Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1957
M. Chartier, G. Munro, D. Jiang, Scott C McCulloch, Wendy Au, M. Brownell, Rob Santos, F. Turner, Leanne Boyd, Nora Murdock, J. Bolton, J. Sareen
ObjectivesPAX, a mental health promotion approach, has been shown to decrease negative mental health outcomes and improve academic achievement. These effects have yet to be shown among Indigenous children. We evaluated PAX for improving First Nations children’s outcomes following a research process wherein community members and researchers work more collaboratively. ApproachBuilding on a long-term relationship with Swampy Cree Tribal Council, community members, First Nations leaders and researchers worked together through all phases of the project. This cluster randomized controlled trial used population-based health, social services, and education administrative data that allowed de-identified individual-level linkages across all databases through a scrambled health number. Our cohort of 725 children from 20 First Nations schools were randomized to PAX (n=469, 11 schools) or wait-list control (n=256, 9 schools). We used propensity score weighting and multi-level modeling to estimate the differences over time (2011 up to 2020) between children exposed to PAX and those who were not. ResultsDifferences in baseline characteristics were found between the two groups of children, despite the cluster randomization. After applying propensity score weights, children in the PAX group had significantly greater decreases in conduct problems (β:-1.08, standard error(se):0.2505, p<.0001), hyperactivity (β:-1.13, se:0.3617, p=.0018 ), and peer problems (β:-1.10, se:0.3043, p=.0003) and a greater increase in prosocial scores (β:2.68, se:0.4139, p<.0001) than control group children. The percentage of children in the PAX group who met academic expectations was higher than those in the control group, however, only grade 3 numeracy (odds ratio (OR):4.30, confidence interval (CI):1.34 – 13.77) and grade 8 reading and writing (OR:2.78, CI:1.01 – 7.67) met statistical significance. We found no evidence that PAX was associated with less emotional problems, diagnosed mental disorders or better student engagement. ConclusionThese findings suggest that PAX was effective in improving First Nations children’s mental health and academic outcomes in First Nations communities. Examining what works in Indigenous communities is crucial because approaches that are effective in some populations may not necessarily be culturally appropriate for remote Indigenous communities.
{"title":"Is PAX-Good Behaviour Game (PAX) Associated with Better Mental Health and Educational Outcomes for First Nations Children?","authors":"M. Chartier, G. Munro, D. Jiang, Scott C McCulloch, Wendy Au, M. Brownell, Rob Santos, F. Turner, Leanne Boyd, Nora Murdock, J. Bolton, J. Sareen","doi":"10.23889/ijpds.v7i3.1957","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1957","url":null,"abstract":"ObjectivesPAX, a mental health promotion approach, has been shown to decrease negative mental health outcomes and improve academic achievement. These effects have yet to be shown among Indigenous children. We evaluated PAX for improving First Nations children’s outcomes following a research process wherein community members and researchers work more collaboratively. \u0000ApproachBuilding on a long-term relationship with Swampy Cree Tribal Council, community members, First Nations leaders and researchers worked together through all phases of the project. This cluster randomized controlled trial used population-based health, social services, and education administrative data that allowed de-identified individual-level linkages across all databases through a scrambled health number. Our cohort of 725 children from 20 First Nations schools were randomized to PAX (n=469, 11 schools) or wait-list control (n=256, 9 schools). We used propensity score weighting and multi-level modeling to estimate the differences over time (2011 up to 2020) between children exposed to PAX and those who were not. \u0000ResultsDifferences in baseline characteristics were found between the two groups of children, despite the cluster randomization. After applying propensity score weights, children in the PAX group had significantly greater decreases in conduct problems (β:-1.08, standard error(se):0.2505, p<.0001), hyperactivity (β:-1.13, se:0.3617, p=.0018 ), and peer problems (β:-1.10, se:0.3043, p=.0003) and a greater increase in prosocial scores (β:2.68, se:0.4139, p<.0001) than control group children. The percentage of children in the PAX group who met academic expectations was higher than those in the control group, however, only grade 3 numeracy (odds ratio (OR):4.30, confidence interval (CI):1.34 – 13.77) and grade 8 reading and writing (OR:2.78, CI:1.01 – 7.67) met statistical significance. We found no evidence that PAX was associated with less emotional problems, diagnosed mental disorders or better student engagement. \u0000ConclusionThese findings suggest that PAX was effective in improving First Nations children’s mental health and academic outcomes in First Nations communities. Examining what works in Indigenous communities is crucial because approaches that are effective in some populations may not necessarily be culturally appropriate for remote Indigenous communities.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46963793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1861
J. Lee, M. Brownell, T. Afifi, L. Turnbull, Marcelo L. Urquia, Nathan C. Nickel
ObjectiveThe objective was to examine the relationship between maternal intimate partner violence (IPV) victimization and children’s developmental health using linked population-wide administrative datasets. We examined developmental vulnerability (DV) at kindergarten of children exposed to maternal IPV victimization aged 0 to 5 using provincial prosecution records compared to unexposed counterparts. ApproachThis retrospective cohort study linked administrative datasets (legal, health, education, social services) from the Population Research Data Repository at the Manitoba Centre for Health Policy. Exposed mother-child pairs with 1+ prosecution records of maternal IPV victimization during early childhood (child aged 0 to 5) between 2003-2018 in Manitoba (n = 5,728) were matched to unexposed pairs (1:3) based on sex/birthdate of child and neighbourhood income. DV at kindergarten was measured across 5 domains (physical, social, emotional, language/cognitive [LC], communication/general knowledge) using the Early Developmental Instrument (EDI). Children without eligible EDI scores were excluded. Multiple logistic regression models were conducted. ResultsThe cohort included 5321 children (exposed n=1365, unexposed n=3956). 32.98% of the cohort was developmentally vulnerable in one or more domains (1/+) and 19.60% was developmentally vulnerable in two or more domains (2/+). Unadjusted relationships between maternal IPV victimization from age 0 to 5 and developmental vulnerability at kindergarten were statistically significant across all 5 domains (e.g., LC OR=2.76 [2.36, 3.23]) and in 1/+ (OR=2.72 [2.39, 3.09]) as well as 2/+ (OR=2.89 [2.51, 3.34]) domains. After adjusting for covariates, children who were exposed to maternal IPV victimization from ages 0 to 5 had increased odds of being developmentally vulnerable in social competence (aOR=1.33 [1.07, 1.66]) and emotional maturity (aOR=1.29 [1.03, 1.62]), also in 2/+ domains (aOR=1.42 [1.15, 1.73]) at kindergarten, compared to unexposed counterparts. ConclusionThe study provided Canadian population-wide evidence of the association between maternal IPV victimization and early childhood development, specifically later socio-emotional vulnerability. Interventions and support systems for this population of families should be developed and implemented, with an emphasis on mitigating long-term socio-emotional developmental risks in children exposed to IPV.
{"title":"Early Childhood Exposure to Intimate Partner Violence and Developmental Vulnerability at Kindergarten: Linking Canadian Population-Level Administrative Data.","authors":"J. Lee, M. Brownell, T. Afifi, L. Turnbull, Marcelo L. Urquia, Nathan C. Nickel","doi":"10.23889/ijpds.v7i3.1861","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1861","url":null,"abstract":"ObjectiveThe objective was to examine the relationship between maternal intimate partner violence (IPV) victimization and children’s developmental health using linked population-wide administrative datasets. We examined developmental vulnerability (DV) at kindergarten of children exposed to maternal IPV victimization aged 0 to 5 using provincial prosecution records compared to unexposed counterparts. \u0000ApproachThis retrospective cohort study linked administrative datasets (legal, health, education, social services) from the Population Research Data Repository at the Manitoba Centre for Health Policy. Exposed mother-child pairs with 1+ prosecution records of maternal IPV victimization during early childhood (child aged 0 to 5) between 2003-2018 in Manitoba (n = 5,728) were matched to unexposed pairs (1:3) based on sex/birthdate of child and neighbourhood income. DV at kindergarten was measured across 5 domains (physical, social, emotional, language/cognitive [LC], communication/general knowledge) using the Early Developmental Instrument (EDI). Children without eligible EDI scores were excluded. Multiple logistic regression models were conducted. \u0000ResultsThe cohort included 5321 children (exposed n=1365, unexposed n=3956). 32.98% of the cohort was developmentally vulnerable in one or more domains (1/+) and 19.60% was developmentally vulnerable in two or more domains (2/+). Unadjusted relationships between maternal IPV victimization from age 0 to 5 and developmental vulnerability at kindergarten were statistically significant across all 5 domains (e.g., LC OR=2.76 [2.36, 3.23]) and in 1/+ (OR=2.72 [2.39, 3.09]) as well as 2/+ (OR=2.89 [2.51, 3.34]) domains. After adjusting for covariates, children who were exposed to maternal IPV victimization from ages 0 to 5 had increased odds of being developmentally vulnerable in social competence (aOR=1.33 [1.07, 1.66]) and emotional maturity (aOR=1.29 [1.03, 1.62]), also in 2/+ domains (aOR=1.42 [1.15, 1.73]) at kindergarten, compared to unexposed counterparts. \u0000ConclusionThe study provided Canadian population-wide evidence of the association between maternal IPV victimization and early childhood development, specifically later socio-emotional vulnerability. Interventions and support systems for this population of families should be developed and implemented, with an emphasis on mitigating long-term socio-emotional developmental risks in children exposed to IPV.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43078214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1805
D. Lopez, D. Preen, W. Raymond, C. Inderjeeth, K. Murray, H. Nossent, G. Dwivedi, H. Keen
