Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA
Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA.
BMJ Open Qual. 2023 Mar;12(1). doi: 10.1136/bmjoq-2023-002269.
Highly visible hospital quality reporting stakeholders in the USA such as the US News & World Report (USNWR) and the Centers for Medicare & Medicaid Services (CMS) play an important health systems role via their transparent public reporting of hospital outcomes and performance. However, during the pandemic, many such quality measurement stakeholders and pay-for-performance programmes in the USA and Europe have eschewed the traditional risk adjustment paradigm, instead choosing to pre-emptively exclude months or years of pandemic era performance data due largely to hospitals' perceived COVID-19 burdens. These data exclusions may lead patients to draw misleading conclusions about where to seek care, while also masking genuine improvements or deteriorations in hospital quality that may have occurred during the pandemic. Here, we assessed to what extent hospitals' COVID-19 burdens (proportion of hospitalised patients with COVID-19) were associated with their non-COVID 30-day mortality rates from March through November 2020 to inform whether inclusion of pandemic-era data may still be appropriate.
This was a retrospective cohort study using the 100% CMS Inpatient Standard Analytic File and Master Beneficiary Summary File to include all US Medicare inpatient encounters with admission dates from 1 April 2020 through 30 November 2020, excluding COVID-19 encounters. Using linear regression, we modelled the association between hospitals' COVID-19 proportions and observed/expected (O/E) ratios, testing whether the relationship was non-linear. We calculated alternative hospital O/E ratios after selective pandemic data exclusions mirroring the USNWR data exclusion methodology.
We analysed 4 182 226 consecutive Medicare inpatient encounters from across 2601 US hospitals.
The association between hospital COVID-19 proportion and non-COVID O/E 30-day mortality was statistically significant (p<0.0001), but weakly correlated (r=0.06). The median (IQR) pairwise relative difference in hospital O/E ratios comparing the alternative analysis with the original analysis was +3.7% (-2.5%, +6.7%), with 1908/2571 (74.2%) of hospitals having relative differences within ±10%.
For non-COVID patient outcomes such as mortality, evidence-based inclusion of pandemic-era data is methodologically plausible and must be explored rather than exclusion of months or years of relevant patient outcomes data.
在美国，像《美国新闻与世界报道》（USNWR）和医疗保险和医疗补助服务中心（CMS）这样的高可见度医院质量报告利益相关者通过透明地报告医院的结果和绩效，在医疗体系中发挥着重要作用。然而，在大流行期间，美国和欧洲的许多此类质量测量利益相关者和按绩效付费计划回避了传统的风险调整范式，而是选择预先排除大流行时代的几个月或几年的绩效数据，主要是因为医院认为 COVID-19 负担过重。这些数据排除可能导致患者对在哪里寻求治疗产生误导性的结论，同时掩盖了大流行期间可能发生的真正的医院质量改善或恶化。在这里，我们评估了医院的 COVID-19 负担（COVID-19 住院患者的比例）与 2020 年 3 月至 11 月的非 COVID-19 30 天死亡率之间的关联程度，以了解是否仍可以包含大流行时代的数据。
这是一项回顾性队列研究，使用 100%CMS 住院标准分析文件和主受益人摘要文件纳入所有 2020 年 4 月 1 日至 2020 年 11 月 30 日期间有入院日期的美国医疗保险住院患者，排除 COVID-19 患者。我们使用线性回归模型来模拟医院 COVID-19 比例与观察到的/预期的（O/E）比值之间的关系，检验关系是否为非线性的。我们计算了在选择性排除大流行数据时，与 USNWR 数据排除方法相匹配的替代医院 O/E 比值。
我们分析了来自 2601 家美国医院的 4182266 例连续医疗保险住院患者。
医院 COVID-19 比例与非 COVID-O/E30 天死亡率之间的关联具有统计学意义（p<0.0001），但相关性较弱（r=0.06）。与原始分析相比，在替代分析中，医院 O/E 比值的中位数（IQR）差值为+3.7%（-2.5%，+6.7%），其中 1908/2571（74.2%）家医院的相对差值在±10%以内。
对于非 COVID-19 患者的结果，如死亡率，从循证的角度来看，纳入大流行时代的数据在方法上是合理的，必须进行探索，而不是排除几个月或几年的相关患者结果数据。
