[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_11256.html
   My bibliography  Save this paper

Early Home Visiting Delivery Model and Maternal and Child Mental Health at Primary School Age

Author

Listed:
  • Gabriella Conti
  • Sören Kliem
  • Malte Sandner
Abstract
We study the impacts of a prenatal and infancy home visiting program targeting disadvantaged families on mental health outcomes, assessed through diagnostic interviews. The program significantly reduced the prevalence of mental health conditions for both mothers and children, measured at primary-school age, and broke the intergenerational association of these conditions. The impacts are predominantly associated with a particular delivery model, wherein a single home visitor interacts with the family, as opposed to a model involving two home visitors.

Suggested Citation

  • Gabriella Conti & Sören Kliem & Malte Sandner, 2024. "Early Home Visiting Delivery Model and Maternal and Child Mental Health at Primary School Age," CESifo Working Paper Series 11256, CESifo.
    • Handle: RePEc:ces:ceswps:_11256
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp11256.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jonas Hjort & Mikkel Sølvsten & Miriam Wüst, 2017. "Universal Investment in Infants and Long-Run Health: Evidence from Denmark's 1937 Home Visiting Program," American Economic Journal: Applied Economics, American Economic Association, vol. 9(4), pages 78-104, October.
    2. Aline Bütikofer & Rita Ginja & Krzysztof Karbownik & Fanny Landaud, 2024. "(Breaking) Intergenerational Transmission of Mental Health," Journal of Human Resources, University of Wisconsin Press, vol. 59(S), pages 108-151.
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    4. Anna Chorniy & Janet Currie & Lyudmyla Sonchak, 2020. "Does Prenatal WIC Participation Improve Child Outcomes?," American Journal of Health Economics, University of Chicago Press, vol. 6(2), pages 169-198.
    5. Pedro Carneiro & Rita Ginja, 2014. "Long-Term Impacts of Compensatory Preschool on Health and Behavior: Evidence from Head Start," American Economic Journal: Economic Policy, American Economic Association, vol. 6(4), pages 135-173, November.
    6. Sarah Cattan & Gabriella Conti & Christine Farquharson & Rita Ginja & Maud Pecher, 2021. "The Health Effects of Universal Early Childhood Interventions: Evidence from Sure Start," Working Papers 2021-051, Human Capital and Economic Opportunity Working Group.
    7. Sandner, Malte & Cornelissen, Thomas & Jungmann, Tanja & Herrmann, Peggy, 2018. "Evaluating the effects of a targeted home visiting program on maternal and child health outcomes," Journal of Health Economics, Elsevier, vol. 58(C), pages 269-283.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jonas Lau-Jensen Hirani & Hans Henrik Sievertsen & Miriam Wust, 2020. "The Timing of Early Interventions and Child and Maternal Health," Bristol Economics Discussion Papers 20/720, School of Economics, University of Bristol, UK.
    2. Hirani, Jonas Cuzulan & Sievertsen, Hans Henrik & Wüst, Miriam, 2020. "Missing a Nurse Visit," IZA Discussion Papers 13485, Institute of Labor Economics (IZA).
      • Miriam Wüst & Jonas Lau-Jensen Hirani & Hans Henrik Sievertsen, 2021. "Missing a Nurse Visit," CEBI working paper series 20-09, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    3. Jonas Lau-Jensen Hirani & Hans Henrik Sievertsen & Miriam Wüst & Johannes Wohlfart, 2020. "Missing a Nurse Visit," Discussion Papers 20-09, University of Copenhagen. Department of Economics.
    4. Gabriella Conti, 2013. "The Developmental Origins of Health Inequality," Research on Economic Inequality, in: Health and Inequality, volume 21, pages 285-309, Emerald Group Publishing Limited.
    5. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
    6. Dan Anderberg & Christina Olympiou, 2023. "Children's social care and early intervention policy: Evidence from sure start," Economica, London School of Economics and Political Science, vol. 90(359), pages 953-977, July.
    7. Elizabeth M. Caucutt & Lance Lochner & Youngmin Park, 2017. "Correlation, Consumption, Confusion, or Constraints: Why Do Poor Children Perform so Poorly?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 119(1), pages 102-147, January.
    8. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    9. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    10. Holla,Alaka & Bendini,Maria Magdalena & Dinarte Diaz,Lelys Ileana & Trako,Iva, 2021. "Is Investment in Preprimary Education Too Low ? Lessons from (Quasi) ExperimentalEvidence across Countries," Policy Research Working Paper Series 9723, The World Bank.
    11. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    12. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2023. "Design-Based Identification with Formula Instruments: A Review," NBER Working Papers 31393, National Bureau of Economic Research, Inc.
    13. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    14. Orazio Attanasio & Helen Baker-Henningham & Raquel Bernal & Costas Meghir & Diana Pineda & Marta Rubio-Codina, 2022. "Early Stimulation and Nutrition: The Impacts of a Scalable Intervention," Journal of the European Economic Association, European Economic Association, vol. 20(4), pages 1395-1432.
    15. Katrina M. Walsemann & Calley E. Fisk & Mateo P. Farina & Emily Abbruzzi & Jennifer A. Ailshire, 2023. "Race, gender, and cohort differences in the educational experiences of Black and White Americans," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(6), pages 1-23, December.
    16. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    17. Guo, Jiaqi & Wang, Qiang & Li, Rongrong, 2024. "Can official development assistance promote renewable energy in sub-Saharan Africa countries? A matter of institutional transparency of recipient countries," Energy Policy, Elsevier, vol. 186(C).
    18. Munday, Tim & Brookes, James, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
    19. Juan Carlos Escanciano & Telmo P'erez-Izquierdo, 2023. "Automatic Locally Robust Estimation with Generated Regressors," Papers 2301.10643, arXiv.org, revised Nov 2023.
    20. Shi, Chengchun & Zhou, Yunzhe & Li, Lexin, 2023. "Testing directed acyclic graph via structural, supervised and generative adversarial learning," LSE Research Online Documents on Economics 119446, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    home visiting; mental health; diagnostic interviews;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ceswps:_11256. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.