ObjectivesCardiovascular disease is the largest contributor of increased mortality in patients with gout. Acute inflammation as seen with gout attacks may have a mechanistic role in developing Major Adverse Cardiovascular Events (MACE). We examined the temporal relationship between admission to hospital with acute gout and MACE. ApproachLinked inpatient and mortality data from the Western Australian Rheumatic Disease Epidemiology Registry were used. We identified patients with an incident acute gout (index) hospitalisation and admission or death records due to MACE (composite of acute coronary syndrome, stroke, heart failure, cardiovascular death). The risk of MACE during the index post-discharge period (1-30 days after index admission) and control period (365 days prior to index admission and 365 days post-discharge) was determined using a self-controlled case-series (SCCS) design. Conditional fixed-effects Poisson regression was used to obtain incidence rate ratios (IRR). Sensitivity analyses were performed excluding deaths and 180-day events. ResultsWe identified 962 patients (mean age=76.2 years [SD=12.2]; 66.8% male) with incident acute gout admission and documented MACE during the control and/or index post-discharge periods. 917 (95.3%) patients experienced MACE during the control period and 114 (11.9%) during the index post-discharge period. The rate of MACE during the control and post-discharge periods were 0.84 and 1.44 events per person-year, respectively, with an IRR=1.67 (95% CI: 1.38-2.02) for the post-discharge period compared with the control period from regression analysis. Sensitivity analyses excluding deaths and 180-day events were IRR=1.68 (95% CI=1.29-2.20) and IRR=1.66 (95% CI=1.34-2.07) respectively. ConclusionOur self-controlled case-series study using linked administrative data found an increased risk of MACE during the 30 days after discharge for index gout hospitalisation. This suggests a temporal association between acute inflammation and MACE.
{"title":"Risk of a major adverse cardiovascular event (MACE) following first-ever hospitalisation for acute gout: a Western Australian population-level linked data study.","authors":"D. Lopez, D. Preen, W. Raymond, C. Inderjeeth, K. Murray, H. Nossent, G. Dwivedi, H. Keen","doi":"10.23889/ijpds.v7i3.1805","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1805","url":null,"abstract":"ObjectivesCardiovascular disease is the largest contributor of increased mortality in patients with gout. Acute inflammation as seen with gout attacks may have a mechanistic role in developing Major Adverse Cardiovascular Events (MACE). We examined the temporal relationship between admission to hospital with acute gout and MACE. \u0000ApproachLinked inpatient and mortality data from the Western Australian Rheumatic Disease Epidemiology Registry were used. We identified patients with an incident acute gout (index) hospitalisation and admission or death records due to MACE (composite of acute coronary syndrome, stroke, heart failure, cardiovascular death). The risk of MACE during the index post-discharge period (1-30 days after index admission) and control period (365 days prior to index admission and 365 days post-discharge) was determined using a self-controlled case-series (SCCS) design. Conditional fixed-effects Poisson regression was used to obtain incidence rate ratios (IRR). Sensitivity analyses were performed excluding deaths and 180-day events. \u0000ResultsWe identified 962 patients (mean age=76.2 years [SD=12.2]; 66.8% male) with incident acute gout admission and documented MACE during the control and/or index post-discharge periods. 917 (95.3%) patients experienced MACE during the control period and 114 (11.9%) during the index post-discharge period. The rate of MACE during the control and post-discharge periods were 0.84 and 1.44 events per person-year, respectively, with an IRR=1.67 (95% CI: 1.38-2.02) for the post-discharge period compared with the control period from regression analysis. Sensitivity analyses excluding deaths and 180-day events were IRR=1.68 (95% CI=1.29-2.20) and IRR=1.66 (95% CI=1.34-2.07) respectively. \u0000ConclusionOur self-controlled case-series study using linked administrative data found an increased risk of MACE during the 30 days after discharge for index gout hospitalisation. This suggests a temporal association between acute inflammation and MACE.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43192361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1784
Nishadi Kirelle, P. Christen, Garrett Eilidh
ObjectivesDemographers are interested in the degree to which marriage is driven by prenuptial pregnancy within particular communities. To help answer this question, we present a novel method which links marriage certificates to birth certificates, where the birth-mother is the marriage-bride, considering only births which occur in the first seven months after a marriage. ApproachTo identify prenuptial births we employed an unsupervised graph-based record linkage method to link birth and marriage certificates. We first extracted related groups of individuals: babies and their parents from birth certificates, and brides, grooms, and their parents from marriage certificates. To link births with marriages, we employed techniques to address challenges such as changing attribute values over time (such as names and addresses), the ambiguity of attribute values giving priority to rare names over common names, and different relationships encountered at different points in time (by applying temporal constraints). Based on the obtained links we then extracted prenuptial births. ResultsUsing two Scottish data sets containing a total of 38,451 births and 8,667 marriage certificates from the period 1861 to 1901 and employing different linkage thresholds we identified between 853 and 945 first birth-marriage links in the smaller (rural) data set, and between 2,165 and 2,232 links in the larger (urban) data set. In the rural data set, between 16.9% and 17.7% of these links were with birth less than 8 months after marriage (i.e. prenuptial births), where the corresponding ground truth contained 17.6% prenuptial births. For the urban data set, we identified between 51.3% and 51.4% prenuptial births. Our results show clear differences between rural and urban prenuptial pregnancies in 19th Century Scotland. ConclusionWe have presented an unsupervised graph-based record linkage method that compares attribute values of individuals and their relationships to link records based on the ambiguity of attribute values, attribute value changes, and temporal constraints. This linking process helps us to identify prenuptial births to help us understand the degree to which marriages may have been driven by pregnancy.
