Assessing Occupancy and Its Relation to Healthcare-Associated Infections

Infection prevention and control research priorities: what do we need to combat healthcare-associated infections and antimicrobial resistance? Results of a narrative literature review and survey analysis

BACKGROUND

Infection prevention and control (IPC) is one of the most cost-effective interventions against antimicrobial resistance (AMR). Yet, IPC knowledge gaps often receive little prominence in AMR research agendas. In this article, we construct IPC research priorities, in order to draw attention to these critical research needs.

METHODS

We developed a 4-step framework to identify IPC knowledge gaps from literature (narrative review). These gaps were then translated into research priorities and sent to two groups of European IPC experts for validation and critique through an online survey.

RESULTS

Seventy-nine publications were retrieved from the literature review, identifying fifteen IPC research gaps. Forty-four IPC experts, clustered in two groups, vetted them. The experts classified all research gaps as medium or high priority. Overall agreement between both groups was average (Kendall's τ = 0.43), with strong alignment on the highest priorities: (i) the assessment of organizational, socio-economic, and behavioural barriers/facilitators for the implementation of IPC programmes, (ii) the impact of overcrowding on the spread of infections and (iii) the impact of infrastructural changes, at facility level, on the reduction of infections. Feedback from experts also identified an additional research gap on the interaction between the human and hospital microbiomes.

CONCLUSIONS

We formulated a list of sixteen research priorities and identified three urgent needs. Now, we encourage researchers, funding agencies, policymakers and relevant stakeholders to start addressing the identified gaps.

On the Convergence of Epidemiology, Biostatistics, and Data Science

Epidemiology, biostatistics, and data science are broad disciplines that incorporate a variety of substantive areas. Common among them is a focus on quantitative approaches for solving intricate problems. When the substantive area is health and health care, the overlap is further cemented. Researchers in these disciplines are fluent in statistics, data management and analysis, and health and medicine, to name but a few competencies. Yet there are important and perhaps mutually exclusive attributes of these fields that warrant a tighter integration. For example, epidemiologists receive substantial training in the science of study design, measurement, and the art of causal inference. Biostatisticians are well versed in the theory and application of methodological techniques, as well as the design and conduct of public health research. Data scientists receive equivalently rigorous training in computational and visualization approaches for high-dimensional data. Compared to data scientists, epidemiologists and biostatisticians may have less expertise in computer science and informatics, while data scientists may benefit from a working knowledge of study design and causal inference. Collaboration and cross-training offer the opportunity to share and learn of the constructs, frameworks, theories, and methods of these fields with the goal of offering fresh and innovate perspectives for tackling challenging problems in health and health care. In this article, we first describe the evolution of these fields focusing on their convergence in the era of electronic health data, notably electronic medical records (EMRs). Next we present how a collaborative team may design, analyze, and implement an EMR-based study. Finally, we review the curricula at leading epidemiology, biostatistics, and data science training programs, identifying gaps and offering suggestions for the fields moving forward.

Spatial and environmental correlates of organism colonization and infection in the neonatal intensive care unit

OBJECTIVE

To examine organism colonization and infection in the neonatal intensive care unit as a result of environmental and spatial factors.

STUDY DESIGN

A retrospective cohort of infants admitted between 2006 and 2015 (n = 11 428), to assess the relationship between location and four outcomes: methicillin-resistant Staphylococcus aureus (MRSA) colonization; culture-confirmed late-onset sepsis; and, if intubated, endotracheal tube colonization with Pseudomonas aeruginosa or Klebsiella pneumonia. Independent risk factors were identified with mixed-effects logistic regression models and Moran's I for spatial autocorrelation.

RESULT

All four outcomes statistically clustered by location; neighboring colonization also influenced risk of MRSA (p < 0.05). For P. aeruginosa, being in a location with space for more medical equipment was associated with 2.61 times the odds of colonization (95% CrI: 1.19, 5.78).

CONCLUSION

Extrinsic factors partially explained risk for neonatal colonization and infection. For P. aeruginosa, infection prevention efforts at locations with space for more equipment may lower future colonization.