Constructing whole of population cohorts for health and social research using the New Zealand Integrated Data Infrastructure

Zhao, Jinfeng; Gibb, Sheree; Jackson, Rod; Mehta, Suneela; Exeter, Daniel J.

doi:10.1111/1753-6405.12781

Cited by 30 publications

(27 citation statements)

References 3 publications

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“…The study area comprises the Northland and Auckland regions of NZ, which include four DHBs, namely Northland, Waitemata, Auckland and Counties Manukau. The study relates to the 1‐year period from the 1st January 2013 to 31st December 2013 and includes 1,679,703 (1,582,881 for census resident population in 2013) people, comprising more than 37% of the total resident IDI population of NZ (Zhao et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…A population cohort was constructed from multiple data sources in the IDI for the study area using an “activity‐based” approach. Activity here refers to individuals' interactions with a particular public service (e.g., health, immigration, education), and can be used to determine whether individuals were “active”, that is, present and resident, in NZ (Gibb, Bycroft, & Matheson‐Dunning, ; Zhao et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Our cohort was constructed based on the method developed by Zhao et al (). Individuals who met all of the following criteria were extracted and linked in the IDI to form a population cohort for the study area if they: (a) were within the IDI spine and were active in at least one of the following data sources: in tax, education or injury claims in the in‐scope year; or in the births or visa dataset in last 5 years; or (b) were active in at least one of the following health datasets in the in‐scope year: general medical subsidy claims, community laboratory test claims, the national non‐admitted patient collection, the community pharmaceutical collection, the publicly‐funded hospitalisation discharge event and diagnosis datasets; and had lived in NZ for more than 6 months in 2013; and were alive on 31st December 2013; and were aged between 0 and 115 years. …”

Section: Methodsmentioning

confidence: 99%

“…The spine therefore facilitates the linkage of individual‐level information from different sources (Black, ). One of the many advantages in using the IDI is the ability to develop comprehensive IDI population cohorts and to associate them with rich socio‐demographic information and meshblock level geographic information (Zhao, Gibb, Jackson, Mehta, & Exeter, ). Meshblocks are the smallest official geographic area in NZ.…”

Section: Introductionmentioning

confidence: 99%

“…Meshblocks are the smallest official geographic area in NZ. Zhao et al () found that a population constructed from the IDI offers the most complete information, with the lowest levels of missing information at the meshblock level (1%) compared to a health service utilisation population (6%) and the 2013 census population (8%). Having a more complete geographic coverage provides an important foundation for geographical analyses.…”

Section: Introductionmentioning

confidence: 99%

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Unintentional non‐fatal hospitalised falls and motor vehicle injuries in the Auckland and Northland regions: Geographical patterns and associations with rurality

et al. 2019

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This paper examined the relationship between rurality and unintentional non-fatal hospitalisations for falls and motorised vehicle transport injuries and their geographic patterns in New Zealand. Data were constructed from Statistics New Zealand's Integrated Data Infrastructure. Logistic regression revealed that the odds of hospitalisations from falls were lower in more rural areas, but motor vehicle injuries were higher. The odds of injured people being discharged from a ward increased with rurality for both injury types. This study discovered geographical disparities in the two injury types.The findings of this study direct attention to underlying contextual factors that require targeted prevention strategies. K E Y W O R D Sfalls, geographic patterns, motorised vehicle transport injuries, Rurality Index, New Zealand

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unintentional non‐fatal hospitalised falls and motor vehicle injuries in the Auckland and Northland regions: Geographical patterns and associations with rurality

