Evidence map›Paper›PMID 38802871›Full record

ArticleBMC health services research2024

Who is your prenatal care provider? An algorithm to identify the predominant prenatal care provider with claims data.

Songyuan Deng, Samantha Renaud, Kevin J Bennett

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Article in BMC health services research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Songyuan DengUniversity of South Carolina School of Medicine, Columbia, SC, USA. songyuan@email.sc.edu.
Samantha RenaudUniversity of South Carolina School of Medicine, Columbia, SC, USA.
Kevin J BennettUniversity of South Carolina School of Medicine, Columbia, SC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUsing claims data to identify a predominant prenatal care (PNC) provider is not always straightforward, but it is essential for assessing access, cost, and outcomes. Previous algorithms applied plurality (providing the most visits) and majority (providing majority of visits) to identify the predominant provider in primary care setting, but they lacked visit sequence information. This study proposes an algorithm that includes both PNC frequency and sequence information to identify the predominant provider and estimates the percentage of identified predominant providers. Additionally, differences in travel distances to the predominant and nearest provider are compared.

methodsThe dataset used for this study consisted of 108,441 live births and 2,155,076 associated South Carolina Medicaid claims from 2015-2018. Analysis focused on patients who were continuously enrolled throughout their pregnancy and had any PNC visit, resulting in 32,609 pregnancies. PNC visits were identified with diagnosis and procedure codes and specialty within the estimated gestational age. To classify PNC providers, seven subgroups were created based on PNC frequency and sequence information. The algorithm was developed by considering both the frequency and sequence information. Percentage of identified predominant providers was reported. Chi-square tests were conducted to assess whether the probability of being identified as a predominant provider for a specific subgroup differed from that of the reference group (who provided majority of all PNC). Paired t-tests were used to examine differences in travel distance.

resultsPregnancies in the sample had an average of 7.86 PNC visits. Fewer than 30% of the sample had an exclusive provider. By applying PNC frequency information, a predominant provider can be identified for 81% of pregnancies. After adding sequential information, a predominant provider can be identified for 92% of pregnancies. Distance was significantly longer for pregnant individuals traveling to the identified predominant provider (an average of 5 miles) than to the nearest provider.

conclusionsInclusion of PNC sequential information in the algorithm has increased the proportion of identifiable predominant providers by 11%. Applying this algorithm reveals a longer distance for pregnant individuals travelling to their predominant provider than to the nearest provider.

Indexed as

AlgorithmsMedicaidPrenatal CareAdultFemaleHumansInsurance Claim ReviewPregnancyPrimary Health CareSouth CarolinaUnited StatesPredominant providerPrenatal careVisit frequencyVisit sequence

Identifiers

PMID38802871
PMCPMC11131320

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.