TALANOA OPEN ACCESS Potential for using New Zealand’s Integrated Data Infrastructure in Pacific health and wellbeing research
Jesse KOKAUA,*1 Seini JENSEN,2 Wilmason JENSEN,3 Debbie SORENSEN,4 Rosalina RICHARDS,5
ABSTRACT
The Integrated Data Infrastructure (IDI) incorporates national data collected by many of New Zealand’s government agencies, some non-government organisations, Census and other national surveys. Using the IDI for research into social, cultural, health, or other outcomes has become much more common, reflecting its research potential. The primary aim of this paper is to discuss the utility of the IDI for use by Pacific researchers. We use the experiences from a research project collaboration between Pasifika Futures and a University of Otago study. A second aim for this paper is to discuss whether Pacific researchers should consider integrating their own data with data in the IDI. This is an option available to some organisations within the IDI, to supplement their own data with that of other government agencies.
For Pacific researchers, the IDI offers sufficient numbers to investigate outcomes to a level of detail that was available to only a handful of previous studies. With its ability to draw information from multiple sources, it seems a valuable addition to the information requirements of Pacific health research. But it is not without its limitations and it falls well short of being the total solution to all Pacific data needs and the need for other contextual research is likely to remain necessary. Furthermore, from a Pacific perspective, it also comes with several caveats that can reduce its usefulness for many Pacific communities. It is particularly lacking in terms of measures that reflect the value of Pacific culture.
The IDI offers a cost effective, secure and timely alternative to that process. We argue that it is important that as a community we encourage Pacific researchers to take leadership in shaping the stories that emerge from the IDI. To uphold Pacific world-views, prevent deficit-framed findings and even add value in terms of measures indicating strength of culture.
Key words: Pacific, Health, Population data, Integrated data, Research
Integrated Data and Health Research
Big data has come to refer to sophisticated data systems usually comprised of very large amounts of complex and variable data that require advanced high-speed computational capture, storage, and management of information as well as high demands for analysis.1 The focus of this paper is the Integrated Data Infrastructure (IDI), a database collated by Statistics NZ (Tatauranga Aotearoa). It is a database comprised of multiple datasets containing de-identified microdata from several government agencies, some non- government organisations, Census and other national surveys.2 Individual unit data, from different sources are merged using a coded identifier for each individual common to each datasets (see Glossary microdata and data
*1 Corresponding author; Research Fellow, Centre for Pacific Health, Va’a O Tautai, Division of Health Sciences, University of Otago, [email protected], 2. Director of Performance & Evaluation, Pasifika Futures, 3. Deputy Chief Executive, Pasifika Futures, 4. Chief Executive, Pasifika Futures, 5. Director, Centre for Pacific Health, Va’a O Tautai, Division of Health Sciences, University of Otago,
Received: 29.03.2019, Published: 23.08.2019
Citation: Kokaua J, et al. Potential for using New Zealand’s Integrated Data Infrastructure in Pacific health and wellbeing research.Pacific Health Dialog 2019;21(4):187- 195 DOI: 10.26635/phd.2019.WOS.623
Copyright: © 2019 Kokaua J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
integration/linking). The IDI houses a variety of information collected as part of administrative record-keeping, compliance with regulatory requirements, and client or patient care. For example, in health research, it allows researchers to link data from the Ministry of Health – Manatū Hauora (MoH), and the New Zealand Census as well as outcomes recorded by other agencies.3 Additional supplementary data held by an external agency can be uploaded into the IDI to
append to data from government agencies already in the IDI.4
Another benefit of using the IDI, in addition to access to linked inter-agency data in a single database (Figure 1), is that it provides an environment that fosters sharing of technical resources such as standardised computer code for consistent analyses, websites, helpful documentation and peer support from other users.
Figure 1 Sources of tables in the Integrated Data Infrastructure
Source: Stats NZ and licensed by Stats NZ for reuse under the Creative Commons Attribution 4.0 International license.
