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April 2006, Vol 96, No. 4 | American Journal of Public Health Myers et al. | Peer Reviewed | Research and Practice | 685


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.


Data Collection: Tuberculosis Cases
Case information was obtained from the

California Department of Health Services,

American Journal of Public Health | April 2006, Vol 96, No. 4686 | Research and Practice | Peer Reviewed | Myers et al.


TABLE 1—Ecological Measures Derived From Year 2000 US Census Tract Data

Summary Census
Measure Operational Definition File Variable


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


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

April 2006, Vol 96, No. 4 | American Journal of Public Health Myers et al. | Peer Reviewed | Research and Practice | 687


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).


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).

American Journal of Public Health | April 2006, Vol 96, No. 4688 | Research and Practice | Peer Reviewed | Myers et al.


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.


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

April 2006, Vol 96, No. 4 | American Journal of Public Health Myers et al. | Peer Reviewed | Research and Practice | 689


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

American Journal of Public Health | April 2006, Vol 96, No. 4690 | Research and Practice | Peer Reviewed | Myers et al.


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 …

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