Decision-Making under Spatial Uncertainty in Downscaled Population Estimates: An Assessment of HIV Prevalence in Tanzania

Spatially explicit impact assessment analyses such as vulnerability studies often require spatially detailed population distribution as input. Downscaled population datasets otherwise known as disaggregated, gridded or fine resolution population datasets are becoming increasingly available at global and regional scales and are based on information from various sources with varying spatial and temporal resolutions as well as reliabilities. Uncertainty is endemic in such downscaled population estimates, particularly in developing countries yet it is hardly assessed. Consequently, decision-makers are potentially faced with a bias problem whereby uncertainties are masked and estimates are presented as unique or expected values even after being derived in a probabilistic context. This research explores how HIV prevalence in three districts of the United Republic of Tanzania might vary with the utilization of simulations of downscaled population estimates. In so doing, this study explores some scenarios in which HIV prevalence that corresponds to minimum expected cost of antiretroviral (ARV) treatment is estimated under three different decision-making attitudes, namely minimax regret, maximin and maximax, followed by a discussion of some implications of any variation in best estimates of HIV prevalence corresponding to the least impact on ARV cost. Our findings show that for effective decision analysis, rather than using coarse aggregated values such as census data at the district level, decision-makers may benefit from the application of multiple simulated spatial distributions of fine scale population along with the associated ARV cost estimates. From this distribution of ARV cost estimates, decision-makers could select best estimates based on an explication of risk attitude, thus avoiding unforeseeable consequences of underestimating or overestimating impact assessment outcomes of HIV prevalence

Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects

Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. Method: We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps. Results: We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots

Geographic Variation in Pneumonia and Influenza in Long-Term Care Facilities:A National Study

There is large county-level geographic variation in pneumonia and influenza hospitalizations among short-stay and long-stay long-term care facility residents in the United States. Long-term care facilities in counties in the Southern and Midwestern regions had the highest rates of pneumonia and influenza from 2013 to 2015. Future research should identify reasons for these geographic differences

Global Landscape Review of Serotype-Specific Invasive Pneumococcal Disease Surveillance among Countries Using PCV10/13: The Pneumococcal Serotype Replacement and Distribution Estimation (PSERENADE) Project.

Serotype-specific surveillance for invasive pneumococcal disease (IPD) is essential for assessing the impact of 10- and 13-valent pneumococcal conjugate vaccines (PCV10/13). The Pneumococcal Serotype Replacement and Distribution Estimation (PSERENADE) project aimed to evaluate the global evidence to estimate the impact of PCV10/13 by age, product, schedule, and syndrome. Here we systematically characterize and summarize the global landscape of routine serotype-specific IPD surveillance in PCV10/13-using countries and describe the subset that are included in PSERENADE. Of 138 countries using PCV10/13 as of 2018, we identified 109 with IPD surveillance systems, 76 of which met PSERENADE data collection eligibility criteria. PSERENADE received data from most (n = 63, 82.9%), yielding 240,639 post-PCV10/13 introduction IPD cases. Pediatric and adult surveillance was represented from all geographic regions but was limited from lower income and high-burden countries. In PSERENADE, 18 sites evaluated PCV10, 42 PCV13, and 17 both; 17 sites used a 3 + 0 schedule, 38 used 2 + 1, 13 used 3 + 1, and 9 used mixed schedules. With such a sizeable and generally representative dataset, PSERENADE will be able to conduct robust analyses to estimate PCV impact and inform policy at national and global levels regarding adult immunization, schedule, and product choice, including for higher valency PCVs on the horizon

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Decision-Making under Spatial Uncertainty in Downscaled Population Estimates: An Assessment of HIV Prevalence in Tanzania

Spatially explicit impact assessment analyses such as vulnerability studies often require spatially detailed population distribution as input. Downscaled population datasets otherwise known as disaggregated, gridded or fine resolution population datasets are becoming increasingly available at global and regional scales and are based on information from various sources with varying spatial and temporal resolutions as well as reliabilities. Uncertainty is endemic in such downscaled population estimates, particularly in developing countries yet it is hardly assessed. Consequently, decision-makers are potentially faced with a bias problem whereby uncertainties are masked and estimates are presented as unique or expected values even after being derived in a probabilistic context. This research explores how HIV prevalence in three districts of the United Republic of Tanzania might vary with the utilization of simulations of downscaled population estimates. In so doing, this study explores some scenarios in which HIV prevalence that corresponds to minimum expected cost of antiretroviral (ARV) treatment is estimated under three different decision-making attitudes, namely minimax regret, maximin and maximax, followed by a discussion of some implications of any variation in best estimates of HIV prevalence corresponding to the least impact on ARV cost. Our findings show that for effective decision analysis, rather than using coarse aggregated values such as census data at the district level, decision-makers may benefit from the application of multiple simulated spatial distributions of fine scale population along with the associated ARV cost estimates. From this distribution of ARV cost estimates, decision-makers could select best estimates based on an explication of risk attitude, thus avoiding unforeseeable consequences of underestimating or overestimating impact assessment outcomes of HIV prevalence

Identifying regional variation in place visit behavior during a global pandemic

The emergence of the SARS-CoV-2 virus in 2019 lead to a global pandemic that altered the activity behavior of most people on our planet. While government regulations and public concern modified visitation patterns to places of interest, little research has examined the nuanced changes in the length of time someone spends at a place, nor the regional variability of these changes. In this work, we examine place visit duration in four major U.S. cities, identify which place types saw the largest and smallest changes, and quantify variation between cities. Furthermore, we identify socio-economic and demographic factors that contribute to changes in visit duration and demonstrate the varying influence of these factors by region. The results of our analysis indicate that the pandemic's impact on visiting behavior varies between cities, though there are commonalities found in certain types of places. Our findings suggest that places of interest within lower income communities experienced less change in visit duration than others. An increase in the percentage of younger, Black or Hispanic populations within a community also resulted in a smaller decrease in visit duration than in other communities. These findings offer insight into the factors that contribute to changes in visiting behavior and the resilience of communities to a global pandemic

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

Abstract Background The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships