Cross-sectional study to predict subnational levels of health workers’ knowledge about severe malaria treatment in Kenya

ObjectivesThis study applied a Bayesian hierarchical ecological spatial model beyond predictor analysis to test for the best fitting spatial effects model to predict subnational levels of health workers’ knowledge of severe malaria treatment policy, artesunate dosing, and preparation.SettingCounty referral government and major faith-based hospitals across 47 counties in Kenya in 2019.Design and participantsA secondary analysis of cross-sectional survey data from 345 health workers across 89 hospitals with inpatient departments who were randomly selected and interviewed.Outcome measuresThree ordinal outcome variables for severe malaria treatment policy, artesunate dose and preparation were considered, while 12 individual and contextual predictors were included in the spatial models.ResultsA third of the health workers had high knowledge levels on artesunate treatment policy; almost three-quarters had high knowledge levels on artesunate dosing and preparation. The likelihood of having high knowledge on severe malaria treatment policy was lower among nurses relative to clinicians (adjusted OR (aOR)=0.48, 95% CI 0.25 to 0.87), health workers older than 30 years were 61% less likely to have high knowledge about dosing compared with younger health workers (aOR=0.39, 95% CI 0.22 to 0.67), while health workers exposed to artesunate posters had 2.4-fold higher odds of higher knowledge about dosing compared with non-exposed health workers (aOR=2.38, 95% CI 1.22 to 4.74). The best model fitted with spatially structured random effects and spatial variations of the knowledge level across the 47 counties exhibited neighbourhood influence.ConclusionsKnowledge of severe malaria treatment policies is not adequately and optimally available among health workers across Kenya. The factors associated with the health workers’ level of knowledge were cadre, age and exposure to artesunate posters. The spatial maps provided subnational estimates of knowledge levels for focused interventions.

The Case Against Censoring of Progression-Free Survival in Cancer Clinical Trials – A Pandemic Shutdown as an Illustration

Missing data may lead to loss of statistical power and introduce bias in clinical trials. The ongoing Covid-19 pandemic has had a profound impact on patient health care and on the conduct of cancer clinical trials. Restricted access to sites, medication and evaluations brings challenges to the analysis of clinical trials due to missing data. Although several endpoints may be affected, progression-free survival (PFS) is of major concern, given its frequent use as primary endpoint in advanced cancer and the fact that missed radiographic assessments are to be expected. If patients with progression have delayed radiographic assessment due to the pandemic, there is controversy between censoring at the last visit prior to a shutdown period or ascribing the progression date to the day the assessment is eventually done after the end of the shutdown. The recent introduction of the estimand framework creates an opportunity to define more precisely the target of estimation and ensure alignment between the scientific question and the statistical analysis. Two basic approaches can be considered for handling missing tumor scans due to the pandemic: a “treatment policy” strategy, which consists in ascribing events to the time they are observed, and a “hypothetical” approach of censoring patients with events during the shutdown period at the last assessment prior to that period. In this article, we show through simulations how these two approaches may affect the overall power of a study and bias the estimated treatment effect and median PFS estimates. As a general rule, we suggest that the treatment policy approach, which conforms with the intent-to-treat principle, should be the primary analysis in order to avoid unnecessary loss of power and minimize bias in median PFS estimates.

Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan

Objective In the diabetes treatment policy after the Kumamoto Declaration 2013, it is difficult to accurately predict the incidence of complications in patients using the JJ risk engine. This study was conducted to develop a prediction equation suitable for the current diabetes treatment policy using patient data from Kitasato University Kitasato Institute Hospital (Hospital A) and to externally validate the developed equation using patient data from Kitasato University Hospital (Hospital B). Outlier tests were performed on the patient data from Hospital A to exclude the outliers. Prediction equation was developed using the patient data excluding the outliers and was subjected to external validation. Results By excluding outlier data, we could develop a new prediction equation for the incidence of coronary heart disease (CHD) as a complication of type 2 diabetes, incorporating the use of antidiabetic drugs with a high risk of hypoglycemia. This is the first prediction equation in Japan that incorporates the use of antidiabetic drugs. We believe that it will be useful in preventive medicine for treatment for people at high risk of CHD as a complication of diabetes or other diseases. In the future, we would like to confirm the accuracy of this equation at other facilities.

Chronic Pulmonary Aspergillosis: Literature Review and Demonstration of Own Observations

Chronic pulmonary aspergillosis (CPA) is a severe disease that develops mainly in patients without obvious immune disorders. Computed tomography is the main instrumental method in the diagnosis of CPA, which is necessary to determine the form of the disease, to choose treatment policy, to combat complications, and to monitor therapy. This makes it important for a radiologist to understand the main aspects of timely and differential diagnosis. There are insufficient Russian studies on this problem. This paper analyzes the 2014–2020 Russian and foreign publications available in PubMed, Web of Science, Elsevier, and eLibrary electronic databases. When searching for information, the following keywords were used: “computed tomography”, “chronic pulmonary aspergillosis”, “aspergilloma”, “air-crescent symptom”, “differential diagnosis”.

