使用转型医疗补助统计信息系统 (Transformed Medicaid Statistical Information System) 识别活产儿的方法进行比较
Samantha G Auty1, Jamie R Daw2, Lindsay K Admon3
1Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, Massachusetts, USA.
Health services research
|September 29, 2023
概括
在转换的医疗补助统计信息系统分析文件 (TAF) 中准确识别活产婴儿至关重要. 方法4,包括住院和其他服务索赔,但不包括婴儿特定的代码,最好与CDC数据匹配活产数.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 准确识别活产婴儿对于公共卫生监督和政策至关重要.
- 医疗补助行政数据,如转换的医疗补助统计信息系统分析文件 (TAF),是跟踪健康趋势的宝贵资源.
- 在TAF中识别活产生的现有方法的准确性可能有局限性.
研究的目的:
- 用TAF数据评估不同算法的性能,以识别活产婴儿.
- 将这些基于TAF的方法与疾病控制和预防中心 (CDC) 的出生数据进行比较,被认为是黄金标准.
主要方法:
- 从2018年1月1日到2018年12月31日使用TAF住院患者 (IP) 和其他服务 (OT) 文件.
- 对比了五种不同的方法来识别活产儿,不同组合的诊断,程序,收入和服务地点代码.
- 已验证的TAF衍生出生数与CDC出生数据在州一级.
主要成果:
- 方法4,包含所有IP和OT索赔,不包括婴儿特定代码,显示了最高的准确性.
- 这种最佳方法确定了1,656,794名活产婴儿,与CDC数据相比,全国超计数仅为3.6%.
- 其他方法导致显著的过度计数 (方法1和3) 或不足计数 (方法2和5).
结论:
- 包括IP和OT索赔,同时不包括非分娩事件代码和婴儿特定服务代码,提高了TAF.中活出生的识别的准确性.
- 这种精细的方法提高了TAF对活产监测的可靠性.
- 这些发现为利用医疗补助行政数据提供了一种有效的方法,用于准确估计出生人数.
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