Pathways through which higher neighborhood crime is longitudinally associated with greater body mass index
Richardson et al. International Journal of Behavioral Nutrition and Physical Activity
Pathways through which higher neighborhood crime is longitudinally associated with greater body mass index
Andrea S. Richardson 0
Wendy M. Troxel 0
Madhumita Ghosh-Dastidar 0
Gerald P. Hunter 0
Robin Beckman 0
Natalie Colabianchi 1
Rebecca L. Collins 0
Tamara Dubowitz 0
0 RAND Corporation, Health Division , 4570 Fifth Avenue, Pittsburgh, PA 15213 , USA
1 University of Michigan, School of Kinesiology , Ann Arbor, MI 48109-2013 , USA
Background: Although crime and perceived safety are associated with obesity and body mass index (BMI), the pathways are less clear. Two likely pathways by which crime and perceived safety may impact obesity are through distress and physical activity. Methods: We examined data from 2013 to 2014 for 644 predominantly African-American adults (mean age 57 years; 77% female) living in low-income Pittsburgh, PA neighborhoods, including self-reported perceptions of safety and emotional distress, interviewer-measured height/weight, and physical activity measured via accelerometry. We used secondary data on neighborhood crime from 2011 to 2013. We built a structural equation model to examine the longitudinal direct and indirect pathways from crime to BMI through perceived safety, distress and physical activity. Results: Long-term exposure to crime was positively associated with lack of perceived safety (β = 0.11, p = 0.005) and lack of perceived safety was positively associated with BMI (β = 0.08, p = 0.03). The beneficial association between physical activity and BMI (β = −0.15, p < 0.001) was attenuated by a negative association between crime and physical activity (β = −0.09, p = 0.01). Although crime was associated with distress we found no evidence of a path from crime to BMI via distress. Conclusions: Our findings suggest decrements in perceived safety and physical activity are important processes that might explain why neighborhood crime is associated with greater BMI.
Obesity; Crime; Physical activity; Neighborhood; Perceived safety; Structural equation modeling
African Americans are disproportionately affected by
] and more likely to live in neighborhoods with
higher crime rates than whites [
]. Neighborhood crime
and perceived lack of safety have been associated with
psychological distress [
], poor health [
physical activity [
], and obesity [
on objective crime measures and their association with
obesity, BMI and physical activity supports associations
between neighborhood safety and activity [
12, 17, 18
However, the role of crime, varying degrees of perceived
safety and the intersection of these factors in influencing
resident distress, physical activity, and BMI are not well
understood (see reviews [
]), especially in
lowincome, African American populations [
Neighborhoods provide environments that may
promote or limit health-related behaviors [
evidence has shown objectively measured crime and
perceived lack of safety to increase stress, limit physical
activity because of safety concerns, and therefore influence
]. Several plausible pathways exist through
which neighborhood crime could influence obesity.
Chronic activation of the physiologic stress system
resulting from crime exposure could lead to obesity,
given how cortisol production increases BMI [
Another path from crime to obesity could operate
through perceived lack of safety to physical activity.
Understanding residents’ perceptions of neighborhood
safety and their role in resident health could help
identify ways to support resilience in disadvantaged
]. Much of the existing evidence is based
on self-reported physical activity and anthropometry,
which are subject to reporting bias [
Furthermore, much of the cross-sectional research focuses on
direct associations between crime/perceived safety and
obesity/BMI without considering two mediating
pathways through distress and physical activity.
Among the few studies examining pathways, Roman
et al., used structural (SEM) equation modeling with data
from two deprived Chicago neighborhoods (n = 328, mean
age 47 years) to examine the direct and indirect pathways
from perceptions of neighborhood violence and disorder
to obesity through fear of walking [
]. Perceived violence
was positively associated with fear of walking but not with
physical activity or obesity. However, this cross-sectional
study relied on self-reported anthropometry and physical
activity. A cross-sectional study of 864 adults from a
lowincome and ethnically mixed neighborhood in Salt Lake
City, UT used objectively measured anthropometry and
physical activity [
]. They found that low perceived
safety was associated with high BMI and that lower
moderate-to-vigorous physical activity partially
explained the relationship between safety and BMI. Yet
the authors were unable to examine how crime may
relate to perceived safety.
It remains unclear how peoples’ perceptions of crime
impact their sense of safety, in part, because no consensus
exists on whether the location and timing of crime
incidents influences resident perceptions. Understanding the
types, location and timing of crimes and their relationship
with perceptions of safety is necessary in order to robustly
model an indirect pathway whereby crime influences BMI
through perceived safety using the objective measure that
is most strongly associated with perceived safety.
Prior studies have examined crime across a wide
range of geographic areas (e.g., census block group
], census tract [
], half-mile [
], and 1-mile [
surrounding residents’ homes in association with
physical activity, diet and BMI. But it is unknown how
proximity from crimes, or the timing at which crimes
occurred, may be associated with physical activity and
health. In one study, agreement was poor between
objective measures of crime and perceived fear across
1-, half-, and eighth-mile buffers but agreement
appeared to increase with decreasing buffer size [
study was limited by small sample size (n = 303) and
crimes were aggregated to an annual rate, ignoring
more detailed timing of the occurrences (e.g., a month
versus a year ago). In addition, much of the research on
the role of crime in neighborhoods has not considered
influences on health from crimes occurring with
varying degrees of proximity [
In sum, limitations of research on neighborhood
crime, perceptions, behaviors, obesity, and distress
include the lack of studies that use objectively measured
data (crime, physical activity, and BMI), to examine
longitudinal pathways to resident health, and to examine
patterns of association with crime across different levels
of crime aggregation (over space and time). To address
these gaps, this longitudinal study examined how
proximity and timing of crime related to perceptions of safety
in two predominantly African American low-income
Pittsburgh neighborhoods. We then derived a long-term
measure of neighborhood crime based on these
associations. In a SEM, we tested direct and indirect pathways
from long-term exposure to neighborhood crime (prior
2 years) to BMI a year later through perceived safety and
the two likely additional mediating pathways, distress
and physical activity.
