Protection and Affordable Care Act (PPACA)—builds on state coverage initiatives, most notably on Massachusetts' 2006 landmark reform effort PPACA includes expansions of existing public programs, efforts to make private insurance more affordable, and individual and employer mandates This study looks at the impacts of state health reform initiatives in New York and Massachusetts on insurance coverage and health care access and use to expand our understanding of the likely impacts of national reform Understanding the impacts of coverage expansions on both insurance coverage and access to health care is critical to designing initiatives that lead to improvements in the health care available to the population and, thereby, population health—which is the ultimate goal of coverage expansion efforts (Hadley 2003; Institute of Medicine 2009; McWilliams 2009;) Prior studies of individual state health reform initiatives have seldom considered impacts on access to and use of health care, largely because of a lack of data This study takes advantage of the availability of state-level data in the National Health Interview Survey (NHIS) to examine the impacts of the health reform initiatives in New York and Massachusetts on coverage and access to and use of health care To our knowledge, this represents the first use of the NHIS, which is the nation's most comprehensive health survey, to study the effects of an individual state's health reform initiative on health care access and use

Organizational Culture and Its Relationship with Hospital Performance in Public Hospitals in China

Abstract: Objective To measure perceptions of organizational culture among employees of public hospitals in China and to determine whether perceptions are associated with hospital performance.