The control of the epidemic of gonorrhea continues to present a significant public health challenge in the United States.1,2 Numerous studies have identified individual-level risk factors and outlined recommendations typically targeted on high-risk persons who are closer to the disease “epicenter” in the causal chain and contribute to disease transmission.3,4 Federally supported public health measures focus mainly upon enhanced surveillance, screening, effective treatment, partner notification, and behavioral intervention. When sustained, these measures appear effective: the national gonorrhea rate decreased by 75% from 468 cases per 100,000 population in 1975 to the record-low rate of 116 cases per 100,000 population in 2004.5 However, despite these important gains, significant reductions must still occur if the Healthy People 2010 (HP2010) objective of 19 cases per 100,000 population is to be realized.
In order to achieve such reductions there is increasing advocacy for paying more attention to social determinants of disease,6,7 and an inverse relationship between community socioeconomic status (SES) and gonorrhea rates have been well documented.8–20 The possible mechanisms linking community SES to gonorrhea might be because of: more prevalent high-risk sexual behaviors, lack of normative standards caused by high mobility in communities with irregular SES, and the absence of public services and resources in communities without financial security.21 Thomas also emphasized the importance of the lingering historical legacy of slavery, unfair economic policies, and disparities in rates of incarceration among men as factors affecting the epidemiology of sexually transmitted disease (STD).22 Within this context, it becomes apparent that merely living within a socioeconomically disadvantaged community may also place a population at a disadvantage for acquisition and transmission of gonorrhea among sexually active persons regardless of individual risk factors.23
Questions remain regarding the SES-gonorrhea relationship from previous studies. In particular, community SES and racial composition have usually been measured at a fixed time point. Thus, the longitudinal impact of fluctuations in community SES and racial composition on gonorrhea rates has not been extensively studied.
Gonorrhea is the second most commonly reported STD in New York State (NYS), exclusive of New York City (NYC). The number of gonorrhea cases declined considerably during 1992 to 2002, correlated with the implementation of a geographically targeted field intervention.24 However, the rate in 2002 (84 cases per 100,000 population) was substantially higher than the HP2010 objective, and wide variation in gonorrhea rates was observed across communities. Geographically, NYS excluding NYC is a combination of large rural areas with population concentrations in urban centers. Whites are still the predominant racial group, but blacks account for a substantial proportion of the population in some major municipalities and represent a growing demographic statewide. In addition to the changing demographics with an increased racial-ethnic diversity, between 1990 and 2000 increasing socioeconomic disparities have been observed characterized by higher poverty rates and growing income inequality.25 This study was designed to quantify these changing community characteristics and ecologically assess their effect on gonorrhea incidence rates at the community level. Specifically, we characterized the distribution of gonorrhea across census tracts in NYS excluding NYC about SES and other demographic factors, and measured the longitudinal association between changes in community SES and changes in gonorrhea rates.
MATERIALS AND METHODS
NYS Public Health Law requires that health care providers and laboratories report suspected newly diagnosed or test-positive gonorrhea cases to local health departments, using the NYS Department of Health Electronic Clinical Laboratory Reporting System or the disease reporting paper form. After a report is received and verified by the local health department, the information is forwarded to the NYS Department of Health via a web-based reporting system, the Communicable Disease Electronic Surveillance System. All laboratory-confirmed gonorrhea cases are then added to a confidential STD surveillance database.
Laboratory-confirmed gonorrhea cases reported between 1992 and 2002 in NYS excluding NYC were considered as incident cases. One person may be included multiple times during the study period if repeated infections were reported. Cases were coded to their 2000 census block group of the recorded home address using MapInfo 7.0 (MapInfo, Corp., Troy, NY), then aggregated at the census tract level by year, and finally matched to the census data.
