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Table 2 Results - observational studies identified in Stage 1 that used more advanced analytical techniques specified in MRC guidance (n = 8)

From: Evaluating causal relationships between urban built environment characteristics and obesity: a methodological review of observational studies

Study details

Description of variables

Results (for two different methods of analysis, when reported)

 

Independent variables

Dependent variables

Main method of analysis:

Alternative method of analysis:

More advanced analytical technique

Single equation analytical technique

First author, date, journal

Study population

Description

Time varying

Areal unit precision

Description

Source

Description of analytical technique

Data type (time periods)

Effect sizes (95% confidence interval)1

Method

Effect sizes (95% confidence interval)1

Results where no statistically significant differences are observed between main and alternative analyses

Results where a mismatch between results is observed2

Cross sectional studies

Anderson, 2011, American Economic Journal [30]

U.S. adults (11 States)

Miles between home and fast-food restaurant

N/A

Telephone/ZIP codes

BMI

BRFSS

Instrumental variable derived from distance to the interstate highway

Cross sectional (1)

0.09 (-0.17, 0.17)

Not reported

Chen, 2012, Health Economics [31]

U.S. adults (Indianapolis, Indiana)

Number of

N/A

Individual addresses

BMI

Obesity Needs Assessment survey

Instrumental variable derived from distance to arterial roads and non-residential zones

Cross sectional (1)

 

OLS

None

Under-estimates:

(a.) restaurants,

(a.) 0.37* (confidence interval missing)

  

(a.) 0.06 (-0.03, 0.14)

(b.) chain grocery stores, and

(b.) 0.90* (0.12, 1.682)

(b.) 0.14 (-0.21, 0.50)

(c.) proportion of park land, within a 0.5 mile radius

(c.) 2.85* (0.03, 5.67)

(c.) 2.39 (-0.66, 5.45)

Dunn, 2010, American Journal of Agricultural Economics [32]

U.S. adults (all States)

Number of fast food restaurants (at county level; author collected)

N/A

County level

BMI

BRFSS, 2004-2006

Instrumental variable derived from number of interstate highway exits in the county

Cross sectional (1)

No statistically significant results were reported, except in two subgroup analyses:

OLS

No statistically significant results were reported, except in two subgroup analyses (see right).

Under-estimates were reported in two subgroup analyses:

Female participants in medium density counties: 0.06* (0.01, 0.11)

  

Female participants in medium density counties: -0.01 (-0.02, 0.01)

Non-white participants in medium density counties: 0.20* (0.02, 0.38)

Non-white participants in medium density counties: 0.01 (-0.02, 0.04)

Dunn, 2012, Economics and Human Biology [33]

U.S. adults (Brazos Valley, Texas)

 

N/A

Individual addresses

Obesity likelihood

A mail survey

Instrumental variable derived from distance to nearest highway

Cross sectional (1)

No statistically significant results were reported, except in two subgroup analyses:

Probit model

No statistically significant results were reported, except in two cases (see right).

Under-estimates in just two cases:

e.g. Non-white participants:

 

Non-white participants:

Non-white participants:

(a.) miles to nearest fast-food restaurant, and number of fast-food restaurants within a

(a.) -0.100* (-0.178, -0.022)

(a.) -0.088 (-0.188, 0.012)

(b.) 1 mile and

(b.) 0.189* (0.030, 0.348)

(b.) 0.052 (-0.021, 0.125)

(c.) 3 mile radius

(c.) 0.058 (0.005, 0.121)

(c.) 0.014 (-0.004, 0.032)

Fish, 2010, Am J Public Health [34]

U.S. adults (Los Angeles County)

Resident perception of neighbourhood safety (self-reported dichotomous variable where 1= extremely or somewhat dangerous and 0=fairly or completely safe)

N/A

Individual level survey data

BMI

Los Angeles Family and Neighbourhood Survey

Instrumental variable derived from measures related to social cohesion and experience of household crime

Cross sectional (1)

2.81* (0.11, 5.52)

OLS (using first wave 2001/2 data)

None

Under-estimate: -0.07 (-1.07, 0.93)

Zick, 2013, IJBNPA [35]

U.S. females (Salt Lake, Utah)

Neighbourhood walkability

N/A

Census block (typically 1,500 people)

BMI

Utah Population Database

Instrumental variable derived from neighbourhood characteristics e.g. churches and schools

Cross sectional (1)

-0.24*

OLS

None

Under-estimate: 0.00

Longitudinal studies

Courtemanche, 2011, Journal of Urban Economics [36]

U.S. adults (all States)

Number of Walmart Supercenters per 100,000 residents (these stores provide low cost food and encourage sedentary lifestyles)

Yes

County level

 

BRFSS, 1996-2005

Instrumental variable derived from distance to Walmart head office (expansion over time of Walmart stores was shown to be correlated with distance from the head office)

Repeated cross sectional (10)

 

OLS

None

Under-estimates:

(i.) BMI

(i.) 0.24* (0.06, 0.41)

  

(i.) 0.02 (-0.00, 0.05)

(ii.) Obesity likelihood

   

(ii.) 0.023* (0.011, 0.035)

  

(ii.) 0.001 (-0.001, 0.003)

Zhao, 2010, Journal of Health Economics [3]

U.S. adults (all States)

Proportion of people living in densely populated areas with >9000 people per square mile

Yes (4; every 10 years)

MSA level (366 of these in U.S.)

(i.) BMI

National Health Interview Survey, 1976-2001

Instrumental variable derived from exogenous expansion over time of the U.S. interstate highway system

Repeated cross sectional (25)

(i.) -0.01 (-0.03, 0.01)

Not reported

(ii.) Obesity likelihood

   

(ii.) -0.0013* (-0.002, 0.000)3

  1. BMI: Body mass index measured in kg/m2 BRFSS: Behavioural Risk Factor Surveillance System dataset. MSA: Metropolitan Statistical Area.
  2. OLS: Ordinary-Least-Squares.
  3. 1 * indicates statistical significance at the p <-0.05 level.
  4. 2 when compared to results in the main analysis: “Under-estimate” if statistically significant results in the main analysis were not statistically significant the cross-sectional, single equation analysis; “Over-estimate” if statistically insignificant results in the main analysis were statistically significant in the cross-sectional, single equation analysis.
  5. 3 The interpretation of this result is that for each additional percentage point decrease in the proportion of population living in the densely populated area, obesity is approximately 0.1-0.2 percentage points higher.