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Objectives. We evaluated the association between ecological factors and rates of tuberculosis within California, using pediatric tuberculosis as an indicator of new transmission.
Methods. Ecological variables such as racial/ethnic distribution, immigration level, education, employment, poverty, and crowding were obtained from the United States Census for each census tract in California. These data were incor- porated into a negative binomial regression model with the rate of pediatric tu- berculosis disease in each census tract as an outcome variable. Disease rates were obtained by geocoding reported cases. Subsections of the state (San Fran- cisco and Los Angeles) were examined independently.
Results. Census tracts with lower median incomes, more racial/ethnic minorities, and more immigrants had higher rates of pediatric tuberculosis. Other frequently cited risk factors such as overcrowding and unemployment were not associated with increased disease after adjusting for other measures. Risks were compara- ble across regions, but subtle differences were noted.
Conclusions. The techniques used in this work provide a way to examine a dis- ease within its social context. The results confirmed that tuberculosis in California continues to be a disease of poverty and racial/ethnic minorities. (Am J Public Health. 2006;96:685–690. doi:10.2105/AJPH.2004.048132)
An Ecological Study of Tuberculosis Transmission in California | Ward P. Myers, MD, MPH, Janice L. Westenhouse, MPH, Jennifer Flood, MD, MPH, and Lee W. Riley, MD
Tuberculosis is a social disease caused by an airborne pathogen with low infectivity. The transmission of tuberculosis depends on human interaction within communities. However, some communities provide a better environment for disease transmission than others. Previous sur- veillance has documented great disparities in rates of tuberculosis among neighborhoods.1
These differences depended in part on commu- nity-level, ecological risk factors that facilitate transmission—poverty, crowding, and other markers of deprivation have long been associ- ated with increased rates of tuberculosis.2,3
Because of its airborne transmission and soci- etal impact, tuberculosis is closely monitored by local, state, and federal health departments. Cases of tuberculosis are subject to mandatory reporting in all 50 states, the District of Colum- bia, US dependencies and possessions, and inde- pendent nations within the United States (Native American lands).4 In addition to ensuring treat- ment, health departments collect case-specific demographic information (e.g., age, race, for- eign-born status) and disease information (e.g., site of infection, drug resistance).5 The focus on individual cases, however, neglects the ecologi- cal context of this disease. Information about community-level, ecological risk factors for con- tracting tuberculosis is important for structuring a public health response to this illness.
Ecological data can be obtained by geocod- ing addresses from reported cases, and then linking these cases to geographic locations such as the census tract. The US Census defines a census tract as a “small, relatively permanent statistical subdivision of a county . . . designed to be relatively homogeneous units with respect to population characteristics, economic status, and living conditions at the time of establish- ment. Census tracts average about 4000 inhab- itants.”6 Every 10 years the US Census collects detailed demographic and socioeconomic infor- mation about the US population. When linked to reported tuberculosis cases, this information permits the examination of ecological factors
that are associated with disease. Use of the cen- sus tract has many advantages over the use of other geographic units such as zip codes. Previ- ous work has shown that populations defined by zip codes, being larger and more heteroge- neous, give more variable results than census tracts in ecological analysis.7
Ecological analysis of tuberculosis is compli- cated by the disease’s long incubation period. A delay of 30 years or more between infection and clinical disease has been documented,8
bringing into question the validity of studies comparing current ecological data to case re- ports from adults. Cases of tuberculosis in chil- dren, compared with cases in adults, have a short delay between infection and onset of clin- ical disease. The incubation period is limited by the child’s lifespan and, thus, a greater propor- tion of cases are likely to be primary disease. Cases occurring in children represent recently acquired infection and serve as a surrogate marker for ongoing transmission. For this rea- son, tuberculosis cases in children are used by state and local health departments to monitor the success of tuberculosis-control activities.
Recent studies have supported the role of ecological risk factors, such as poverty, lack of social capital, and overcrowding, in tuberculosis disease.1,7,9–15 Although these studies have used a variety of techniques, there are limited data using exclusively pediatric cases to look at eco- logical risks for tuberculosis.16 In this work, we developed a multivariate model for prediction of tuberculosis transmission on the basis of eco- logical measures and pediatric cases from cen- sus tracts in the state of California. Data from California are particularly useful for under- standing tuberculosis in the United States. In 2002, California reported 3159 cases of tuber- culosis, or 21% of the national total.4 Further- more, much of the United States is now begin- ning demographic and ethnic shifts that mirror the changes that have occurred in California over the past 10 years.