Census tracts were used as the study unit to approximate the concept of “community.” Census tract characteristics in 1990 and 2000 were obtained from the Neighborhood Change Database (GeoLytics, Inc., East Brunswick, NJ). The 1990 census variables were standardized to the 2000 boundaries and categories in order to assess changes in community characteristics across censuses.26 This study initially included all 2690 census tracts in NYS excluding NYC, and the associations between community characteristics and gonorrhea rates were investigated separately for census tracts by their level of urbanization based on the 1993 rural-urban continuum codes and the population density. However, the analyses was subsequently focused on 740 census tracts from 15 urban counties with high population densities where reported gonorrhea morbidity was overwhelmingly concentrated (i.e., 73% of cases were reported from these tracts) and community characteristics were more diverse; there was wide variation in gonorrhea rates between the 2 time periods in the remaining census tracts. The details of urban classification have been described elsewhere.25
Variables of Interest
The outcome variable was the census-tract level gonorrhea rate, determined by the number of reported gonorrhea cases among the source population at 2 points in time, using 2-year averages to stabilize rates (1992–1993 and 2001–2002). Because community SES was a primary interest in this study, we chose to use existing indicators of SES contained within the US Census data. Those selected, listed in Table 1, were based on findings from previous research with specific interests in measuring both wealth and income.27–31 Although factors measure different aspects of a community’s resources, substantial correlations between these factors exist. We initially chose to include multiple SES factors in the analysis for the most complete assessment possible. In the final multivariate model, the proportion of working class population, high school dropout rate, and household poverty rate were selected as measures of community SES primarily based on the study focus, with consideration of the Pearson correlation coefficient between the SES variables (<0.6), the degree of change that occurred during 1990 to 2000 and the statistical significance from the bivariate analysis. All community demographic and SES variables were measured at 2 points in time (1990 and 2000) and the percent change for each characteristic was calculated.
The community characteristics in 1990 and 2000 were linked with the average gonorrhea rates in 1992–1993 and 2001–2002, respectively. The descriptive analysis was conducted to depict the relationships between community characteristics and gonorrhea rates at 2 points of time. To assess the longitudinal impact of community SES and racial composition on gonorrhea, a negative binomial regression model was utilized to estimate the expected change in the gonorrhea rate over time according to changes in community characteristics between 1990 and 2000.32 The logarithm of the number of gonorrhea cases in each census tract in 1992–1993 and in 2001–2002 was modeled with the 1990 baseline community characteristics and changes in these characteristics (the change was set to zero at the baseline); the logarithm of the total census tract population was included as an offset. All variables were modeled as continuous and the coefficients for changes in community characteristics and their 95% confidence intervals (CI) were used to estimate the rate ratio (RR) of the 2001–2002 gonorrhea rates to the 1992–1993 rates given a 5% increase in a particular community characteristic. Generalized estimating equations were used to adjust for the correlation among repeated observations.
Additional analyses were also conducted to assess the possible interaction effects between community racial distribution, SES, and gonorrhea rates. All data were analyzed using SAS, Version 8.2, software (SAS Institute, Inc., Cary, NC).
From 1992 to 2002, a total of 100,756 gonorrhea cases were reported from 57 counties. Of those cases, 95,671 (94.9%) were successfully coded and matched to a census tract. Gonorrhea was concentrated in urban areas (Buffalo-Niagara Falls, Rochester, Syracuse, Albany-Schenectady-Troy, and Nassau-Suffolk) and some densely populated yet isolated rural towns located near military bases and colleges. These census tracts with high gonorrhea rates are primarily located along the Erie Canal, now lined with major highways in NYS (Fig. 1). The 740 urban census tracts accounted for 73% of total reported gonorrhea cases over the decade. Gonorrhea rates within these urban tracts were substantially higher among those with a low SES, poor housing conditions, and a high proportion (i.e., greater than 10%) of the population comprised of non-Hispanic blacks (Table 2). The gonorrhea rate was correspondingly low in census tracts with minimal urban profiles.
Statewide, the gonorrhea rates decreased by 25% from 112 cases per 100,000 in 1992 to 84 cases per 100,000 in 2002, largely because of pronounced declines in urban areas. Conversely, gonorrhea gradually increased in some suburban and rural areas in the lower Hudson Valley. However, changes in the gonorrhea rates between baseline (1992–1993) and follow-up (2001–2002) were not homogenous across the 740 urban communities about their baseline characteristics. Specifically, a noticeable decrease in gonorrhea rates occurred among socioeconomically disadvantaged and minority communities, but remained unchanged or slightly increased among many communities with better SES or low proportion of non-Hispanic black population at baseline (Table 2). Further analyses revealed that among communities with a 10% or higher household poverty rate in 1990, the gonorrhea rates declined by 26% (686.7 cases per 100,000 in 2001–2002 vs. 928.2 cases per 100,000 in 1992–1993) among those communities that had a decreased poverty rate between 1990 and 2000. Minority communities (10% or higher non-Hispanic black population) that experienced a decline in household poverty also had a 34% decrease in the gonorrhea rates (714.5 cases per 100,000 in 2001–2002 vs. 1084.3 cases per 100,000 in 1992–1993).
