Abstract
Methods
The study employs a secondary analysis of data from the Te Rau Hinengaro Mental Health Survey of 12,992 adults aged 16 years
and over from the household population. It uses small area estimation (SAE) methods involving: (1) estimation of a logistic
model of risk of SMI; (2) use of the foregoing model for computing estimates, using census data, for District Board areas;
(3) validation of estimates against an alternative indicator of SMI prevalence.
and over from the household population. It uses small area estimation (SAE) methods involving: (1) estimation of a logistic
model of risk of SMI; (2) use of the foregoing model for computing estimates, using census data, for District Board areas;
(3) validation of estimates against an alternative indicator of SMI prevalence.
Results
The model uses age, ethnicity, marital status, employment, and income to predict 92.2 % of respondents’ SMI statuses, with
a specificity of 95.9 %, sensitivity of 16.9 %, and an AUC of 0.73. The resulting estimates for the District Board areas ranged
between 4.1 and 5.7 %, with confidence intervals from ±0.3 to ±1.1 %. The estimates demonstrated a correlation of 0.51 (p = 0.028) with rates of psychiatric hospitalization.
a specificity of 95.9 %, sensitivity of 16.9 %, and an AUC of 0.73. The resulting estimates for the District Board areas ranged
between 4.1 and 5.7 %, with confidence intervals from ±0.3 to ±1.1 %. The estimates demonstrated a correlation of 0.51 (p = 0.028) with rates of psychiatric hospitalization.
- Content Type Journal Article
- Category Original Paper
- Pages 1-12
- DOI 10.1007/s00127-012-0519-4
- Authors
- Christopher G. Hudson, School of Social Work, Salem State University, 352 Lafayette Street, Salem, MA 01970, USA
- Max W. Abbott, Faculty of Health and Environmental Sciences, Auckland University of Technology, North Shore, 90 Akoranga Drive, Private Bag 92006, Northcote, Auckland, 1142 New Zealand
- Journal Social Psychiatry and Psychiatric Epidemiology
- Online ISSN 1433-9285
- Print ISSN 0933-7954