P50 Development of a national cohort from linked administrative data for understanding child health and informing early years’ policy

Background. The recently enacted State Children's Health Insurance Program (SCHIP), designed to provide affordable health insurance for uninsured children, was modeled in part on New York State's Child Health Plus (CHPlus), which was implemented in 1991. All SCHIP programs involve voluntary enrollment of eligible children. Little is known about characteristics of children who enroll in these programs. Objectives. To provide a profile of children enrolled in CHPlus between 1993 and 1994 in the 6-county upstate New York study area, and to estimate the participation rate in CHPlus. Methods. A parent interview was conducted to obtain information about children, 0 to 6.9 years old, who enrolled in CHPlus in the study area. Two school-based surveys and the Current Population Survey were used to estimate health insurance coverage. Enrollment data from New York State's Department of Health, together with estimates of the uninsured, were used to estimate participation rates in CHPlus. Results. Most children enrolled in CHPlus in the study area were white. Although 17% of all children in the study area who were <13 years old and living in families with incomes below 160% of the federal poverty level were black, only 9% of CHPlus-enrolled children were black. Twenty-one percent of enrolled children were uninsured during the entire year before enrollment and 61% of children had a gap in coverage lasting >1 month. Children were generally healthy; only 4% had fair or poor health. Eighty-eight percent of parents of enrolled children had completed high school or a higher level of education. Parents reported that loss of a job was the main reason for loss of prior health insurance for their child. Most families learned about CHPlus from a friend (30%) or from their doctor (26%). The uninsured rate among children in the study area was approximately 4.1%. By 1993, the participation rate in CHPlus was about 36%. Conclusion. Blacks were underrepresented in CHPlus. Because the underlying uninsured rate was relatively low and parental education and family income were relatively high, the effects of CHPlus observed in this evaluation may be conservative in comparison to the potential effects of CHPlus for other populations of children. Participation rates during the early years of the program were modest.

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Information and choice of A-level subjects: A cluster randomised controlled trial with linked administrative data

British Educational Research Journal ◽

10.1002/berj.3289 ◽

2017 ◽

Vol 43 (4) ◽

pp. 647-670 ◽

Cited By ~ 1

Author(s):

Peter Davies ◽

Neil M. Davies ◽

Tian Qiu

Keyword(s):

Randomised Controlled Trial ◽

Administrative Data ◽

Controlled Trial ◽

Cluster Randomised Controlled Trial ◽

Cluster Randomised ◽

Linked Administrative Data ◽

Randomised Controlled

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Analyzing establishment nonresponse using an interpretable regression tree model with linked administrative data

The Annals of Applied Statistics ◽

10.1214/11-aoas521 ◽

2012 ◽

Vol 6 (2) ◽

pp. 772-794 ◽

Cited By ~ 10

Author(s):

Polly Phipps ◽

Daniell Toth

Keyword(s):

Administrative Data ◽

Regression Tree ◽

Tree Model ◽

Linked Administrative Data ◽

Regression Tree Model

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Building Research Capacity and Organizational Empathy Among Students: Making Connections Beyond the Data

International Journal for Population Data Science ◽

10.23889/ijpds.v5i5.1557 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

Anita Durksen ◽

Shannon Struck ◽

Alexandra Guemili ◽

Soomin Han ◽

Emily Brownell ◽

...

Keyword(s):

Social Policy ◽

Administrative Data ◽

Linked Data ◽

Common Ground ◽

Social Issues ◽

Policy Research ◽

Complex Data ◽

Research Experience ◽

Data Repositories ◽

Linked Administrative Data

IntroductionThe leveraging of multi-sector, whole-population, linked administrative data is advantageous for conducting research on complex real-world problems. However, such large and complex data repositories can sometimes appear impersonal and overwhelming. Establishing organizational empathy (OE) in thecontext of a multi-sector partnership between academic, government and community representatives can help us understand the data better for social policy research. Evidence stemming from this research can then inform policy decisions, ultimately increasing the potency of linked data analysis and creating more meaningful student experiences. Our objective is to examine the role of OE in the student research experience. Objectives and ApproachSPECTRUM (Social Policy Evaluation Collaborative Team Research at Universities in Manitoba) is a multi-disciplinary partnership working to provide evidence-based solutions to ‘wicked’ social issues by using linked data from multiple sectors. SPECTRUM provides fellowships to students to become partners in the collaboration. Students have participated in quarterly workshops, building relationships with community leaders, government decision-makers and academic researchers. Students are from various faculties, bringing their unique frameworks and research interests to the collective. Through OE, students observeand participate in SPECTRUM, relating its goals and outcomes to society and their own research. ResultsStudent inclusion in SPECTRUM enhances the partnership by providing a greater range of perspectives and facilitates the development of OE among SPECTRUM members. Students are using linked administrative data, while actively engaging in dialogue with stakeholders, thereby enriching their knowledge and understanding of research. Conclusion / ImplicationsData linkage involves more than just use of the repository; it requires establishing common ground since the data have different meaning to each partner. OE developed through SPECTRUM provides invaluable insight into and context for the data. Knowledge transfer among members of the partnership will enrich SPECTRUM’s research outcomes while building capacity among Students.

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Creating Additional Census Income Topics from Linked Administrative Data

International Journal for Population Data Science ◽

10.23889/ijpds.v5i5.1482 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

James Mowle

Keyword(s):

Income Distribution ◽

Social Services ◽

Administrative Data ◽

Census Data ◽

Community Services ◽

Wide Range ◽

Australian Community ◽

Linked Administrative Data ◽

Australian Bureau Of Statistics ◽

Integration Project

IntroductionThe Census is the largest statistical collection undertaken by the Australian Bureau of Statistics (ABS), with its data critical to informing the planning and delivery of Government and community services. While the Census measure of income supports a wide range of analysis, demand exists for additional income topics to complement and extend the range of socio economic analysis that can be undertaken. The ABS has recently developed three experimental income topics for the 2016 Census using linked administrative data: main source of income; main source of government payments; and previous financial year income. Objectives and ApproachThis research utilised administrative data integrated by the ABS for the Multi-Agency Data Integration Project (MADIP). Taxation data from the Australian Taxation Office (ATO) and social security data from the Department of Social Services (DSS) were used in conjunction with the 2016 Census data to derive the additional topics. ResultsOverall, the three measures compare relatively closely to similar measures from the ABS Survey of Income and Housing (SIH). The ‘Main source of income’ and ‘Main source of government payments’ measures exhibit similar distributions to those from the SIH. The ‘Previous financial year income’ measures compare more closely with Census and SIH at the higher end of the income distribution, with some differences apparent at the lower end of the income distribution. Conclusion / ImplicationsThis work demonstrates the potential to supplement and enhance existing Census topics with linked administrative data. Further research, development and consultation with data users and the Australian community is needed.

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