Suchergebnisse

IntroductionA main goal of policy makers is to understand various transition pathways between services to optimize program design and to achieve successful outcomes, for example, securing stable income or having access to disability supports.
Objectives and ApproachUsing linked administrative data (between 2006 and 2011), this study examines two types of program transition pathways: 1) Toward understanding who may be at risk for relying on income support, we profile Albertans transitioning from an income support training program to a program providing income support but no training, and 2) Toward understanding service delivery from child/youth to adult disability programs, we profile students receiving child/youth disability services, but who did not transition into an adult disabilities program. The socio-demographic characteristics and government service use of these students, including health and education-related services, were examined.
ResultsFirst, among Albertans who transitioned from an income support training program to one in which only income support was provided: 57% were female; 72% were living in low socio-economic neighbourhoods; 14% were high cost health service users; 20% received mental health services; 30% received an injury/harm diagnosis; and 12% were involved in corrections. Second, among children/youth who received child disability services but did not transition to an adult disability program, lower than expected proportions received Assured Income for the Severely Handicapped (45%) and Income Support (14%), while higher than expected proportions had criminal offences (17%).
Conclusion/ImplicationsProfiles produced from linked data helps Alberta government to understand clients' program transitions which allows for: 1) the identification of Albertans requiring supports for securing employment that reduce or eliminate the need for income support, and 2) improvements in service delivery to individuals transitioning from child/youth to adult disability programs.

IntroductionThis study uses linked administrative data to identify Albertan children and youth (aged 0 to 25 years) with Autism Spectrum Disorder (ASD), Cerebral Palsy (CP) or Fetal Alcohol Spectrum Disorder (FASD), and to examine their government service use patterns from 2005/06 to 2010/11.
Objectives and ApproachThe analysis looks at whether children with ASD, CP and FASD have similar government service use patterns and how their service use patterns changed over time. Six Government of Alberta ministries' data were linked (Health, Justice, Education, Advanced Education, Children's Services and Community and Social Services). Individuals were defined as having ASD, CP or FASD if, at any point during the study period, they had a diagnostic code for one of three conditions (ASD, CP or FASD) from a health service (i.e., physician visits, or emergency room visits, or hospitalizations), or accessed disability support programs for ASD, CP or FASD.
ResultsThe results showed different service use patterns for the three conditions: (1) a higher proportion of children with ASD were students with special needs compared to children with FASD and CP; (2) a lower proportion of children with FASD received FSCD services compared to children with ASD and CP; (3) a higher proportion of children with FASD were involved in the Child Intervention system and were Income Support recipients compared to children with ASD and CP. Moreover, the study showed that there was a decrease in accessing services and programming from Persons with Development Disabilities (PDD) for young Albertans with ASD, CP and FASD from 2005/06 to 2010/11. In contrast, the use of another adult disability program that provides only financial assistance, Assured Income for Severely Handicapped, increased over time.
Conclusion/ImplicationsThe use of population-based linked administrative data allows a comprehensive and comparative study for government service use patterns. The findings have implications for cross-ministry policy and program development, resource allocation, and service coordination and delivery to young Albertans with ASD, CP and FASD.

IntroductionLinking administrative data provides valuable information about individuals using government services and can be very useful for policy-makers in improving and developing services and policies. The Child and Youth Data Laboratory (CYDL) links and analyses administrative data from Alberta Government ministries to provide evidence for policy and program development.
Objectives and ApproachData from 20 programs of six Government of Alberta ministries (Advanced Education, Education, Health, Children's Services, Community and Social Services, and Justice and Solicitor General) were linked anonymously. The data spans six years from 2005/06 to 2010/11 and consists of almost 50 million records corresponding to over 2 million unique Albertans aged 0 to 25 years. A data visualization tool called the Program Overlap Matrix summarises the overlap rates among the programs. It is comprised of a matrix of squares, where each cell represents the overlap between two programs.
ResultsThe Program Overlap Matrix is publically available at https://visualization.policywise.com/P2matrix/. It consists of overlap rates between programs in any study year (2005/06 to 2010/11), individual years, the first year vs. future years, and the last year vs. previous years which can be used to answer many policy-related questions such as: other service use (e.g., what other services do ESL students use?), over-represented programs (e.g., in what programs are Child Care Subsidy clients over-represented?), resilience (e.g., what is the proportion of Child Intervention clients in post-secondary institutions?), transitions (e.g., what types of services do students with special needs receive as they transition to adulthood?), and time trend (e.g., what types of services did Income Support clients receive in the past?)
Conclusion/ImplicationsThe program overlap matrix is a powerful tool to discover relationships between programs. It is a useful instrument to inform public and policy-makers about the overlap rates between government programs. It can be used to answer a variety of policy-related questions.

