Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention.
<h4>Introduction</h4>Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinants of mental health service rec...
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0319396 |
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| author | Nicole M Marlow Jessica M Kramer Anne V Kirby Molly M Jacobs |
| author_facet | Nicole M Marlow Jessica M Kramer Anne V Kirby Molly M Jacobs |
| author_sort | Nicole M Marlow |
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| description | <h4>Introduction</h4>Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinants of mental health service receipt among this population are unknown. This study will identify socioecological factors associated with increased risk of SI and SA for autistic people and evaluate determinants of mental health care receipt.<h4>Methods</h4>This study will link information for individuals aged 12-64 years in healthcare claims data (IBM® MarketScan® Research Database and CMS Medicaid) to publicly available databases containing community and policy factors, thereby creating a unique, multilevel dataset that includes health, demographic, community, and policy information. Machine learning data reduction methods will be applied to reduce the dimensionality prior to nested, multilevel empirical estimation. These techniques will allow for robust identification of clusters of socioecological factors associated with 1) risk of SI and SA and 2) receipt of mental health services (type, dose, delivery modality). Throughout, the research team will partner with an established group of autistic partners to promote community relevance, as well as receive input and guidance from a council of policy and practice advisors.<h4>Discussion</h4>We hypothesize that nested individual (co-occurring conditions, age, sex), community (healthcare availability, social vulnerabilities), and policy factors (state mental health legislation, state Medicaid expansion) will be associated with heightened risk of SI and SA, and that receipt, dose, and delivery of mental health services will be associated with interdependent factors at all three levels. The approach will lead to identification of multilevel clusters of risk and factors that facilitate or impede mental health service delivery. The study team will then engage the community partners, and policy and practice advisors to inform development of recommendations to reduce risk and improve mental health for the autistic population. |
| format | Article |
| id | doaj-art-8ef2bc2e787b4ddb9d91df57466416e4 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-8ef2bc2e787b4ddb9d91df57466416e42025-08-20T03:47:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031939610.1371/journal.pone.0319396Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention.Nicole M MarlowJessica M KramerAnne V KirbyMolly M Jacobs<h4>Introduction</h4>Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinants of mental health service receipt among this population are unknown. This study will identify socioecological factors associated with increased risk of SI and SA for autistic people and evaluate determinants of mental health care receipt.<h4>Methods</h4>This study will link information for individuals aged 12-64 years in healthcare claims data (IBM® MarketScan® Research Database and CMS Medicaid) to publicly available databases containing community and policy factors, thereby creating a unique, multilevel dataset that includes health, demographic, community, and policy information. Machine learning data reduction methods will be applied to reduce the dimensionality prior to nested, multilevel empirical estimation. These techniques will allow for robust identification of clusters of socioecological factors associated with 1) risk of SI and SA and 2) receipt of mental health services (type, dose, delivery modality). Throughout, the research team will partner with an established group of autistic partners to promote community relevance, as well as receive input and guidance from a council of policy and practice advisors.<h4>Discussion</h4>We hypothesize that nested individual (co-occurring conditions, age, sex), community (healthcare availability, social vulnerabilities), and policy factors (state mental health legislation, state Medicaid expansion) will be associated with heightened risk of SI and SA, and that receipt, dose, and delivery of mental health services will be associated with interdependent factors at all three levels. The approach will lead to identification of multilevel clusters of risk and factors that facilitate or impede mental health service delivery. The study team will then engage the community partners, and policy and practice advisors to inform development of recommendations to reduce risk and improve mental health for the autistic population.https://doi.org/10.1371/journal.pone.0319396 |
| spellingShingle | Nicole M Marlow Jessica M Kramer Anne V Kirby Molly M Jacobs Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention. PLoS ONE |
| title | Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention. |
| title_full | Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention. |
| title_fullStr | Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention. |
| title_full_unstemmed | Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention. |
| title_short | Protocol for socioecological study of autism, suicide risk, and mental health care: Integrating machine learning and community consultation for suicide prevention. |
| title_sort | protocol for socioecological study of autism suicide risk and mental health care integrating machine learning and community consultation for suicide prevention |
| url | https://doi.org/10.1371/journal.pone.0319396 |
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