The analytical models highlighted significant associations between changes in community SES and gonorrhea rate changes between 1992–1993 and 2001–2002 (Table 3). The bivariate analyses indicated that changes in the household poverty rate, the relative size of the working class, unemployment rate, and vacant housing units were positively related to the gonorrhea rates. Increases in the non-Hispanic black population also was significantly associated with gonorrhea rate changes. Conversely, increases in the relative size of college educated, males, and the foreign-born populations were associated with decreases in gonorrhea rates.
Multivariate analysis showed that significant associations persisted between changes in both race and the 3 key community SES indicators and changes in gonorrhea rates, after adjusting for alterations in other community demographics (Table 3). Specifically, the model indicated that a 5% increase in the household poverty rate was associated with an 8% increase in the gonorrhea rates (RR = 1.08; 95% CI: 1.02, 1.14), after adjusting for other community characteristics. In addition, the association between changes in the proportion of non-Hispanic black population and the gonorrhea rates remained even when multiple SES variables were adjusted (RR = 1.20 for a 5% increase in non-Hispanic black population; 95% CI: 1.16, 1.24).
However, understanding the relationship and interaction of race and community SES and their effect on gonorrhea infection is complicated. Although the interaction term between these 2 factors was significant at baseline, only the changes in the black population and SES, but not the interaction term, were associated with changes in gonorrhea rates between 1992 and 2002. Furthermore, within the subset of 458 communities with a low proportion (<10% of the population) of non-Hispanic blacks in 1990, where increases in the gonorrhea rates were commonly seen, a 5%increase in household poverty rate was associated with a 23% increase in the gonorrhea rate (95% CI: 1.07, 1.40) (Table 4). The significant association remained between changes in the proportion of non-Hispanic black population and the gonorrhea rates (RR = 1.29 for a 5% increase in non-Hispanic black population; 95% CI: 1.22, 1.37).
In comparison, within the 282 urban communities with a 10% or higher of non-Hispanic blacks at baseline, the association between the change in household poverty rate and gonorrhea rates was largely attenuated (RR = 1.02) although a change in the working class population was significantly related to the gonorrhea rate (Table 4). However, additional analysis revealed that communities with declining household poverty rate experienced an 18% greater reduction in the gonorrhea rate (RR = 0.68) when compared to those with an increased poverty rate (RR = 0.83; 2-sided P < 0.01 for the ratio difference). Inclusion of non-Hispanic black population change in the model also resulted in a weakened yet significant association with the gonorrhea rates (RR = 1.07). The association between changes in community demographics and SES and the gonorrhea rate were also modeled separately for female and male cases, and the results did not differ substantially, except within the subset of communities with a high proportion of non-Hispanic blacks at baseline where a 5% increase in the male population was associated with a significant (16%) decrease in the gonorrhea rate (Table 4).
This study is consistent with prior research findings that community SES is significantly associated with gonorrhea risk. The cross-sectional results at both time periods reflected clear socioeconomic gradients for gonorrhea rates, demonstrating that inner-city urban census tracts with low SES had the highest gonorrhea rates. It also presents new longitudinal evidence supporting a temporal association between community SES and gonorrhea rates. In urban communities where we had the power to assess the effects of community changes, gonorrhea rates decreased with SES improvements. Results from this longitudinal analysis indicate that changes in gonorrhea rates were significantly associated with changes in community poverty, working class population and the high school dropout rate over the last decade of the 20th century. Research in other fields has also demonstrated that changes in area-based socioeconomic characteristics were related to changes in mortality, tuberculosis incidence, reproductive outcomes, and in limiting long-term illness.33–37 The consistency of these findings suggests a possible causal link between the social environment and the risk of disease. However, proving such a link remains a methodological challenge even in some randomized social intervention studies.38,39 The fact that gonorrhea rates remain low among communities with better SES suggests that such communities have material and social resources, which may have a buffer effect against the spread of disease.40 Proponents of improvements in community socioeconomic resources suggest such enhancements may encourage healthier behaviors and facilitate better health outcomes.41
The substantial reductions in gonorrhea rates observed among urban census tracts with a low SES and high proportion of black population complicated our analysis. This occurrence may, at least in part, be attributable to the effect of STD control interventions initiated in the late 1980s which have been targeted predominantly in urban areas with low SES and high gonorrhea morbidity in NYS exclusive of NYC.24,42 Despite the masking effect of the morbidity decline within these areas, our results showed that SES variables were still significantly associated with gonorrhea rates and that the declines were greater within those communities experiencing improvements in SES, suggesting that low community SES may predispose urban, disadvantaged communities to higher gonorrhea rates.
