We consider adaptive robust methods for lung cancer that are also dose-reactive, wherein the treatment is modified after each treatment session to account for the dose delivered in prior treatment sessions. Such methods are of interest because they potentially allow for errors in the delivered dose to be corrected as the treatment progresses, thereby ensuring that the tumor receives a sufficient dose at the end of the treatment. We show through a computational study with real lung cancer patient data that while dose reaction is beneficial with respect to the final dose distribution, it may lead to exaggerated daily underdose and overdose relative to non-reactive methods that grows as the treatment progresses. However, by combining dose reaction with a mechanism for updating an estimate of the uncertainty, the magnitude of this growth can be mitigated substantially. The key finding of this paper is that reacting to dose errors - an adaptation strategy that is both simple and intuitively appealing - may backfire and lead to treatments that are clinically unacceptable.

BACKGROUND: In urbanized, low-income cities with high rates of congestion, delivery of antiepileptic drugs (AEDs) by unmanned aerial vehicles (drones) to people with epilepsy for both emergency and non-urgent distribution may prove beneficial. METHODS: Conakry is the capital of the Republic of Guinea, a low-income sub-Saharan African country (2018 per capita gross national income US$830). We computed the number of drones and delivery times to distribute AEDs from a main urban hospital to 27 pre-identified gas stations, mosques and pharmacies and compared these to the delivery times of a personal vehicle. RESULTS: We predict that a single drone could serve all pre-identified delivery locations in Conakry within a 20.4-h period. In an emergency case of status epilepticus, 8, 20 and 24 of the 27 pre-identified destinations can be reached from the hub within 5, 10 and 15 min, respectively. Compared with the use of a personal vehicle, the response time for a drone is reduced by an average of 78.8% across all times of the day. CONCLUSIONS: Drones can dramatically reduce the response time for both emergency and routine delivery of lifesaving medicines. We discuss the advantages and disadvantages of such a drone delivery model with relevance to epilepsy. However, the commissioning of a trial of drones for drug delivery in related diseases and geographies is justified.

BACKGROUND: Immediate access to an automated external defibrillator (AED) increases the chance of survival for out-of-hospital cardiac arrest (OHCA). Current deployment usually considers spatial AED access, assuming AEDs are available 24 h a day. OBJECTIVES: The goal of this study was to develop an optimization model for AED deployment, accounting for spatial and temporal accessibility, to evaluate if OHCA coverage would improve compared with deployment based on spatial accessibility alone. METHODS: This study was a retrospective population-based cohort trial using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all nontraumatic public location OHCAs in Toronto, Ontario, Canada (January 2006 through August 2014) and obtained a list of registered AEDs (March 2015) from Toronto Paramedic Services. Coverage loss due to limited temporal access was quantified by comparing the number of OHCAs that occurred within 100 meters of a registered AED (assumed coverage 24 h per day, 7 days per week) with the number that occurred both within 100 meters of a registered AED and when the AED was available (actual coverage). A spatiotemporal optimization model was then developed that determined AED locations to maximize OHCA actual coverage and overcome the reported coverage loss. The coverage gain between the spatiotemporal model and a spatial-only model was computed by using 10-fold cross-validation. RESULTS: A total of 2,440 nontraumatic public OHCAs and 737 registered AED locations were identified. A total of 451 OHCAs were covered by registered AEDs under assumed coverage 24 h per day, 7 days per week, and 354 OHCAs under actual coverage, representing a coverage loss of 21.5% (p < 0.001). Using the spatiotemporal model to optimize AED deployment, a 25.3% relative increase in actual coverage was achieved compared with the spatial-only approach (p < 0.001). CONCLUSIONS: One in 5 OHCAs occurred near an inaccessible AED at the time of the OHCA. Potential AED use was significantly improved with a spatiotemporal optimization model guiding deployment.