Study population and participants
Pittsburgh Research on Neighborhoods, Exercise and
Health (also known as ‘PHRESH Plus’) was designed to
document and evaluate neighborhood investments in
greenspace and housing on physical activity and active
transport in lower-income African American
neighborhoods in Pittsburgh. The sample includes randomly
selected households from two communities who are part
of a cohort followed over time. The Hill District
neighborhood has and continues to undergo neighborhood
economic investments, including renovation of
greenspace for recreational activities. Homewood, the
comparison neighborhood, although experiencing some
investment and change, did not experience the same
degree of investment. This study uses the PHRESH Plus
baseline data (collected Spring 2013), prior to major
greenspace and housing renovations, and follow-up data
on the same cohort members from a sister PHRESH
study (collected in Spring 2014). Data collection
included neighborhood-level built and social
characteristics, and detailed individual-level data. All study
protocols were approved by the institution’s Institutional
Outcome variable: Body mass index (BMI) (2014)
Interviewers measured height (without shoes) to the
nearest eighth inch using a carpenter’s square and an 8-ft
folding wooden ruler marked in inches. Weight was measured
to the nearest tenth of a pound using the SECA Robusta
813 digital scale. BMI was calculated as weight in kg
vided by height in m .
Exposure variable: Neighborhood-level crime (2011–2013)
Incident-level crime data was provided by the City
of Pittsburgh police department which contained
comprehensive lists of all reported crimes in Pittsburgh
for 2 years preceding the household survey administration
(i.e., 2011–2013). We calculated street network distances
from each household to crime locations for all types
(e.g., robbery, assault, etc.) using ArcGIS 10.2. We
geocoded 95% of the incidents using address information.
To assess how different crime measures are associated
with perceived safety we first calculated them across
varying distances from the residents’ homes, summing the
number of crimes that occurred within 1/10-, 1/4-, 1/2-,
and 3/4-mile radial distances from each household
address. To assess how timing of the criminal activity is
associated with perceived safety we summed the number of
crimes that occurred within 1 month, 3 months, 6 months,
1 year and 2 years prior to the date the respondent was
interviewed in 2013. We created 20 crime measures total
(e.g., crimes that occurred within last month and ½ mile
of the residents’ residence) for each combination of timing
and proximity. Because the crime measures are aggregated
to buffers surrounding the respondents’ residence they are
respondent-based exposures (i.e., a crime can be counted
multiple times in the sample for different individuals
depending on their residential location). Thus, we used
counts of crime rather than neighborhood
To assess long-term exposure to neighborhood crime
we used the measure that was most sensitive to
perceived safety and averaged it over the 2 years preceding
the interview date.
Intermediate variables (2013)
Study interviewers administered questionnaires and
residents answered the question “Your neighborhood is safe
from crime” using a five-point disagree-agree scale. We
reverse coded responses so that higher values reflected
lack of perceived safety because of neighborhood crime.
The Kessler 6 (K6) psychological distress scale is a
selfreport instrument to assess psychological distress [
We chose the K6 because it is a well-validated measure
of psychological distress that can detect the presence of
mild to severe psychological problems with high levels
of sensitivity and specificity in a wide variety of
]. As such, it has indeed been linked with
cortisol in prior research [
]. Data collectors asked residents
the frequency with which they experienced distress
symptoms (e.g., “feel hopeless”; “feel restless or fidgety”)
in the last 30 days. Responses were on a five-point scale.
Scores were summed into a single score where high values
reflect distress. K6 scores can be interpreted as no or low
distress = 0–4 points, moderate distress = 5–12 points, or
severe distress = 13–24 points [
Objectively measured physical activity
Participants were given a tri-axial accelerometer (i.e.,
ActiGraph GT3X+) and asked to wear the device on
their non-dominant wrist for 7 consecutive (24 h) days.
Data were sampled at 30 hz and summarized into 60 s
epochs. Non-wear was determined based on the Choi
algorithm using vector magnitude values [
intervals were identified from daily bed and wake times
reported by the participant. When these times were not
available or irregular (e.g., > 12 h or < 3 h sleep interval;
suspected inaccurate date recorded by the participant),
further visual inspection of the raw accelerometer signal
was used to supplement the recorded data. All sleep
interval data was removed from analyses. Further,
because participants did not report their bed times on the
first day the monitor was worn, the first full 24 h of
recording was removed to ensure that no sleep time was
included in vector magnitude calculations. Vector
magnitude is the square root of the sum of the three squared
axes [e.g., (x2 + y2 + z2)1/2]. Data were processed in
ActiLife v6.13.1. The daily average of vector magnitude
counts per minute was averaged across all days with at
least 10 h of wear time. A participant was included in
the analysis if he/she had at least 3 days of valid wear
and their vector magnitude counts were analyzed
continuously. While no vector magnitude cut points for the
ActiGraph wrist exist yet for sedentary activity, a 2000
counts per minute cut point was identified in 94 older
women to be very low activity [
Individual-level covariates (2013)
Below, we describe the variables we used as covariates in
models. Residents provided information on date of birth,
gender, education (categorized into at least some
college/bachelor’s degree versus less than college), and
marital/cohabitation status (married or living with a
partner versus living alone). We also included annual
income per capita (we imputed missing values n = 45).
