Phil Hurvitz
phurvitz@u.washington.edu
UDP 591, Alberti
Autumn, 2016
23.November.2004
Exercise 3: Literature Critique
Paper: Sturm, R. and Cohen, D. Suburban sprawl and physical and mental health. Public
Health. 2004. 118:488–496.
1.
Statement of the research problem:
The authors studied the relationship between suburban sprawl (independent variable) on
“common medical and mental health disorders” (dependent variables). The hypothesis, though
not formally stated, is
H0: Chronic negative health conditions are not significantly related to urban sprawl.
H1: Chronic negative health conditions are not significantly related to urban sprawl.
2.
Underlying theoretical model(s) used by the authors:
The authors do a good job of relating their study to others that have looked at the
relationship between urban sprawl and health-related effects. There currently exists a small but
growing number of studies generating empirical evidence of a relationship between urban form
and general mental/physical health, so even though possible causal relationships have been
proposed, there is not enough convincing evidence that urban sprawl or its effects is a cause of ill
health. However, the underlying theoretical models are analogous to those that have found a
relationship between urban sprawl and other health factors. The proposed mechanism of
causation is a reduced daily level of physical activity due to the necessity of automobile
transportation for navigating sprawling urban landscapes.
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3.
Literature review:
The authors cite a number of studies that have looked at other measures of both
urbanization and health. On the whole, the authors have found that other research supports a
case for the negative effect of urban sprawl on general mental and medical health. Previous
studies have found a relationship between urban sprawl and health-related effects such as traffic
fatalities, air quality, decreased exercise, and higher body mass index. However, few studies
have addressed the relationship between urban sprawl and quantifiable measures of mental and
medical health (though they do cite a study that looked at urban sprawl and hypertension).
Although there exists a large body of medical evidence that higher levels of physical activity has
a protective effect, I think the disconnect is that it is difficult to establish causation between
urban sprawl and lower activity levels.
4.
Research Methods:
Methods The authors used a data from a cross-sectional study to measure health.
Measures of health were self-reported (via a telephone survey which was actually part of a
different research project, but the data were available). It is not stated whether the Healthcare for
Communities study used a random sample (indeed, any telephone survey cannot currently be
considered truly random), but supposedly the results of this are representative of the US
population. Thirty-eight cities were used in the telephone survey, and about 1500 samples were
taken (an overall response rate greater than 60%).
Measures of sprawl were obtained from a study by Ewing et al. which used principal
components analysis to rank the 4 measures of sprawl into a single normalized index score. In
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this respect, the authors were quite efficient in using data that were already available, and did not
need to collect any data.
Variables Medical and mental health was measured as self-reported values for 16
operationalized variables (asthma/allergies, diabetes, hypertension, arthritis, physical disability,
trouble breathing, cancer, neurological condition, stroke ,angina/heart disease, back pain,
abdominal/digestive problems, liver disease, migraine/chronic headaches, urinary tract problems,
and other chronic pain). Sprawl was measured with four operationalized variables (residential
density, land use mix, degree of centering, and street accessibility) at a metropolitan spatial scale.
The variables were agglomerated into a single interval index score. Other explanatory variables
included demographic factors (age, race/ethnicity, education, income, marital status, family size,
employment status, and gender).
By using the data available from other studies, the authors assume these data were
generated with an acceptable degree of both validity and reliability. Without looking at the
source studies, it is not possible to determine whether these variables are either valid or reliable.
5.
Statistical Analysis:
Statistical analyses included linear and logistic regression to adjust for sociodemographic
differences” and the Wald test of covariance matrices in the software package Strata to test for
relationship between urban sprawl and the number of reported negative chronic health
conditions.
Multivariate logit regressions were also used for comparing different quantized levels of
sprawl against the 16 health conditions. This is appropriate because of the lumping of individual
cities with ratio sprawl measures into interval classes.
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Testing for covariance is an appropriate statistical method for determining covariance,
though I’m not sure why they did not simply use a linear regression. The sprawl index is a ratio
measure, as is the mean number of chronic illnesses.
I’m not sure how logistic regression could be used to adjust for sociodemographic
differences. If these are possible confounders they should be teased apart separately. If any of
these are confounders, this could have an effect on the findings in general. Simply to state that
an adjustment was made does not provide me enough information to know whether they have
adequately addressed confounding.
6.
Data Analysis:
Of the 16 health measures, five were found to be statistically associated with sprawl at
the 0.05 level of significance. Fourteen of the 16 health measures were associated in the same
direction as sprawl (i.e., less sprawl is associated with lower prevalence of ill health). Both of
these results support the hypothesis that urban sprawl and chronic ill health are related.
Although they found a relationship between sprawl and physical health, they found no
such relationship with mental health measures.
7.
Conclusions:
I believe their conclusions are justified based on the evidence presented. First, there is a
mounting body of evidence that urban form has an effect on many different aspects of health;
this study adds to the growing body of knowledge. Second, five of the 16 measures exhibited a
high level of statistical significance (< 0.05). Third, as the authors state, there is a very low
chance (0.002) that 14 out of 16 measures would have negative associations with sprawl.
8.
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Limitations:
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There are a number of limitations to the conclusions presented here, and the authors do
acknowledge some of these limitations. Being a cross-sectional study, it is not possible to
ascertain order of temporal precedence. Were people suffering poor health effects at the time of
the survey self-selected to live in locations of greater sprawl? Did they succumb to illnesses
while living elsewhere, and then moved to more sprawling cities later? Furthermore, crosssectional studies can only measure prevalence, not incidence. Knowing how many new cases of
ill health over a span of time would be much more informative. Especially because some of
these chronic conditions have a long duration, we do not know if the subjects had poor health
before the time of sprawl.
The “relatively small number of sites” has implications on the external validity of the
study. Analyzing only 38 cities, and analyzing only cities (and not analyzing rural areas) limits
the generalizability of the findings.
To me, the biggest limitation to this study is its ecologic design. This has an effect in two
spheres. First, aggregating the entire metropolitan area with a single sprawl index ignores
within-city variation. Within any of these cities it is possible to find widely varying conditions,
ranging from more extreme sprawl (e.g., Issaquah Highlands) to highly dense (e.g., Belltown).
Performing the same kind of analysis stratified by varying sprawl may show an even stronger
relationship, but may actually show there is no effect! Second, aggregating individual responses
into mean numbers of ill effects also removes the variability of health status across the
population. While one city may have a higher mean number of chronic diseases, the same city
may also have a higher variance, indicating a class of very sick people and also a class of
extremely healthy people. Combining both ecologic effects does not necessarily throw doubt
upon their findings, but actually begs for more research measuring health and sprawl or other
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urban form measurements on a localized level: health of the individual and measures of urban
form in that individual’s spatial proximity. With GIS methods, it will be possible to gather these
kinds of individual data and actually come closer to finding more solid relationships and
hopefully causative effect.
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