Andrew Ward

Disparities in potentially avoidable emergency department (ED) care: ED visits for ambulatory care sensitive conditions.

Introduction:Hospital care for ambulatory care sensitive conditions (ACSC) is potentially avoidable and often viewed as an indicator of suboptimal primary care. However, potentially preventable encounters with the health care system also occur in emergency department (ED) settings. We examined ED visits to identify subpopulations with disproportionate use of EDs for ACSC care. Methods:We analyzed data from the 2007–2009 National Hospital Ambulatory Medical Care Survey for 78,114 ED visits by adults aged 18 and older. Outcomes were ACSC visits determined from the primary ED diagnosis. We constructed analytic groups aligned with Agency for Healthcare Research and Quality’s priority populations. Multivariate logistic regression was used to estimate the odds of all-cause, acute, and chronic ACSC visits. We used Stata SE survey techniques to account for the complex survey design. Results:Overall, 8.4% of ED visits were for ACSC, representing over 8 million potentially avoidable ED visits annually. ACSC visits were more likely to result in hospitalization than non-ACSC visits (34.4% vs. 14.0%, P<0.001). Multivariate models revealed significant disparities in ACSC visits to the ED by race/ethnicity, insurance status, age group, and socioeconomic status, although patterns differed for acute and chronic ACSC. Conclusions:Disproportionately higher use of EDs for ACSC care exists for many priority populations and across a broader range of priority populations than previously documented. These differences constitute disparities in potentially avoidable ED visits for ACSC. To avoid exacerbating disparities, health policy efforts to minimize economic inefficiencies in health care delivery by limiting ED visits for ACSC should first address their determinants.

How much health insurance is enough? Revisiting the concept of underinsurance.

There is little consensus on what constitutes adequate health insurance coverage. The concept of a lack of adequate coverage, or underinsurance, is a matter of ongoing debate. A measure of adequate coverage is of critical importance as the nature of health insurance products evolves. Changes to health coverage include more direct out-of-pocket spending by consumers and a reduction of covered benefits. This article updates and extends an earlier review of underinsurance measurement published in 1993. We present a conceptual approach to measuring underinsurance and provide a review of the empirical findings obtained from the application of these approaches. A discussion of the limitations in the selection of a measurement approach includes a review of the extant data sources used. We recommend a national effort to develop a consistent approach to monitor changes in the economic and structural dimensions of health insurance coverage with a concerted effort to define and measure underinsurance.

Personal Use of Complementary and Alternative Medicine (CAM) by U.S. Health Care Workers

OBJECTIVE To examine personal use of complementary and alternative medicine (CAM) among U.S. health care workers. DATA Data are from the 2007 Alternative Health Supplement of the National Health Interview Survey. We examined a nationally representative sample of employed adults (n = 14,329), including a subsample employed in hospitals or ambulatory care settings (n = 1,280). STUDY DESIGN We used multivariate logistic regression to estimate the odds of past year CAM use. PRINCIPAL FINDINGS Health care workers are more likely than the general population to use CAM. Among health care workers, health care providers are more likely to use CAM than other occupations. CONCLUSIONS Personal CAM use by health care workers may influence the integration of CAM with conventional health care delivery. Future research on the effects of personal CAM use by health care workers is therefore warranted.

Persistent Disparities in Pap Test Use: Assessments and Predictions for Asian Women in the U.S., 1982–2010

Disparities in cancer screening among U.S. women are well documented. However, little is known about Pap test use by Asian women living in the U.S. Data for women, ages 18 and older, living in the U.S. were obtained from National Health Interview Survey (NHIS) files from 1982 to 2005. Outcomes were ever having a Pap test and having a Pap test within the preceding 3 years. Pap test prevalence trends were estimated by race and ethnicity and for Asian subgroups. Fractional logit models were used to predict Pap test use in 2010. Although the rate of having a Pap test within the preceding 3 years increased slightly from 1982 to 2005 for all U.S. women, Asian women continue to have the lowest rate. Pap test use also varied within Asian subpopulations living in the U.S. None of the races and ethnicities are predicted to reach the Pap test targets of Healthy People 2010. To reduce or eliminate continuing disparities in Pap test use requires targeted policy interventions.

Ensuring the Enfranchisement of People With Disabilities

In a representational democracy, the process of selecting people to represent the electorate is critical. To accomplish this goal, it is crucial that elections be fair and accurate reflections of the decisions of the voters. However, a significant and relatively unacknowledged constituency, people with disabilities, faces a variety of barriers to full participation in the U.S. electoral democracy. Recent research has provided evidence that how people with disabilities vote is just as important as the physical barriers they face when casting their votes. This article presents an overview of the literature addressing issues that affect how people with disabilities vote, with an especial focus on the role of election officials as both facilitators and inhibitors of voting by people with disabilities.