We did not control for race/ethnicity because the
majority of the sample (92%) self-identified as Black or African
American. To capture physical activity limitation we
used their responses to the question “Does your health
limit you when walking one block?” which we
dichotomized to “a little or a lot” versus “not at all.” To account
for unmeasured differences across neighborhoods that
could confound associations between crime and obesity,
we controlled for neighborhood, using a binary indicator
of Hill District versus Homewood. Social networks may
mitigate negative effects of crime on health and
] by providing positive support and social
] and might also influence whether crimes occur
in one’s neighborhood. Therefore, we also controlled for
social network size which was measured using a
previously validated scale [
]. Participants reported the
number of people they knew (e.g., family, close friends,
neighbors, etc.). The number of people in each category
was summed to compute each participant’s social
We excluded residents if they did not live in Hill District
or Homewood at the time of the 2013 interview (n = 27)
(i.e., if they had moved out of the neighborhood from
initial enrollment into the study), were missing
accelerometry data (n = 98), had less than 3 valid (≥ 10 h)
accelerometer days (n = 15), were lost to follow up in 2014
when the BMI outcome was collected (n = 248), were
missing crime data (n = 8), or were missing response to
the question about perceived safety (n = 198). The final
analytic sample included 644 adults who wore the
accelerometer for an average of 5.7 ± 0.6 days. We calculated
T-tests (continuous variables) and Chi-Square tests
(categorical variables) to compare the included versus
excluded (n = 397) residents. We compared all covariate,
intermediate and, outcome variables between excluded
individuals and individuals included, and only age and
social network size differed. Those individuals excluded
were younger (mean age 53 years vs 57 years among the
included, p = < 0.001) and had fewer social network
members (mean 28 vs 48 among the included, p = < 0.001).
We performed descriptive analyses and tested
multivariable models using Stata 14.0 (StataCorp, College
Station, TX). We calculated means and standard
deviations (continuous variables) and percentages (categorical
variables) of individual-level – and neighborhood-level
crime. To identify the timing and proximity of crime most
sensitive (based on relative magnitude of association) to
perceived safety, we used logit models to predict perceived
safety as a function of 20 different crime measures. We
estimated separate models for counts of crime by time and
proximity. We controlled for age, gender, education,
married or living with a partner, physical limitation,
neighborhood, social network size, and household income in these
crime-safety tests. To illustrate the magnitude of the
associations we plotted a three-dimensional bar graph of the
logit estimates (y-axis) by crime timing (x-axis) and
To examine longitudinal indirect pathways from crime
to later BMI through perceived safety, psychological
distress and physical activity, we used Mplus version 7.11
] to build a SEM. We used the measure of crime that
emerged as the best predictor of perceived safety in the
above analyses and averaged it over the 2 years
preceding the respondents’ interview to derive a measure that
is sensitive to perceived safety and also captures
longterm exposure to crime. We allowed for a direct
pathway from crime to BMI. Given that there may be
bidirectional associations between distress and physical
], we also tested whether distress and
physical activity covaried (double-headed curved arrow
in Fig. 1). Figure 1 presents our conceptual model. We
controlled for the above-mentioned covariates to address
confounding of associations between: (1) crime and BMI
(exposure to outcome); (2) perceived safety, physical
activity, and BMI (mediators to outcome); (3) crime,
perceived safety, distress, and physical activity (exposure to
mediators) that were not likely to be affected by crime
. A statistically non-significant Chi-Square test
], Root Mean Square Error of Approximation
(RMSEA) < 0.06 [
], and Comparative Fit Index (CFI)
values approaching 1.0 [
] imply the model fits the
In 2013, the analytic sample was on average 57 years of
age, low-income (mean per capita income $13,400),
burdened with low mobility, and overweight in 2014
(mean body mass index (kg/m2) was 31 kg/m2 (Table 1).
Fig. 1 Conceptual model of direct and indirect paths from crime to BMI
About one fifth of the cohort was married or living
with a partner (19%), and an average vector magnitude
of 2140 cpm suggests very low activity [
cohort reports relatively low distress with a mean of 4.3.
The average number of crime exposures ranged from
about 2 to 1424 depending on distance from residence
and time frame (Table 2).
We associated 1) residents’ perceived neighborhood
safety with 2) counts of crimes in varying time frames
preceding the 2013 interview and 3) distance from the
residence. Figure 2 shows plots of the magnitude of the
logit beta estimates (y-axis) across the time frame of the
crimes (x-axis) and the distance from residents’
residence (z-axis). Most estimates were statistically
significant (p < 0.05, data not shown). Magnitude of the
association between crime and perceived safety
increased as distance between crime and participants’
residence decreased and as timing was closer to the
interview. Perceived safety was most strongly associated
with crimes that occurred within 1/10-mile from the
resident’s home, and those that that happened within the
month preceding the interview. Although the correlation
between perceived safety and crimes within 1/10-mile
and 1 month was not significant (Pearson’s correlation
coefficient = 0.06, p = 0.11), which is likely due to the
small number of crimes that occurred within 1/10 mile
and the 1 month timeframe.
To address temporality, we created an average of
crime over a longer period of time. First, we derived a
monthly count of crime for each respondent for each
Within last month
Within 1/10 mile
Within 1/4 mile
Within 1/2 mile
Within 3/4 mile
Within last 3 months
Within 1/10 mile
Within 1/4 mile
Within 1/2 mile
Within 3/4 mile
Within last 6 months
Within 1/10 mile
Within 1/4 mile
Within 1/2 mile
Within 3/4 mile
Within last year
Within 2 years
Within 1/10 mile
Within 1/4 mile
Within 1/2 mile
Within 3/4 mile
Within 1/10 mile
Within 1/4 mile
Within 1/2 mile
Within 3/4 mile
aCounts of crimes obtained from Pittsburgh Police Department and aggregated
by timing preceding interview and radial distance from resident household
month over 2 years (2011–2013) preceding the date of
the resident’s interview (2013). Next, we averaged the
monthly counts across the 2 years by summing the
counts and dividing by 24 (mean = 1.84 crimes per
month SD = 1.5). This captures the importance of crime
timing to concurrent perceptions of safety, while
providing an indicator of such over a longer period of time in
order to better predict slow-changing BMI.