{"title":"Identifying prenuptial births from family pedigrees using record linkage.","authors":"Nishadi Kirelle, P. Christen, Garrett Eilidh","doi":"10.23889/ijpds.v7i3.1784","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1784","url":null,"abstract":"ObjectivesDemographers are interested in the degree to which marriage is driven by prenuptial pregnancy within particular communities. To help answer this question, we present a novel method which links marriage certificates to birth certificates, where the birth-mother is the marriage-bride, considering only births which occur in the first seven months after a marriage. \u0000ApproachTo identify prenuptial births we employed an unsupervised graph-based record linkage method to link birth and marriage certificates. We first extracted related groups of individuals: babies and their parents from birth certificates, and brides, grooms, and their parents from marriage certificates. To link births with marriages, we employed techniques to address challenges such as changing attribute values over time (such as names and addresses), the ambiguity of attribute values giving priority to rare names over common names, and different relationships encountered at different points in time (by applying temporal constraints). Based on the obtained links we then extracted prenuptial births. \u0000ResultsUsing two Scottish data sets containing a total of 38,451 births and 8,667 marriage certificates from the period 1861 to 1901 and employing different linkage thresholds we identified between 853 and 945 first birth-marriage links in the smaller (rural) data set, and between 2,165 and 2,232 links in the larger (urban) data set. In the rural data set, between 16.9% and 17.7% of these links were with birth less than 8 months after marriage (i.e. prenuptial births), where the corresponding ground truth contained 17.6% prenuptial births. For the urban data set, we identified between 51.3% and 51.4% prenuptial births. Our results show clear differences between rural and urban prenuptial pregnancies in 19th Century Scotland. \u0000ConclusionWe have presented an unsupervised graph-based record linkage method that compares attribute values of individuals and their relationships to link records based on the ambiguity of attribute values, attribute value changes, and temporal constraints. This linking process helps us to identify prenuptial births to help us understand the degree to which marriages may have been driven by pregnancy.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43211296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1991
Elisa Jones, L. Frith, A. Chiumento, S. Rodgers, Alan Clarke, S. Markham
ObjectivesPublic involvement and engagement (PIE)) is playing an increasingly important role in big data initiatives and projects. It is therefore important to gain a deeper understanding of the different approaches used. ApproachThis study explores PIE using ethnographically-informed qualitative case studies. The case studies include: three citizen juries, each one carried out over eight days and that asked jurors to consider different real-world health data initiatives; and a public panel set up by a regional databank that carries out data linking. Data collection is ongoing and I will be continuing to carry out close observations of activities, and conducting semi-structured 1:1 interviews with those that organise and have taken part in the activities. ResultsData collection so far comprises completed observations at the citizen juries (~96 hours), ongoing observations of the public panel meetings (~15 hours), and thirty semi-structured 1:1 interviews with public contributors and other stakeholders about their experiences of the activities they were involved in. Early data analysis indicates key themes of: jurors feeling heard, but unsure whether anybody was listening; stakeholders being impressed by informed jurors, but raising concerns over contributors becoming too ‘expert’; how who is at the table and what information is presented impacts what is discussed; differences between online and in-person participation; and public involvement not being a substitute for informing the public about how their data is used. Conclusion‘Who’ is involved, and ‘how’ PPIE activities are designed and run can facilitate or constrain discussion, enhancing or limiting public contributions. If public involvement is to achieve its aims, including increasing trustworthiness, deeper consideration of these factors by those who seek the public’s views in their data projects is recommended.
