Annual Review of Public Health - Volume 21, 2000

A dramatic rise in homicide in the latter half of the 1980s peaked during the 1990s and then declined at an equally dramatic rate. Such trends in homicide rates can be understood only by examining rates in specific age, sex, and racial groups. The increase primarily involved young males, especially black males, occurred first in the big cities, and was related to the sudden appearance of crack cocaine in the drug markets of the big cities around 1985. This development led to an increased need for and use of guns and was accompanied by a general diffusion of guns into the larger community. The decline in homicide since the early 1990s has been caused by changes in the drug markets, police response to gun carrying by young males, especially those under 18 years old, the economic expansion, and efforts to decrease general access to guns, as well as an increase in the prison population and a continued decline in homicide among those over age 24. The lessons learned from the recent homicide trends and the factors associated with them have important implications for public health and the criminal justice system.

This paper reviews the large and growing body of literature on the apparently negative effects of income inequality on population health. Various hypotheses are identified and described that explain the empirically observed association between measures of income inequality and population health. We have concluded that data from aggregate-level studies of the effect of income inequality on health, i.e. studies at the population and community (e.g. state) levels, are largely insufficient to discriminate between competing hypotheses. Only individual-level studies have the potential to discriminate between most of the advanced hypotheses. The relevant individual-level studies to date, all on U.S. population data, provide strong support for the “absolute-income hypothesis,” no support for the “relative-income hypothesis,” and little or no support for the “income-inequality hypothesis.” Results that provide some support for the income-inequality hypothesis suggest that income inequality at the state level affects mainly the health of the poor. There is only indirect evidence for the “deprivation hypothesis,” and no evidence supports the “relative-position hypothesis.” Overall, the absolute-income hypothesis, although >20 years old, is still the most likely to explain the frequently observed strong association between population health and income inequality levels.

The Balanced Budget Act of August 1997 was intended to improve the insurance coverage of low-income children. Although no specific mandates for evaluation accompanied the legislation, states are expected to assess its impact. Insurance improves access to and use of health services. The extent to which this is associated with better health depends on the kinds of services received. Access to a regular source of care is a critical characteristic. If this source of care provides good primary care, a variety of benefits would be expected to accrue. Thus, evaluation of the benefits of the State Children's Health Insurance Program should address not only improved coverage by health insurance for eligible children, but also the extent to which children receive high-quality primary care, including appropriate referrals when needed. Maternal access to and use of services is also a critical factor. Targets for evaluation include state activities to (a) enroll through outreach, inform through education, and develop information systems for tracking; (b) assess the degree of penetration of insurance among low-income children and their mothers, the availability of health care personnel, and the adequacy of benefits conferred by the insurance, primary-care services, and back-up specialty services; and (c) obtain evidence of improved health status. This article details approaches that can appropriately be used to address each of these aspects of concern.

Estimating preferences for states of health has been an active area of research in recent years. Unlike psychophysical approaches, which discriminate levels of health status, preference-based approaches incorporate values or utilities for health outcomes and can be used in cost-effectiveness analyses to aid resource allocation decisions. This chapter considers issues and controversies involved in using preference-based measures in economic evaluation in health care, with a particular emphasis on cost-utility analysis and the estimation of quality-adjusted life years. Topics considered include techniques for measuring preferences, the use of preference-based classification systems, the relationship between patient and community preferences, methods for obtaining utilities from clinical trials, mapping health status from health utilities, the development of “off-the-shelf” preference weights, and proposed alternatives to quality-adjusted life years. We also consider applications of cost-utility analyses to public health interventions. Although cost-utility analyses have become more popular recently, many challenges remain for the field. Widespread acceptance of the methodology likely awaits more consensus on measurement techniques, as well as educational efforts in the public health and medical communities on the usefulness of the approach.

The resurgence of telemedicine can be attributed to its potential for addressing intransigent problems in health care, including limited accessibility, cost inflation, and uneven quality. After discussing definitions and the genesis of telemedicine, this review focuses on conceptual issues and an assessment of past research. The scope and methodological rigor necessary for sustained development and policy making have been limited in this area of research, owing to the nature of extant telemedicine projects and the lack of a comprehensive research strategy that specifies the objectives of telemedicine research regarding accessibility, cost, and quality. Research strategies and a framework for analysis are discussed. Without a commitment to the types of research objectives, framework, and strategy presented here, the considerable promise of telemedicine, as an innovative system of care, may not be fully realized.

The rural health care system has changed dramatically over the past decade because of a general transformation of health care financing, the introduction of new technologies, and the clustering of health services into systems and networks. Despite these changes, resources for rural health systems remain relatively insufficient. Many rural communities continue to experience shortages of physicians, and the proportion of rural hospitals under financial stress is much greater than that of urban hospitals. The health care conditions of selected rural areas compare unfavorably with the rest of the nation. The market and governmental policies have attempted to address some of these disparities by encouraging network development and telemedicine and by changing the rules for Medicare payments to providers. The public health infrastructure in rural America is not well understood but is potentially the most fragile aspect of the rural health care continuum.

Long-term care has begun to rely heavily on assessment as a basis for determining eligibility and payment for services, as well as for planning needed care. Mandated assessments have been introduced into nursing homes and will soon be required for home health care and rehabilitation. Many states use a formal structured assessment process to establish clients' eligibility for institutional or community-based care. The common feature of such assessment is attention to physical functioning, but other domains are also relevant, including affect, social function, cognition, pain and discomfort, and satisfaction. Taken together, this cluster is often referred to as quality of life. While some measures attempt to infer this information from clients' behavior, it is best obtained directly from clients' responses whenever possible. The other major component of a long term care assessment relates to obtaining information on clients' preferences and values. These data are important both for weighting the individual components of an assessment and for directly addressing preferences about the care and lifestyle available.