METHODS
Data Collection: Tuberculosis Cases Case information was obtained from the
California Department of Health Services,
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TABLE 1—Ecological Measures Derived From Year 2000 US Census Tract Data
Summary Census Measure Operational Definition File Variable
Demographic
Asian race Percentage of population in census tract that self-reports Asian 1 P4
race (1 race only, non-Hispanic)
Black race Percentage of population in census tract that self-reports black 1 P4
race (1 race only, non-Hispanic)
Hispanic ethnicity Percentage of population in census tract that self-reports 1 P4
Hispanic ethnicity
Immigration Percentage of population that was born outside the United States 3 P21
Education: Low attainment Percentage of persons 25 years and older with less than a 3 P37
high-school diploma
Occupation: Unemployment Percentage of persons aged 16 and older in the labor force who 3 P43
are unemployed
Economy: Median income Median household income for census tract in 1999 3 P53
Housing
Crowded households Percentage of households with > 1 person per room 3 H20
Population density Number of people per square mile 1 P1
Note. P = population subjects; H = housing subjects.
TABLE 2—Descriptive Characteristics of 7018 Census Tracts in Californiaa
Variable Mean SD Range
Total population per census tract 4819.7 2129.8 3–36 146
Pediatric (0–14 years) population 1109.1 662.8 1–7962
Cases of TB aged 0–14 years from 1993–2002 0.5 1.0 0–15
Pediatric case rate (per 100 000 person-years) 3.8 9.0 0–230
Asian race, % 10.6 12.9 0–95
Black race, % 6.4 11.4 0–91
Hispanic ethnicity, % 31.0 25.5 0–98
Foreign born, % 25.5 16.1 0–100
Lower educated, % 24.4 19.3 0–100
Unemployed, % 7.4 5.6 0–100
Median household income, $ 51 615.7 24 685.4 0–200 001
Living in crowded housing, % 16.9 16.5 0–100
Population density (people/square mile) 8064.3 9205.1 0–156 015
Note. TB = tuberculosis; SD = standard deviation. aCalifornia has 7049 census tracts. Prior to analysis, 31 tracts were excluded because their pediatric population was 0. No TB cases were present in the excluded census tracts.
Tuberculosis Control Branch. We analyzed all 3208 cases of tuberculosis in children aged 0 to 14 years that were reported in the 10 years between January 1, 1993, and December 31, 2002. The cases were geocoded, and each case was linked to a census tract from the 2000 US Census. A census tract number was available for 3164 cases (98.6% of total). Use
of nonidentifying case information was ap- proved by the California Department of Health Services, Tuberculosis Control Branch. Tuber- culosis case rates per 100 000 person-years were calculated on the basis of populations from the 2000 Census.
The analysis was repeated, limiting tubercu- losis cases to children aged 0 to 4 years. As this
approach yielded similar results, the final analy- sis used cases in patients aged 0 to 14 years.
Data Collection: Ecological Measures Ecological measures were obtained from the
2000 US Census Web site.17,18 Individual vari- ables were selected from summary files 1 and 3 (Table 1). Prior to analysis, variables were chosen that characterized traditional risk fac- tors for transmission of tuberculosis.
Means and standard distributions for predic- tor variables were calculated for all included census tracts and are reported in Table 2. Vari- ables were standardized to a z scale on the basis of their mean and standard deviation ([X – mean] / SD). This standardization of vari- ability permitted the generation of tuberculosis incidence rate ratios that could be compared among ecological measures (e.g., how does the incidence rate change for a 1-standard-devia- tion increase in population density, compared with a 1-standard-deviation increase in percent- age of residents in poverty?).