In our initial model, the covariance path between
distress and physical activity was not statistically
significant (p = 0.35) and model fit was not ideal (RMSEA = 0.00,
CFI = 1.00, Chi-Square = 0.00 (0 df) p = < 0.001). The
SEM model deleting this path fit the data well
(RMSEA = 0.00, CFI = 1.00, Chi-Square = 0.02 (1 df)
p = 0.88. Figure 3 presents this result. For clarity, we
present only associations that were statistically significant
(p < 0.05). Long-term exposure to crime was indirectly
associated with BMI through perceived safety. We saw a
positive association between the average monthly number
of nearby crimes and lack of perceived safety (β = 0.11,
p = 0.005) and lack of perceived safety was positively
associated with BMI a year later (β = 0.08, p = 0.03). Physical
activity also appeared to serve as an indirect pathway
between crime and later BMI. Crime was associated with
reduced physical activity (β = −0.09, p = 0.009), and physical
activity was negatively associated with BMI (β = −0.15,
p < 0.001). Crime was also associated with distress, but
because distress was not predictive of BMI, distress does not
serve as a pathway between crime and this outcome, nor
does it explain associations between perceived safety and
BMI. Finally, we find no direct association between crime
and BMI, suggesting that the indirect paths through
perceived safety and physical activity, account entirely for the
association between these two variables.
We found that objectively measured long-term exposure
to crime is associated with higher BMI at a later point in
time through two separate paths, lack of perceived safety
and decreased physical activity. Unexpectedly, distress did
not explain associations between objectively measured
crime, lack of perceived safety and BMI, suggesting other
processes could be at play along this particular path.
Future work may be warranted exploring other potential
pathways. For example, lack of perceived safety may
impact neighborhood cohesion or disorder and subsequently
have downstream impacts on BMI via social
fragmentation and isolation [
] that promote poor dietary
behaviors. Our covariate measure of social network size may
not have adequately captured such a process.
Lack of perceived safety appeared to mediate the
pathway between objective crime and BMI. Thus, it is
possible that perception of crime is more acutely relevant to
BMI than objectively reported crime events in one’s
neighborhood. This is consistent with other studies
showing that perceptions of neighborhood conditions
are more strongly associated with health outcomes than
objective measures [
Perceived safety was not associated with physical
activity, contradicting prior findings indicating that residents
who felt safe in their neighborhood were more likely to
12, 57, 58
] and be physically active [
some studies have found no association between safety
perceptions and physical activity [
]. One of the first
studies to examine how changes in crime and perceptions
associated with walking over time found little evidence
that perceived safety or police-reported crime associated
with walking [
]. Fear is another reaction to crime and is
a distinct construct from perceived safety, although the
two constructs are correlated and both empirically linked
with health [
]. Given the apparent links between
neighborhood crime, fear and health [
], fear may be more
salient to physical activity than perceived safety. A
longitudinal study in Perth, Australia, (n = 531) among adults
(mean age 40 years) reported that perceived safety was
associated with increased time walking . Yet, in an
earlier publication this same group reported that fear of crime
was also associated with walking time and the effect size
was stronger for fear than for perceived safety (22 min/wk.
vs 20 min/wk). Future research should test whether fear is
more salient to ones’ decision to engage in physical
activity than perceived safety. While future research should
examine pathways through which both perceived safety
and fear may mediate associations between crime, and
BMI, it is beyond the scope of this paper. Our survey
asked the residents how safe their neighborhood was from
crime so we opted to use this specific measure of
Our study population is urban, low-income,
predominantly African American and sedentary so findings may
not be generalizable. Given uncertainty in cut points, we
analyzed vector magnitude as a continuous variable
where vector magnitude is a volume measure of activity
that may (or not) happen in the neighborhood. The
average vector magnitude in our study was 2124.7 which
is not much more than the 2000 cut point identified in
94 older women as sedentary [
]. Nonetheless, it is an
important group of interest to both the study of crime
and the study of overweight.
Another issue underlying inconsistencies may be the
approaches used to quantify feeling safe in prior studies,
such as using composite scores that include questions
about walking at night and aesthetics [
unattended dogs, and safety jogging , or worry about
being attacked [
]. These measures may tap into
slightly different aspects of perceived safety or they may
confound perceived safety with other variables. That is,
composite measures of perceived safety may conflate
other safety-related issues with safety specific to crime.
For this reason, we opted to use a single-item measure
that directly asked how safe their neighborhood was
Our finding that lack of perceived safety was
associated with higher BMI is consistent with other studies
]. Of the few that examine both physical
activity and BMI [
11, 66, 68, 69
], perceived social
nuisances (e.g., incivilities) among 14,836 English adults
were positively associated with obesity but it was not
mediated by physical activity . While the strengths
of Poortinga’s study include measured anthropometry
and examination of mediation by physical activity
they lacked objective measures of crime and physical
activity. Another study among 9252 American adults
living in urban areas found a positive association
between county-level crime and BMI but not between
county-level crime and walking [
]. Yet, this study
was limited by variation within counties and by
modeling physical activity and BMI separately, which does
not address potential mediating pathways. Our
pathway findings do support the theory that African
American adults living in deprived neighborhoods are
obese because crime deters physical activity [
we found associations between crime, perceived
safety, and physical activity.
Some research suggests that people living in deprived
areas with high crime rates experience stress that
translates into dysregulation of the
hypothalamicpituitary-adrenal that can cause higher BMI [
Our findings suggest that high levels of crime increase
distress. In another large longitudinal study of older
Australian adults that used objective measures of crime
an increase in risk of experiencing distress was
associated with an increase in neighborhood crime .
