The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2024
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| author | Evan D. Peet Dana Schultz Susan Lovejoy Fuchiang (Rich) Tsui |
| author_facet | Evan D. Peet Dana Schultz Susan Lovejoy Fuchiang (Rich) Tsui Evan D. Peet Dana Schultz Susan Lovejoy Fuchiang (Rich) Tsui |
| collection | Wiley Open Access |
| contents | The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting Evan D. Peet Dana Schultz Susan Lovejoy Fuchiang (Rich) Tsui Health Economics AbstractDoula services represent an underutilized maternal and child health intervention with the potential to improve outcomes through the provision of physical, emotional, and informational support. However, there is limited evidence of the infant health effects of doulas despite well‐established connections between maternal and infant health. Moreover, because the availability of doulas is limited and often not covered by insurers, existing evidence leaves unclear if or how doula services should be allocated to achieve the greatest improvements in outcomes. We use unique data and machine learning to develop accurate predictive models of infant health and doula service participation. We then combine these predictive models within the double machine learning method to estimate the effects of doula services. We show that while doula services reduce risk on average, the benefits of doula services increase as the risk of negative infant health outcomes increases. We compare these benefits to the costs of doula services under alternative allocation schemes and show that leveraging the risk predictions dramatically increases the cost effectiveness of doula services. Our results show the potential of big data and novel analytic methods to provide cost‐effective support to those at greatest risk of poor outcomes. 10.1002/hec.4821 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/hec.4821 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_hec_4821 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting Evan D. Peet Dana Schultz Susan Lovejoy Fuchiang (Rich) Tsui Health Economics The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting Evan D. Peet Dana Schultz Susan Lovejoy Fuchiang (Rich) Tsui Health Economics AbstractDoula services represent an underutilized maternal and child health intervention with the potential to improve outcomes through the provision of physical, emotional, and informational support. However, there is limited evidence of the infant health effects of doulas despite well‐established connections between maternal and infant health. Moreover, because the availability of doulas is limited and often not covered by insurers, existing evidence leaves unclear if or how doula services should be allocated to achieve the greatest improvements in outcomes. We use unique data and machine learning to develop accurate predictive models of infant health and doula service participation. We then combine these predictive models within the double machine learning method to estimate the effects of doula services. We show that while doula services reduce risk on average, the benefits of doula services increase as the risk of negative infant health outcomes increases. We compare these benefits to the costs of doula services under alternative allocation schemes and show that leveraging the risk predictions dramatically increases the cost effectiveness of doula services. Our results show the potential of big data and novel analytic methods to provide cost‐effective support to those at greatest risk of poor outcomes. 10.1002/hec.4821 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting |
| topic | Health Economics |
| url | https://onlinelibrary.wiley.com/doi/10.1002/hec.4821 |