Antimicrobial resistance: The complex challenge of measurement to inform policy and the public.
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ABSTRACT:
SUBMITTER: Wernli D
PROVIDER: S-EPMC5560527 | biostudies-other | 2017 Aug
REPOSITORIES: biostudies-other
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Project description:BACKGROUND:Health system expenditure on cancer drugs is rising rapidly in many OECD countries given the costly new treatments and increased rates of use due to a growing and ageing population. These factors put considerable strain on the sustainability of health systems worldwide, sparking public debate among clinicians, pharmaceutical companies, policy-makers and citizens on issues of affordability and equity. We engaged Canadians through a series of deliberative public engagement events to determine their priorities for making cancer drug funding decisions fair and sustainable in Canada's publicly financed health system. METHODS:An approach to deliberation was developed based on the McMaster Health Forum's citizen panels and the established Burgess and O'Doherty model of deliberative public engagement. Six deliberations were held across Canada in 2016. Transcripts were coded in NVivo and analysed to determine where participants' views converged and diverged. Recommendations were grouped thematically. RESULTS:A total of 115 Canadians participated in the deliberative events and developed 86 recommendations. Recommendations included the review and regular re-review of approved drugs using 'real-world' evidence on effectiveness and cost-effectiveness; prioritisation of treatments that restore patients' independence, mental health and general well-being; ensuring that decision processes, results and their rationales are transparent; and commitment to people with similar needs receiving the same care regardless of where in Canada they live. CONCLUSIONS:The next steps for policy-makers should be to develop mechanisms for (1) re-reviewing effectiveness and cost-effectiveness data for all cancer drugs; (2) making disinvestments in cancer drugs that satisfy requirements relating to grandfathering and compassionate access; (3) ensuring fair and equitable access to cancer drugs for all Canadians; and (4) fostering a pan-Canadian approach to cancer drug funding decisions.
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Project description:BACKGROUND:SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. OBJECTIVE:The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. METHODS:Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. RESULTS:A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (?210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (?2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. CONCLUSIONS:DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19-free only when there is an effective vaccine, and the "social" end of the pandemic will occur before the "medical" end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.
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