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Association Between High-Need Education-Based Funding and School Suspension Rates for Autistic Students in New Zealand

et al. 2022

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IMPORTANCE Autistic students often experience poor educational outcomes that have implications for later life, including unemployment, interactions with the criminal justice system, increased risk for substance abuse, and low socioeconomic status. Improving educational outcomes is critical for ensuring that autistic young people can reach their potential.OBJECTIVE To quantify differences in suspension rates between autistic and nonautistic students and to assess whether high-need education-based funding for autistic students is associated with reduced rates of school suspension. DESIGN, SETTING, AND PARTICIPANTSThis national cohort study used linked health and education data from New Zealand's Integrated Data Infrastructure. Data were obtained for students aged 5 to 16 years from January 1 to December 31, 2018, and analyzed July 7, 2021, to January 1, 2022. A novel case identification method was used to identify autistic students.EXPOSURES High-need education-based funding (Ongoing Resourcing Scheme [ORS]) obtained before 2019. MAIN OUTCOMES AND MEASURESRates of suspension from school. Crude and adjusted analyses of the association between suspension rates and autism among the full population with adjustment made for sociodemographic characteristics (sex, age, ethnicity, deprivation, and urban or rural profile of residence) were conducted using complete-case, 2-level random intercept logistic multivariable regressions. To assess the association between ORS funding and suspension, analysis was restricted to autistic students. RESULTSOf the 736 911 students in the study population, 9741 (1.3%) were identified as autistic (median [SD] age, 10 [3.2] years; 7710 [79.1%] boys), and 727 170 (98.7%) as nonautistic (median [SD] age, 10 [3.4] years; 369 777 [50.9%] boys). School suspension was experienced by 504 autistic students (5.2%) and 13 845 nonautistic students (1.9%). After adjustment for demographic characteristics, autistic students had significantly higher odds of suspension than their nonautistic peers (adjusted odds ratio, 2.81; 95% CI, 2.55-3.11). Of the 9741 autistic students, 2895 (29.7%) received high-need education-based (ORS) funding. Suspensions were experienced by 57 autistic students (2.0%) with high-need funding and 447 autistic students (6.5%) without high-need funding. After adjustment for demographic characteristics, co-occurring conditions, and level of disability support need, autistic students with high-need funding had significantly lower odds of suspension than autistic students without high-need funding (adjusted odds ratio, 0.29; 95% CI, 0.21-0.40). CONCLUSIONS AND RELEVANCEIn this cohort study, the findings of disparities in suspension rates between autistic and nonautistic students underscore the challenges faced in providing inclusive education for all young people, regardless of disability status. This study found that high-need funding was associated with reduced suspension rates among autistic students, suggesting that if appropriate supports are afforded to autistic students, a more ...

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Developing a New Index of Rurality for Exploring Variations in Health Outcomes in Auckland and Northland

et al. 2019

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Health inequalities between urban and rural areas are prevalent in many countries. However, rurality is a multifaceted concept so it is important to specify which aspects of rurality are most relevant to the analysis of urban/rural health disparities. Drawing from international research, we identified four domains that are most relevant to measuring rurality:(1) access to service facilities; (2) percentage of natural space; (3) population density; and (4) percentage of people employed in farming-related occupations. For the Auckland and Northland regions of New Zealand, we calculated a score for each of the four domains and combined them into a multi-dimensional index of relative rurality for health research using network analysis and various statistical approaches. We further developed an urban/ rural typology that characterises urban and rural areas using quantitative criteria including thresholds and different combinations of domains and indicators of rurality. The rurality index, the typology and existing classifications were assessed in relation to all-cause mortality. The findings of this preliminary study provide useful insights into the potential benefits of a conceptually rigorous, granular and multi-dimensional index which incorporates characteristics that can differentiate levels of rurality in New Zealand more than existing classifications. Validation studies and assessment of associations with specific health outcomes would be useful next steps in considering the utility of this rurality index for New Zealand more generally. Keywords Rurality index • Urban/rural typology • Health research • New Zealand

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Constructing whole of population cohorts for health and social research using the New Zealand Integrated Data Infrastructure

Abstract: The New Zealand IDI population is the most comprehensive and appropriate national cohort for use in health and social research.

Cited by 30 publications

References 3 publications

Unintentional non‐fatal hospitalised falls and motor vehicle injuries in the Auckland and Northland regions: Geographical patterns and associations with rurality

Unintentional non‐fatal hospitalised falls and motor vehicle injuries in the Auckland and Northland regions: Geographical patterns and associations with rurality

Association Between High-Need Education-Based Funding and School Suspension Rates for Autistic Students in New Zealand

Developing a New Index of Rurality for Exploring Variations in Health Outcomes in Auckland and Northland

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