Application of the Haddon matrix to COVID-19 prevention and containment in nursing homes.

COVID-19 has exacted a disproportionate toll on the health of persons living in nursing homes. Healthcare providers and other decision-makers in those settings must refer to multiple evolving sources of guidance to coordinate care delivery in such a way as to minimize the introduction and spread of the causal virus, SARS-CoV-2. It is essential that guidance be presented in an accessible and usable format to facilitate its translation into evidence-based best practice. In this article, we propose the Haddon matrix as a tool well-suited to this task. The Haddon matrix is a conceptual model that organizes influencing factors into pre-event, event, and post-event phases, and into host, agent, and environment domains akin to the components of the epidemiologic triad. The Haddon matrix has previously been applied to topics relevant to the care of older persons, such as fall prevention, as well as to pandemic planning and response. Presented here is a novel application of the Haddon matrix to pandemic response in nursing homes, with practical applications for nursing home decision-makers in their efforts to prevent and contain COVID-19.

In-hospital Mortality and the Predictive Ability of the Modified Early Warning Score in Ghana: Single-Center, Retrospective Study.

BACKGROUND: The modified early warning score (MEWS) is an objective measure of illness severity that promotes early recognition of clinical deterioration in critically ill patients. Its primary use is to facilitate faster intervention or increase the level of care. Despite its adoption in some African countries, MEWS is not standard of care in Ghana. In order to facilitate the use of such a tool, we assessed whether MEWS, or a combination of the more limited data that are routinely collected in current clinical practice, can be used predict to mortality among critically ill inpatients at the Korle-Bu Teaching Hospital in Accra, Ghana. OBJECTIVE: The aim of this study was to identify the predictive ability of MEWS for medical inpatients at risk of mortality and its comparability to a measure combining routinely measured physiologic parameters (limited MEWS [LMEWS]). METHODS: We conducted a retrospective study of medical inpatients, aged ≥13 years and admitted to the Korle-Bu Teaching Hospital from January 2017 to March 2019. Routine vital signs at 48 hours post admission were coded to obtain LMEWS values. The level of consciousness was imputed from medical records and combined with LMEWS to obtain the full MEWS value. A predictive model comparing mortality among patients with a significant MEWS value or LMEWS ≥4 versus a nonsignificant MEWS value or LMEWS <4 was designed using multiple logistic regression and internally validated for predictive accuracy, using the receiver operating characteristic (ROC) curve. RESULTS: A total of 112 patients were included in the study. The adjusted odds of death comparing patients with a significant MEWS to patients with a nonsignificant MEWS was 6.33 (95% CI 1.96-20.48). Similarly, the adjusted odds of death comparing patients with a significant versus nonsignificant LMEWS value was 8.22 (95% CI 2.45-27.56). The ROC curve for each analysis had a C-statistic of 0.83 and 0.84, respectively. CONCLUSIONS: LMEWS is a good predictor of mortality and comparable to MEWS. Adoption of LMEWS can be implemented now using currently available data to identify medical inpatients at risk of death in order to improve care.