Developing Geostatistical Space–Time Models to Predict Outpatient Treatment Burdens from Incomplete National Data

Basic health system data such as the number of patients utilizing different health facilities and the types of illness for which they are being treated are critical for managing service provision. These data requirements are generally addressed with some form of national Health Management Information System (HMIS), which coordinates the routine collection and compilation of data from national health facilities. HMIS in most developing countries are characterized by widespread underreporting. Here we present a method to adjust incomplete data to allow prediction of national outpatient treatment burdens. We demonstrate this method with the example of outpatient treatments for malaria within the Kenyan HMIS. Three alternative modeling frameworks were developed and tested in which space–time geostatistical prediction algorithms were used to predict the monthly tally of treatments for presumed malaria cases (MC) at facilities where such records were missing. Models were compared by a cross-validation exercise and the model found to most accurately predict MC incorporated available data on the total number of patients visiting each facility each month. A space–time stochastic simulation framework to accompany this model was developed and tested in order to provide estimates of both local and regional prediction uncertainty. The level of accuracy provided by the predictive model, and the accompanying estimates of uncertainty around the predictions, demonstrate how this tool can mitigate the uncertainties caused by missing data, substantially enhancing the utility of existing HMIS data to health-service decision makers.     

The Index of Economic Disparity: Measuring trends in economic disparity across Canadian Census Subdivisions and rural and urban communities

Territorial inequalities have long been a subject of study and concern in Canada. In the face of large structural changes such as industrial shifts and the decarbonization of our economies, there is an urgency to understand such inequalities and design effective policy interventions for those places facing persistent economic decline. This paper shares a novel composite index that measures economic disparity across Canadian Census Subdivisions (CSDs) using Census data from 2001 through 2016 and the 2011 National Household Survey. Named the “Index of Economic Disparity,” it is comprised of an equally weighted average of four sub-indices that assign percentile rankings for all CSDs based on whether they experience persistent and substantial decline in key economic areas: population, labour force outcomes, working-age share of population, and industrial diversity. The variation of outcomes across geographies—urban and rural—highlights the importance of place-based policies.     

Best practices for measuring community resources across Canada: A comparison of coding classifications

Social scientists, geographers, criminologists, and health scientists are often tasked with finding data to best capture the impact of “community context” on individual outcomes, including residential services, physical resources, and social institutions. One outlet for such data in Canada is Digital Map Technologies Inc. (DMTI) Spatial, which offers a national repository of over one million businesses and recreational points of interest. The database is generated through CanMap Streetfiles, which includes geocodes of each point's precise location. These data are available to researchers from their university data library and Esri Canada, but primarily available to private sector and government markets. That said, the goal of the current paper is to encourage researchers to access this rich yet under-utilized data source. Each service, business, or resource in the DMTI Spatial database is assigned to a respective category using Standard Industrial Classification codes and North American Industrial Classification System codes. It is not clear, however, which is the more reliable coding criteria. We provide an overview of our review of DMTI Spatial data and take-away suggestions for using this valuable resource for future research on meso-level residential markers.     

Beyond the Genome: Genetically Modified Crops in Africa and the Implications for Genome Editing

Genome editing — a plant-breeding technology that facilitates the manipulation of genetic traits within living organisms — has captured the imagination of scholars and professionals working on agricultural development in Africa. Echoing the arrival of genetically modified (GM) crops decades ago, genome editing is being heralded as a technology with the potential to revolutionize breeding based on enhanced precision, reduced cost and increased speed. This article makes two interventions. First, it identifies the discursive continuity linking genome editing and the earlier technology of genetic modification. Second, it offers a suite of recommendations regarding how lessons learned from GM crops might be integrated into future breeding programmes focused on genome editing. Ultimately, the authors argue that donors, policy makers and scientists should move beyond the genome towards systems-level thinking by prioritizing the co-development of technologies with farmers; using plant material that is unencumbered by intellectual property restrictions and therefore accessible to resource-poor farmers; and acknowledging that seeds are components of complex and dynamic agroecological production systems. If these lessons are not heeded, genome-editing projects are in danger of repeating mistakes of the past.     

Police unions and use-of-force reforms in American cities

Following the murder of George Floyd, there was widespread debate about the policies that govern the use of force by police, but municipal leaders suggested that police unions kept many of the proposed changes from being adopted. Although there is anecdotal and scholarly evidence that unions frequently oppose policing reform, the limited literature in this area actually offers somewhat mixed conclusions about the relationship between union strength and the success of previous reform efforts. In this study, we draw on Halpin's theory of interest groups as politically adaptive organizations to develop expectations about the behavior of police unions in police governance subsystems. We hypothesize that union attempts to influence policy will correlate positively with reform adoption when the political environment is conducive to victory on that front or makes the cost of opposition too high and negatively when the opposite conditions hold. Analyses of the impact of union campaign contributions on the adoption of use-of-force policies in the 100 largest U.S. cities demonstrate that the nature and direction of union influence are moderated by the political climate of the jurisdiction in which they operate.