{"title":"Public involvement in big data projects: an ethnographically-informed study.","authors":"Elisa Jones, L. Frith, A. Chiumento, S. Rodgers, Alan Clarke, S. Markham","doi":"10.23889/ijpds.v7i3.1991","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1991","url":null,"abstract":"ObjectivesPublic involvement and engagement (PIE)) is playing an increasingly important role in big data initiatives and projects. It is therefore important to gain a deeper understanding of the different approaches used. \u0000ApproachThis study explores PIE using ethnographically-informed qualitative case studies. The case studies include: three citizen juries, each one carried out over eight days and that asked jurors to consider different real-world health data initiatives; and a public panel set up by a regional databank that carries out data linking. Data collection is ongoing and I will be continuing to carry out close observations of activities, and conducting semi-structured 1:1 interviews with those that organise and have taken part in the activities. \u0000ResultsData collection so far comprises completed observations at the citizen juries (~96 hours), ongoing observations of the public panel meetings (~15 hours), and thirty semi-structured 1:1 interviews with public contributors and other stakeholders about their experiences of the activities they were involved in. Early data analysis indicates key themes of: jurors feeling heard, but unsure whether anybody was listening; stakeholders being impressed by informed jurors, but raising concerns over contributors becoming too ‘expert’; how who is at the table and what information is presented impacts what is discussed; differences between online and in-person participation; and public involvement not being a substitute for informing the public about how their data is used. \u0000Conclusion‘Who’ is involved, and ‘how’ PPIE activities are designed and run can facilitate or constrain discussion, enhancing or limiting public contributions. If public involvement is to achieve its aims, including increasing trustworthiness, deeper consideration of these factors by those who seek the public’s views in their data projects is recommended.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43560839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1852
M. Chartier, W. Phillips-Beck, M. Brownell, L. Star, Nora Murdock, Wendy Au, J. Bowes, Brooke Cochrane, R. Campbell
ObjectivesGiven the impact of colonization and responding to Canada’s Truth and Reconciliation Commission, we aimed to provide baseline measures of First Nations children’s health and social outcomes in Manitoba, Canada. We also aimed to create a research process where Indigenous and non-Indigenous researchers work collaboratively and in culturally safe ways. ApproachWe formed a team consisting of members of First Nation organizations and academic researchers. Knowledge Keepers from Anishinaabe, Cree, Anishininew, Dakota and Dene Nations guided the study, interpreted results and ensured meaningful knowledge translation. This retrospective cohort study utilized population-based health, social services, education and justice administrative data that allowed de-identified individual-level linkages across all databases through a scrambled health number. Adjusted rates and rate ratios were calculated using a generalized liner modeling approach to compare First Nations children (n=61,726) and all other Manitoba children (n=279,087) and comparing First Nations children living on and off-reserve. ResultsLarge disparities between First Nations and other Manitoba children were found in birth outcomes, physical and mental health, health services, education, social services, justice system involvement and mortality. First Nations infants had higher rates of preterm births, large-for-gestational-age births, newborn readmissions to hospital and lower rates of breastfeeding initiation compared with other Manitoba infants. Suicide rates among First Nations adolescents were ten times higher than among other adolescents in Manitoba, yet we found few differences in diagnosis of mood and anxiety disorders between the groups. First Nations children were also seven times more likely to apprehended by child protection services and youth were ten times more likely to be criminally accused. Knowledge Keepers offered their perspectives on these findings. ConclusionThese findings demonstrate that an enormous amount of work is required in virtually every area – health, social, education and justice – to improve First Nations children’s lives. There is an urgent need for equitable access to services, and these services should be self-determined, planned and implemented by First Nations people.
{"title":"Our Children, Our Future: The Health and Well-being of First Nations Children in Manitoba, Canada.","authors":"M. Chartier, W. Phillips-Beck, M. Brownell, L. Star, Nora Murdock, Wendy Au, J. Bowes, Brooke Cochrane, R. Campbell","doi":"10.23889/ijpds.v7i3.1852","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1852","url":null,"abstract":"ObjectivesGiven the impact of colonization and responding to Canada’s Truth and Reconciliation Commission, we aimed to provide baseline measures of First Nations children’s health and social outcomes in Manitoba, Canada. We also aimed to create a research process where Indigenous and non-Indigenous researchers work collaboratively and in culturally safe ways. \u0000ApproachWe formed a team consisting of members of First Nation organizations and academic researchers. Knowledge Keepers from Anishinaabe, Cree, Anishininew, Dakota and Dene Nations guided the study, interpreted results and ensured meaningful knowledge translation. This retrospective cohort study utilized population-based health, social services, education and justice administrative data that allowed de-identified individual-level linkages across all databases through a scrambled health number. Adjusted rates and rate ratios were calculated using a generalized liner modeling approach to compare First Nations children (n=61,726) and all other Manitoba children (n=279,087) and comparing First Nations children living on and off-reserve. \u0000ResultsLarge disparities between First Nations and other Manitoba children were found in birth outcomes, physical and mental health, health services, education, social services, justice system involvement and mortality. First Nations infants had higher rates of preterm births, large-for-gestational-age births, newborn readmissions to hospital and lower rates of breastfeeding initiation compared with other Manitoba infants. Suicide rates among First Nations adolescents were ten times higher than among other adolescents in Manitoba, yet we found few differences in diagnosis of mood and anxiety disorders between the groups. First Nations children were also seven times more likely to apprehended by child protection services and youth were ten times more likely to be criminally accused. Knowledge Keepers offered their perspectives on these findings. \u0000ConclusionThese findings demonstrate that an enormous amount of work is required in virtually every area – health, social, education and justice – to improve First Nations children’s lives. There is an urgent need for equitable access to services, and these services should be self-determined, planned and implemented by First Nations people.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45279857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.2037
