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| cc image: ZoL87 on Flickr |
So, imagine my surprise when
I learned about a randomized social experiment with obesity as the
outcome. Randomization balances the
exposure [neighbourhood characteristic(s)] on known and unknown confounders,
and rectifies the issue of temporality. Randomized controlled trials are the
gold-standard in clinical epidemiology, but for ethical and economic reasons,
are usually not feasible in social epidemiology (randomizing people to smoke,
for instance, would never fly).
The study, published by
Jens Ludwig and crew in the New England Journal of Medicine, was based on the
Moving to Opportunity for Fair Housing Program, conducted by the US Department
of Housing and Urban Development (HUD). The basic premise of this experiment
was to determine how best to provide housing for those in need. Briefly, 4498 families
with children living in public/project housing in high poverty neighbourhoods
in Baltimore, Boston, Chicago, Los Angeles, or New York, were randomly
allocated to one of three groups in the years 1994-1998 (one quarter of those
eligible):
1. The MTO low poverty voucher group which
received rental vouchers usable only in low-poverty areas (where < 10% of
residents were poor), along with counseling and assistance in the search to
find a private rental unit (n = 1788)
2. The traditional voucher group, which
received rental vouchers where there were no restrictions on where the family
could relocate, as well as support ordinarily given to families by local public
housing authorities (n =1312)
3. The control group, which received no
vouchers but remained eligible for public/project housing and other social
programs, otherwise the status quo (n=1398)
For the most part, families
were headed by African-American or Hispanic single mothers. From 2008-2010,
health outcomes of female adults (usually the family head) were measured and
included height, weight, and level of glycated hemoglobin.
Now, not all families moved
or used the vouchers. The study used an intent-to-treat analysis which analyzes
individuals based on groups to which they were assigned. This is the least biased and most
conservative way to analyze a study like this.
So even though a family may have been assigned to the MTO group but did
not move to a low poverty neighbourhood, they would still be analyzed as part
of the MTO group.
In the MTO group, 48% used
the vouchers, in contrast to 63% in the traditional group. All groups were comparable at baseline in
terms of 57 characteristics including age, race/ethnicity, marital status,
employment, education, and federal assistance, for example. One year after randomization, the
neighbourhood poverty rate was significantly lower in the MTO group, but this
difference attenuated (still remained significant) at 5 and 10 y, as
families in the control group moved to lower poverty areas on their own. Additionally, the proportion of women that
said they felt safe/very safe in their neighbourhood, and the proportion that
said neighbourhood adults would intervene in youth anti-social activity (defined as collective efficacy) were significantly
higher in the MTO compared to the
control group at 4-7 y and 10-15 y post-randomization. These same significant
differences were seen for the traditional versus the control group, although there
was no difference in collective efficacy at 10-15 y.
At 10-15 y of follow-up, after
adjustment for baseline characteristics and allocation procedures, the prevalence
in each category of extreme obesity was significantly lower in the MTO group (BMI
≥ 35 = 31.1%, and BMI ≥ 40 = 14.4%) compared to the control group (BMI ≥ 35 = 35.5%,
and BMI ≥ 40 = 17.7%). There was no difference in obesity defined as BMI ≥ 30. The
prevalence of elevated glycated hemoglobin was also lower in the MTO versus the
control group (16.3% versus 20%). Differences
were in the same direction but not significant between the traditional versus the
control group.
This study was interesting
to me mainly because of its design. Yes, significant differences were found, and
interestingly, even with such low compliance. But there are some important
things to keep in mind when interpreting the results of this study:
=> Significant differences
were for severe obesity, not for overweight or obesity in general.
=> No baseline data was
available for BMI or glycated hemoglobin so changes could not be assessed (the
authors state that this should not affect internal validity, which I tend to
agree with, especially if they found no significant differences in 57 baseline
characteristics).
=> Allocation of participants
and data collection procedures were extremely complicated; in many cases
information was not collected from participants (even though they were eligible
and appeared not to have refused), or they were randomly excluded. Perhaps
because of word limits, reasons for treatment of participants during these processes
were not clear to me.
=> Only one quarter of those
eligible actually applied to be randomized.
=> I am wondering if exposure
to environments after the initial move (e.g. subsequent moves) may have
confounded associations. But I can’t
really work out why this would be different across groups, given randomization,
unless attrition was higher in one group versus another. Attrition is an issue
in longitudinal study designs in general, but doesn’t appear to be an issue in
this study (although, in light of what I said in the previous paragraph, I have
trouble following calculation of response rates). I think the issue of multiple moves, and
duration of time spent in each neighbourhood, warranted more of a discussion in
the actual paper (some descriptive measures of neighbourhood characteristics
were weighted by time spent in each neighbourhood, but I don’t think the main
analysis accounted for this).
=> Is it the change in
environment characteristics (and which ones are important), or just the move itself
that is responsible for significant differences? Even though there were no significant
differences between the MTO (had to move to a low poverty area) and the
traditional group (who had no restrictions of where to move) in terms of the
health outcomes, the authors say that differences approached significance for
glycated hemoglobin, which they say, suggests that a change in the environment is
important. I’m not sure if the results they are referring to can really support
this assertion. It’s also evident that the traditional group moved to more
affluent areas anyway so a comparison of the two groups in this regard may be
moot.
=> This is a high-poverty,
minority sample that examined adult women only. Although it may have higher
relative internal validity for a social study, it lacks external validity or “generalizability”
to other population subgroups.
- To expand a little on the
high-poverty issue, I hypothesize that lower income individuals are more tied
to their residential neighbourhoods (less mobile) than more affluent people
(due largely to lack of access to a car). Thus, they accrue more exposure time
than more affluent people. In this vein, I think residential environments are
less important for more affluent individuals compared to those who are worse
off. I also think that context in the US is likely not generalizable to the Canadian
context (e.g. ghettoization
based on racial segregation and poverty).
=> There is evidence that MTO families
moved to areas lower in poverty but similar in racial distribution. These new areas
still had more poverty than the country average.
=> Neighbourhoods themselves are not
static entities, but were treated as such in this study. Some research has indicated that when change is
considered, disadvantage is the same in the MTO versus the control group
=> Even though neighbourhood
cohesion and safety were not outcomes, they are potential reasons for why
significant differences were seen. However, the measures employed in the study
were based on single items, which I find hard to accept that they accurately
captured what they were supposed to measure.
=> Finally, this study was
based on individuals as the unit of allocation and analysis, not neighborhoods.
Thus, this was not a study of a neighbourhood-level intervention. Population interventions such as those for
neighbourhoods are generally more cost-effective than those targeted to
individuals. A discussion of the two in
regards to the MTO study is provided by Sampson (2008).
All in all, the MTO is, and
I’ll quote Sampson, “a major contribution to the long tradition of experimental
social science.” There are certainly methodological issues with it, but I think
that the NEJM study provides fairly strong evidence that small decreases in
neighbourhood poverty can decrease prevalence of diabetes and extreme obesity in a highly disadvantaged population.
Ludwig J, Sanbonmatsu L, Gennetian L, Adam E, Duncan GJ, Katz LF, Kessler RC, Kling JR, Lindau ST, Whitaker RC, & McDade TW (2011). Neighborhoods, obesity, and diabetes--a randomized social experiment. The New England journal of medicine, 365 (16), 1509-19 PMID: 22010917
