University of South Carolina School of Medicine, Columbia, SC, USA.
BMC Health Serv Res. 2024 May 27;24(1):665. doi: 10.1186/s12913-024-11080-2.
Using 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.
The 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.
Pregnancies 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.
Inclusion 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.
利用索赔数据识别主要产前保健 (PNC) 提供者并不总是那么直接，但这对于评估可及性、成本和结果至关重要。先前的算法应用多数原则（提供最多的就诊次数）和多数原则（提供多数就诊次数）来确定初级保健环境中的主要提供者，但它们缺乏就诊顺序信息。本研究提出了一种既包含 PNC 频率又包含就诊顺序信息的算法来识别主要提供者，并估计识别出的主要提供者的百分比。此外，还比较了到主要提供者和最近提供者的旅行距离差异。
本研究使用的数据集包括 2015-2018 年来自南卡罗来纳州医疗补助计划的 108441 例活产和 2155076 例相关索赔。分析重点是在整个孕期持续参保且有任何 PNC 就诊的患者，共涉及 32609 例妊娠。通过诊断和程序代码以及估计的孕龄内的专业知识来识别 PNC 就诊。为了对 PNC 提供者进行分类，根据 PNC 频率和就诊顺序信息创建了七个亚组。该算法是通过考虑频率和顺序信息来开发的。报告了识别出的主要提供者的百分比。卡方检验用于评估特定亚组被识别为主要提供者的概率是否与参考组（提供所有 PNC 就诊次数的多数）不同。配对 t 检验用于检查旅行距离的差异。
样本中的妊娠平均有 7.86 次 PNC 就诊。不到 30%的样本有独家提供者。通过应用 PNC 频率信息，可以识别出 81%的妊娠的主要提供者。在添加顺序信息后，92%的妊娠可以识别出主要提供者。与最近的提供者相比，前往识别出的主要提供者的孕妇的距离明显更长（平均 5 英里）。
在算法中纳入 PNC 顺序信息将可识别的主要提供者的比例提高了 11%。应用该算法表明，前往主要提供者的孕妇的距离比前往最近提供者的距离更长。