However, this study did not include perceptions of
safety and our findings suggest that distress does not
play a role in pathways from crime to BMI.
We also found that the association between number
of crimes and lack of perceived safety got progressively
stronger as the reference period was defined closer in
time to interview and distance from residents’ homes
was smaller. We provide evidence that when and where
crimes occur is important to consider in studies that
link objective measures of crime with resident
perceptions of safety. Thus, researchers might not need a long
reference period or wide area of assessment in studies
of crime and perceived safety. However, in the case
when the study outcome is slow to change, such as with
BMI, it is also important to consider a method of
aggregating such a measure to tap a longer history of
immediate (proximal in time and space) exposure.
No consensus exists about the geographic area that
best represents a neighborhood [
] and to our
knowledge only one study examined how counts of crime
aggregated within different distances from residents homes
associated with perceived safety [
]. Among 303 adults
living in Winston-Salem, NC the number of police
service calls within 1-, 1/2-, and 1/8-mile and normalized
by population size had low agreement with perceived
safety (weighted kappas [95% CI]: 1-mile, .12 [.04–.20];
half-mile, .18 [.10–.26]; eighth-mile, .22 [.14–.30]).
However, agreement appears to have been highest when
using the crime rate within the closest distance. To our
knowledge, no study has explored how the timing of
when crimes occur influences perceptions of safety and
key health outcomes.
Our study is longitudinal, capturing changes in
environment, perceptions, behaviors or health. However, this
study has limitations. We could not control for how long
participants lived at their current address, which could
contribute to a mismeasurement of their crime
exposure. However, we did collect information regarding years
lived in neighborhood of residence. The participants in
our study are a stable population. Only 6% reported
living in their neighborhood for less than a year, while 80%
of the residents reported living in their neighborhood
for over 5 years, and 50% of the residents reported living
in their neighborhood for 20 years or more. Participants
excluded from this analysis were younger which may
have biased our results. Residential location choice is
complex and driven by more than health-related
preferences. Yet, individual behaviors and health may be tied
to unobserved characteristics (e.g., health consciousness
]) that underlie an individual’s residential location.
Thus, residential selection could bias our results. We
lacked dietary data at this assessment, so we were unable
to test alternate pathways through energy balance.
Lastly, our participants were mostly sedentary, so the
lack of variation may have limited our ability to detect
associations with physical activity.
Despite these limitations we present longitudinal data
from a low-income and predominantly African American
cohort living in underserved urban neighborhoods that
includes historic crime data combined with
individuallevel perceptions, behaviors, and objectively measured
anthropometry and physical activity. Accelerometry is
superior to self-report where over-reporting can bias
]. Similarly, measured heights and weights
are less vulnerable to reporting bias than self-report
]. Further, our study population is often at increased
risk of residing in disadvantaged neighborhoods [
limited physical activity [
] and suffering higher rates
of inactivity-related cardiometabolic conditions [
To our knowledge, this is the first analysis linking
objective measures of crime and perceived safety, to
psychological distress, accelerometry-derived activity, and
measured anthropometry in an older, disadvantaged,
and predominantly African American population.
This work adds evidence that among African Americans
living in urban low-income neighborhoods, lack of
perceived safety in one’s neighborhood because of high
crime rates is associated with higher BMI, independent
of physical activity and distress. Importantly, crime is
also associated with higher BMI through less physical
activity, but this process is a separate one. Neighborhood
investments that reduce crime and improve resident
perception of safety remain critical for the wellbeing of
communities. Public health professionals and policy
makers may consider crime and perception of safety as
salient neighborhood factors that could exacerbate
obesity in the United States.
BMI: Body mass index; CFI: Comparative fit index; RMSEA: Root mean square
error of approximation; SEM: Structured equation modeling
The authors express sincere appreciation and gratitude to La’Vette Wagner,
field coordinator of the Pittsburgh Hill/Homewood Research on Eating,
Shopping, and Health study and the data collection staff. The authors thank
the Hill House Association, Operation Better Block, and Homewood
Children’s Village. Without their participation, the study could not have
Funding was provided by the National Cancer Institute (Grant No. R01CA164137
“Impact of Greenspace Improvement on Physical Activity in a Low Income
Community” and National Heart Lung Blood Institute (Grant No. R01 HL122460
“Neighborhood Change: Impact on Sleep and Obesity-Related Health
Availability of data and materials
Data for research purposes are will be available upon request after the study
ASR conceptualized, performed statistical analyses, and drafted the manuscript.
TD and WT acquired the data. RB and GPH created variables and data sets. NC,
MGD, RLC, WT and TD made substantial contributions to conception and
design of the study. All authors involved in drafting the manuscript, revising
critically for important content, agreed to be accountable for all aspects of the
work in ensuring that questions related to the accuracy or integrity of any part
of the work are appropriately investigated and resolved, and approved the final
Ethics approval and consent to participate
All study protocols were approved by the institution’s Institutional Review
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Submit your next manuscript to BioMed Central
and we will help you at every step:
1. World Health Organization: WHO global Infobase. 2011 .
2. White K , Borrell LN . Racial/ethnic residential segregation: framing the context of health risk and health disparities . Health Place . 2011 ; 17 ( 2 ): 438 - 48 .
3. Williams DR , Collins C. Racial residential segregation: a fundamental cause of racial disparities in health . Public Health Rep . 2001 ; 116 ( 5 ): 404 - 16 .
4. Roberts B , Stickley A , Petticrew M , McKee M. The influence of concern about crime on levels of psychological distress in the former soviet union . J Epidemiol Community Health . 2012 ; 66 ( 5 ): 433 - 9 .
5. Roman CG , Knight CR , Chalfin A , Popkin SJ . The relation of the perceived environment to fear, physical activity, and health in public housing developments: evidence from Chicago . J Public Health Policy . 2009 ; 30 ( Suppl 1 ): S286 - 308 .