BACKGROUND: Planning for the reliable and cost-effective supply of a health service commodity such as medical oxygen requires an understanding of the dynamic need or demand for the commodity over time. In developing country health systems, however, collecting longitudinal clinical data for forecasting purposes is very difficult. Furthermore, approaches to estimating demand for supplies based on annual averages can underestimate demand some of the time by missing temporal variability. METHODS: A discrete event simulation model was developed to estimate variable demand for a health service commodity using the important example of medical oxygen for childhood pneumonia. The model is based on five key factors affecting oxygen demand: annual pneumonia admission rate, hypoxaemia prevalence, degree of seasonality, treatment duration, and oxygen flow rate. These parameters were varied over a wide range of values to generate simulation results for different settings. Total oxygen volume, peak patient load, and hours spent above average-based demand estimates were computed for both low and high seasons. FINDINGS: Oxygen demand estimates based on annual average values of demand factors can often severely underestimate actual demand. For scenarios with high hypoxaemia prevalence and degree of seasonality, demand can exceed average levels up to 68% of the time. Even for typical scenarios, demand may exceed three times the average level for several hours per day. Peak patient load is sensitive to hypoxaemia prevalence, whereas time spent at such peak loads is strongly influenced by degree of seasonality. CONCLUSION: A theoretical study is presented whereby a simulation approach to estimating oxygen demand is used to better capture temporal variability compared to standard average-based approaches. This approach provides better grounds for health service planning, including decision-making around technologies for oxygen delivery. Beyond oxygen, this approach is widely applicable to other areas of resource and technology planning in developing country health systems.

INTRODUCTION: The COVID-19 pandemic has led to closures of non-essential businesses and buildings. The impact of such closures on automated external defibrillator (AED) accessibility compared to changes in foot traffic levels is unknown. METHODS: We identified all publicly available online AED registries in Canada last updated May 1, 2019 or later. We mapped AED locations to location types and classified each location type as completely inaccessible, partially inaccessible, or unaffected based on government-issued closure orders as of May 1, 2020. Using location and mobility data from Googles COVID-19 Community Mobility Reports, we identified the change in foot traffic levels between February 15-May 1, 2020 (excluding April 10-12) compared to the baseline of January 3-February 1, 2020, and determined the discrepancy between foot traffic levels and AED accessibility. RESULTS: We identified four provincial and two municipal AED registries containing a total of 5848 AEDs. Of those, we estimated that 69.9% were completely inaccessible, 18.8% were partially inaccessible, and 11.3% were unaffected. Parks, retail and recreation locations, and workplaces experienced the greatest reduction in AED accessibility. The greatest discrepancies between foot traffic levels and AED accessibility occurred in parks, retail and recreation locations, and transit stations. CONCLUSION: A majority of AEDs became inaccessible during the COVID-19 pandemic due to government-mandated closures. In a substantial number of locations across Canada, the reduction in AED accessibility was far greater than the reduction in foot traffic.

BACKGROUND: Efforts to guide automated external defibrillator placement for out-of-hospital cardiac arrest (OHCA) treatment have focused on identifying broadly defined location categories without considering hours of operation. Broad location categories may be composed of many businesses with varying accessibility. Identifying specific locations for automated external defibrillator deployment incorporating operating hours and time of OHCA occurrence may improve automated external defibrillator accessibility. We aim to identify specific businesses and municipal locations that maximize OHCA coverage on the basis of spatiotemporal assessment of OHCA risk in the immediate vicinity of franchise locations. METHODS: This study was a retrospective population-based cohort study using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all nontraumatic public OHCAs occurring in Toronto, ON, Canada, from January 2007 through December 2015. We identified 41 unique businesses and municipal location types with ≥20 locations in Toronto from the YellowPages, Canadian Franchise Association, and the City of Toronto Open Data Portal. We obtained their geographic coordinates and hours of operation from Web sites, by phone, or in person. We determined the number of OHCAs that occurred within 100 m of each location when it was open (spatiotemporal coverage) for Toronto overall and downtown. The businesses and municipal locations were then ranked by spatiotemporal OHCA coverage. To evaluate temporal stability of the rankings, we calculated intraclass correlation of the annual coverage values. RESULTS: There were 2654 nontraumatic public OHCAs. Tim Hortons ranked first in Toronto, covering 286 OHCAs. Starbucks ranked first in downtown, covering 110 OHCAs. Coffee shops and bank machines from the 5 largest Canadian banks occupied 8 of the top 10 spots in both Toronto and downtown. The rankings exhibited high temporal stability with intraclass correlation values of 0.88 (95% confidence interval, 0.83-0.93) in Toronto and 0.79 (95% confidence interval, 0.71-0.86) in downtown. CONCLUSIONS: We identified and ranked businesses and municipal locations by spatiotemporal OHCA risk in their immediate vicinity. This approach may help policy makers and funders to identify and prioritize potential partnerships for automated external defibrillator deployment in public-access defibrillator programs.