Our findings also showed a significant association between race and gonorrhea infection, which was independent of the effect of the SES indicators included in our model. Although race is obviously strongly correlated with SES, these findings support the position that there are factors of race that transcend poverty.43 Health disparities across racial groups have been considered to be related to historical factors including racial discrimination and residential segregation which may influence sexual networks, and access to health care and preventive services.44–53 Thomas has argued that the historical legacy of “social forces” such as racism and segregation have produced an ecological environment that facilitates the transmission of sexually transmitted infection.22 These environments not only “predispose individuals to behaviors facilitating transmission,”22 but also have produced disproportionate levels of incarceration of black males, which contributes to social disorganization, disrupts sex ratios, and may affect sexual networks and transmission of infection in ways that transcend individual risk behaviors. The results of our stratified analysis which showed a 16% decline in the gonorrhea rate within urban, high minority census tracts that experienced a 5% increase in the population of males, might be an indication of some quantitative support for this hypothesis.
Several specific caveats to this analysis should be noted. First, variations in gonorrhea reporting may impact disease trends across time and geography. Although the surveillance system is well established in NYS for mandatory gonorrhea case reports and laboratory tests are reported consistently during the study period, the use of syndromic management without laboratory confirmation by, and the extent of compliance with case reporting from, private physicians is unknown. Results from a US physician survey showed low rates of routine screening and reporting of STDs from physicians, and presumptive treatment for gonorrhea without confirmatory tests was common.54 Because this study included a variety of communities, selective underreporting by providers may bias results. As women are more likely to have routine screening for gonorrhea across SES and race/ethnic groups, we stratified the data by gender; no differences were found that would indicate a detection bias of the surveillance system.
Second, census tracts are commonly used in epidemiologic research to approximate the concept of community because they are considered to be a relatively homogeneous unit. However, evidence from this study and others indicates that census tracts mainly reflect relatively arbitrary geographic or physical boundaries and are not necessarily homogeneous.55–57 The operational definition of community is still open for debate and needs refinement. In addition, the spatial autocorrelation between census tracts was not estimated because of the limitations of available software which does not assess the spatial autocorrelations for longitudinal research. This problem may have produced a small underestimation of standard errors.20
Third, evidence suggests that blacks were more likely to be undercounted than whites in the 1990 census,58 which may lead to an underestimation of indicators of low SES in urban communities with a high proportion of black population, whereas gonorrhea rates may be overestimated among these communities. Thus, the association between changes in community SES and gonorrhea rates may be underestimated.
Although numerous studies have been conducted to investigate the effects of SES on population health, consensus on measuring SES in public health research has yet to be reached. Selecting the relevant community SES factors is complex because these factors are likely interrelated and may influence each other, and the relevance of community SES for health may differ from one outcome to another. Meanwhile, some social epidemiologists may be interested in predicting linear effects of community SES, whereas others may prefer threshold effects. In this study, multiple SES factors were measured in both continuous and categorical formats at 2 different points of time. In the multivariate model the focus was on 3 SES variables that had relatively low correlations compared to other SES measures. To develop confidence in the findings, we conducted multiple analyses to assess the robustness of the model presented. These included inclusion of all SES factors, different subsets of SES factors, and a summary measure of SES factors developed, using a regression equation. Results from analyses with these variations are consistent with the overall finding presented.
In recent years, STD control strategies have focused primarily upon individual biologic and behavioral interventions.6,59,60 The latter became particularly important in the context of the HIV/AIDS epidemic. In the short term, such strategies may have an impact on interrupting disease transmission.61 However, as inquiries into health disparities have grown, it has become increasingly appreciated that STD risk cannot be fully explained by individual sexual behavior,53,62 and that sustained changes in sexual behavior are difficult to maintain in an “unhealthy” social and economic environment.