6. Kitchen P , Williams A . Quality of life and perceptions of crime in Saskatoon, Canada . Soc Indic Res . 2010 ; 95 ( 1 ): 33 - 61 .
7. Lorenc T , Clayton S , Neary D , Whitehead M , Petticrew M , Thomson H , Cummins S , Sowden A , Renton A . Crime, fear of crime, environment, and mental health and wellbeing: mapping review of theories and causal pathways . Health Place . 2012 ; 18 ( 4 ): 757 - 65 .
8. Ross CE , Mirowsky J . Neighborhood disadvantage, disorder, and health . J Health Soc Behav . 2001 ; 42 ( 3 ): 258 - 76 .
9. White M , Kasl SV , Zahner GEP , Will JC . Perceived crime in the neighborhood and mental-health of women and children . Environ Behav . 1987 ; 19 ( 5 ): 588 - 613 .
10. Bracy NL , Millstein RA , Carlson JA , Conway TL , Sallis JF , Saelens BE , Kerr J , Cain KL , Frank LD , King AC . Is the relationship between the built environment and physical activity moderated by perceptions of crime and safety? Int J Behav Nutr Phys . 2014 ; 11 https://doi.org/10.1186/ 1479 -5868- 1111-1124.
11. Brown BB , Werner CM , Smith KR , Tribby CP , Miller HJ . Physical activity mediates the relationship between perceived crime safety and obesity . Prev Med . 2014 ; 66 : 140 - 4 .
12. Foster S , Hooper P , Knuiman M , Christian H , Bull F , Giles-Corti B . Safe RESIDential environments? A longitudinal analysis of the influence of crimerelated safety on walking . Int J Behav Nutr Phys Act . 2016 ; 13 : 22 .
13. Kerr Z , Evenson KR , Moore K , Block R , Diez Roux AV. Changes in walking associated with perceived neighborhood safety and police-recorded crime: the multi-ethnic study of atherosclerosis . Prev Med . 2015 ; 73 : 88 - 93 .
14. Van Dyck D , Cerin E , De Bourdeaudhuij I , Salvo D , Christiansen LB , Macfarlane D , Owen N , Mitas J , Troelsen J , Aguinaga-Ontoso I , et al. Moderating effects of age, gender and education on the associations of perceived neighborhood environment attributes with accelerometer-based physical activity: the IPEN adult study . Health Place . 2015 ; 36 : 65 - 73 .
15. Mujahid MS , Diez Roux AV , Shen M , Gowda D , Sanchez B , Shea S , Jacobs DR Jr , Jackson SA . Relation between neighborhood environments and obesity in the multi-ethnic study of atherosclerosis . Am J Epidemiol . 2008 ; 167 ( 11 ): 1349 - 57 .
16. Pham do Q , Ommerborn MJ , Hickson DA , Taylor HA , Clark CR . Neighborhood safety and adipose tissue distribution in African Americans: the Jackson heart study . PLoS One . 2014 ; 9 ( 8 ): e105251 .
17. McDonald NC . The effect of objectively measured crime on walking in minority adults . Am J Health Promot . 2008 ; 22 ( 6 ): 433 - 6 .
18. McGinn AP , Evenson KR , Herring AH , Huston SL , Rodriguez DA . The association of perceived and objectively measured crime with physical activity: a cross-sectional analysis . J Phys Act Health . 2008 ; 5 ( 1 ): 117 - 31 .
19. Foster S , Giles-Corti B . The built environment, neighborhood crime and constrained physical activity: an exploration of inconsistent findings . Prev Med . 2008 ; 47 ( 3 ): 241 - 51 .
20. Van Cauwenberg J , De Bourdeaudhuij I , De Meester F , Van D yck D , Salmon J , Clarys P , Deforche B . Relationship between the physical environment and physical activity in older adults: a systematic review . Health Place . 2011 ; 17 ( 2 ): 458 - 69 .
21. Ding D , Gebel K. Built environment, physical activity, and obesity: what have we learned from reviewing the literature? Health Place . 2012 ; 18 ( 1 ): 100 - 5 .
22. Feng J , Glass TA , Curriero FC , Stewart WF , Schwartz BS . The built environment and obesity: a systematic review of the epidemiologic evidence . Health Place . 2010 ; 16 ( 2 ): 175 - 90 .
23. Kumanyika SK , Gary TL , Lancaster KJ , Samuel-Hodge CD , Banks-Wallace J , Beech BM , Hughes-Halbert C , Karanja N , Odoms-Young AM , Prewitt TE , et al. Achieving healthy weight in African-American communities: research perspectives and priorities . Obes Res . 2005 ; 13 ( 12 ): 2037 - 47 .
24. Sallis JF , Bauman A , Pratt M. Environmental and policy interventions to promote physical activity . Am J Prev Med . 1998 ; 15 ( 4 ): 379 - 97 .
25. Rachele JN , Ghani F , Loh VH , Brown WJ , Turrell G . Associations between physical activity and the neighbourhood social environment: baseline results from the HABITAT multilevel study . Prev Med . 2016 ;
26. Barrington WE , Stafford M , Hamer M , Beresford SA , Koepsell T , Steptoe A . Neighborhood socioeconomic deprivation, perceived neighborhood factors, and cortisol responses to induced stress among healthy adults . Health Place . 2014 ; 27 : 120 - 6 .
27. Champaneri S , Xu X , Carnethon MR , Bertoni AG , Seeman T , DeSantis AS , Diez Roux A , Shrager S , Golden SH . Diurnal salivary cortisol is associated with body mass index and waist circumference: the multiethnic study of atherosclerosis . Obesity . 2013 ; 21 ( 1 ): E56 - 63 .
