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BackgroundPublic health mitigation strategies in British Columbia during the pandemic included stay-at-home orders and closure of non-essential services. While most primary physicians’ offices were closed, hospitals prepared for a pandemic surge and emergency departments (EDs) stayed open to provide care for urgent needs. We sought to determine whether ED paediatric presentations prior and during the COVID-19 pandemic changed and review acuity compared with seasonal adjusted prior year.MethodsWe analysed records from 18 EDs in British Columbia, Canada, serving 60% of the population. We included children 0–16 years old and excluded those with no recorded acuity or discharge disposition and those left without being seen by a physician. We compared prepandemic (before the first COVID-19 case), early pandemic (after first COVID-19 case) and peak pandemic (during public health emergency) periods as well as a similar time from the previous year.ResultsA reduction of 57% and 70% in overall visits was recorded in the children’s hospital ED and the general hospitals EDs, respectively. Average daily visits declined significantly during the peak-pandemic period (167.44±40.72) compared with prepandemic period (543.53±58.8). Admission rates increased mainly due to the decrease in the rate of visits with lower acuity. Children with complaints of ‘fever’ and ‘gastrointestinal’ symptoms had both the largest overall volume and per cent reduction in visits between peak-pandemic and prior year (79% and 74%, respectively).ConclusionPaediatric emergency medicine attendances were reduced to one-third of normal numbers during the 2020 COVID-19 lockdown in British Columbia, Canada, with the reduction mainly seen in minor illnesses that do not usually require admission.
Background and aimsThe province of British Columbia (BC) Canada has experienced a rapid increase in illicit drug overdoses and deaths during the last 4 years, with a provincial emergency declared in April 2016. These deaths have been driven primarily by the introduction of synthetic opioids into the illicit opioid supply. This study aimed to measure the combined impact of large‐scale opioid overdose interventions implemented in BC between April 2016 and December 2017 on the number of deaths averted.DesignWe expanded on the mathematical modelling methodology of our previous study to construct a Bayesian hierarchical latent Markov process model to estimate monthly overdose and overdose‐death risk, along with the impact of interventions.Setting and CasesOverdose events and overdose‐related deaths in BC from January 2012 to December 2017.InterventionsThe interventions considered were take‐home naloxone kits, overdose prevention/supervised consumption sites and opioid agonist therapyMeasurementsCounterfactual simulations were performed with the fitted model to estimate the number of death events averted for each intervention and in combination.FindingsBetween April 2016 and December 2017, BC observed 2177 overdose deaths (77% fentanyl‐detected). During the same period, an estimated 3030 (2900–3240) death events were averted by all interventions combined. In isolation, 1580 (1480–1740) were averted by take‐home naloxone, 230 (160–350) by overdose prevention services and 590 (510–720) were averted by opioid agonist therapy.ConclusionsA combined intervention approach has been effective in averting overdose deaths during British Columbia's opioid overdose crisis in the period since declaration of a public health emergency (April 2016–December 2017). However, the absolute numbers of overdose deaths have not changed.
BackgroundWith ambitious targets to eliminate lymphatic filariasis over the coming years, there is a need to identify optimal strategies to achieve them in areas with different baseline prevalence and stages of control. Modelling can assist in identifying what data should be collected and what strategies are best for which scenarios.MethodsWe develop a new individual-based, stochastic mathematical model of the transmission of lymphatic filariasis. We validate the model by fitting to a first time point and predicting future timepoints from surveillance data in Kenya and Sri Lanka, which have different vectors and different stages of the control programme. We then simulate different treatment scenarios in low, medium and high transmission settings, comparing once yearly mass drug administration (MDA) with more frequent MDA and higher coverage. We investigate the potential impact that vector control, systematic non-compliance and different levels of aggregation have on the dynamics of transmission and control.ResultsIn all settings, increasing coverage from 65 to 80 % has a similar impact on control to treating twice a year at 65 % coverage, for fewer drug treatments being distributed. Vector control has a large impact, even at moderate levels. The extent of aggregation of parasite loads amongst a small portion of the population, which has been estimated to be highly variable in different settings, can undermine the success of a programme, particularly if high risk sub-communities are not accessing interventions.ConclusionEven moderate levels of vector control have a large impact both on the reduction in prevalence and the maintenance of gains made during MDA, even when parasite loads are highly aggregated, and use of vector control is at moderate levels. For the same prevalence, differences in aggregation and adherence can result in very different dynamics. The novel analysis of a small amount of surveillance data and resulting simulations highlight the need for more individual level data to be analysed to effectively tailor programmes in the drive for elimination.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-1152-3) contains supplementary material, which is available to authorized users.
Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination ‘as a public health problem’ when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models’ predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020.
Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11–0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the “contact ratio” to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19–0.60) in BC. We developed an R package ‘covidseir’ to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11–0.34]), New York (0.60 [0.43–0.74]), Washington (0.84 [0.79–0.90]) and Florida (0.86 [0.76–0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07–1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures—if sufficiently strong and robustly followed—could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.
Canadian Institutes of Health Research Partnerships for Health Systems Improvement programme (grant 318068) and Natural Science and Engineering Research Council of Canada (grant 04611).
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