Statistical Analysis The number of pediatric cases for each cen-
sus tract was modeled as a negative binomial distribution. In contrast to the Poisson distribu- tion, a negative binomial distribution does not assume that the variance equals the mean and allows for more zero counts and overdisper- sion.19 Therefore, it is a useful model when the variance of a population exceeds the mean. In this analysis, the model took the form of
log λi = β0 + β1xi 1 + β2x i 2 + . . . + βk x ik + σε + log ( popi )
for each census tract [i = 1, . . . 7018], where λ is the expected cases in each census tract, xj is each standardized ecological measure (with its associated βj regression coefficient), σε is the disturbance or error term, and pop is the 2000 population (age 0–14) in the census tract times the years exposed (times 10, for time exposed). The log( popi ) term has no re- gression coefficient because it serves as an offset (log λi – log( popi ) = log [case ratei ]). The σε term represents error and dispersion in the form of a negative binomial distribu- tion. The exponent of each βj regression coef- ficient provides the incidence rate ratio for a
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TABLE 3—Univariate and Multivariate Incidence Rate Ratios for Pediatric Tuberculosis and Selected Ecological Measures in the State of Californiaa
Univariate Analysis Intermediate Model Full Multivariate Analysis US-Born Stratum Only
Area-based measure IRR 95% CI IRR 95% CI IRR 95% CI IRR 95% CI
Asian race 1.08 (1.04, 1.13) 1.22 (1.14, 1.30) 1.18 (1.08, 1.28)
Black race 1.21 (1.17, 1.24) 1.19 (1.14, 1.23) 1.27 (1.22, 1.33)d
Hispanic ethnicity 1.56 (1.51, 1.62) 1.25 (1.12, 1.40) 1.38 (1.2, 1.58)
Foreign born 1.65 (1.59, 1.71) 1.26 (1.14, 1.40) 1.26 (1.11, 1.44)
Lower educated 1.67 (1.62, 1.73) 1.13 (1.01, 1.27) 1.12 (0.99, 1.27)c 1.13 (0.96, 1.32)
Unemployed 1.44 (1.40, 1.48) 1.04 (0.99, 1.10) 1.02 (0.97, 1.08)c 0.97 (0.9, 1.04)
Median incomeb 2.25 (2.11, 2.40) 1.55 (1.42, 1.70) 1.62 (1.48, 1.78) 1.75 (1.55, 1.97)
Crowded housing 1.59 (1.54, 1.64) 1.16 (1.05, 1.28) 0.87 (0.77, 0.98)c 0.81 (0.7, 0.93)
Population density 1.32 (1.28, 1.35) 1.07 (1.03, 1.12) 1 (0.95, 1.04)c 1 (0.95, 1.06)
Note. IRR = incidence rate ratio; CI = confidence interval. aIRRs reflect the change in the incidence rate that occurs when the area-based measure increases by 1 standard deviation. The multivariate analysis holds all other variables constant. bStandardized values for median income are inverted. IRR shows change for a 1-standard-deviation decrease in median income. cFour variables showed a loss of significance as a risk factor or changed to a mildly protective factor in the model that included all variables. d The IRR for 1 variable in the US-born stratum was outside the 95% confidence intervals for the full multivariate analysis model.
1-standard-deviation change in the correspon- ding ecological measure.
Each ecological measure was initially exam- ined alone and then as a part of a multivariate model with the other measures. To better un- derstand the loss of significance for many socio- economic variables in the full model, we ana- lyzed an intermediate multivariate model (without race, ethnicity, or immigration). Inci- dence rate ratios with 95% confidence inter- vals for each measure are reported in Table 3. The multivariate model is reported in full. All variables were selected prior to analysis, and none were eliminated.
To assess goodness of fit, deviance residuals were calculated for the multivariate negative bi- nomial model with constant dispersion. Greater than 99% of predicted standardized deviances fell within 2 standard deviations, signifying a very good fit.20 We also modeled the data using a Poisson distribution. Goodness of fit for the Poisson model, however, was poor (P < .01). Because additional evidence that the negative binomial model was more appropriate than the Poisson, the likelihood ratio test for dispersion parameter being equal to 0 (in the Poisson model, dispersion parameter equals zero) was P < .001. To assess the extent to which the population adjustment factor (log[popi ]) might explain the goodness of fit, a correlation coeffi- cient with the number of tuberculosis cases was
calculated (r 2 = 0.1). This value was significant (in part because of the larger number of census tracts), but was also too close to the null to solely explain the model’s goodness of fit.
To reduce error from the inclusion of tuber- culosis cases representing transmission that oc- curred outside the United States, a stratified analysis was also performed on the basis of country of origin. Analysis was repeated as in the full multivariate model, but the dependent variable included only cases in children born in the United States from each census tract. Inci- dence rate ratios and 95% confidence intervals for the stratum of cases in children born in the United States are reported in Table 3.
