An economic analysis of the cost of survival of micro preemies: A systematic review.

OBJECTIVE: This study aimed to systematically review the current literature on the economic costs of micro preemie as well as evidence on the cost-effectiveness of interventions to improve outcomes for micro preemie babies with a birth weight of ≤500 g. METHOD: We searched MEDLINE, CINAHL, Scopus, ECONLIT, Business Source Premier and Cochrane Library for studies reporting costs of micro preemie from January 2000. Costs were inflated to 2019 United States dollars (US$). All full-text articles were assessed for eligibility and a quality assessment of included articles was conducted using the Drummond and the Larg and Moss checklists. RESULTS: The search identified three studies that met the inclusion criteria; two cost-of-illness studies and one cost-effectiveness study. Across studies, the mean healthcare spending per micro preemie survivor (in 2019 US$) ranged from US$61,310 (birth admission) to US$263,958 (inpatient and outpatient for the first six months of life). One modelling study reported exclusive human milk diet for micro preemies at birth was more cost-effective compared to the standard approach with cow milk diet from the third-party payer and societal perspectives. CONCLUSION: Despite significant advances in perinatal care and expanded access to life-saving equipment to improve survival outcomes of micro preemie, there remains a paucity of research on economic costs associated with these babies. No study has utilised quality-adjusted life-years as an outcome measure. Given the chronic conditions and long-term neurologic disability associated with micro preemie survivors, an estimate of the lifetime cost to the individual, healthcare providers and society would provide a benchmark of the potential cost-savings that could accrue from cost-effective interventions to improve the survival rate of micro preemies.

Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review.

In the more than 15 years since its introduction, quantitative microbial risk assessment (QMRA) has become a widely used technique for assessing population health risk posed by waterborne pathogens. However, the variation in approaches taken for QMRA in relation to drinking water supply is not well understood. This systematic review identifies, categorises, and critically synthesises peer-reviewed and academic case studies of QMRA implementation for existing distributed public drinking water supplies. Thirty-nine English-language, peer-reviewed and academic studies published from 2003 to 2019 were identified. Key findings were synthesised in narrative form. The overall designs of the included studies varied widely, as did the assumptions used in risk calculation, especially in relation to pathogen dose. There was also substantial variation in the degree to which the use of location-specific data weighed with the use of assumptions when performing risk calculation. In general, the included studies' complexity did not appear to be associated with greater result certainty. Factors relating to pathogen dose were commonly influential on risk estimates whereas dose-response parameters tended to be of low relative influence. In two of the included studies, use of the 'susceptible fraction' factor was inconsistent with recognised guidance and potentially led to the underestimation of risk. While approaches and assumptions used in QMRA need not be standardised, improvement in the reporting of QMRA results and uncertainties would be beneficial. It is recommended that future authors consider the water supply QMRA reporting checklist developed for the current review. Consideration of the broad types of uncertainty relevant to QMRA is also recommended. Policy-makers should consider emergent discussion on acute microbial health-based targets when setting normative guidelines. The continued representation of QMRA case studies within peer-reviewed and academic literature would also enhance future implementation. Further research is needed on the optimisation of QMRA resourcing given the application context.