Causal criteria and the problem of complex causation

Nancy Cartwright begins her recent book, Hunting Causes and Using Them, by noting that while a few years ago real causal claims were in dispute, nowadays “causality is back, and with a vengeance.” In the case of the social sciences, Keith Morrison writes that “Social science asks ‘why?’. Detecting causality or its corollary—prediction—is the jewel in the crown of social science research.” With respect to the health sciences, Judea Pearl writes that the “research questions that motivate most studies in the health sciences are causal in nature.” However, not all data used by people interested in making causal claims come from experiments that use random assignment to control and treatment groups. Indeed, much research in the social and health science depends on non-experimental, observational data. Thus, one of the most important problems in the social and health sciences concerns making warranted causal claims using non-experimental, observational data; viz., “Can observational data be used to make etiological inferences leading to warranted causal claims?” This paper examines one method of warranting causal claims that is especially widespread in epidemiology and the health sciences generally—the use of causal criteria. It is argued that cases of complex causation generally, and redundant causation—both causal overdetermination and causal preemption—specifically, undermine the use of such criteria to warrant causal claims.

Addressing confounding errors when using non-experimental, observational data to make causal claims

In their recent book, Is Inequality Bad for Our Health?, Daniels, Kennedy, and Kawachi claim that to “act justly in health policy, we must have knowledge about the causal pathways through which socioeconomic (and other) inequalities work to produce differential health outcomes.” One of the central problems with this approach is its dependency on “knowledge about the causal pathways.” A widely held belief is that the randomized clinical trial (RCT) is, and ought to be the “gold standard” of evaluating the causal efficacy of interventions. However, often the only data available are non-experimental, observational data. For such data, the necessary randomization is missing. Because the randomization is missing, it seems to follow that it is not possible to make epistemically warranted claims about the causal pathways. Although we are not sanguine about the difficulty in using observational data to make warranted causal claims, we are not as pessimistic as those who believe that the only warranted causal claims are claims based on data from (idealized) RCTs. We argue that careful, thoughtful study design, informed by expert knowledge, that incorporates propensity score matching methods in conjunction with instrumental variable analyses, provides the possibility of warranted causal claims using observational data.

Health Care Coverage and the Health Care Industry

OBJECTIVES We examined rates of uninsurance among workers in the US health care workforce by health care industry subtype and workforce category. METHODS We used 2004 to 2006 National Health Interview Survey data to assess health insurance coverage rates. Multivariate logistic regression analyses were conducted to estimate the odds of uninsurance among health care workers by industry subtype. RESULTS Overall, 11% of the US health care workforce is uninsured. Ambulatory care workers were 3.1 times as likely as hospital workers (95% confidence interval [CI]=2.3, 4.3) to be uninsured, and residential care workers were 4.3 times as likely to be uninsured (95% CI=3.0, 6.1). Health service workers had 50% greater odds of being uninsured relative to workers in health diagnosing and treating occupations (odds ratio [OR]=1.5; 95% CI=1.0, 2.4). CONCLUSIONS Because uninsurance leads to delays in seeking care, fewer prevention visits, and poorer health status, the fact that nearly 1 in 8 health care workers lacks insurance coverage is cause for concern.

Necessary Health Care and Basic Needs: Health Insurance Plans and Essential Benefits

According to HealthCare.gov, by improving access to quality health for all Americans, the Affordable Care Act (ACA) will reduce disparities in health insurance coverage. One way this will happen under the provisions of the ACA is by creating a new health insurance marketplace (a health insurance exchange) by 2014 in which “all people will have a choice for quality, affordable health insurance even if a job loss, job switch, move or illness occurs”. This does not mean that everyone will have whatever insurance coverage he or she wants. The provisions of the ACA require that each of the four benefit categories of plans (known as bronze, silver, gold and platinum) provides no less than the benefits available in an “essential health benefits package”. However, without a clear understanding of what criteria must be satisfied for health care to be essential, the ACA’s requirement is much too vague and open to multiple, potentially conflicting interpretations. Indeed, without such understanding, in the rush to provide health insurance coverage to as many people as is economically feasible, we may replace one kind of disparity (lack of health insurance) with another kind of disparity (lack of adequate health insurance). Thus, this paper explores the concept of “essential benefits”, arguing that the “essential health benefits package” in the ACA should be one that optimally satisfies the basic needs of the people covered.

The social epidemiologic concept of fundamental cause

The goal of research in social epidemiology is not simply conceptual clarification or theoretical understanding, but more importantly it is to contribute to, and enhance the health of populations (and so, too, the people who constitute those populations). Undoubtedly, understanding how various individual risk factors such as smoking and obesity affect the health of people does contribute to this goal. However, what is distinctive of much on-going work in social epidemiology is the view that analyses making use of individual-level variables is not enough. In the spirit of Durkheim and Weber, S. Leonard Syme makes this point by writing that just “as bad water and food may be harmful to our health, unhealthful forces in our society may be detrimental to our capacity to make choices and to form opinions” conducive to health and well-being. Advocates of upstream (distal) causes of adverse health outcomes propose to identify the most important of these “unhealthful forces” as the fundamental causes of adverse health outcomes. However, without a clear, theoretically precise and well-grounded understanding of the characteristics of fundamental causes, there is little hope in applying the statistical tools of the health sciences to hypotheses about fundamental causes, their outcomes, and policies intended to enhance the health of populations. This paper begins the process of characterizing the social epidemiological concept of fundamental cause in a theoretically respectable and robust way.