Jason W. Flindall, Saiganesh Dhannewar, Mikhail Skrigitil, Siddharth Chadda, Samantha Magnus, Heather Richards, L. Corscadden
ObjectiveWhile overall health service use declined following the start of the pandemic, the aim of this analysis is to generate insights to inform public health priorities by identifying higher-than-expected patterns of health care service use for some health condition and population groups. ApproachHealth care encounters for hospital, emergency department, and primary care encounters between 2011 and 2021 were categorized into condition groups according to the CIHI Population Grouping Methodology (British Columbia version). Actual health condition encounters were compared with ARIMA-based encounter forecasts to identify conditions with different-from-expected encounter rates in 2020 and 2021. For each of 225 CIHI-defined health conditions, we identified health conditions for which service use was higher-than-expected. Area-based socioeconomic status and virtual care visit data are examined to further explore conditions that continue to differ from their pre-pandemic encounter patterns. ResultsThis analysis demonstrates that some health condition groups have seen dramatic increases in service use. The three most impacted groups with higher-than-expected encounters are hypercholesterolaemia/high cholesterol [47.8% increase in average monthly encounters since 2019], emotional and behavioural disorder (w/onset generally in childhood) [+37.3%] and neurotic/anxiety/obsessive compulsive disorder [+28.0%]. Since the start of the pandemic in British Columbia, the health condition groups with both the highest volumes of services and higher than expected service use included: hypercholesterolemia & hypothyroidism, mental health conditions (eating disorder, depression, and others), hypertension and heart failure, and diabetes. Additional descriptive analysis explores potential inequities in encounters by socio-economic status and how virtual care has changed service patterns. ConclusionIncreased service use may reflect greater need, better access to virtual care or potential changes in diagnoses. Identifying patterns of higher-than-expected use can support program planning to address growing need in certain regions or populations. Additional exploration will be undertaken to examine lower-than-expected service use as potential unmet need.
{"title":"Pandemic effects on health condition specific healthcare encounters in British Columbia, Canada.","authors":"Jason W. Flindall, Saiganesh Dhannewar, Mikhail Skrigitil, Siddharth Chadda, Samantha Magnus, Heather Richards, L. Corscadden","doi":"10.23889/ijpds.v7i3.2037","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.2037","url":null,"abstract":"ObjectiveWhile overall health service use declined following the start of the pandemic, the aim of this analysis is to generate insights to inform public health priorities by identifying higher-than-expected patterns of health care service use for some health condition and population groups. \u0000ApproachHealth care encounters for hospital, emergency department, and primary care encounters between 2011 and 2021 were categorized into condition groups according to the CIHI Population Grouping Methodology (British Columbia version). Actual health condition encounters were compared with ARIMA-based encounter forecasts to identify conditions with different-from-expected encounter rates in 2020 and 2021. For each of 225 CIHI-defined health conditions, we identified health conditions for which service use was higher-than-expected. Area-based socioeconomic status and virtual care visit data are examined to further explore conditions that continue to differ from their pre-pandemic encounter patterns. \u0000ResultsThis analysis demonstrates that some health condition groups have seen dramatic increases in service use. The three most impacted groups with higher-than-expected encounters are hypercholesterolaemia/high cholesterol [47.8% increase in average monthly encounters since 2019], emotional and behavioural disorder (w/onset generally in childhood) [+37.3%] and neurotic/anxiety/obsessive compulsive disorder [+28.0%]. Since the start of the pandemic in British Columbia, the health condition groups with both the highest volumes of services and higher than expected service use included: hypercholesterolemia & hypothyroidism, mental health conditions (eating disorder, depression, and others), hypertension and heart failure, and diabetes. Additional descriptive analysis explores potential inequities in encounters by socio-economic status and how virtual care has changed service patterns. \u0000ConclusionIncreased service use may reflect greater need, better access to virtual care or potential changes in diagnoses. Identifying patterns of higher-than-expected use can support program planning to address growing need in certain regions or populations. Additional exploration will be undertaken to examine lower-than-expected service use as potential unmet need.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44434002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1801
R. Urquhart, C. Kendell, Julia Kaal, J. Vickery, L. Lethbridge
ObjectivesTo link population-based survey data to routinely collected administrative health data to enable investigation of how cancer survivors' ongoing physical, emotional, and practical needs and experiences after completing cancer treatment impact their healthcare utilization, including discharge from oncology to primary care. ApproachThe "Cancer Transitions Survey" is a population-based survey examining survivors' experiences and needs after completing cancer treatment. The survey was administered by the Nova Scotia Cancer Registry (NSCR) as part of a national study, the largest of its kind in Canada. Respondents included Nova Scotian survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1-3 years after treatment. Survey responses were linked to cancer registry, physicians' claims, hospitalization, and ambulatory care data. The data linkage provided a full four years of healthcare utilization data for each cancer survivor, beginning one year after their cancer diagnosis. Results1557 survivors responded to the survey and subsequently had their data linked. Collectively, breast, colorectal, and prostate cancer survivors represented 78.5% of survey respondents. Most respondents (65.3%) were 65 years of age or older and 69.8% had an existing co-morbid condition. Regression analyses are now being conducted to investigate whether the type and magnitude of post-treatment care needs, and the interventions (services and supports) received, impact health care utilization in the survivorship period, including discharge to primary care. ConclusionThis study represents a unique opportunity to link data unavailable in administrative health data: namely, self-reported needs and use of non-physician services and supports (e.g., support groups, counselling). As such, this dataset permits investigation of healthcare utilization and patterns of care that cannot be accomplished using administrative health data alone.
