Transcript Natural amenities and psychological depression: … Regional

Place or Prozac?
Regional planning, natural amenities, and
psychological depression
Zacharia Levine, University of Utah
Depression: What’s the problem?
*Social impacts
2nd leading cause of disability globally—leading source of years lived with a
disability (Ferrari et al. 2010)
Depression affects an estimated 350 million people worldwide (WHO 2010)
*Economic
impacts
3rd most costly medical condition for total expenditure (AHRQ 2013)
Total cost of all mental illnesses in U.S. = $317.6B (Insel 2008)
The planner’s role?
Environmental context  mental health and well-being
Income
Demographics
SES
“Selection” or “drifter effect”
Environmental Context (IVs)
Mental health (DV)
“Causation” or “breeder effect”
Time
Physical activity
Aesthetics
Opportunity
Access
Biophilia
Restoration (ART/SRT)
Positive Emotions
Soliphilia/solistalgia
Topophilia
Environmental quality
Natural amenities
Urban form
Water resources
Air quality
Questions
Are county-level measures of urban form and environmental
context related to individual-level psychological depression?
If so, what can planners do to mitigate
or prevent psychological depression?
Data
• Environmental context
• Natural Amenities Scale (McGranahan, USDA ERS) n=3000
• Public parks (2006 ESRI parks layer) n=3000
• Compactness (i.e. urban form) (Ewing & Hamidi 2010) n=967
• Mental Illness
• Psychological depression (CDC—BRFSS 2012) n=447,000
• Additional variables
• Demographic and socioeconomic (CDC—BRFSS 2012) n=447,000
Individual-level (L1) variables mean (SD) or percentage (n=201,467)
Depression (DV)
16%
Age
54.01 (17.20)
Married
55%
College educated
42%
Female
57%
Divorced
14%
Employed (any level)
58%
White
78%
Other relationship status
31%
Income level (1-8)
5.99
(2.06)
Black
10%
# of children
0.59 (1.06)
Winter interview
25%
Asian
2%
Tenure
76%
# of poor physical health
days last month
3.28
(7.56)
Other Race
10%
Veteran
13%
# of poor mental health
days last month
2.97
(6.96)
“Good Health”
86%
Obese
64%
Respondent physically
active last month
80%
County-level (L2) variables mean (SD) (n = 945)
Natural Amenities Scale
.28 (2.39)
Mean Income (‘12)
$86,304 ($20,302.91)
Park fraction (land cover)
6.93 E+3 (1.70 E+2)
Park acres per capita
7.28 E+3 (2.18 E+2)
Compactness Score
100.28 (24.98)
Natural Amenities Scale (1999)
Statistical method: 2-level binary logistic MLM
• Nesting Structure
• Level 1: Individual characteristics (BRFSS)
• Depression diagnosis = binary outcome variable
• Demographics
• Socio-economics
• Level 2: County-level characteristics
• Natural amenities scale
• Public parks
• Median Household income
County-level variables
Natural amenities
Parks
Sprawl
Individual
(BRFSS)
Tau = .11130; likelihood function at iteration 2 = -2.800797E+005 (“best fit”)
Selected & Significant Results
Fixed Effect
Coefficient Standard Error
P-value
Odds Ratio
Exp(dir&strength)?
ANOVA (“baseline” or “null” model) … τ = 0.116  Intraclass Correlation Coefficient (ICC = .034) … L = -2.849 e5
β0 
γ00 (grand mean)
-1.802
0.020
.000
0.164
Yes
Slopes- and Intercepts-as-outcomes (specified model) … τ = 0.111 … L = -2.801 e5 … McFadden Pseudo R2 = .001
β0 
γ00 (grand mean)
γ01 (Natural amenities)
β1 
γ10 (Poor phys hlth days)
γ11 (Park fraction)
βj 
γ30 (Age)
γ40 (Female)
γ50 (Black)
γ60 (Asian)
γ80 (Divorced)
γ80 (College)
γ80 (Employed)
-1.606
-0.065
0.131
0.010
0.000
0.000
0.201
0.937
Yes
Yes
0.106
0.111
0.001
0.063
0.000
0.027
0.010
0.112
Yes
Yes
-0.006
0.681
-0.793
-0.879
0.389
0.119
-0.160
.001
0.032
0.058
0.128
0.041
0.028
0.030
.000
.000
.000
.000
.000
.000
.000
.994
1.975
0.453
0.415
0.389
1.127
0.852
No (strength)
Yes
No (direction)
No (direction)
Yes
No (direction)
Yes
Results
• Greater natural amenities correspond to lesser odds of depression diagnosis
Each unit increase in natural amenities  6.5% decrease in likelihood
• More park space corresponds to better physical health, which, in turn, leads to
lesser odds of psychological depression
• L1 Control Variables of Interest
• ~96% of variation due to individual-level differences
• Winter variable was intended to look at seasonal affective disorder
Discussion
• City and regional planners can and should work to address mental illness
Place matters!
• Ecological planning can protect natural amenities
• Parks/open space planning is an important tool: physical health  mental
health
• Compactness may become significant at smaller geographies
Evaluate difference between individuals in “most vs. least” compact places
Limitations
Future Research
Geographic scale of BRFSS public health data
Health data at census block or block group
Cross-sectional design
Longitudinal design (mixed or panel data)
Regression-based (MLM) correlative analysis
Structural Equation Modeling & causal pathways
Operationalization of environmental context
Include additional context variables at both levels
Only 1 level of nesting
Include MSA and/or region-level IVs
Operationalization of mediator/moderator IVs
Measure interaction (use) and immersion (access)
Relationship between variables & planning application
Relate mental illness to economic development
Time: natural amenities change with climate change
Forecast climate change impacts on depression rates
Questions?
Zacharia Levine, University of Utah
30 years of anecdotal, theoretical,
and empirical research
• Evolutionary affinity (biophilia)
• Place attachment (Wilson 1984; Kaplan & Kaplan, 1989)
• Preference (population change, home value) (Herzog et al. 2000; McGranahan
1999; Wu & Gopinath 2008)
• Restorative benefits (cognitive)
• Attention Restoration Theory (Kaplan 1995, Kuo 2001, Berto 2005)
• Stress Recovery Theory (Ulrich et al. 1991, 2003)
• Well-being impacts (emotional)
• Joy, happiness, self-confidence (Kuo & Sullivan 2001)
What’s new here?
Operationalization of “nature”
Specificity of “mental illness”
Geographic scale (county)
Spatial planning view (in the US)
Selected Results
Tau = 0.11632 (ICC=.034); likelihood function at iteration 2 = -2.84947 E+5