To allow the greater San Francisco and Los Angeles areas to vary independently from each other and the rest of the state, indicator vari- ables were created for corresponding metropol- itan statistical areas. The US Census defines a Metropolitan Statistical Area (MSA) as “a core area with a large population nucleus, plus adja- cent communities having a high degree of eco- nomic and social integration with that core.”21
Lists of counties and census tracts included in the Los Angeles and San Francisco MSAs are available from the US Census Web site.21
To compare differences in the predictive powers of ecological measures between the San Francisco and Los Angeles MSAs, an additional model was generated. This model included
cross-products that allowed coefficients for eco- logical measures from the 2 MSAs to vary in- dependently. For clarity, cross-products that were less significant than P = .05 were removed by backward elimination. The results are de- picted in Figure 1.
All analyses were conducted using Stata, Version 7.0 (Stata Corp, College Station, Tex).
RESULTS
Over the 10 years included in this study, Cal- ifornia had 3208 cases of tuberculosis in its pe- diatric population. On the basis of the 2000 census, there were 7.78 million individuals aged 0 to 14 years, yielding a crude incidence rate of 4.1 cases per 100 000 person-years. Individual census tracts, however, showed very divergent rates. Incidence rates ranged from 0 to 230 per 100 000 person-years.
Results of univariate, intermediate, multivari- ate, and stratified models are depicted in Table 3. In the univariate models, the tradi- tional ecological measures were all strongly as- sociated with pediatric tuberculosis. However, when the variables were combined into a single multivariate model, measures such as lower ed- ucation, unemployment, crowding, and popula- tion density became less predictive. Census tracts with lower median incomes and more ra- cial/ethnic minorities and foreign-born individ- uals were particularly likely to have increased rates of disease when the other variables were held constant. Notably, Asian race appeared to be a greater risk factor in the multivariate model than in the univariate model, and crowded housing became a mildly protective factor in the multivariate model.
The intermediate model suggested that much of the loss of significance for lower education, unemployment, crowding, and population den- sity was attributable to each factor’s collinearity with income. The incidence rate ratios in Table 3 are best conceptualized as changes to a hypothetical “average census tract.” This aver- age census tract is characterized by the ecologi- cal measures shown in Table 2. As the percent- age of foreign-born residents increases to 1 standard deviation above the average census tract (approximately from 26% to 42%) the in- cidence of pediatric tuberculosis would be ex- pected to increase 1.3-fold (assuming all other variables were held constant).
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Note. Incidence rate ratios reflect the change in the incidence rate that occurs when the area-based measure increases by 1 standard deviation. Standardized values for median income are inverted. Incidence rate ratio shows change for a 1-standard- deviation decrease in median income.
FIGURE 1—Regional differences in incidence rate ratios for pediatric tuberculosis and ecologic variables, by race/ethnicity (a) and sociodemographic variables (b).
Differences between the US-born stratum and the full multivariate analysis were small but informative. Compared with the full model, census tracts with more Blacks showed an in- creased risk of disease. Additionally, Asian race seemed less strongly correlated (but still signifi- cant), and income became a slightly stronger risk factor.
Figure 1 depicts incidence rates for pedi- atric tuberculosis that were allowed to vary
independently across regions (i.e., other Cali- fornia [i.e., San Diego, Sacramento, Arcata, and so on], Los Angeles, San Francisco). For many ecological measures, the effects on incidence rates in the different regions were the same. Notable exceptions included differences in the effect of race/ethnicity, unemployment, and population density. In adjusted analysis, San Francisco–area census tracts with more Black residents had higher rates of tuberculosis
than equivalent census tracts in the rest of Cal- ifornia. This trend reversed itself for measures of the Hispanic population; increasing Hispanic population was less of a risk factor for disease in Los Angeles and San Francisco than in the rest of California. Population density was an important risk factor for disease in areas other than Los Angeles and San Francisco.
DISCUSSION
General Findings Using a multivariate model and ecological
data from census tract–level geography, we have shown that minority race/ethnicity, immi- gration, and low income are strong risk factors for new tuberculosis transmission.