{"title":"Understanding how cancer survivors’ needs and experiences after treatment impact their health care utilization: a survey-administrative health data linkage study.","authors":"R. Urquhart, C. Kendell, Julia Kaal, J. Vickery, L. Lethbridge","doi":"10.23889/ijpds.v7i3.1801","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1801","url":null,"abstract":"ObjectivesTo link population-based survey data to routinely collected administrative health data to enable investigation of how cancer survivors' ongoing physical, emotional, and practical needs and experiences after completing cancer treatment impact their healthcare utilization, including discharge from oncology to primary care. \u0000ApproachThe \"Cancer Transitions Survey\" is a population-based survey examining survivors' experiences and needs after completing cancer treatment. The survey was administered by the Nova Scotia Cancer Registry (NSCR) as part of a national study, the largest of its kind in Canada. Respondents included Nova Scotian survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1-3 years after treatment. Survey responses were linked to cancer registry, physicians' claims, hospitalization, and ambulatory care data. The data linkage provided a full four years of healthcare utilization data for each cancer survivor, beginning one year after their cancer diagnosis. \u0000Results1557 survivors responded to the survey and subsequently had their data linked. Collectively, breast, colorectal, and prostate cancer survivors represented 78.5% of survey respondents. Most respondents (65.3%) were 65 years of age or older and 69.8% had an existing co-morbid condition. Regression analyses are now being conducted to investigate whether the type and magnitude of post-treatment care needs, and the interventions (services and supports) received, impact health care utilization in the survivorship period, including discharge to primary care. \u0000ConclusionThis study represents a unique opportunity to link data unavailable in administrative health data: namely, self-reported needs and use of non-physician services and supports (e.g., support groups, counselling). As such, this dataset permits investigation of healthcare utilization and patterns of care that cannot be accomplished using administrative health data alone.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44581340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1851
V. Harish, Mathieu Ravaut, S. Yi, Jahir M. Gutierrez, H. Sadeghi, Kin Kwan Leung, T. Watson, K. Kornas, T. Poutanen, M. Volkovs, L. Rosella
There has been considerable growth in the development of machine learning models for clinical applications; however, less attention has been paid to applications at the health systems level. Here, we survey recent models developed using provincial administrative health data holdings in Ontario, Canada to synthesize key learnings across use cases. We have developed four models in the areas of diabetes incidence and complications, hospitalization due to ambulatory care sensitive conditions, and hospitalization due to SARS-CoV-2 infection. Our team was highly multidisciplinary with expertise across clinical medicine, administrative health data, epidemiology, and computer science. We used a “sliding window” approach to aggregate healthcare events across multiple health administrative data sets chronologically and map them dynamically onto a patient timeline. Tree-based algorithms, specifically gradient boosted decision trees, are well suited for the underlying tabular structure of administrative data and were used for each prediction task. Our models achieved excellent discrimination, measured by the area under the receiver operating characteristic curve, between 0.77-0.85 at prediction windows between 30 days and 3 years in advance. They were also well-calibrated, both in-the-large and in population subgroups such as older adults, those living in rural areas, and the materially deprived. Measures of feature importance revealed that our models were leveraging predictors across administrative datasets (e.g. demographics, interactions with the healthcare system, medications) in intuitive and defensible ways. Finally, we demonstrated the utility of our models with “recall at top k” metrics - for example, the top 1% of patients predicted at risk of diabetes complications represented a cost of over $400 million to the healthcare system. We identify three key learnings needed for the successful application of machine learning methods to health administrative data: synergy between nature of training data and intended algorithm use, adherence to methodological best practices for rigour and transparency, and multidisciplinary teams with expertise across data provenance, methodological approach, and impact assessment.
