Cargando…

Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand

OBJECTIVES: Examine the impact of two generic—urban–rural experimental profile (UREP) and urban accessibility (UA)—and one purposely built—geographic classification for health (GCH)—rurality classification systems on the identification of rural–urban health disparities in Aotearoa New Zealand (NZ)....

Descripción completa

Detalles Bibliográficos
Autores principales: Whitehead, Jesse, Davie, Gabrielle, de Graaf, Brandon, Crengle, Sue, Lawrenson, Ross, Miller, Rory, Nixon, Garry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10106021/
https://www.ncbi.nlm.nih.gov/pubmed/37055208
http://dx.doi.org/10.1136/bmjopen-2022-067927
_version_ 1785026332652994560
author Whitehead, Jesse
Davie, Gabrielle
de Graaf, Brandon
Crengle, Sue
Lawrenson, Ross
Miller, Rory
Nixon, Garry
author_facet Whitehead, Jesse
Davie, Gabrielle
de Graaf, Brandon
Crengle, Sue
Lawrenson, Ross
Miller, Rory
Nixon, Garry
author_sort Whitehead, Jesse
collection PubMed
description OBJECTIVES: Examine the impact of two generic—urban–rural experimental profile (UREP) and urban accessibility (UA)—and one purposely built—geographic classification for health (GCH)—rurality classification systems on the identification of rural–urban health disparities in Aotearoa New Zealand (NZ). DESIGN: A comparative observational study. SETTING: NZ; the most recent 5 years of available data on mortality events (2013–2017), hospitalisations and non-admitted hospital patient events (both 2015–2019). PARTICIPANTS: Numerator data included deaths (n=156 521), hospitalisations (n=13 020 042) and selected non-admitted patient events (n=44 596 471) for the total NZ population during the study period. Annual denominators, by 5-year age group, sex, ethnicity (Māori, non-Māori) and rurality, were estimated from Census 2013 and Census 2018. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary measures were the unadjusted rural incidence rates for 17 health outcome and service utilisation indicators, using each rurality classification. Secondary measures were the age-sex-adjusted rural and urban incidence rate ratios (IRRs) for the same indicators and rurality classifications. RESULTS: Total population rural rates of all indicators examined were substantially higher using the GCH compared with the UREP, and for all except paediatric hospitalisations when the UA was applied. All-cause rural mortality rates using the GCH, UA and UREP were 82, 67 and 50 per 10 000 person-years, respectively. Rural–urban all-cause mortality IRRs were higher using the GCH (1.21, 95% CI 1.19 to 1.22), compared with the UA (0.92, 95% CI 0.91 to 0.94) and UREP (0.67, 95% CI 0.66 to 0.68). Age-sex-adjusted rural and urban IRRs were also higher using the GCH than the UREP for all outcomes, and higher than the UA for 13 of the 17 outcomes. A similar pattern was observed for Māori with higher rural rates for all outcomes using the GCH compared with the UREP, and 11 of the 17 outcomes using the UA. For Māori, rural–urban all-cause mortality IRRs for Māori were higher using the GCH (1.34, 95% CI 1.29 to 1.38), compared with the UA (1.23, 95% CI 1.19 to 1.27) and UREP (1.15, 95% CI 1.10 to 1.19). CONCLUSIONS: Substantial variation in rural health outcome and service utilisation rates were identified with different classifications. Rural rates using the GCH are substantially higher than the UREP. Generic classifications substantially underestimated rural–urban mortality IRRs for the total and Māori populations.
format Online
Article
Text
id pubmed-10106021
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-101060212023-04-17 Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand Whitehead, Jesse Davie, Gabrielle de Graaf, Brandon Crengle, Sue Lawrenson, Ross Miller, Rory Nixon, Garry BMJ Open Epidemiology OBJECTIVES: Examine the impact of two generic—urban–rural experimental profile (UREP) and urban accessibility (UA)—and one purposely built—geographic classification for health (GCH)—rurality classification systems on the identification of rural–urban health disparities in Aotearoa New Zealand (NZ). DESIGN: A comparative observational study. SETTING: NZ; the most recent 5 years of available data on mortality events (2013–2017), hospitalisations and non-admitted hospital patient events (both 2015–2019). PARTICIPANTS: Numerator data included deaths (n=156 521), hospitalisations (n=13 020 042) and selected non-admitted patient events (n=44 596 471) for the total NZ population during the study period. Annual denominators, by 5-year age group, sex, ethnicity (Māori, non-Māori) and rurality, were estimated from Census 2013 and Census 2018. