The association between opening a short stay paediatric assessment unit and trends in short stay hospital admissions

Interventions in primary and community care to reduce urgent paediatric hospital admissions: systematic review

BACKGROUND

There has been a rise in urgent paediatric hospital admissions and interventions to address this are required.

OBJECTIVE

To systemically review the literature describing community (or non-hospital)-based interventions designed to reduce emergency department (ED) visits or urgent hospital admissions.

DATA SOURCES

MEDLINE, Embase, OVIS SP, PsycINFO, Science Citation Index Expanded/ISI Web of Science (1981-present), the Cochrane Library database and the Database of Abstracts of Reviews of Effectiveness.

STUDY ELIGIBILITY CRITERIA

Randomised controlled trials (RCTs) and before-and-after studies.

PARTICIPANTS

Individuals aged <16 years.

STUDY APPRAISAL AND SYNTHESIS METHODS

Papers were independently reviewed by two researchers. Data extraction and the Critical Appraisals Skills Programme checklist was completed (for risk of bias assessment).

RESULTS

Seven studies were identified. Three studies were RCTs, three were a comparison between non-randomised groups and one was a before-and-after study. Interventions were reconfiguration of staff roles (two papers), telemedicine (three papers), pathways of urgent care (one paper) and point-of-care testing (one paper). Reconfiguration of staff roles resulted in reduction in ED visits in one study (with a commensurate increase in general practitioner visits) but increased hospital admissions from ED in a second. Telemedicine was associated with a reduction in children's admissions in one study and reduced ED admissions in two further studies. Interventions with pathways of care and point-of-care testing did not impact either ED visits or urgent admissions.

CONCLUSIONS AND IMPLICATIONS

New out-of-hospital models of urgent care for children need to be introduced and evaluated without delay.

PROSPERO REGISTRATION NUMBER

CRD42021274374.

A comparison of statistical methods for modeling count data with an application to hospital length of stay

Background

Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models using simulated and empirical data.

Methods

Data were generated under different simulation scenarios with varying sample sizes, proportions of zeros, and levels of overdispersion. Analysis of hospital LOS was conducted using empirical data from the Medical Information Mart for Intensive Care database.

Results

Results showed that Poisson and ZIP models performed poorly in overdispersed data. ZIP outperformed the rest of the regression models when the overdispersion is due to zero-inflation only. NB and ZINB regression models faced substantial convergence issues when incorrectly used to model equidispersed data. NB model provided the best fit in overdispersed data and outperformed the ZINB model in many simulation scenarios with combinations of zero-inflation and overdispersion, regardless of the sample size. In the empirical data analysis, we demonstrated that fitting incorrect models to overdispersed data leaded to incorrect regression coefficients estimates and overstated significance of some of the predictors.

Conclusions

Based on this study, we recommend to the researchers that they consider the ZIP models for count data with zero-inflation only and NB models for overdispersed data or data with combinations of zero-inflation and overdispersion. If the researcher believes there are two different data generating mechanisms producing zeros, then the ZINB regression model may provide greater flexibility when modeling the zero-inflation and overdispersion.