IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0181260.html
   My bibliography  Save this article

The New Zealand Indices of Multiple Deprivation (IMD): A new suite of indicators for social and health research in Aotearoa, New Zealand

Author

Listed:
  • Daniel John Exeter
  • Jinfeng Zhao
  • Sue Crengle
  • Arier Lee
  • Michael Browne

Abstract

For the past 20 years, the New Zealand Deprivation Index (NZDep) has been the universal measure of area-based social circumstances for New Zealand (NZ) and often the key social determinant used in population health and social research. This paper presents the first theoretical and methodological shift in the measurement of area deprivation in New Zealand since the 1990s and describes the development of the New Zealand Index of Multiple Deprivation (IMD).We briefly describe the development of Data Zones, an intermediary geographical scale, before outlining the development of the New Zealand Index of Multiple Deprivation (IMD), which uses routine datasets and methods comparable to current international deprivation indices. We identified 28 indicators of deprivation from national health, social development, taxation, education, police databases, geospatial data providers and the 2013 Census, all of which represented seven Domains of deprivation: Employment; Income; Crime; Housing; Health; Education; and Geographical Access. The IMD is the combination of these seven Domains. The Domains may be used individually or in combination, to explore the geography of deprivation and its association with a given health or social outcome.Geographic variations in the distribution of the IMD and its Domains were found among the District Health Boards in NZ, suggesting that factors underpinning overall deprivation are inconsistent across the country. With the exception of the Access Domain, the IMD and its Domains were statistically and moderately-to-strongly associated with both smoking rates and household poverty.The IMD provides a more nuanced view of area deprivation circumstances in Aotearoa NZ. Our vision is for the IMD and the Data Zones to be widely used to inform research, policy and resource allocation projects, providing a better measurement of area deprivation in NZ, improved outcomes for Māori, and a more consistent approach to reporting and monitoring the social climate of NZ.