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary measures were the unadjusted rural incidence rates for 17 health outcome and service utilisation indicators, using each rurality classification. Secondary measures were the age-sex-adjusted rural and urban incidence rate ratios (IRRs) for the same indicators and rurality classifications. RESULTS: Total population rural rates of all indicators examined were substantially higher using the GCH compared with the UREP, and for all except paediatric hospitalisations when the UA was applied. All-cause rural mortality rates using the GCH, UA and UREP were 82, 67 and 50 per 10 000 person-years, respectively. Rural–urban all-cause mortality IRRs were higher using the GCH (1.21, 95% CI 1.19 to 1.22), compared with the UA (0.92, 95% CI 0.91 to 0.94) and UREP (0.67, 95% CI 0.66 to 0.68). Age-sex-adjusted rural and urban IRRs were also higher using the GCH than the UREP for all outcomes, and higher than the UA for 13 of the 17 outcomes. A similar pattern was observed for Māori with higher rural rates for all outcomes using the GCH compared with the UREP, and 11 of the 17 outcomes using the UA. For Māori, rural–urban all-cause mortality IRRs for Māori were higher using the GCH (1.34, 95% CI 1.29 to 1.38), compared with the UA (1.23, 95% CI 1.19 to 1.27) and UREP (1.15, 95% CI 1.10 to 1.19). CONCLUSIONS: Substantial variation in rural health outcome and service utilisation rates were identified with different classifications. Rural rates using the GCH are substantially higher than the UREP. Generic classifications substantially underestimated rural–urban mortality IRRs for the total and Māori populations. BMJ Publishing Group 2023-04-13 /pmc/articles/PMC10106021/ /pubmed/37055208 http://dx.doi.org/10.1136/bmjopen-2022-067927 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Epidemiology
Whitehead, Jesse
Davie, Gabrielle
de Graaf, Brandon
Crengle, Sue
Lawrenson, Ross
Miller, Rory
Nixon, Garry
Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand
title Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand
title_full Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand
title_fullStr Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand
title_full_unstemmed Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand
title_short Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand
title_sort unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in aotearoa new zealand
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10106021/
https://www.ncbi.nlm.nih.gov/pubmed/37055208
http://dx.doi.org/10.1136/bmjopen-2022-067927
work_keys_str_mv AT whiteheadjesse unmaskinghiddendisparitiesacomparativeobservationalstudyexaminingtheimpactofdifferentruralityclassificationsforhealthresearchinaotearoanewzealand
AT daviegabrielle unmaskinghiddendisparitiesacomparativeobservationalstudyexaminingtheimpactofdifferentruralityclassificationsforhealthresearchinaotearoanewzealand
AT degraafbrandon unmaskinghiddendisparitiesacomparativeobservationalstudyexaminingtheimpactofdifferentruralityclassificationsforhealthresearchinaotearoanewzealand
AT crenglesue unmaskinghiddendisparitiesacomparativeobservationalstudyexaminingtheimpactofdifferentruralityclassificationsforhealthresearchinaotearoanewzealand
AT lawrensonross unmaskinghiddendisparitiesacomparativeobservationalstudyexaminingtheimpactofdifferentruralityclassificationsforhealthresearchinaotearoanewzealand
AT millerrory unmaskinghiddendisparitiesacomparativeobservationalstudyexaminingtheimpactofdifferentruralityclassificationsforhealthresearchinaotearoanewzealand
AT nixongarry unmaskinghiddendisparitiesacomparativeobservationalstudyexaminingtheimpactofdifferentruralityclassificationsforhealthresearchinaotearoanewzealand