Pacific findings using the IDI
Research outputs using IDI data is growing and several academic publications have using data on Pacific peoples have resulted.5-8 Four such papers, from the Better Start National Science Challenge (BSNSC), explored trends in childhood obesity, oral health and literacy. The first paper included ethnic specific Pacific groupings and described declining obesity rates over time. 5 The second paper highlighted the association between socio-economic deprivation and higher and increasing levels of dental caries among Pacific children.6 Two other publications, looked at need and access to literacy interventions in primary schools.7,8 One publication used ethnicity as an independent variable while the other focussed solely upon Pacific children.
Aims of this paper
The primary purpose of this paper is to discuss the potential value of the IDI in Pacific health
research by Pasifika Futures Limited (PFL).
Pasifika Futures is New Zealands Pacific Whānau Ora Commissioning Agency and a Pacific organisation with substantial evaluation capability and expertise in Pacific methodologies. The second purpose is whether they should they consider uploading their data into the IDI?
It is hoped that this will provide a useful discussion for other Pacific researchers looking to undertake health related research with the IDI about opportunities and challenges in this space.
Applying to access data in the IDI for a Pacific Health Research project.
The online resources outline the process for successful negotiation of the application process.4 They provide background and helpful information about the IDI as well as links to application materials. Some data dictionaries,
documents that list the content of what is available in the dataset from different agencies, can be viewed prior to applying for access. For a new project, there are five steps to gain access to IDI data (see Figure 2). Once access has been granted a final check is required before release of results for wider dissemination
The first and most demanding step involves planning a successful application to access IDI data. It is a requirement of Statistics NZ that research teams satisfy a range of criteria beginning with the “Five safes”: Safe people; Safe projects; safe settings; safe data; and safe outputs.2 The virtual health information network (VHIN) have outlined requirements for research proposals that incorporate the five safes as the apply to recommendations for HRC reviewers assessing IDI research proposals: Research impact, Design and methodology; Practical issues of working in the IDI; Responsiveness to Maori;
Expertise and track record.9
Composition of teams applying for access to IDI microdata
For Pacific communities, “safe people” extend beyond a requirement for topical expertise and a proven track record in research. It is essential that a team is comprised of people who are aware of and prioritise the values that are carried by Pacific communities. In our example research project, how we have applied our Pacific values has been described by an application of a Tivaivai/Tivaevae model.10
Prospective projects applying to use IDI microdata
A requirement for “Safe projects” dictates that each project have a clearly defined research questions. The requirement states that projects be of some public value, have sound methodological application, and uphold accepted research ethical practices. Having identified the projects research questions to investigate, a clear research protocol will help elaborate on the principals of a safe project. This will describe the projects purpose, background, intent and proposed methods of the proposed research project.
For Pacific communities, the fundamental question and method should be of value to those communities and framed in a way that promotes positive development. The application form, Step 2 in Figure 2, is comparatively straightforward and Statistics NZ website provides helpful advice to fill in necessary fields. Application timelines and the forms can be accessed online.4
Ethics in the IDI: Step 2, in Figure 2, requires evidence of peer review and an application to the Health and Disability Ethics committee (HDEC) may also be required by Statistics NZ as part of their application process. For HDEC approval, only an assessment of scope, rather than a full application, may be necessary. The IDI uses de- identified data and fall outside the scope of HDEC criteria. However, University researchers require further ethics application from their individual academic institution or other HRC approved ethics committee.
For example, in our project, the University of Otago requires that a study protocol is prepared, and peer reviewed for internal ethics approval.
Many Pacific people have concerns about the IDI and linked government data: its potential for surveillance; privacy and confidentiality; a lack of consent; and the legitimacy of the use of data held in the IDI. Much of the data was gathered under compulsion and not specifically for the purpose of research. Thus, many research questions may fall outside the purpose for which the data was originally gathered. In terms of consent, privacy laws state that anonymised data, such as that used in the IDI, can be used without consent if the project is of public good, has sound methodology, and a trusted research team.11
Māori of Aotearoa have covered much ground in the area of data sovereignty and ownership that is still to be fully explored for Pacific communities.12 Like Māori, it is likely that Pacific communities consider data an extension of those communities and therefore they are likely to express a desire for ownership in terms of its use.