28. Karb RA , Elliott MR , Dowd JB , Morenoff JD . Neighborhood-level stressors, social support, and diurnal patterns of cortisol: the Chicago community adult health study . Soc Sci Med . 2012 ; 75 ( 6 ): 1038 - 47 .
29. Do DP , Diez Roux AV , Hajat A , Auchincloss AH , Merkin SS , Ranjit N , Shea S , Seeman T. Circadian rhythm of cortisol and neighborhood characteristics in a population-based sample: the multi-ethnic study of atherosclerosis . Health Place . 2011 ; 17 ( 2 ): 625 - 32 .
30. Ball K , Abbott G , Cleland V , Timperio A , Thornton L , Mishra G , Jeffery RW , Brug J , King A , Crawford D . Resilience to obesity among socioeconomically disadvantaged women: the READI study . Int J Obes . 2012 ; 36 ( 6 ): 855 - 65 .
31. Helmerhorst HJ , Brage S , Warren J , Besson H , Ekelund U . A systematic review of reliability and objective criterion-related validity of physical activity questionnaires . Int J Behav Nutr Phys Act . 2012 ; 9 : 103 .
32. Villanueva EV . The validity of self-reported weight in US adults: a population based cross-sectional study . BMC Public Health . 2001 ; 1 : 11 .
33. Roman CG , Chalfin A . Fear of walking outdoors. A multilevel ecologic analysis of crime and disorder . Am J Prev Med . 2008 ; 34 ( 4 ): 306 - 12 .
34. Morenoff JD . Neighborhood mechanisms and the spatial dynamics of birth weight . Am J Sociol . 2003 ; 108 ( 5 ): 976 - 1017 .
35. van Hees VT , Gorzelniak L , Dean Leon EC , Eder M , Pias M , Taherian S , Ekelund U , Renstrom F , Franks PW , Horsch A , et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity . PLoS One . 2013 ; 8 ( 4 ): e61691 .
36. Arnaud B , Malet L , Teissedre F , Izaute M , Moustafa F , Geneste J , Schmidt J , Llorca PM , Brousse G . Validity study of Kessler's psychological distress scales conducted among patients admitted to French emergency Department for Alcohol Consumption-Related Disorders . Alcohol Clin Exp Res . 2010 ; 34 ( 7 ): 1235 - 45 .
37. Berger M , Leicht A , Slatcher A , Kraeuter AK , Ketheesan S , Larkins S , Sarnyai Z : Cortisol awakening response and acute stress reactivity in first nations people . Sci Rep-Uk . 2017 . 7 Digital ObjectIdentifier . doi: 10 .1038/srep41760.
38. Kessler RC , Barker PR , Colpe LJ , Epstein JF , Gfroerer JC , Hiripi E , Howes MJ , Normand SL , Manderscheid RW , Walters EE , et al. Screening for serious mental illness in the general population . Arch Gen Psychiatry . 2003 ; 60 ( 2 ): 184 - 9 .
39. Choi L , Ward SC , Schnelle JF , Buchowski MS . Assessment of wear/nonwear time classification algorithms for triaxial accelerometer . Med Sci Sports Exerc . 2012 ; 44 ( 10 ): 2009 - 16 .
40. Kamada M , Shiroma EJ , Harris TB , Lee IM . Comparison of physical activity assessed using hip- and wrist-worn accelerometers . Gait Posture . 2016 ; 44 : 23 - 8 .
41. Ross CE , Jang SJ . Neighborhood disorder, fear, and mistrust: the buffering role of social ties with neighbors . Am J Community Psychol . 2000 ; 28 ( 4 ): 401 - 20 .
42. Vardavas E , Marcum SC . Modeling influenza vaccination behavior via inductive REasoning games . In: d'Onofrio A, editor. Modeling the interplay between human behavior and spread of infectious disease . Manfredi P: Springer; 2012 .
43. Muthén LK , Muthén BO . Mplus User's Guide . Los Angeles, CA: Muthén & Muthén; 1998 - 2010 .
44. Stults-Kolehmainen MA , Sinha R. The effects of stress on physical activity and exercise . Sports Med . 2014 ; 44 ( 1 ): 81 - 121 .
45. Perales F , Pozo-Cruz JD , Pozo-Cruz BD . Impact of physical activity on psychological distress: a prospective analysis of an Australian national sample . Am J Public Health . 2014 ; 104 ( 12 ): e91 - 7 .
46. Valeri L , Vanderweele TJ . Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros . Psychol Methods . 2013 ; 18 ( 2 ): 137 - 50 .
47. Bollen KA . Front Matter, in Structural Equations with Latent Variables . Hoboken: Wiley; doi:10.1002/9781118619179.fmatter.
48. Hu L , Bentler PM . Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new laternatives . Struct Equ Model . 1999 ; 6 ( 1 ): 1 - 55 .
49. Tucker LR , Lewis C . Reliability coefficient for maximum likelihood factoranalysis . Psychometrika . 1973 ; 38 ( 1 ): 1 - 10 .
50. Koster A , Shiroma EJ , Caserotti P , Matthews CE , Chen KY , Glynn NW , Harris TB . Comparison of sedentary estimates between activPAL and hip- and wrist-worn ActiGraph . Med Sci Sports Exerc . 2016 ;
51. Ross CE . Fear of victimization and health . J Quant Crim . 1993 ; 9 : 159 - 75 .
52. Kawachi I , Kennedy BP , Wilkinson RG . Crime: social disorganization and relative deprivation . Soc Sci Med . 1999 ; 48 ( 6 ): 719 - 31 .
53. Sampson RJ , Raudenbush SW , Earls F . Neighborhoods and violent crime: a multilevel study of collective efficacy . Science . 1997 ; 277 ( 5328 ): 918 - 24 .
54. Weden MM , Carpiano RM , Robert SA . Subjective and objective neighborhood characteristics and adult health . Soc Sci Med . 2008 ; 66 ( 6 ): 1256 - 70 .