Suggested Citation

  • Daniel John Exeter & Jinfeng Zhao & Sue Crengle & Arier Lee & Michael Browne, 2017. "The New Zealand Indices of Multiple Deprivation (IMD): A new suite of indicators for social and health research in Aotearoa, New Zealand," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    • Handle: RePEc:plo:pone00:0181260
    DOI: 10.1371/journal.pone.0181260
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181260
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0181260&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0181260?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. Norman, Paul & Boyle, Paul & Exeter, Daniel & Feng, Zhiqiang & Popham, Frank, 2011. "Rising premature mortality in the UK’s persistently deprived areas: Only a Scottish phenomenon?," Social Science & Medicine, Elsevier, vol. 73(11), pages 1575-1584.
    3. Michael Noble & Helen Barnes & Gemma Wright & Benjamin Roberts, 2010. "Small Area Indices of Multiple Deprivation in South Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 95(2), pages 281-297, January.
    4. N. T. Longford, 1999. "Multivariate shrinkage estimation of small area means and proportions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 227-245.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinfeng Zhao & Lisa Mackay & Kevin Chang & Suzanne Mavoa & Tom Stewart & Erika Ikeda & Niamh Donnellan & Melody Smith, 2019. "Visualising Combined Time Use Patterns of Children’s Activities and Their Association with Weight Status and Neighbourhood Context," IJERPH, MDPI, vol. 16(5), pages 1-16, March.
    2. Watkins, A. & Curl, A. & Mavoa, S. & Tomintz, M. & Todd, V. & Dicker, B., 2021. "A socio-spatial analysis of pedestrian falls in Aotearoa New Zealand," Social Science & Medicine, Elsevier, vol. 288(C).
    3. Daniel J. Exeter & Olivia Healey & Jessie Colbert & Nichola Shackleton, 2023. "Developing SEP65: A Census-Derived Index of Socio-Economic Position Specifically for the Older Population in Aotearoa New Zealand," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 169(3), pages 973-991, October.
    4. Vivienne C. Ivory & Joanne R. Stevenson, 2019. "From contesting to conversing about resilience: kickstarting measurement in complex research environments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(2), pages 935-947, June.
    5. Éadaoin M Butler & José G B Derraik & Marewa Glover & Susan M B Morton & El-Shadan Tautolo & Rachael W Taylor & Wayne S Cutfield, 2019. "Acceptability of early childhood obesity prediction models to New Zealand families," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    6. Beere, Paul & Keeling, Sally & Jamieson, Hamish, 2019. "Ageing, loneliness, and the geographic distribution of New Zealand's interRAI-HC cohort," Social Science & Medicine, Elsevier, vol. 227(C), pages 84-92.
    7. Jonathan Page, 2018. "Well-Being Assessment in Hawaii: Creating community-level composite indices in paradise," Working Papers 2018-5, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    8. Wiki, Jesse & Kingham, Simon & Campbell, Malcolm, 2021. "A geospatial analysis of Type 2 Diabetes Mellitus and the food environment in urban New Zealand," Social Science & Medicine, Elsevier, vol. 288(C).
    9. Jinfeng Zhao & Shanthi Ameratunga & Arier Lee & Michael Browne & Daniel J. Exeter, 2019. "Developing a New Index of Rurality for Exploring Variations in Health Outcomes in Auckland and Northland," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 955-980, July.
    10. Moon, Graham & Twigg, Liz & Jones, Kelvyn & Aitken, Grant & Taylor, Joanna, 2019. "The utility of geodemographic indicators in small area estimates of limiting long-term illness," Social Science & Medicine, Elsevier, vol. 227(C), pages 47-55.
    11. James M. Fitton & Jim D. Hansom & Alistair F. Rennie, 2018. "A method for modelling coastal erosion risk: the example of Scotland," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 931-961, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Renato Assunção & Carl Schmertmann & Joseph Potter & Suzana Cavenaghi, 2005. "Empirical bayes estimation of demographic schedules for small areas," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 537-558, August.
    2. Katie Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    3. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    4. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    5. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    6. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    7. Vanessa Santos-Sánchez & Juan Antonio Córdoba-Doña & Javier García-Pérez & Antonio Escolar-Pujolar & Lucia Pozzi & Rebeca Ramis, 2020. "Cancer Mortality and Deprivation in the Proximity of Polluting Industrial Facilities in an Industrial Region of Spain," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    8. Berti, Patrizia & Dreassi, Emanuela & Rigo, Pietro, 2014. "Compatibility results for conditional distributions," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 190-203.
    9. Louise Choo & Stephen G. Walker, 2008. "A new approach to investigating spatial variations of disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 395-405, April.
    10. Young‐Geun Choi & Lawrence P. Hanrahan & Derek Norton & Ying‐Qi Zhao, 2022. "Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records," Biometrics, The International Biometric Society, vol. 78(1), pages 324-336, March.
    11. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    12. Eric C. Tassone & Marie Lynn Miranda & Alan E. Gelfand, 2010. "Disaggregated spatial modelling for areal unit categorical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 175-190, January.
    13. Junming Li & Xiulan Han & Xiao Li & Jianping Yang & Xuejiao Li, 2018. "Spatiotemporal Patterns of Ground Monitored PM 2.5 Concentrations in China in Recent Years," IJERPH, MDPI, vol. 15(1), pages 1-15, January.
    14. Sanjay Chaudhuri & Debashis Mondal & Teng Yin, 2017. "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 293-320, January.
    15. Dolores Catelan & Annibale Biggeri & Corrado Lagazio, 2009. "On the clustering term in ecological analysis: how do different prior specifications affect results?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 49-61, March.
    16. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    17. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    18. Exeter, Daniel John & Rodgers, Sarah & Sabel, Clive Eric, 2014. "“Whose data is it anyway?” The implications of putting small area-level health and social data online," Health Policy, Elsevier, vol. 114(1), pages 88-96.
    19. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.
    20. Arden Finn & Murray Leibbrandt & Ingrid Woolard, 2013. "What happened to multidimensional poverty in South Africa between 1993 and 2010?," SALDRU Working Papers 099, Southern Africa Labour and Development Research Unit, University of Cape Town.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0181260. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.