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. 2024 Jun 26;25(1):230.
doi: 10.1186/s12875-024-02463-9.

Multi-level factors linked to young adult primary care transitions: evidence from a state all-payer claims analysis

Affiliations

Multi-level factors linked to young adult primary care transitions: evidence from a state all-payer claims analysis

Sarah A Nowak et al. BMC Prim Care. .

Abstract

Objective: Delayed transitions from pediatric to adult primary care leads to gaps in medical care. State all-payer claims data was used to assess multilevel factors associated with timely transition from pediatric to adult primary care.

Materials and methods: We created a cohort of 4,320 patients aged 17-20 in 2014-2017 continuously enrolled in health insurance 36 months between 2014 and 2019 and attributed to a pediatric provider in months 1-12. We also constructed primary care provider networks identifying links between providers who saw members of the same family. Logistic regression was used to predict adult primary care in months 25-36 on family, provider, and county-level factors. Finally, we modeled the effect of county and network cluster membership on care transitions.

Results: Male sex, having another family member seeing a pediatrician, and residing in a county with high pediatric care capacity or low adult primary care capacity were associated with lower odds of adult primary care transition.

Discussion: We investigated factors associated with successful transitions from pediatric to adult primary care. Family ties to a pediatrician and robust county capacity to provide primary care to children were associated with non-transition to adult primary care.

Conclusion: Multiple level factors contribute to non-transition to adult primary care. Understanding the factors associated with appropriate transition can help inform state and national policy.

Keywords: Adolescent primary care; All-payer claims; Network analysis; Pediatric primary care; Primary care; Transitions of care; Young adult primary care.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Network visualization of 14 network communities identified. Nodes (providers) have connections (edges) between them if they treat patients in the same family. Those more connected to others in the community are more central in the network visualization
Fig. 2
Fig. 2
Provider network communities with colors representing primary care specialty

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