Zacharia Levine, University of Utah

Place or Prozac?Regional planning, natural amenities, and

psychological depression

Depression: What’s the problem?

*Social impacts2nd 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

Environmental Context (IVs) Mental health (DV)

“Selection” or “drifter effect”

“Causation” or “breeder effect”

Physical activity Environmental quality

AestheticsOpportunity

Access

Natural amenitiesUrban form

Water resourcesAir quality

Income SESDemographics

Time

BiophiliaRestoration (ART/SRT)

Positive Emotions

Soliphilia/solistalgiaTopophilia

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 amenitiesParksSprawl

Individual(BRFSS)

Tau = .11130; likelihood function at iteration 2 = -2.800797E+005 (“best fit”)

Selected & Significant ResultsFixed 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)

-1.606-0.065

0.1310.010

0.0000.000

0.2010.937

YesYes

β1 γ10 (Poor phys hlth days)γ11 (Park fraction)

βj γ30 (Age)γ40 (Female)γ50 (Black)γ60 (Asian)γ80 (Divorced)γ80 (College)γ80 (Employed)

0.1060.111

-0.0060.681-0.793-0.8790.3890.119-0.160

0.0010.063

.0010.0320.0580.1280.0410.0280.030

0.0000.027

.000

.000

.000

.000

.000

.000

.000

0.0100.112

.9941.9750.4530.4150.3891.1270.852

YesYes

No (strength)YesNo (direction)No (direction)YesNo (direction)Yes

Results

• Greater natural amenities correspond to lesser odds of depression diagnosisEach 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

Zacharia Levine, University of Utah

Questions?

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