The impact of the first wave of the COVID‐19 pandemic on health service use by persons with dementia in Ontario, Canada: A population‐based time series analysis

Introduction Streptococcus pneumoniae is a cause of infections that range in severity from acute otitis media (AOM) to pneumonia and invasive pneumococcal disease (IPD). The 10-valent pneumococcal conjugate vaccine (PHiD-CV10) was introduced into the Icelandic paediatric immunisation programme in 2011. The aim was to estimate the population impact and cost-effectiveness of PHiD-CV10 introduction. Methods Data on primary care visits from 2005–2015 and hospitalisations from 2005–2017 were obtained from population-based registries. A Bayesian time series analysis with synthetic controls was employed to estimate the number of cases of AOM, pneumonia and IPD that would have occurred between 2013–2017, had PHiD-CV10 not been introduced. Prevented cases were calculated by subtracting the observed number of cases from this estimate. The cost of the programme was calculated accounting for cost-savings due to prevented cases. Results The introduction of PHiD-CV10 prevented 13,767 (95% credible interval [CI] 2,511–29,410) visits for AOM from 2013–2015, and prevented 1,814 (95%CI -523-4,512) hospitalisations for pneumonia and 53 (95%CI -17-177) admissions for IPD from 2013–2017. Visits for AOM decreased both among young children and among children 4–19 years of age, with rate ratios between 0.72–0.89. Decreases were observed in both pneumonia hospitalisations (rate ratios between 0.67–0.92) and IPD (rate ratios between 0.27–0.94). The total cost of implementing PHiD-CV10 in Iceland was -7,463,176 United States Dollars (USD) (95%CI -16,159,551–582,135) with 2.1 USD (95%CI 0.2–4.7) saved for every 1 USD spent. Conclusions The introduction of PHiD-CV10 was associated with large decreases in visits and hospitalisations for infections commonly caused by pneumococcus and was cost-saving during the first five years of the immunisation programme.

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The Impact of Lockdown in England on Daily Confirmed Cases of COVID-19 and Related Deaths Compared to Sweden: A Comparative Interrupted Time Series Analysis

SSRN Electronic Journal ◽

10.2139/ssrn.3629457 ◽

2020 ◽

Author(s):

Moaath Mustafa Ali ◽

Yazan Samhouri ◽

Marwa Sabha ◽

Lynna Alnimer

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Interrupted Time Series ◽

Interrupted Time Series Analysis ◽

Series Analysis ◽

The Impact

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Does admission prevalence change after reconfiguration of inpatient services? An interrupted time series analysis of the impact of reconfiguration in five centres

BMC Health Services Research ◽

10.1186/s12913-021-06070-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Joanne Martin ◽

Edwin Amalraj Raja ◽

Steve Turner

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Emergency Admission ◽

Interrupted Time Series ◽

Interrupted Time Series Analysis ◽

Emergency Admissions ◽

Series Analysis ◽

Inpatient Services ◽

The Impact ◽

Admission Prevalence

Abstract Background Service reconfiguration of inpatient services in a hospital includes complete and partial closure of all emergency inpatient facilities. The “natural experiment” of service reconfiguration may give insight into drivers for emergency admissions to hospital. This study addressed the question does the prevalence of emergency admission to hospital for children change after reconfiguration of inpatient services? Methods There were five service reconfigurations in Scottish hospitals between 2004 and 2018 where emergency admissions to one “reconfigured” hospital were halted (permanently or temporarily) and directed to a second “adjacent” hospital. The number of emergency admissions (standardised to /1000 children in the regional population) per month to the “reconfigured” and “adjacent” hospitals was obtained for five years prior to reconfiguration and up to five years afterwards. An interrupted time series analysis considered the association between reconfiguration and admissions across pairs comprised of “reconfigured” and “adjacent” hospitals, with adjustment for seasonality and an overall rising trend in admissions. Results Of the five episodes of reconfiguration, two were immediate closure, two involved closure only to overnight admissions and one with overnight closure for a period and then closure. In “reconfigured” hospitals there was an average fall of 117 admissions/month [95% CI 78, 156] in the year after reconfiguration compared to the year before, and in “adjacent” hospitals admissions rose by 82/month [32, 131]. Across paired reconfigured and adjacent hospitals, in the months post reconfiguration, the overall number of admissions to one hospital pair slowed, in another pair admissions accelerated, and admission prevalence was unchanged in three pairs. After reconfiguration in one hospital, there was a rise in admissions to a third hospital which was closer than the named “adjacent” hospital. Conclusions There are diverse outcomes for the number of emergency admissions post reconfiguration of inpatient facilities. Factors including resources placed in the community after local reconfiguration, distance to the “adjacent” hospital and local deprivation may be important drivers for admission pathways after reconfiguration. Policy makers considering reconfiguration might consider a number of factors which may be important determinants of admissions post reconfiguration.

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