Figure 2: Steps for Application to the IDI for a Research Project
Is the data in the IDI sufficient to answer your research questions
The “safe data” requirement by Statistics NZ is to ensure that data confidentiality, and privacy has been maintained. Firstly, all personal microdata data has been de-identified, and rules about the use of the data lab prohibit sharing or viewing individual record details with any unauthorised person. Confidentiality is maintained also by the
“safe outputs” criteria.
Imbedded in Step 2 is whether the data in the IDI is sufficient to answer the stated research question. Our project example seeks to understand the link between education and health outcomes of Pacific families. For this project the data considerations include:
Ethnicity: Ethnicity is fundamental to ethnic based health research. In NZ, ethnicity is a measure of self-perceived cultural identity and different from ancestry or race based research from many other countries. Not all government agencies in NZ have consistently gathered ethnicity data. Many collections have changed their definitions of ethnicity over time.13,14 The Ministry of health published guidelines, for the health and disability sector, to standardise the collection of ethnicity in their data.15 Those guidelines were consistent with ethnic group codes as defined by Statistics NZ.13 This improved analyses involving ethnicity by making data collected by the health sector more transparent in terms of definitions and consistent.
In the IDI, ethnicity has four levels of classification: from the highest most common groups where Pacific overall are identified; to the most detailed level that codes individual Pacific Island ethnicities. The five most common ethnic groups in NZ are identified in a table of all people uniquely identified in the IDI. Thus, all people in NZ who identify with a Pacific ethnicity can be identified. Our research looks at all Pacific families so avoids the use of a “prioritised”
ethnicity. That is where ethnicity is categorised into mutually exclusive groups. In order to do this, “Prioritised Pacific” numbers exclude those who were Māori and Pacific. The latter are included as “Prioritised Māori”.
Health Data: There is a wide variety of health- related data in the IDI. The health data sets included in our research are: Hospital admissions to publicly funded Hospitalisations from the National Minimum Data Set (NMDS);
The B4 Schools Check (B4Sc); Pharmaceutical prescriptions (PHARMS); Primary Healthcare Organisation (PHO) registrations; The Programme for the Integration of Mental Health
Data (PRIMHD, which includes mental health and addictions service use and outcomes); Birth events (which includes birthweight and other birth details); Mortality data (includes all deaths and their associated cause). Data dictionaries for these datasets and more are available on the Ministry of Health website.16
Education: Education data sets include: early childhood registrations; primary school;
secondary school; training and tertiary enrolments; as well as interventions and qualification results. Parental education is also available as educational status was recorded in the 2013 census.
Household Income: Incomes can be compiled in wide bands from Census data but is available in detail using Inland Revenue (IRD) data.
Other indicators used: The other variables used in our project were from the Census, a valuable source of individual demographic, family and household specific data. Its main constraint is that it is only captured at one point in time and currently only 2013 is available in the IDI.
However, it provides a valuable source for child, parental, family and household information.
Other datasets are also available from Housing, Justice, Corrections, Courts, and employment from Inland Revenue (Figure 1). All data sources are supported by documentation, including data- dictionaries, with varying levels of quality.2,4 Project Assessment
In Step 3 of the application process (Figure 2), Statistics NZ formally assess each project in terms of its subject, legal implications and methodology. In addition to the projects content, they may approach named referees. This step may take as long as six weeks. After which, Step 4 is the final approval stage that is given by or on behalf of the Government Statistician.
Working in the IDI
In the final step of application process (Step 5), once the required documentation has been filed, approved researchers are given confidentiality training and ultimately access to the IDI within available data labs. The data labs are secure computer sites approved by Statistics NZ that meet their criteria for “safe settings”. An approved data lab is the only space where researchers can access the IDI.
A range of appropriate statistical tools is available in the data lab computing environment to address a wide array of complicated analyses.
By the time a researcher is given access to the data lab, much of their analyses will already be thought out. In particular, for Pacific research the values that will be reflected in their
consideration and interpretation of the expected results.