This study demonstrated that longitudinal changes in community racial distribution and SES were significantly associated with changes in gonorrhea rates at the census tract level, adding to the accumulating evidence which suggests that in order to achieve and sustain reductions in community-level disparities of sexually transmitted infections, public health programs need to pay greater attention to the social and economic environment. The barriers to incorporating such interventions through economic and health policies are considerable,63 but not impossible.64–66 The US Department of Health and Human Services with the publication of the HP2010,67 specifically pointed to the importance of and need to address social determinants of health, while at the same time recognizing that any intervention efforts focused in this area will require collaboration across many disciplines and cooperation between a variety of government agencies and private institutions. At the very least, continued discussion is needed that will foster development of improved methodological approaches to guide future SES-disease research. In addition, inclusion of data on social factors into existing surveillance systems is advocated to facilitate the study of their relationship to patterns of disease at the community level.
1.Eng TR, Butler WT, eds. The Hidden Epidemic: Confronting Sexually Transmitted Diseases, Institute of Medicine. Washington, DC: National Academy Press, 1997.
2.Centers for Disease Control and Prevention. Gonorrhea–United States, 1998. MMWR 2000; 49:538–542.
3.Rothman KJ, Adami HO, Trichopoulos D. Should the mission of epidemiology include the eradication of poverty? Lancet 1998; 352:810–813.
4.Centers for Disease Control and Prevention. Program Operations Guidelines for STD Prevention. Atlanta, GA: US Department of Health and Human Services, 2001. Available at: http://www.cdc.gov/std/program/#guidelines
. Accessed September 29, 2005.
5.Centers for Disease Control and Prevention. STD Surveillance 2004. Atlanta, GA: US Department of Health and Human Services, Public Health Services, 2005. Available at: http://www.cdc.gov/std/stats/toc2004.html
. Accessed November 4, 2005.
6.Metzler M. Social determinants of health: What, how, why, and now. Prev Chronic Dis 2007; 4:A85. Available at: http://www.cdc.gov/pcd/issues/2007/oct/07_0136.html
. Accessed March 27, 2008.
7.Isaacs S, Schoeder S. Class—the ignored determinant of the nation’s health. N Engl J Med 2004; 351:1137–1142.
8.Morton WE, Horton HB, Baker HW. Effects of socioeconomic status on incidences of three sexually transmitted diseases. Sex Transm Dis 1979; 6:206–210.
9.Rothenberg RB. The geography of gonorrhea: Empirical demonstration of core group transmission. Am J Epidemiol 1983; 117:688–694.
10.Rice RJ, Roberts PL, Handsfield HH, et al. Sociodemographic distribution of gonorrhea incidence: Implications for prevention and behavioral research. Am J Public Health 1991; 81:1252–1258.
11.Ellen JM, Kohn RP, Bolan GA, et al. Socioeconomic differences in sexually transmitted disease rates among black and white adolescents, San Francisco, 1990 to 1992. Am J Public Health 1995; 85:1546–1548.
12.Thomas JC, Schoenbach VJ, Weiner DH, et al. Rural gonorrhea in the southeastern United States: A neglected epidemic? Am J Epidemiol 1996; 143:269–277.
13.Cohen D, Spear S, Scribner R, et al. “Broken windows” and the risk of gonorrhea. Am J Public Health 2000; 90:230–236.
14.Cohen DA, Mason K, Bedimo A, et al. Neighborhood physical conditions and health. Am J Public Health 2003; 93:467–471.
15.Holtgrave DR, Crosby RA. Social capital, poverty, and income inequality as predictors of gonorrhea, syphilis, Chlamydia, and AIDS case rates in the United States. Sex Transm Infect 2003; 79:62–64.
16.Krieger N, Waterman PD, Chen JT, et al. Monitoring socioeconomic inequalities in sexually transmitted infections, tuberculosis, and violence: Geocoding and choice of area-based socioeconomic measures–the public health disparities geocoding project (US). Public Health Rep 2003; 118:240–260.
17.Thomas JC, Gaffield ME. Social structure, race, and gonorrhea rates in the southeastern United States. Ethn Dis 2003; 13:362–368.
18.Dombrowski JC, Thomas JC, Kaufman JS. A study in contrasts: Measures of racial disparity in rates of sexually transmitted disease. Sex Transm Dis 2004; 31:149–153.
