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. 2017 May 22;44(2):348–358. doi: 10.1093/schbul/sbx060

Table 1.

Association Between Childhood Urbanicity and Adolescent Psychotic Experiences

Model Specification Level of Urbanicitya Covariates Association Between Childhood Urbanicity and Adolescent Psychotic Experiencesb
OR 95% CI P Value
Model 1 Rural [Reference]
Intermediate 1.37 1.01–1.86 .042
Urban 1.67 1.21–2.30 .002
Model 2 Rural [Reference]
Intermediate 1.11 0.81–1.54 .513
Urban 1.43 1.01–2.03 .042
Family socioeconomic status 1.20 1.02–1.41 .029
Family psychiatric history 1.99 1.30–3.06 .002
Maternal psychotic symptoms 1.09 0.96–1.23 .187
Adolescent alcohol dependence 2.20 1.66–2.92 <.001
Adolescent cannabis dependence 4.21 2.60–6.82 <.001
Neighborhood-level deprivation 1.10 1.00–1.20 .044
Model 3 Rural [Reference]
Intermediate 1.17 0.85–1.62 .329
Urban 1.35 0.94–1.92 .103
Neighborhood social conditions 1.28 1.11–1.48 .001

Note: OR, odds ratio from ordinal logistic regression.

a3-level urbanicity at age 12: Rural = rural towns and fringes, villages, hamlets, isolated dwellings; Intermediate = urban cities and towns; Urban = major and minor conurbations.

bThe association of childhood urbanicity (and other covariates) with adolescent psychotic experiences was calculated with ordinal logistic regression because adolescent psychotic experiences are on an ordinal (0–3) rather than binary scale. Model 1—the unadjusted association between childhood urbanicity and adolescent psychotic experiences (sample size = 1978 participants). Model 2—adjusted for family-level characteristics (family socioeconomic status, family psychiatric history, maternal psychotic symptoms), individual-level characteristics (adolescent alcohol dependence and adolescent cannabis dependence), and neighborhood-level deprivation at age 12 (sample size = 1900 participants). Model 3—adjusted for neighborhood social conditions (social cohesion and neighborhood disorder) at age 12 (sample size = 1956 participants). Sample sizes vary slightly between models due to small numbers of participants missing data on independent variables. All analyses account for the nonindependence of twin observations.