This analysis is further support for earlier studies showing that minority race/ethnicity is a risk factor for disease. However, whereas previous research11 has suggested that the risk of race/ethnicity is largely secondary to its cor- relation with socioeconomic risk factors such as low education, high unemployment, crowd- ing, and high population density, our data did not support this conclusion. In our multivariate analysis, the variability in cases of tuberculosis was better explained by immigration, racial/ ethnic minority groupings, and median income than by other variables such as low education, high unemployment, crowded housing, and high population density. The risk of race for disease could be caused by a combination of factors. Although genetic differences have been linked to increased mycobacterial suscep- tibility,22–25 it seems more likely that minority populations are surrogates for larger reservoirs of latent tuberculosis infection. Many minori- ties have emigrated from regions with higher baseline rates of latent tuberculosis infection, and African Americans have for the past few generations lived disproportionately in urban centers with higher rates of tuberculosis dis- ease. In California, these groups are known to have high rates of active disease.26 Addition- ally, race and ethnicity are complex social con- structs that may be markers for other socioeco- nomic factors that are difficult to capture in such a model.
Like previous studies, our initial univariate analysis demonstrated that crowding is a risk factor for tubercular disease. However, after adjusting for other factors in the multivariate
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model, crowding was noted as developing a protective effect. Part of this change was likely because of its correlation with other variables that better explained the variability in tubercu- losis cases (most significantly, low education [ r 2 = 0.8], foreign birth [ r 2 = 0.8], and Hispanic ethnicity [ r 2 = 0.6]). Nevertheless, its reemer- gence as a significant protective factor suggests some benefit may remain after the negative ef- fects are removed by adjusting for other vari- ables. These results could be explained within the context of recent research on “social capi- tal” as a protective factor for tuberculosis.15
Crowding may be associated with a more tightly woven social network (i.e., increased so- cial capital) that could protect against disease. Although this research has shown potential, much controversy still exists on the precise measurement of social capital. Further research in this area is clearly warranted.
Our study also supports the association be- tween family income and tuberculosis disease. This finding is consistent with previous re- search showing a close link between tuberculo- sis and poverty. Although many racial or ethnic minorities may have higher rates of disease be- cause of historical reservoirs of tuberculosis in- fection, current levels of economic deprivation are of critical importance.
Regional Differences The effects for various ecological risk factors
were generally consistent across the 3 regions studied. Differences were noted in the risk of population density and in the risk of high ra- cial/ethnic minority populations. The lack of ef- fect for population density in San Francisco and Los Angeles was not unexpected because these 2 regions have uniformly high population den- sities in comparison to the rest of the state.
Conversely, the regional differences in the risk factors for Black and Hispanic populations were somewhat surprising. These risk differ- ences were not explained by differences in in- come or recent immigration. The increased rate of tuberculosis noted in predominantly Black census tracts near San Francisco may be at least partially attributable to a known persistent cluster of cases in a Black community in Contra Costa County (part of the San Francisco MSA).27
To assess the impact of this cluster on the gen- eral finding, the analysis was repeated, exclud- ing census tracts that corresponded to the
geographic location of the previously men- tioned cluster. In the new analysis, the inci- dence rate ratio decreased slightly, but not completely (1.4 to 1.34), suggesting that the known cluster may reflect a larger trend in the San Francisco area.
Also worthy of additional investigation is the lower baseline rate of tuberculosis in the Los Angeles MSA. After adjusting for variables in the model, the disease rate in Los Angeles was one third lower than expected. This finding is reflected by the crude rate of disease in Los Angeles. Despite Los Angeles having a higher level of diversity and immigration than the rest of the state, the crude rate of pediatric tubercu- losis there is roughly the same as that for the state as a whole.
Strengths and Limitations This analysis, in comparison to other studies
of ecological risk factors for tuberculosis, has the advantage of a focus on pediatric cases. This focus permits the results to more directly reflect risk factors for disease transmission. Previous studies of molecular epidemiology have shown that between 4% and 31% of all cases are the result of recent transmission.28,29 This means that for a vast majority of all cases, ecological data obtained at the time of disease onset may not represent factors relevant to transmission.
Insufficient data exist for similar estimations for pediatric cases, but it is generally assumed that pediatric cases represent recent transmis- sion. Therefore, analyses using exclusively pedi- atric cases would be expected to provide results with less misclassification and greater precision. Stratification by country of birth could also the- oretically reduce misclassification. Foreign-born children, compared with US-born children, may have been more likely to have acquired their infection overseas. Because the incidence rate ratios from the US-born–only stratum in our analysis are remarkably similar to the results from the full multivariate model, the degree of misclassification may be small.