BACKGROUND: Cities worldwide have underground or above-ground enclosed walkway systems for pedestrian travel, representing unique environments for studying out-of-hospital cardiac arrests (OHCAs). The characteristics and outcomes of OHCAs that occur in such systems are unknown. OBJECTIVE: To determine whether OHCAs occurring in enclosed pedestrian walkway systems have differing demographics, prehospital intervention, and survival outcomes compared to the encompassing city, by examining the PATH walkway system in Toronto. METHODS: We identified all atraumatic, public-location OHCAs in Toronto from April 2006 to March 2016. Exclusion criteria were obvious death, existing DNR, and EMS-witnessed OHCAs. OHCAs were classified into mutually exclusive location groups: Toronto, Downtown, and PATH-accessible. PATH-accessible OHCAs were those that occurred within the PATH system between the first basement and third floor. We analyzed demographic, prehospital intervention, and survival data using t-tests and chi-squared tests. RESULTS: We identified 2172 OHCAs: 1752 Toronto, 371 Downtown, and 49 PATH-accessible. Compared to Toronto, a significantly higher proportion of PATH-accessible OHCAs was bystander-witnessed (62.6% vs 83.7%, p=0.003), had bystander CPR (56.6% vs 73.5%, p=0.019), bystander AED use (11.0% vs 42.6%, p<0.001), shockable initial rhythm (45.5% vs 72.9%, p<0.001), and overall survival (18.5% vs 33.3%, p=0.009). Similar significant differences were observed when compared to Downtown. CONCLUSIONS: This study suggests that OHCAs in enclosed pedestrian walkway systems are uniquely different from other public settings. Bystander resuscitation efforts are significantly more frequent and survival rates are significantly higher. Urban planners in similar infrastructure systems worldwide should consider these findings when determining AED placement and public engagement strategies.

Background Mathematical optimization of automated external defibrillator (AED) placement may improve AED accessibility and out-of-hospital cardiac arrest (OHCA) outcomes compared with American Heart Association (AHA) and European Resuscitation Council (ERC) placement guidelines. We conducted an in silico trial (simulated prospective cohort study) comparing mathematically optimized placements with placements derived from current AHA and ERC guidelines, which recommend placement in locations where OHCAs are usually witnessed. Methods and Results We identified all public OHCAs of presumed cardiac cause from 2008 to 2016 in Copenhagen, Denmark. For the control, we computationally simulated placing 24/7-accessible AEDs at every unique, public, witnessed OHCA location at monthly intervals over the study period. The intervention consisted of an equal number of simulated AEDs placements, deployed monthly, at mathematically optimized locations, using a model that analyzed historical OHCAs before that month. For each approach, we calculated the number of OHCAs in the study period that occurred within a 100-m route distance based on Copenhagens road network of an available AED after it was placed (OHCA coverage). Estimated impact on bystander defibrillation and 30-day survival was calculated by multivariate logistic regression. The control scenario involved 393 AEDs at historical, public, witnessed OHCA locations, covering 15.8% of the 653 public OHCAs from 2008 to 2016. The optimized locations provided significantly higher coverage (24.2%; P<0.001). Estimated bystander defibrillation and 30-day survival rates increased from 15.6% to 18.2% (P<0.05) and from 32.6% to 34.0% (P<0.05), respectively. As a baseline, the 1573 real AEDs in Copenhagen covered 14.4% of the OHCAs. Conclusions Mathematical optimization can significantly improve OHCA coverage and estimated clinical outcomes compared with a guidelines-based approach to AED placement.

BACKGROUND: Operations research (OR) is a discipline that uses advanced analytical methods (e.g. simulation, optimisation, decision analysis) to better understand complex systems and aid in decision-making. Herein, we present a scoping review of the use of OR to analyse issues in global health, with an emphasis on health equity and research impact. A systematic search of five databases was designed to identify relevant published literature. A global overview of 1099 studies highlights the geographic distribution of OR and common OR methods used. From this collection of literature, a narrative description of the use of OR across four main application areas of global health - health systems and operations, clinical medicine, public health and health innovation - is also presented. The theme of health equity is then explored in detail through a subset of 44 studies. Health equity is a critical element of global health that cuts across all four application areas, and is an issue particularly amenable to analysis through OR. Finally, we present seven select cases of OR analyses that have been implemented or have influenced decision-making in global health policy or practice. Based on these cases, we identify three key drivers for success in bridging the gap between OR and global health policy, namely international collaboration with stakeholders, use of contextually appropriate data, and varied communication outlets for research findings. Such cases, however, represent a very small proportion of the literature found. CONCLUSION: Poor availability of representative and quality data, and a lack of collaboration between those who develop OR models and stakeholders in the contexts where OR analyses are intended to serve, were found to be common challenges for effective OR modelling in global health.