Sharing findings outside the IDI
The final requirement for Statistics NZ is for “safe outputs”. Before any results of analyses can be made publicly available, they must undergo a process of confidentialisation. For example, one of the most commonly applied rules, to ensure individuals cannot be identified, is that results with counts of fewer than six individuals are suppressed. Other rules are applied to reduce the likelihood that any individual may be identified.
Finally, all results must be checked and released by Statistics NZ before they can be shared outside of the data lab. For Pacific researchers, this may have undesirable consequences. Some Pacific Island populations, even at an aggregate level, are small making analyses within population subgroups difficult if there are fewer than six individuals.
Uploading data to the IDI
Why upload data to IDI microdata
In the context of the IDI and developing a research proposal, it is important to understand how the IDI will be used to answer the stated research question, particularly for Pacific projects. In addition to data that is available in the IDI (figure 1), sometimes other sources of research or data collections may provide additional evidence. Having exhausted available data sources, it may be worth considering whether the IDI may be a useful platform to add information to the data already collected.
For example, PFL gathers information about people who have received Whānau Ora services.
More than 14,000 Pacific families (over 74,000 individuals) engaged in Pacific Whānau Ora since 2014, 25% of all Pacific people in NZ. In the PFL collection, family affiliation is self-identified which opens up the possibility of performing some valuable family-level research. Unlike other organisations, PFL have developed a suite of indicators that measure family outcomes based upon their outcomes frawork.17 Those measures capture a range of self-assessed family needs within four key domains: Educational success through lifelong learning; Economic independence and resilience through financial freedom; Healthy families living longer and better; and Leading and caring for families, communities and country. Pasifika Futures gathers and uses this data to support Pacific families to shape better futures for themselves;
debt reduction; enrolling in early childhood education; stopping smoking; starting new
businesses; from gaining trades apprenticeships;
and completing tertiary qualifications.
One possible approach is to undertake a quasi- experimental study, where either the study cohort is an identified subset of the national population within the IDI or a set of identified individuals collated outside the IDI. Data integration of PFL and IDI data links the two allowing for expansive analyses of the PFL cohort using other data within the IDI. This quasi-cohort as recipients of an intervention, can be compared with those not included in the cohort as in a case- control study.
Ethical considerations for uploading data: In addition to the ethical considerations described in previous sections, all of which still apply when uploading and linking data in the IDI. Two issues stand out for PFL: consent and maintaining sovereignty of data once it has been linked and de-identified.
PFL have obtained individual consent, upon registration to a Whānau Ora service, for use of data for individuals and families for research and monitoring purposes by PFL. While there are certain conditions that allow de-identified data to be used, PFL add that enrolled families, and individual family members are able to exclude themselves, should they wish their data not be uploaded to the IDI for future research. However, if they are uploading their own data, it is important that the organisation is comfortable and trusts that confidentiality is maintained within the IDI. In terms of data security, Statistics NZ afford a very high level of security, and the main risk is in the transfer of raw data, which may contain identifiable data, prior to its upload.
After which, identifiable data is removed and deleted.
In terms of data governance, PFL would need to be satisfied with their ability to review and constrain proposed research projects that wish to use their data. They may pursue this avenue for a single project only.
Application to upload data to the IDI
The application to access microdata in the Statistics data lab or to upload data into the IDI is relatively easy to fill in and is found through the microdata link on the Statistics NZ website.4 However, the schedule for data uploads into the IDI is prolonged and it would be misguided to consider this a quick solution to data needs.
Strengths of the IDI
Using the IDI for Pacific research
This discussion considers whether research in the IDI or collaborative research projects could
benefit Pacific organisations or Pacific communities. For a Pacific organisation that meets the criteria to apply for access to the IDI they will be afforded the ability to describe their populations of interest and draw comparisons with other groups at a population level. It affords a Pacific organisation the ability to describe the needs of their target population in terms of outcomes that are relevant to their purpose, thereby providing some quantitative input into planning by a better understanding of some of the needs of all people they serve.