Research that makes comparisons among different measures of social inequalities is chal- lenging; social measures of income, education, and ethnic heritage all use different units and scales. Furthermore, the shape of each distribu- tion differs, and threshold effects are often un- known. To address these challenges, we stan- dardized variables to a scale on the basis of
mean and variance. Because each independent variable is transformed through addition and multiplication of constants, the magnitude of the resulting incidence rate ratio changes, but its direction and significance do not.
Alternative methods of standardization for predictor variables have been used elsewhere. These include use of raw variables,13,15 compar- ison by quartiles,7 use of the relative index of inequality,7,30 use of a multiple variable index score,7,9 and numerous others.30 Each of these techniques has advantages and disadvantages (the full discussion of which is beyond the scope of this paper). Broadly speaking, these techniques tend to sacrifice either ease of com- parison to other variables (in the case of raw scores and log transformations) or clarity of technique (in the case of indices). We propose that although the technique of standardization by mean and variance is by no means perfect, it is an acceptable compromise that permits the clear comparison between ecological measures by nonstatisticians.
This analysis, however, is not without limita- tions. Collinearity, which occurs when indepen- dent variables are identical or very similar to each other, can be problematic in ecological studies. This occurs because aggregated socio- economic variables tend to be more highly cor- related with each other than individual socio- economic variables.31 This effect is magnified in studies with a small number of large hetero- geneous regions. Generally speaking, collinear- ity reduces the significance of a study’s findings by increasing the variance of its regression coef- ficients. This effect may have resulted in the underestimation of the incidence rate ratios reported in this article. We attempted to mini- mize this effect by analyzing 7018 census tracts and by selecting a variety of differing socioeco- nomic variables. Additionally, we confirmed that the potential collinearity because of crowd- ing did not destabilize the full model, because the remaining statistics changed only minimally (0.5% to 5%) when crowding was removed from the analysis.
Some misclassification may have occurred through the use of cases reported between January 1993 and December 2002 and eco- logical measures taken from the 2000 US Census. Although ecological measures for each census tract do shift over time, data from the national census is only collected
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every 10 years. Because there are insufficient cases of pediatric tuberculosis each year to analyze individually, this study combined cases over 10 years and used census data that were obtained during that time period.
Aggregated ecological measures, such as those used in this study, are distinct from their analogous individual-level characteristics.32 For example, having a low income affects an indi- vidual differently than living in a poor neigh- borhood. Because the California Department of Health does not currently collect data on in- come, education, or household crowding from individual tuberculosis cases, we were unable to directly compare ecological and individual- level factors. However, such a multilevel analy- sis would be informative and should be pur- sued in future research.
Finally, tuberculosis transmission is a com- plex process that depends on many factors. The models developed in this investigation include several variables, but other important variables may be missing.
CONCLUSIONS
Ecological studies such as this provide valu- able information. Disease transmission within a population depends both on individual host risk factors and community-level risk factors that govern the individual’s exposure to disease. This research suggests specific ecological factors that are associated with increased rates of tuberculo- sis disease. State and local tuberculosis control programs may use this information to identify “at risk” geographic areas that merit increased disease surveillance. These techniques under- score both the importance of geographic infor- mation in case reporting and its contribution to the better understanding of disease.
About the Authors Ward P. Myers is with the Children’s Hospital, Boston, and Boston Medical Center, Boston, Mass. Janice L. Westenhouse and Jennifer Flood are with the Tuberculosis Control Branch, California Department of Health Services, Berkeley, Calif. Lee W. Riley is with the University of California, Berkeley, School of Public Health.
Request for reprints should be sent to Lee Riley, 140 War- ren Hall, Berkeley, CA 94720 (e-mail: lwriley@berkeley.edu).
This article was accepted May 30, 2005.
Contributors W. P. Myers originated the study and led the analysis and writing. J. L. Westenhouse assisted with the data collection
and analysis. J. Flood supervised data collection and anal- ysis. L. W. Riley supervised the analysis and writing.
Acknowledgments Material and financial support were provided by the Cali- fornia Department of Health Services and the University of California, Berkeley, School of Public Health.
We would like to thank Arthur Reingold for his guid- ance and assistance in reviewing the article.
Human Participant Protection No institutional review board protocol approval was needed for this study.
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