Current guidelines for malaria treatment in Somalia: evidence-based recommendations

Case management – rapid diagnosis and prompt administration of artemisinin-based combination therapy (ACT) – is a fundamental pillar of recommended malaria interventions in Somalia. Unfortunately, the emergence and spread of drug resistant falciparum parasites continues to pose a considerable threat to effective case management. With technical and financial support from WHO, the efficacy of recommended ACTs has been regularly monitored in sentinel sites since 2003. These studies provided evidence that supported the adoption of artesunate-sulfadoxine/pyrimethamine as first-line treatment in 2005 and artemether-lumefantrine as second-line treatment in 2011. Efficacy studies conducted between 2011 and 2015 showed high artesunate-sulfadoxine/pyrimethamine treatment failure rates of 12.3% - 22.2%, above the threshold (10%) for a change of treatment policy as recommended by WHO. This was also associated with high prevalence of quadruple and quintuple mutations in the dihydrofolate reductase (Pfdhfr) and dihydropteroate synthase (Pfdhps) genes, which are associated with sulfadoxine/pyrimethamine resistance. Based on these findings, national malaria treatment guidelines were updated in 2016, with artesunate-sulfadoxine/pyrimethamine replaced by artemether-lumefantrine as first-line treatment and dihydroartemisinin-piperaquine recommended as second-line treatment. Subsequent efficacy studies in 2016 and 2017 confirmed that both the current first- and second-line treatments remain highly efficacious (cure rate above 97%). Technical and financial support from WHO has been instrumental in generating evidence that informs malaria treatment policy and should therefore continue to ensure that effective treatments are available to malaria patients in the country.

Impute the Missing Data Using Retrieved Dropouts

Background: In the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of primary results. Some of the methods are based on the assumption of missing at random (MAR) which assumes subjects who discontinue the treatment will maintain the treatment effect after discontinuation. The agency, however, has expressed concern over methods based on this overly optimistic assumption, because it hardly holds for subjects discontinuing the investigational product (IP). Although in recent years a good number of sensitivity analyses based on missing not at random (MNAR) assumptions have been proposed, some use very conservative assumption on which it might be hard for sponsors and regulators to reach common ground.Methods: Here we propose a multiple imputation method targeting at “treatment policy” estimand based on the MNAR assumption. This method can be used as the primary analysis, in addition to serving as a sensitivity analysis. It imputes missing data using information from retrieved dropouts defined as subjects who remain in the study despite occurrence of intercurrent events. Then imputed data long with completers and retrieved dropouts are analyzed altogether and finally multiple results are summarized into a single estimate. According to definition in ICH E9 (R1), this proposed approach fully aligns with the treatment policy estimand but its assumption is much more realistic and reasonable. Results: Our approach has well controlled type I error rate with no loss of power. As expected, the effect size estimates take into account any dilution effect contributed by retrieved dropouts, conforming to the MNAR assumption.Conclusions: Although multiple imputation approaches are always used as sensitivity analyses, this multiple imputation approach can be used as primary analysis for trials with sufficient retrieved dropouts or trials designed to collect retrieved dropouts.

Dynamics of an Epidemic Model under the Influence of Environmental Stress

We have considered a compartmental epidemiological model with infectious disease to observe the influence of environmental stress on disease transmission. The proposed model is well-defined as the population at each compartment remains positive and bounded with time. Dynamical behaviour of the model is observed by the stability and bifurcation analysis at the equilibrium points. Also, numerical simulation supports the theoretical proofs and the result shows that the system undergoes a forward bifurcation around the disease-free equilibrium. Our results indicate that with the increase of environmental pollution, the overall infected population increases. Also, the disease transmission rate among the susceptible and stressed population from asymptomatically infected individuals plays a crucial role to make a system endemic. A corresponding optimal control problem has also been proposed to control the disease prevalence as well as to minimize the cost by choosing the vaccination policy before being infected and treatment policy to the infected as control variables. Numerical figures indicate that the vaccination provided to susceptible needs some time to reduce the disease transmission but the vaccination provided to stressed individuals works immediately after implementation. The treatment policy for symptomatically infected individuals works with a higher rate at an earlier stage but the intensity decreases with time. Simultaneous implementation of all control interventions is more useful to reduce the size of overall infective individuals and also to minimize the economic burden. Hence, this research clearly expresses the impact of environmental pollution (specifically the influence of environmental stress) on the disease transmission in the population.