HCA 675 Week 2 Discussion Question Two
HCA 675 Week 2 Discussion Question Two
Discuss the impacts of a large portion of the population being uninsured and how this affects the health care system. If this continues, how do you anticipate that the health care system will respond to it? What are the ethical issues and the practical ones?
Context: Uninsured adults have less access to recommended care, receive poorer quality of care, and experience worse health outcomes than insured adults do. The potential health benefits of expanding insurance coverage for these adults may provide a strong
rationale for reform. However, evidence of the adverse health effects of uninsurance has been largely based on observational studies with designs that do not support causal conclusions. Although recent research using more rigorous methods may offer a better understanding of this important subject, it has not been comprehensively reviewed.
Methods: The clinical and economic literature since 2002 was systematically searched. New research contributions were reviewed and evaluated based on their methodological strength. Because the effectiveness of medical care varies considerably by clinical risk and across conditions, the consistency of study findings with clinical expectations was considered in their interpretation. Updated conclusions were formulated from the current body of research.
Findings: The quality of research has improved significantly, as investigators have employed quasi-experimental designs with increasing frequency to address limitations of earlier research. Recent studies have found consistently positive and often significant effects of health insurance coverage on health across a range of outcomes. In particular, significant benefits of coverage have now been robustly demonstrated for adults with acute or chronic conditions for which there are effective treatments.
Conclusions: Based on the evidence to date, the health consequences of uninsurance are real, vary in magnitude in a clinically consistent manner, strengthen the argument for universal coverage in the United States, and underscore the importance of evidence-based determinations in providing health care to a diverse population of adults.
In its comprehensive study of the health consequences of uninsurance, Care without Coverage: Too Little, Too Late, the Institute of Medicine in 2002 found that uninsured adults in the United States have less access to recommended care, receive poorer quality of care, and experience worse health outcomes than insured adults do (IOM 2002). Derived from a systematic review of a large body of research, these findings led to the conclusion that providing health insurance coverage to uninsured adults would likely improve their health status and reduce their risk of premature death. Since this report was published, the number of Americans who lack health insurance rose to 46 million in 2007, including 37 million, or 19.6 percent, of the nonelderly adult population (DeNavas-Walt, Proctor, and Smith 2008). If health insurance coverage indeed improves health, then the benefits of policies to expand coverage could be substantial.
The Institute of Medicine’s findings were distilled from consistent evidence provided by more than 130 research articles and substantiated by a subsequent rigorous literature review (Hadley 2003). These conclusions could not be stated with great certainty, however, because they were based largely on observational studies that had fundamental design limitations. Most of these studies compared health-related outcomes of insured and uninsured adults and used statistical techniques to adjust for other predictors of health that may be related to health insurance status.
Such comparisons are problematic for two principal reasons. First, insured and uninsured adults may differ greatly in their sociodemographic characteristics, environmental influences, clinical risk factors, health behaviors, preferences, or other predictors of health. It is virtually impossible to measure all systematic differences among these groups, some of which may be unobservable, let alone measure them all precisely. Moreover, comparisons of insured and uninsured adults often rely on data collected on a limited set of variables. Therefore, important differences may remain after statistical adjustments that explain observed differences in health between insured and uninsured adults. Econometricians commonly call this threat to validity the omitted variables bias; epidemiologists label it unmeasured confounding.