The IDI has the potential to enable analyses that are intergenerational, at least to second generation. Most of the data sets are longitudinal but are restricted to those years where data is available. However, few research datasets outside the IDI allow analyses of almost everyone in a given target population including children, identified siblings and parents. Even fewer afford a researcher the ability to view that data over time.
Only a handful of studies are capable of finely detailed analysis with sufficient New Zealand Pacific families. One such study is the Pacific Island Families (PIF) Study.16 The PIF study captures a large amount of information about their cohort from the past 18 years making it an extremely valuable resource for Pacific research.
However, one potential limitation is that PIF study is limited to Pacific families of children born at Middlemore hospital in the year 2000 and does not enable comparisons groups not included in that study. Other studies that have Pacific and other families rarely have enough power to allow analysis to the level of detail that the IDI delivers.
Advantages of uploading data to the IDI Deficit framed analyses can be avoided by presenting findings in a way that maintains Pacific worldviews, to uphold Pacific research values and benefit Pacific communities.
Furthermore, adding metrics that are reflective of Pacific values can also support positive framed research that support Pacific communities.
The IDI also offers the opportunity to track outcomes over time and understand factors that help improve outcomes or investigate positive trajectories of those in our community who might otherwise be worse off. There is also potential to evaluate pre and post intervention outcomes over time.
Shortcomings of the IDI
The nature of how the data was gathered, can encourage negatively (deficit) framed analyses,
such as reinforcing stereotypes of poor health behaviours or engagement with justice and social services. Unlikely to reflect positive Pacific values, the data often captured in stressful circumstances, not created for research purposes. There are issues around the need for better data quality, understanding how appropriate the current measures are for Pacific communities and, refining or adding new measures that incorporate Pacific worldviews.
For a number of reasons, not all individuals who are in the IDI can be linked to other datasets.
Linkage rates, the proportion of people successfully linked between datasets, vary across sectors and ethnic groups. Pacific often have the poorest linkage rates meaning they are less likely to be fully represented in some datasets. That said, health data has some of the best linkage rates in the IDI.
Also, while in principle the IDI covers all people in NZ, not everyone is captured in the IDI. Those who were not born in New Zealand and have had no interaction with a government agency are not captured by any dataset except possibly migration. This may be relevant to Pacific services that are provided to Pacific families, some of whom may be in New Zealand temporarily or awaiting migration processes to be completed.
Sometimes the actual data is not in the IDI or is captured in a way that makes it unfit to reflect the outcome required by the research question.
Finally, there may be too few observed incidences of some events to satisfy confidentiality checks. Confidentiality protections enforced by Statistics NZ also limits the granular level to which analyses can be performed. This means that final results for some infrequent events, like a rare condition or a small population group, for which a researcher might like to know some detail about, are manually supressed due to small numbers in the final results.
Alternatives to the IDI for community organisations
While the IDI provides an opportunity to augment organisational data with other government organisations, if your organisation wishes to keep data in-house, whether for perceived data security, sovereignty or other reasons, and has the data infrastructure and budget to consider such an undertaking then there are several other options. Most, except the largest organisations allow their data to be managed by a third party which has security
issues in itself, depending upon the organistion or the country of the actual facility that stores their data.
Those issues aside, there are alternative approaches to adding external data to complement an organisations own administration data. These include: gaining access to individuals’ data from individual government agencies to emulate the IDI in- house; client surveys; or using available web based statistical resources. Each has its own advantages and disadvantages. For example, both allow in-house research to be performed.
This precludes the ability of an organisation to use routinely collected client feedback commentary and mine the wealth of data they may already gather for administrative purposes.
The shortcoming is that the quality of those results requies a serious investment into the maintenance and cleaning of the data that is collected. However, even smaller organisations with limited capacity who wish to future proof or evaluate the practices of their organisation can look at any of the options suggested.