55. Mackenbach JD , Lakerveld J , Van Lenthe FJ , Teixeira PJ , Compernolle S , De Bourdeaudhuij I , Charreire H , Oppert JM , Bardos H , Glonti K , et al. Interactions of individual perceived barriers and neighbourhood destinations with obesity-related behaviours in Europe . Obes Rev . 2016 ; 17 ( Suppl 1 ): 68 - 80 .
56. Troxel WM , Shih RA , Ewing B , Tucker JS , Nugroho A , D'Amico EJ . Examination of neighborhood disadvantage and sleep in a multi-ethnic cohort of adolescents . Health Place . 2017 ; 45 : 39 - 45 .
57. Foster C , Hillsdon M , Thorogood M. Environmental perceptions and walking in English adults . J Epidemiol Community Health . 2004 ; 58 ( 11 ): 924 - 8 .
58. Jack E , McCormack GR . The associations between objectively-determined and self-reported urban form characteristics and neighborhood-based walking in adults . Int J Behav Nutr Phys Act . 2014 ; 11 : 71 .
59. Hinkle JC . Emotional fear of crime vs. perceived safety and risk: implications for measuring "fear" and testing the broken windows thesis . Am J Crim Justice . 2015 ; 40 ( 1 ): 147 - 68 .
60. Stafford M , Chandola T , Marmot M. Association between fear of crime and mental health and physical functioning . Am J Public Health . 2007 ; 97 ( 11 ): 2076 - 81 .
61. Chandola T. The fear of crime and area differences in health . Health Place . 2001 ; 7 ( 2 ): 105 - 16 .
62. Craig CL , Brownson RC , Cragg SE , Dunn AL . Exploring the effect of the environment on physical activity - a study examining walking to work . Am J Prev Med . 2002 ; 23 ( 2 ): 36 - 43 .
63. Wilson DK , Kirtland KA , Ainsworth BE , Addy CL : Socioeconomic status and perceptions of access and safety for physical activity . Ann Behav Med 2004 , 28 ( 1 ): 20 - 28 .
64. Jackson J. A psychological perspective on vulnerability in the fear of crime . Psychol Crime Law . 2009 ; 15 ( 4 ): 365 - 90 .
65. Tamayo A , Karter AJ , Mujahid MS , Warton EM , Moffet HH , Adler N , Schillinger D , Hendrickson O'Connell B , Laraia B . Associations of perceived neighborhood safety and crime with cardiometabolic risk factors among a population with type 2 diabetes . Health Place . 2016 ; 39 : 116 - 21 .
66. Christian H , Giles-Corti B , Knuiman M , Timperio A , Foster S. The influence of the built environment, social environment and health behaviors on body mass index . Results from RESIDE. Prev Med . 2011 ; 53 ( 1-2 ): 57 - 60 .
67. Lange D , Wahrendorf M , Siegrist J , Plachta-Danielzik S , Landsberg B , Muller MJ . Associations between neighbourhood characteristics, body mass index and health-related behaviours of adolescents in the Kiel obesity prevention study: a multilevel analysis . Eur J Clin Nutr . 2011 ; 65 ( 6 ): 711 - 9 .
68. Lovasi GS , Bader MD , Quinn J , Neckerman K , Weiss C , Rundle A. Body mass index, safety hazards, and neighborhood attractiveness . Am J Prev Med . 2012 ; 43 ( 4 ): 378 - 84 .
69. Poortinga W. Perceptions of the environment, physical activity, and obesity . Soc Sci Med . 2006 ; 63 ( 11 ): 2835 - 46 .
70. Doyle S , Kelly-Schwartz A , Schlossberg M , Stockard J . Active community environments and health: the relationship of Walkable and safe communities to individual health . J Am Plan Assoc . 2006 ; 72 ( 1 ): 19 - 31 .
71. Daniel M , Moore DS , Decker S , Belton L , DeVellis B , Doolen A , Campbell MK . Associations among education, cortisol rhythm, and BMI in blue-collar women . Obesity . 2006 ; 14 ( 2 ): 327 - 35 .
72. Astell-Burt T , Feng X , Kolt GS , Jalaludin B . Does rising crime lead to increasing distress? Longitudinal analysis of a natural experiment with dynamic objective neighbourhood measures . Soc Sci Med . 2015 ; 138 : 68 - 73 .
73. Diez Roux AV , Mair C . Neighborhoods and health . Ann N Y Acad Sci . 2010 ; 1186 : 125 - 45 .
74. Cervero R . Transit-oriented development's ridership bonus: a product of self-selection and public policies . Environ Plan A . 2007 ; 39 ( 9 ): 2068 - 85 .
75. Tucker JM , Welk GJ , Beyler NK . Physical activity in U.S.: adults compliance with the physical activity guidelines for Americans . Am J Prev Med . 2011 ; 40 ( 4 ): 454 - 61 .
76. Parks SE , Housemann RA , Brownson RC . Differential correlates of physical activity in urban and rural adults of various socioeconomic backgrounds in the United States . J Epidemiol Community Health . 2003 ; 57 ( 1 ): 29 - 35 .
77. Beckles GL , Zhu J , Moonesinghe R . Centers for disease C, prevention : diabetes - United States , 2004 and 2008. MMWR Suppl . 2011 ; 60 ( 1 ): 90 - 3 .
78. May AL , Freedman D , Sherry B , Blanck HM . Obesity - United States , 1999 - 2010 . Mmwr-Morbid Mortal W. 2013 ; 62 ( 3 ): 120 - 8 .
79. Keenan NL , Rosendorf KA . Centers for disease C, prevention: prevalence of hypertension and controlled hypertension - United States , 2005 - 2008 . MMWR Suppl . 2011 ; 60 ( 1 ): 94 - 7 .