Browsing by Subject "Markov model"

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    A hypothetical implementation of 'Termination of Resuscitation' protocol for out-of-hospital cardiac arrest.
    (Resuscitation plus, 2021-06) Nazeha, Nuraini; Ong, Marcus Eng Hock; Limkakeng, Alexander T; Ye, Jinny J; Joiner, Anjni Patel; Blewer, Audrey; Shahidah, Nur; Nadarajan, Gayathri Devi; Mao, Desmond Renhao; Graves, Nicholas

    Background

    Out-of-hospital cardiac arrests with negligible chance of survival are routinely transported to hospital and many are pronounced dead thereafter. This leads to some potentially avoidable costs. The 'Termination of Resuscitation' protocol allows paramedics to terminate resuscitation efforts onsite for medically futile cases. This study estimates the changes in frequency of costly events that might occur when the protocol is applied to out-of-hospital cardiac arrests, as compared to existing practice.

    Methods

    We used Singapore data from the Pan-Asian Resuscitation Outcomes Study, from 1 Jan 2014 to 31 Dec 2017. A Markov model was developed to summarise the events that would occur in two scenarios, existing practice and the implementation of a Termination of Resuscitation protocol. The model was evaluated for 10,000 hypothetical patients with a cycle duration of 30 days after having a cardiac arrest. Probabilistic sensitivity analysis accounted for uncertainties in the outcomes: number of urgent transports and emergency treatments, inpatient bed days, and total number of deaths.

    Results

    For every 10,000 patients, existing practice resulted in 1118 (95% Uncertainty Interval 1117 to 1119) additional urgent transports to hospital and subsequent emergency treatments. There were 93 (95% Uncertainty Interval 66 to 120) extra inpatient bed days used, and 3 fewer deaths (95% Uncertainty Interval 2 to 4) in comparison to using the protocol.

    Conclusion

    The findings provide some evidence for adopting the Termination of Resuscitation protocol. This policy could lead to a reduction in costs and non-beneficial hospital admissions, however there may be a small increase in the number of avoidable deaths.
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    Health benefits and economic advantages associated with increased utilization of a smoking cessation program.
    (Journal of comparative effectiveness research, 2020-08-20) Datta, Santanu K; Dennis, Paul A; Davis, James M
    Rationale, aim & objective: The goal of this study was to examine the health and economic impacts related to increased utilization of the Duke Smoking Cessation Program resulting from the addition of two relatively new referral methods - Best Practice Advisory and Population Outreach. Materials & methods: In a companion paper 'Comparison of Referral Methods into a Smoking Cessation Program', we report results from a retrospective, observational, comparative effectiveness study comparing the impact of three referral methods - Traditional Referral, Best Practice Advisory and Population Outreach on utilization of the Duke Smoking Cessation Program. In this paper we take the next step in this comparative assessment by developing a Markov model to estimate the improvement in health and economic outcomes when two referral methods - Best Practice Advisory and Population Outreach - are added to Traditional Referral. Data used in this analysis were collected from Duke Primary Care and Disadvantaged Care clinics over a 1-year period (1 October 2017-30 September 2018). Results: The addition of two new referral methods - Best Practice Advisory and Population Outreach - to Traditional Referral increased the utilization of the Duke Smoking Cessation Program in Primary Care clinics from 129 to 329 smokers and in Disadvantaged Care clinics from 206 to 401 smokers. The addition of these referral methods was estimated to result in 967 life-years gained, 408 discounted quality-adjusted life-years saved and total discounted lifetime direct healthcare cost savings of US$46,376,285. Conclusion: Health systems may achieve increased patient health and decreased healthcare costs by adding Best Practice Advisory and Population Outreach strategies to refer patients to smoking cessation services.
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    Influenza Vaccination Implementation in Sri Lanka: A Cost-Effectiveness Analysis.
    (Vaccines, 2023-05) Neighbors, Coralei E; Myers, Evan R; Weerasinghe, Nayani P; Wijayaratne, Gaya B; Bodinayake, Champica K; Nagahawatte, Ajith; Tillekeratne, L Gayani; Woods, Christopher W
    Influenza causes an estimated 3 to 5 million cases of severe illness annually, along with substantial morbidity and mortality, particularly in low- and middle-income countries (LMICs). Currently, Sri Lanka has no influenza vaccination policies and does not offer vaccination within the public healthcare sector. Therefore, we performed a cost-effectiveness analysis of influenza vaccine implementation for the Sri Lankan population. We designed a static Markov model that followed a population cohort of Sri Lankans in three age groups, 0-4, 5-64, and 65+ years, through two potential scenarios: trivalent inactivated vaccination (TIV) and no TIV across twelve-monthly cycles using a governmental perspective at the national level. We also performed probabilistic and one-way sensitivity analyses to identify influential variables and account for uncertainty. The vaccination model arm reduced influenza outcomes by 20,710 cases, 438 hospitalizations, and 20 deaths compared to no vaccination in one year. Universal vaccination became cost-effective at approximately 98.01% of Sri Lanka's 2022 GDP per capita (incremental cost-effectiveness ratio = 874,890.55 Rs/DALY averted; 3624.84 USD/DALY averted). Results were most sensitive to the vaccine coverage in the 5-64-year-old age group, the cost of the influenza vaccine dose in the 5-64-years-old age group, vaccine effectiveness in the under-5-years-old age group, and the vaccine coverage in the under-5-years-old age group. No value for a variable within our estimated ranges resulted in ICERs above Rs. 1,300,000 (USD 5386.15) per DALY adverted. Providing influenza vaccines was considered highly cost-effective compared to no vaccines. However, large-scale national studies with improved data are needed to better inform estimates and determine the impact of vaccination implementation.