ABSTRACT
BACKGROUND: The COVID-19 pandemic has highlighted the critical importance of robust healthcare capacity planning and preparedness for emerging crises. However, healthcare systems must also adapt to more gradual temporal changes in disease prevalence and demographic composition over time. To support proactive healthcare planning, statistical capacity forecasting models can provide valuable information to healthcare planners. This systematic literature review and evidence mapping aims to identify and describe studies that have used statistical forecasting models to estimate healthcare capacity needs within hospital settings. METHOD: Studies were identified in the databases MEDLINE and Embase and screened for relevance before items were defined and extracted within the following categories: forecast methodology, measure of capacity, forecast horizon, healthcare setting, target diagnosis, validation methods, and implementation. RESULTS: 84 studies were selected, all focusing on various capacity outcomes, including number of hospital beds/ patients, staffing, and length of stay. The selected studies employed different analytical models grouped in six items; discrete event simulation (N = 13, 15 %), generalized linear models (N = 21, 25 %), rate multiplication (N = 15, 18 %), compartmental models (N = 14, 17 %), time series analysis (N = 22, 26 %), and machine learning not otherwise categorizable (N = 12, 14 %). The review further provides insights into disease areas with infectious diseases (N = 24, 29 %) and cancer (N = 12, 14 %) being predominant, though several studies forecasted healthcare capacity needs in general (N = 24, 29 %). Only about half of the models were validated using either temporal validation (N = 39, 46 %), cross-validation (N = 2, 2 %) or/and geographical validation (N = 4, 5 %). CONCLUSION: The forecasting models' applicability can serve as a resource for healthcare stakeholders involved in designing future healthcare capacity estimation. The lack of routine performance validation of the used algorithms is concerning. There is very little information on implementation and follow-up validation of capacity planning models.
ABSTRACT
OBJECTIVES: Breastfeeding problems are common and associated with early cessation. Still length of postpartum hospital stay has been reduced. This leaves new mothers to establish breastfeeding at home with less support from health care professionals. The objective was to explore mothers' perspectives on when breastfeeding problems were the most challenging and prominent early postnatal. The aim was also to identify possible factors associated with the breastfeeding problems. METHODS: In a cross-sectional study, a mixed method approach was used to analyse postal survey data from 1437 mothers with full term singleton infants. Content analysis was used to analyse mothers' open text descriptions of their most challenging breastfeeding problem. Multiple logistic regression was used to calculate odds ratios for early breastfeeding problems according to sociodemographic- and psychosocial factors. RESULTS: Up to 40% of the mothers had experienced early breastfeeding problems. The problems were associated with the mother, the infant and to lack of support from health care professionals. Most prominent problems were infant's inability to latch on (40%) and mothers having sore, wounded and cracked nipples (38%). Pain often occurred when experiencing breastfeeding problems. Factors associated with the problems were primiparity, lower self-efficacy and lower self-perceived knowledge of breastfeeding. Mothers with no or short education reported less frequently breastfeeding problems. CONCLUSIONS: Breastfeeding problems occurred frequently in the early postnatal period and often caused breastfeeding to be painful. Health care professionals should prepare mothers to deal with possible breastfeeding problems. New support options should be reviewed in an early postnatal discharge setting.
