摘要：Objectives. We describe a method to facilitate routine monitoring of socioeconomic health disparities in the United States. Methods. We analyzed geocoded public health surveillance data including events from birth to death (c. 1990) linked to 1990 census tract (CT) poverty data for Massachusetts and Rhode Island. Results. For virtually all outcomes, risk increased with CT poverty, and when we adjusted for CT poverty racial/ethnic disparities were substantially reduced. For half the outcomes, more than 50% of cases would not have occurred if population rates equaled those of persons in the least impoverished CTs. In the early 1990s, persons in the least impoverished CT were the only group meeting Healthy People 2000 objectives a decade ahead. Conclusions. Geocoding and use of the CT poverty measure permit routine monitoring of US socioeconomic inequalities in health, using a common and accessible metric. The extent of US socioeconomic inequalities in health and their contribution to racial/ethnic health disparities is poorly documented and cannot readily be monitored, given the lack of socioeconomic data in most US public health surveillance systems, apart from educational level in the birth and death certificates. 1, 2 This failure to include socioeconomic data severely impedes efforts to understand, routinely monitor, and address social disparities in health in the United States. Because of an absence of baseline data, 70% of the 467 US public health objectives for the year 2010 lack socioeconomic targets. 3 The critical importance of documenting the social patterning of disease and death has been recognized since the rise of the public health movement in the mid-19th century, 4 and such documentation is of national and global significance. 1, 5 As Sydenstricker noted when establishing the first US population-based morbidity studies in the 1920s, data on the social patterns of health are crucial to “give glimpses of what the sanitarian has long wanted to see—a picture of the public-health situation as a whole, drawn in proper perspective and painted in true colors.” 6 (p280) These health statistics, generated through cycles of ongoing data collection, analysis, interpretation, and dissemination, 1 not only provide vital information about the population burden of disease, relevant for allocation of resources, but also provide critical stimuli for—and tests of—etiologic hypotheses about disease causation. 7 Yet in contrast to Europe, where health statistics have routinely included socioeconomic data, 4, 5 in the United States, most public health surveillance systems have not collected data on socioeconomic factors as they have on race/ethnicity. 1, 2 The net effect has been to remove from view—and from policy discourse—the pervasive patterning of US health disparities by socioeconomic position within and across racial/ethnic groups, as well as to retard understanding of the contribution of economic and noneconomic aspects of racial discrimination to US racial/ ethnic health disparities. 8– 10 To address this gap, we employed a methodology rigorously validated in the United States for the first time by our Public Health Disparities Geocoding Project. 11– 14 Our approach builds on a technique eclectically employed in US health research for more than 75 years 15– 17 and increasingly used in European research during the past 25 years 18, 19 : that of categorizing individuals—both cases and the population from which they arise—in relation to the socioeconomic characteristics of the immediate area in which they reside. Our objective was to demonstrate the feasibility and salience of augmenting US public health surveillance systems with socioeconomic data, both to quantify socioeconomic inequalities in health and to investigate their contribution to racial/ethnic disparities in health and to hampering attainment of Healthy People objectives. 3, 20