{"title":"Developing Machine Learning Algorithms on Routinely Collected Administrative Health Data - Lessons from Ontario, Canada.","authors":"V. Harish, Mathieu Ravaut, S. Yi, Jahir M. Gutierrez, H. Sadeghi, Kin Kwan Leung, T. Watson, K. Kornas, T. Poutanen, M. Volkovs, L. Rosella","doi":"10.23889/ijpds.v7i3.1851","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1851","url":null,"abstract":"There has been considerable growth in the development of machine learning models for clinical applications; however, less attention has been paid to applications at the health systems level. Here, we survey recent models developed using provincial administrative health data holdings in Ontario, Canada to synthesize key learnings across use cases. \u0000We have developed four models in the areas of diabetes incidence and complications, hospitalization due to ambulatory care sensitive conditions, and hospitalization due to SARS-CoV-2 infection. Our team was highly multidisciplinary with expertise across clinical medicine, administrative health data, epidemiology, and computer science. We used a “sliding window” approach to aggregate healthcare events across multiple health administrative data sets chronologically and map them dynamically onto a patient timeline. Tree-based algorithms, specifically gradient boosted decision trees, are well suited for the underlying tabular structure of administrative data and were used for each prediction task. \u0000Our models achieved excellent discrimination, measured by the area under the receiver operating characteristic curve, between 0.77-0.85 at prediction windows between 30 days and 3 years in advance. They were also well-calibrated, both in-the-large and in population subgroups such as older adults, those living in rural areas, and the materially deprived. Measures of feature importance revealed that our models were leveraging predictors across administrative datasets (e.g. demographics, interactions with the healthcare system, medications) in intuitive and defensible ways. Finally, we demonstrated the utility of our models with “recall at top k” metrics - for example, the top 1% of patients predicted at risk of diabetes complications represented a cost of over $400 million to the healthcare system. \u0000We identify three key learnings needed for the successful application of machine learning methods to health administrative data: synergy between nature of training data and intended algorithm use, adherence to methodological best practices for rigour and transparency, and multidisciplinary teams with expertise across data provenance, methodological approach, and impact assessment.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44624157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.1865
R. Trubey, I. Thomas, R. Cannings‐John, Peter Mackie
ObjectivesAdministrative data linkage is relatively under-utilised as a way of generating evidence to guide homelessness policy and service delivery in the UK. Our objective is to contribute insight into the ethical, legal, and practical challenges of using data linkage with data from people experiencing homelessness (PEH). ApproachWe outline the data collection and linkage methodologies for two UK-based studies related to PEH. The first design aimed to explore the acceptability and feasibility of consented linkage of trial data (‘Moving On’ trial) to NHS Digital records in a cohort of recruited PEH in two English local authorities (n=50). The second design used administrative data originating from a local authority homelessness service in Wales (n=17,000 cases) to explore educational outcomes of children in homeless households. The resultant data linkage rates are contrasted and discussed in relation to the mechanisms for obtaining and linking personal data. ResultsThe Moving On trial demonstrated high rates of consent for data linkage and the ability to collect sufficient personal identifiable data to increase the chance of successful matching. Aggregate match rates will be discussed. Of the roughly 17,000 cases included in the local authority administrative data, 75% could be linked to unique individuals using probabilistic matching and were therefor ‘useable’ in linkage research. The proportion of useable cases rapidly decreased as the cut-off for matching quality was increased, to roughly 50% of cases being useable when a 99% match probability cut-off was used. Matching rates were higher amongst priority need homeless cases, possibly reflecting business need to identify and work closely with these people. ConclusionWhere homelessness administrative data systems are not designed to enable data linkage, low matching rates can result, reducing study sample sizes and potentially leading to bias towards more extreme cases of homelessness if missed-matches are not random. Consented linkage within large-scale trials offers one possibility for generating long-term evidence.
{"title":"Linkage of people experiencing homeless using two consent models.","authors":"R. Trubey, I. Thomas, R. Cannings‐John, Peter Mackie","doi":"10.23889/ijpds.v7i3.1865","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1865","url":null,"abstract":"ObjectivesAdministrative data linkage is relatively under-utilised as a way of generating evidence to guide homelessness policy and service delivery in the UK. Our objective is to contribute insight into the ethical, legal, and practical challenges of using data linkage with data from people experiencing homelessness (PEH). \u0000ApproachWe outline the data collection and linkage methodologies for two UK-based studies related to PEH. The first design aimed to explore the acceptability and feasibility of consented linkage of trial data (‘Moving On’ trial) to NHS Digital records in a cohort of recruited PEH in two English local authorities (n=50). The second design used administrative data originating from a local authority homelessness service in Wales (n=17,000 cases) to explore educational outcomes of children in homeless households. The resultant data linkage rates are contrasted and discussed in relation to the mechanisms for obtaining and linking personal data. \u0000ResultsThe Moving On trial demonstrated high rates of consent for data linkage and the ability to collect sufficient personal identifiable data to increase the chance of successful matching. Aggregate match rates will be discussed. Of the roughly 17,000 cases included in the local authority administrative data, 75% could be linked to unique individuals using probabilistic matching and were therefor ‘useable’ in linkage research. The proportion of useable cases rapidly decreased as the cut-off for matching quality was increased, to roughly 50% of cases being useable when a 99% match probability cut-off was used. Matching rates were higher amongst priority need homeless cases, possibly reflecting business need to identify and work closely with these people. \u0000ConclusionWhere homelessness administrative data systems are not designed to enable data linkage, low matching rates can result, reducing study sample sizes and potentially leading to bias towards more extreme cases of homelessness if missed-matches are not random. Consented linkage within large-scale trials offers one possibility for generating long-term evidence.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44647974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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