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The healthcare sector has significant environmental impacts, particularly through greenhouse gas emissions. Reducing its climate footprint is therefore essential for achieving political goals such as net-zero and climate-friendly healthcare. While health economic evaluation (HEE) methods compare the costs and consequences of two or more interventions, these analyses rarely consider climate impacts. Some studies have begun to determine climate impacts parallel to or integrated into HEEs. Life cycle assessment (LCA) could be used to integrate climate impacts by considering these results as effects or monetised climate footprints. However, a reporting standard is needed for using these climate-extended economic evaluations in evidence-based decision-making. This protocol describes using an online Delphi process to incorporate climate impacts into the Consolidated Health Economic Evaluation Reporting Standard (CHEERS), called CHEERS Climate Extension (CHEERS ClimatE).
The development of CHEERS ClimatE will proceed through five key stages. First, the preliminary steering group develops in consultation with an advisory board a proposal for the CHEERS ClimatE reporting standard based on a transparency checklist that combines three standards for carbon footprint calculations into the CHEERS framework. The mapping was complemented by reviewing studies that incorporate climate impacts in HEE. Second, for the Delphi process, international experts in HEE and LCA with at least one year of academic experience will be invited to participate in an online pre-survey. We aim to recruit at least 40 participants. Expecting various drop-outs, we aim to reach a consensus with at least 20 participants per Delphi round. Third, an expected three-round Delphi process will be conducted to validate and refine the proposed elements. Participants will rate each item using a 9-point Likert scale and will have the opportunity to comment on each item and propose new items. Consensus is defined with the target of a 70% agreement. Unless consensus is reached, a moderated video conference may be held as a fourth round. Fourth, following other CHEERS extensions, the consented checklist will be piloted using thematically relevant case studies. While substantial changes are not anticipated, minor revisions to individual items may be considered and ratified by the steering group and advisory board. The fifth stage is the publication of the final checklist.
This study has been approved by the ethics committee of the University of Bremen (2024–25). The findings of the Delphi study will be published in a peer-reviewed journal and presented at conferences.
Background
In hospitals that serve disproportionately patients of Black race, here termed Black-serving hospitals (BSH), nurse staffing is worse, mortality rates are higher, and nursing-sensitive indicators may be worse than in other hospitals, but this evidence has not been compiled.
Objective
The study objective was to examine whether nursing-sensitive indicators, which measure changes in patient health status directly affected by nursing care, differ in hospitals where Black patients predominantly access their care, as compared to other hospitals.
Methods
To fulfill the objective, a cross-sectional design using publicly available 2019 to 2022 Hospital Compare, 2019 Medicare Provider Analysis and Review (MEDPAR), and case mix index (CMI) file databases were used. Four nursing-sensitive indicators were evaluated: pressure ulcer, postoperative sepsis, perioperative pulmonary embolus/deep vein thrombosis, and death rate among surgical inpatients with serious treatable complications (“failure to rescue”) in hospitals classified into high, medium, and low BSHs according to the percentage of patients of Black race in the MEDPAR data. Mean outcome differences across BSH categories were assessed through analyses of variance and regression models, which controlled for hospital CMI.
Results
The 3,101 hospitals were predominantly urban nonteaching hospitals in metropolitan areas. Although 12% of hospitals had Magnet designation, BSHs were disproportionately Magnet (14%). The outcome rates were 0.59 for pressure ulcers, 3.38 for perioperative pulmonary embolus/deep vein thrombosis, 143.58 for failure to rescue, and 4.12 for sepsis. Rates were significantly higher for pressure ulcers, perioperative pulmonary embolus/deep vein thrombosis, and sepsis in high BSHs. The mean failure to rescue rate was similar across low-to-high BSHs and did not show significant differences. These results were unchanged in models adjusting for CMI.
Discussion
The evidence suggests that several nursing-sensitive indicators are worse in high BSHs. Research linking nursing-sensitive indicators to nursing resources such as staffing is needed to explicate the mechanism underlying these findings. Poorer nursing-sensitive indicators in combination with poorer nurse staffing in high BSHs presents a priority for policy and management intervention. Background
Patients in hospitals that serve disproportionately patients of Black race have worse outcomes than patients in other hospitals, but the modifiable nursing factors that may contribute to such disparities have not been explored.
Objective
The study objective was to examine whether nurse staffing differs in hospitals that serve predominantly patients of Black race (Black-serving hospitals) as compared to other hospitals.
Methods
A cross-sectional correlational design using a nurse survey in a national hospital sample was used to fulfill the study objective. Nurse staffing was measured as the maximum number of patients cared for on the last shift from the 2015 annual registered nurse survey conducted in National Database of Nursing Quality Indicators hospitals. Hospitals were classified into subgroups of low, medium, and high percentages of patients of Black race using the 2019 Medicare Provider Analysis and Review database.
Results
In survey data from 179,336 registered nurses in 574 hospitals, nurse staffing was significantly worse in high-Black-serving hospitals as compared to medium- and low-Black-serving hospitals. In Poisson regression models that adjusted for nursing unit type and hospital characteristics, nurses in high-Black-serving hospitals and medium-Black-serving hospitals had more patients-per-nurse than did nurses in low-Black-serving hospitals.
Discussion
Small, statistically significant differences in nurse staffing that are worse in hospitals where Black patients disproportionately access their care were found using nurse survey data accounting for nursing unit type. The poorer nurse staffing in Black-serving hospitals may compromise the care and outcomes of the seven in 10 hospitalized Black older adults who receive care in Black-serving hospitals. The consequences for patient outcome disparities of poorer nurse staffing in Black-serving hospitals deserve investigation. Policies to increase nurse staffing in hospitals serving a higher proportion of patients of Black race are needed to contribute to efforts to reduce health disparities. 
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