Zacharia Levine, University of Utah Place or Prozac? Regional planning, natural amenities, and psychological depression

Depression: What’s the problem? * Social impacts 2 nd 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 3 rd 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 Aesthetics Opportunity Access Natural amenities Urban form Water resources Air quality Income SES Demographics Time Biophilia Restoration (ART/SRT) Positive Emotions Soliphilia/solistalgia Topophilia

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% Age54.01 (17.20)Married55%College educated42% Female57%Divorced14%Employed (any level)58% White78%Other relationship status31%Income level (1-8) 5.99 (2.06) Black10%# of children0.59 (1.06)Winter interview25% Asian2%Tenure76% # of poor physical health days last month 3.28 (7.56) Other Race10%Veteran13% # of poor mental health days last month 2.97 (6.96) “Good Health”86%Obese64% 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 capita7.28 E+3 (2.18 E+2) Compactness Score (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 = E+005 (“best fit”)

Selected & Significant Results Fixed EffectCoefficientStandard ErrorP-valueOdds RatioExp(dir&strength)? ANOVA (“baseline” or “null” model) … τ =  Intraclass Correlation Coefficient (ICC =.034) … L = e 5 β 0  γ 00 (grand mean) Yes Slopes- and Intercepts-as-outcomes (specified model) … τ = … L = e 5 … McFadden Pseudo R 2 =.001 β 0  γ 00 (grand mean) γ 01 (Natural amenities) Yes β 1  γ 10 (Poor phys hlth days) γ 11 (Park fraction) β j  γ 30 (Age) γ 40 (Female) γ 50 (Black) γ 60 (Asian) γ 80 (Divorced) γ 80 (College) γ 80 (Employed) Yes No (strength) Yes 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

LimitationsFuture Research Geographic scale of BRFSS public health dataHealth data at census block or block group Cross-sectional designLongitudinal design (mixed or panel data) Regression-based (MLM) correlative analysisStructural Equation Modeling & causal pathways Operationalization of environmental contextInclude additional context variables at both levels Only 1 level of nestingInclude MSA and/or region-level IVs Operationalization of mediator/moderator IVsMeasure interaction (use) and immersion (access) Relationship between variables & planning applicationRelate mental illness to economic development Time: natural amenities change with climate changeForecast 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 = (ICC=.034); likelihood function at iteration 2 = E+5