19.Monteiro EF, Lacey CJ, Merrick D. The interrelation of demographic and geospatial risk factors between four common sexually transmitted diseases. Sex Transm Infect 2005; 81:41–46.
20.Semaan S, Sternberg M, Zaidi A, et al. Social capital and rates of gonorrhea and syphilis in the United States: Spatial regression analyses of state-level associations. Soc Sci Med 2007; 64:2324–2341.
21.Baumer EP, South SJ. Community effects on youth sexual activity. J Marriage Fam 2001; 63:540–554.
22.Thomas JC. From slavery to incarceration: Social forces affecting the epidemiology of sexually transmitted diseases in the rural south. Sex Transm Dis 2006; 33:S6–S10.
23.Sionean C, DiClemente RJ, Wingood GM, et al. Socioeconomic status and self-reported gonorrhea among African American female adolescents. Sex Transm Dis 2001; 28:236–239.
24.Du P, Coles FB, Gerber T, et al. Effects of partner notification on reducing gonorrhea incidence rate. Sex Transm Dis 2007; 34:189–194.
25.Du P, Coles FB, O’Campo P, et al. Changes in population characteristics and their implication on public health research. Epidemiol Perspect Innov 2007; 4:6.
26.Geolytics Inc. Special issues. Data user’s guide. Neighborhood Change Database (NCDB) 1970–2000 Tract Data. CD-ROM Long Form Release 1.0, 2003.
27.Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: Concepts, methodologies, and guidelines. Annu Rev Public Health 1997; 18:341–378.
28.Lynch JW, Kaplan GA. Socioeconomic position. In: Berkman LF, Kawachi I, eds. Social Epidemiology. New York, NY: Oxford University Press Inc, 2000:13–35.
29.Pickett KE, Pearl M. Multilevel analyses of neighborhood socioeconomic context and health outcomes: A critical review. J Epidemiol Community Health 2001; 55:111–122.
30.Braveman PA, Cubbin C, Egerter S, et al. Socioeconomic status in health research: One size does not fit all. JAMA 2005; 294:2879–2888.
31.Galobardes B, Shaw M, Lawlor DA, et al. Indicators of socioeconomic position. In: Oakes JM, Kaufman JS, eds. Methods in Social Epidemiology. San Francisco, CA: Jossey-Bass, 2006: 47–85.
32.Diggle PJ, Heagerty PJ, Liang KY, et al. Analysis of longitudinal data, 2nd ed. New York, NY: Oxford University Press Inc, 2002.
33.Kaplan GA, Pamuk ER, Lynch JW, et al. Inequality in income and mortality in the United States: Analysis of mortality and potential pathways. BMJ 1996; 312:999–1003.
34.Barr RG, Diez Roux AV, Knirsch CA, et al. Neighborhood poverty and the resurgence of tuberculosis in New York City, 1984–1992. Am J Public Health 2001; 91:1487–1493.
35.Pickett KE, Ahern JE, Selvin S, et al. Neighborhood socioeconomic status, maternal race, and preterm delivery: A case-control study. Ann Epidemiol 2002; 12:410–418.
36.English PB, Kharrazi M, Davies S, et al. Changes in the spatial pattern of low birth weight in a southern California county: The role of individual and neighborhood level factors. Soc Sci Med 2003; 56:2073–2088.
37.Boyle P, Norman P, Rees P. Changing places. Do changes in the relative deprivation of areas influence limiting long-term illness and mortality among non- migrant people living in non-deprived households? Soc Sci Med 2004; 58:2459–2471.
38.Oakes JM. The (mis)estimation of neighborhood effects: Causal inference for a practicable social epidemiology. Soc Sci Med 2004; 58:1929–1952.
39.Kaufman JS, Kaufman S, Poole C. Causal inference from randomized trials in social epidemiology. Soc Sci Med 2003; 57:2397–2409.
40.Stafford M, Marmot M. Neighborhood deprivation and health: Does it affect us all equally? Int J Epidemiol 2003; 32:357–366.
41.Lynch JW, Kaplan GA, Salonen JT. Why do poor people behave poorly? Variation in adult health behaviors and psychosocial characteristics by stages of socioeconomic lifecourse. Soc Sci Med 1997; 44:809–819.
