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Modeling the geographic distribution of serious mental illness in New Zealand

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Abstract

Purpose

This study aims to estimate, apply, and validate a model of the risk of serious mental illness (SMI) in local service areas throughout New Zealand.

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.

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.

Conclusions

The use of SAE methods demonstrated the capacity for deriving local prevalence rates of SMI, which can be validated against an available indicator.

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Acknowledgments

The authors would like to acknowledge the consultative assistance provided by: Peggy Fairbaine-Dunlop, Foundation Professor of Policy Studies, Institute of Public Policy, AUT University, Auckland, NZ; Monique Faleafa, National Manager, La Va, Te Pou, Auckland, NZ; Te Kani Kingi, Director, The Academy for Maori Scholarship and Research, Massey University, Wellington, NZ; and Jane Vanderpyl, Research and Evaluation Manager, Te Pou, Auckland, NZ. The gracious sponsorship of the Auckland University of Technology, as well as the partial financial assistance of the School of Graduate Studies, Salem State University, are also acknowledged. The authors note the generous assistance of the New Zealand Ministry of Health in providing access to the Te Rau Hinengaro and New Zealand Health Survey data sets, as well as supplemental hospitalization data, which they have funded. The role of the New Zealand Crown as copyright owner is hereby acknowledged. This report does not necessarily represent the viewpoints of any individual or institution acknowledged above.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. School of Social Work, Salem State University, 352 Lafayette Street, Salem, MA, 01970, USA

    Christopher G. Hudson

  2. Faculty of Health and Environmental Sciences, Auckland University of Technology, North Shore, 90 Akoranga Drive, Private Bag 92006, Northcote, Auckland, 1142, New Zealand

    Max W. Abbott

Authors
  1. Christopher G. Hudson
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  2. Max W. Abbott
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Corresponding author

Correspondence to Christopher G. Hudson.

Appendices

Appendix 1

See Table 5.

Table 5 Descriptive statistics on demographic and socioeconomic predictors used in the study (n = 7,439)
Full size table

Appendix 2

Syntax program for computation of SMI rates for District Board areas, based on results from estimated logistic model, using census data:

compute smidhb = −5.27955 + (age16_29p × 2.30678894) + (age30_49p × 2.28344888) + (age50_64p × 1.89572214) + (age65_plusp ×  0.00000000) + (marriedp × −0.60126291) + (sepdivp ×  0.31067703) + (marnevp × 0.00000000) + (employp ×  −0.51198054) + (emp_unempp ×  0.54605975) + (emp_notlabforp ×  0.00000000) + (finc_lthalfmedp ×  0.85339089) + (finc_half_medp ×  0.62929413) + (finc_med_plushalfp × 0.24669290) + (finc_above1_5medp ×  0.00000000) + (ethnicity_maorip × 1.27175039) + (ethnicity_pacificp ×  0.90615763) + (ethnicity_asianp ×  −0.25910878) + (ethnicity_euro_otherp × 1.04488877).

execute.

compute smidhb2 = exp(smidhb).

execute.

compute Est_smidhb = smidhb2/(1 + smidhb2).

execute.

Key: Smidhb—logit for SMI for each area; smidhb2—odds ratio for SMI; Est_smidhb—probability/proportion of smi. Other variable names represent the categories of the various predictors.

Note: A separate program is used for computation of asymptotic standard errors and confidence intervals, available upon request. (Parts of this section were adapted from an earlier study of one of the authors [4]).

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Hudson, C.G., Abbott, M.W. Modeling the geographic distribution of serious mental illness in New Zealand. Soc Psychiatry Psychiatr Epidemiol 48, 25–36 (2013). https://doi.org/10.1007/s00127-012-0519-4

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  • DOI: https://doi.org/10.1007/s00127-012-0519-4

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