Malaria elimination in Ghana: recommendations for reactive case detection strategy implementation in a low endemic area of Asutsuare, Ghana.

BACKGROUND: Progress toward malaria elimination is increasing as many countries near zero indigenous malaria cases. In settings nearing elimination, interventions will be most effective at interrupting transmission when targeted at the residual foci of transmission. These foci may be missed due to asymptomatic infections. To solve this problem, the World Health Organization recommends reactive case detection (RACD). This case study was conducted to identify individuals with asymptomatic malaria, their predisposing risk factors and recommend RACD in Asutsuare, Ghana based on literature review and a cross sectional study. METHODS: The study involved a search on PubMed and Google Scholar of literature published between 1st January, 2009-14th August, 2023 using the search terms "malaria" in "Asutsuare". Furthermore, structured questionnaires were administered to one hundred individuals without symptoms of malaria and screened using rapid diagnostic test (RDT) kits, microscopy and real-time polymerase chain reaction (rt-PCR). Malaria prevalence based on the three diagnostic techniques as well as potential malaria risk factors were assessed through questionnaires in a cross-sectional study. RESULTS: Cumulatively, sixty-four (64) studies (Google Scholar, 57 and PubMed, 7) were reviewed and 22 studies included in the literature on malaria in Asutsuare, Ghana. Significant risk factors were occupation, distance from a house to a waterbody, age group and educational level. Out of the 100 samples, 3 (3%) were positive by RDT, 6 (6%) by microscopy and 9 (9%) by rt-PCR. Ages 5-14.9 years had the highest mean malaria parasite densities of 560 parasites/µl with Plasmodium falciparum as the dominant species in 4 participants. Moreover, in the age group ≥ 15, 2 participants (1 each) harboured P. falciparum and Plasmodium malariae parasites. RDT had a higher sensitivity (76.54%; CI95 66.82-85.54) than rt-PCR (33.33%; CI95 4.33-77.72), while both rt-PCR and RDT were observed to have a higher specificity (92.55; CI95 85.26-96.95) and (97.30; CI95 93.87-99.13), respectively in the diagnosis of malaria. CONCLUSION: In Asutsuare, Ghana, a low endemic area, the elimination of malaria may require finding individuals with asymptomatic infections. Given the low prevalence of asymptomatic individuals identified in this study and as repleted in the literature review, which favours RACD, Asutsuare is a possible setting receptive for RACD implementation.

The Information Geometry of Sensor Configuration.

In problems of parameter estimation from sensor data, the Fisher information provides a measure of the performance of the sensor; effectively, in an infinitesimal sense, how much information about the parameters can be obtained from the measurements. From the geometric viewpoint, it is a Riemannian metric on the manifold of parameters of the observed system. In this paper, we consider the case of parameterized sensors and answer the question, "How best to reconfigure a sensor (vary the parameters of the sensor) to optimize the information collected?" A change in the sensor parameters results in a corresponding change to the metric. We show that the change in information due to reconfiguration exactly corresponds to the natural metric on the infinite-dimensional space of Riemannian metrics on the parameter manifold, restricted to finite-dimensional sub-manifold determined by the sensor parameters. The distance measure on this configuration manifold is shown to provide optimal, dynamic sensor reconfiguration based on an information criterion. Geodesics on the configuration manifold are shown to optimize the information gain but only if the change is made at a certain rate. An example of configuring two bearings-only sensors to optimally locate a target is developed in detail to illustrate the mathematical machinery, with Fast Marching methods employed to efficiently calculate the geodesics and illustrate the practicality of using this approach.

Evidence-based out-of-hours hospital medicine.

Out-of-hours (OOH) hospital ward cover is generally provided by junior doctors and is typified by heavy workloads, reduced staff numbers and various non-urgent nurse-initiated requests. The present inefficiencies and management problems with the OOH service are reflected by the high number of quality improvement projects recently published. In this narrative review, five common situations peculiar to the OOH general ward setting are discussed with reference to potential areas of inefficiency and unnecessary management steps: (1) prescription of hypnotics and sedatives; (2) overnight fluid therapy; (3) fever; (4) overnight hypotension and (5) chasing outstanding routine diagnostic tests. It is evident that research and consensus guidelines for many clinical situations in the OOH setting are a neglected arena. Many recommendations made herein are based on expert opinion or first principles. In contrast, the management of significant abnormalities in outstanding blood results is based on well-established guidelines using high-quality systematic reviews.

New Jersey COVID-19 municipal dataset.

Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.