42.Han Y, Coles FB, Muse A, et al. Assessment of a geographically targeted field intervention on gonorrhea incidence in two New York State counties. Sex Transm Dis 1999; 26:296–302.
43.Krieger N. Refiguring “race”: Epidemiology, racialized biology, and biological expressions of race relations. Int J Health Serv 2000; 30:211–216.
44.Denton NA, Massey DS. Residential segregation of blacks, Hispanics, and Asians by socioeconomic status and generation. Soc Sci Q 1988; 69:797–817.
45.Krieger N, Rowley DL, Herman AA, et al. Racism, sexism, and social class: Implications for studies of health, disease, and well-being. Am J Prev Med 1993; 9:82–122.
46.South SJ, Crowder KD. Residential mobility between cities and suburbs: Race, suburbanization, and back-to-the-city moves. Demography 1997; 34:525–538.
47.Williams DR, Collins C. Racial residential segregation: A fundamental cause of racial disparities in health. Public Health Rep 2001; 116:404–416.
48.Brewster KL. Race differences in sexual activity among adolescents women: The role of neighborhood characteristics. Am Social Rev 1994; 59:408–424.
49.Laumann EO, Youm Y. Racial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: A network explanation. Sex Transm Dis 1999; 26:250–261.
50.Ford K, Sohn W, Lepkowski JM. Ethnicity or race, area characteristics, and sexual partner choice among American adolescents. J Sex Res 2003; 40:211–218.
51.Adimora AA, Schoenbach VJ. Social context, sexual networks, and racial disparities in rates of sexually transmitted infections. J Infect Dis 2005; 191:S115–S122.
52.Singer MC, Erickson PI, Badiane L, et al. Syndemics, sex and the city: Understanding sexually transmitted diseases in social and cultural context. Soc Sci Med 2006; 63:2010–2021.
53.Hallfors DD, Iritani BJ, Miller WC, et al. Sexual and drug behavior patterns and HIV and STD racial disparities: The need for new directions. Am J Public Health 2007; 97:125–132.
54.St Lawrence JS, Montaño DE, Kasprzyk D, et al. STD screening, testing, case reporting, and clinical and partner notification practices: A national survey of US physicians. Am J Public Health 2002; 92:1784–1788.
55.Sampson RJ, Raudenbush SW, Earls F. Neighborhoods and violent crime: A multilevel study of collective efficacy. Science 1997; 277:918–924.
56.Diez-Roux AV. Investigating neighborhood and area effects on health. Am J Public Health 2001; 91:1783–1789.
57.Krieger N, Chen JT, Waterman PD, et al. Race/ethnicity, gender, and monitoring socioeconomic gradients in health: A comparison of area-based socioeconomic measures–the public health disparities geocoding project. Am J Public Health 2003; 93:1655–1671.
58.Hogan H. The 1990 post-enumeration survey: Operations and results. J Am Stat Assoc 1993; 88:1047–1060.
59.Syme SL. Social determinants of disease. Ann Clin Res 1987; 19:44–52.
60.Syme SL. Social and economic disparities in health: Thoughts about intervention. Milbank Q 1998; 76:493–505.
61.Low N, FitzGerald MR. Success and failure in gonorrhea control. Dermatol Clin 1998; 16:713–722.
62.Ellen JM, Aral SO, Madger LS. Do differences in sexual behaviors account for the racial/ethnic differences in adolescents’ self-reported history of a sexually transmitted disease? Sex Transm Dis 1998; 25:125–129.
63.Syme SL, Lefkowitz B, Krimgold BK. Incorporating socioeconomic factors into US health policy: Addressing the barriers. Health Aff 2002; 21:113–118.
64.Kaplan GA, Lynch JW. Socioeconomic considerations in the primordial prevention of cardiovascular disease. Prev Med 1999; 29:S30–S35.
65.Smedley BD, Syme SL; Committee on Capitalizing on Social Science and Behavioral Research to Improve the Public’s Health. Promoting health: Intervention strategies from social and behavioral research. Am J Health Promot. 2001; 15:149–166.
66.Mechanic D. Disadvantage, inequality, and social policy. Health Aff 2002; 21:48–59.
67.US Department of Health and Human Services. Healthy People 2010. 2nd ed. With Understanding and Improving Health and Objectives for Improving Health. Vol. 2. Washington, DC: US Government Printing Office, 2000.