CONCLUSIONS
The IDI can provide a valuable platform for Pacific communities, and the organisations that serve them, to identify needs and plan services for their populations of interest. It offers potential, when framed in an appropriate manner, to give valuable insights into health outcomes and contribute to the growing pool of Pacific health research. The IDI can offer a cost- effective way of obtaining detailed analysis of those communities.
To return to the original question regarding Pasifika Futures use (or not) of the IDI? The process of gaining access for analyses, particularly for the first time, has a steep learning curve and can be overly burdened by bureaucracy. This means it would be best used for long term research and evaluation undertakings and not recommended for organisational questions where a quick response is required. In spite of this, there will be many questions for which the IDI may offer some definitive and nuanced answers. So with using appropriate Pacific research frameworks there is no reason why PFL should not consider using the IDI. The benefits to an organisation like PFL could be significant in terms of supporting Pacific families to achieve enhanced planning, research and evaluation.
Should PFL consider uploading their own data? It is important that the organisation is comfortable
and trusts the protections of the data afforded within the IDI. In terms of data security and confidentiality, Statistics NZ maintain a very high standard that minimises risk to individuals or organisations data. In terms of data governance, PFL must be satisfied with their ability to review and constrain proposed research projects that wish to use their data. Uploading data to the IDI enables Pacific organisations to undertake their own research into the needs of their target populations alongside valuable metrics from their own collection.
The quantitative data in the IDI means that, for most community organisations, the need for other contextual research is likely to remain necessary to establish the full inherent value of each organisation’s purpose. As it exists, it is particularly lacking in terms of measures that reflect the value of Pacific culture. However, it is important that as a community we encourage Pacific researchers to take a leadership role in shaping the stories that emerge from the IDI. To uphold Pacific world-views, prevent deficit- framed stereotypes and even add value in terms of measures indicating strength of culture.
Otherwise, others will tell them, and in spite of their best intentions, sometimes without the sensitivity required by our communities.
Statistics New Zealand (NZ) Disclaimer: The opinions, findings, recommendations, and conclusions expressed in this paper are those of the author(s), not Statistics NZ, or The University of Otago. Access to the anonymised data used in this study was provided by Statistics NZ under the security and confidentiality provisions of the Statistics Act 1975. Only people authorised by the Statistics Act 1975 are allowed to see data about a particular person, household, business, or organisation, and the results in this paper have been confidentialised to protect these groups from identification and to keep their data safe.
Careful consideration has been given to the privacy, security, and confidentiality issues associated with using administrative and survey data in the IDI. Further detail can be found in the Privacy impact assessment for the Integrated Data Infrastructure available from www.stats.govt.nz
Glossary
This glossary includes Stats NZ’s terms that are licensed by Stats NZ for reuse under the Creative Commons Attribution 4.0 International licence.
Anonymized: Term most commonly used to refer to data from which direct identifiers have been removed (de-identified data).
Confidential information: Data and information about a person, household, iwi, or organisation that not disclosed to people who are not authorised to have access to it.
Confidentialisation: The statistical methods used to protect against confidential information being disclosed to people who are not authorised to have access to it, in a way that could identify an individual, household or organisation.
Confidentiality: The protection of information provided by people and organisations to ensure that it is not disclosed or made available to people or organisations who are not authorised to access it.
Data integration/linking: The merging of data about the same entity, person, household, organisation, or other unit, from two or more unit record datasets, originally collected for different purposes.
De-identification: The process of removing information from microdata to reduce risk of spontaneous recognition. It typically includes removing names, exact dates of birth or death, and exact addresses.
Information security: The measures put in place to protect against data and information being disclosed to unauthorised people or organisations, and to ensure appropriate availability and integrity of information.
Integrated Data Infrastructure (IDI): Database containing de-identified people-centred microdata from a range of government agencies, Stats NZ surveys and non-government organisations.
Integrity: Assurance about the accuracy and consistency of data and information and that it is authentic and complete.
Microdata: Data about individual people, organisations, households, or other units in a population.
Personal information: Data and information about a person that we should not disclose to people who are not authorised to have access to it.
Privacy: The individual’s rights relating to control of the provision, use, and disclosure of their personal information.
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