IntroductionLinked datasets are important resources for research, but linkage errors can lead to incorrect results. For data security and privacy concerns, when linkage of personal identifiers is performed anonymously, it is difficult to assess the quality of the linked dataset. We describe the method used to perform linkage quality.
Objectives and ApproachWe explored how to check the quality of linkages while preserving the privacy of individuals. We also adopted an approach that minimized time and burden on data providers involved in physical verification using randomly-generated appropriate sample sizes.
To validate these linkages, data providers were given random samples of 50 unique records from both linked and unlinked individuals across two other Government programs. Data providers were asked to look at the records associated with those individuals in their original datasets. Three types of linkage results were validated: cross-program linkages, cross-program non-linkages, and within-program linkages. Proportions of false-matches and missed-matches were estimated.
ResultsTwenty data providers checked their samples with two other programs which gave us a sample of 2000 individuals. The linkage process, based on anonymized personal identifiers, resulted in high true positive and high true negative rates. Agreement between human judges and the linkage software was strong. Results of this exercise and other linkage validation examinations provided confidence in the accuracy of the linkage process. With false matches occurring approximately only 3% of the time and virtually no missed-matches occurring, no adjustments were deemed necessary. Although linkage rates were reassuring, the sample sizes used for comparison were small, so it is expected that there would be significant variation associated with this 3% estimate; caution is advised in its use.
Conclusion/ImplicationsProportions of false-matches and missed-matches determine linkage quality which is the base for research when linkages are performed anonymously. A low proportion of false-matches and an absence of missed-matches was an indication of robust linkages.

IntroductionThe cultural revolution of data sharing is becoming a global movement. It allows for scientific replication and verification of research results, avoiding research duplication, and enabling greater transparency and knowledge mobilization with a relatively low cost. However, privacy protection and data security are critical concerns for human-subject related data sharing.
Objectives and ApproachIn order to facilitate data sharing and engage various stakeholders to better inform policy and practice while protecting privacy, SAGE (Secondary Analysis to Generate Evidence) was established by PolicyWise for Children and Families. It is a collaborative data repository platform that connects stakeholders through secondary use of data. SAGE was built to link, manage, and share research data, community service data, and administrative data related to health and social well-being. Governance and technical processes are in place to ensure that data depositors are involved in decision-making, and data accessed by collaborators are secured and re-identification risks assessed.
ResultsSAGE has been in operation for over a year. Through engagement with the research and non-profit communities, SAGE now offers ten data assets. Discovery is facilitated through well-documented metadata through NADA and Dataverse. Six new collaborative projects have been initiated through SAGE. SAGE is working actively with local non-profits to liberate data to generate evidence and collaborate with each other on common goals. SAGE has helped these organizations understand the legal and legislative barriers to data sharing, and build the technical capacity to further this goal. Discussions are underway with Alberta public entities on how SAGE can support the linkage and governance processes in the use of administrative data.
Conclusion/ImplicationsSAGE is putting the governance processes and security practices in place to fill a need for a facilitated data sharing model for sensitive data. SAGE is supporting the cultural shift towards data sharing and reuse by fostering trust and collaboration among researchers, non-profit and government ministries.

IntroductionCompared to the public sector, not-for-profits are less focused on cross-agency data linkage. Technical capacity is often secondary to addressing caseloads and protecting clients. Clients' privacy is paramount and can be perceived as a barrier to collaboration between agencies. However, collaboration could streamline referrals and better assist vulnerable populations.
Objectives and ApproachSix not-for-profit agencies in Calgary, Canada participated in a data sharing project to measure various aspects of poverty and link data to determine cross-agency service usage. With this goal in mind, agencies examined their consent and data sharing practices to assess barriers to data sharing. There was a thorough exploration of client consent and how a client's context can enable or limit data sharing. Cross-agency program usage was assessed among participating agencies using a privacy-preserving record linkage (PPRL) methodology.
ResultsAmongst the six participating agencies, four were deemed to have adequate technical capacity to share data. A contributing factor to the willingness of agencies to share data was the development of LinkWise: a PPRL software created and developed by PolicyWise. Linkage rates amongst three agencies were compared. Rates ranged from 47.8% to 0.23%. A higher linkage rate between two agencies indicated a small community based agency which provided many referrals to a larger agency, such as a food bank. Lower linkage rates on client intake may indicate an agency with many clients. It may also indicate differing socio-economic brackets for their clients' catchment area.
Conclusion/ImplicationsWhile capacity, caseloads, and privacy protection restrict data sharing, not-for-profit agencies would benefit from a data sharing strategy. Linking data represent opportunities for collaboration within a significantly resource constrained sector. Moreover, it could more effectively address issues of vulnerable populations, streamline referrals for services, and facilitate quality improvement.