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Not all COVID-19 deaths are officially reported, and particularly in low-income and humanitarian settings, the magnitude of reporting gaps remains sparsely characterized. Alternative data sources, including burial site worker reports, satellite imagery of cemeteries, and social media-conducted surveys of infection may offer solutions. By merging these data with independently conducted, representative serological studies within a mathematical modeling framework, we aim to better understand the range of underreporting using examples from three major cities: Addis Ababa (Ethiopia), Aden (Yemen), and Khartoum (Sudan) during 2020. We estimate that 69 to 100%, 0.8 to 8.0%, and 3.0 to 6.0% of COVID-19 deaths were reported in each setting, respectively. In future epidemics, and in settings where vital registration systems are limited, using multiple alternative data sources could provide critically needed, improved estimates of epidemic impact. However, ultimately, these systems are needed to ensure that, in contrast to COVID-19, the impact of future pandemics or other drivers of mortality is reported and understood worldwide.

Background: Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution., Methods: We describe here a 'small-area estimation' method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method's implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts., Results: Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates., Conclusions: The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development., (© 2022. The Author(s).)

Some of the glutathione S-transferases (GSTs) are polymorphic and may play a role in lung cancer susceptibility. Our previous study in a French Caucasian study population suggested GSTM1 null genotype as a moderate risk factor for lung cancer. Here we extended the study to investigate the potential role of GSTT1 and GSTP1 polymorphisms in susceptibility to lung cancer, either separately or in combination. The study population consisted of 268 controls and 251 cases. Nineteen percent of the controls and 15% of the cases had GSTT1 null genotype. The distribution of GSTP1*A/*A, *A/*B and *B/*B genotypes were 46.9, 45.5 and 7.6% in controls, and 47.8, 40.2 and 12.0% in cases, respectively. No statistically significant effects in the lung cancer risk were observed for the GSTT1 genotypes, but the GSTP1*B/*B genotype posed a 2-fold risk [odds ratio (OR) = 2.0, 95% confidence interval (CI) 1.0-4.1] of this malignancy compared with the GSTP1*A allele containing genotypes; this association was mainly attributable to small cell lung cancer (OR = 3.6, 95% CI 1.3-9.8). The most remarkable risk was seen for the small cell carcinoma among subjects with the GSTP1*B/*B genotype and concurrent lack of the GSTM1 gene (OR = 6.9, 95% CI 1.6-30.2). The deficient genotypes for GSTM1 and GSTP1 seem thus to be important risk modifiers for lung cancer, especially in combination.

Glutathione S-transferases (GSTs) are metabolic phase II enzymes that promote reactive metabolite elimination by conjugating them to glutathione (GSH). Because of their important role in xenobiotic metabolism and detoxification, they have been implicated in carcinogenesis processes, especially epithelium transformation. Moreover, their influence on response to chemotherapy in cancer patients has been demonstrated. Genetic polymorphisms for GSTM1, GSTT1 and GSTP1 have been found in human populations and have been shown to have phenotypic consequences. To investigate the role of GST enzymes in carcinogenesis and in response to chemotherapy in patients with head and neck squamous cell carcinoma (HNSCC), GSTP1, GSTM1 and GSTT1 were studied prospectively in a large series of HNSCC patients. Correlations between GST alterations, p53 mutation status and clinical response to chemotherapy were investigated. We showed that the risk of developing laryngeal cancer was increased by 2.6-fold [95% CI 1.6--6.1] in patients with the GSTM1 null genotype and by 2.8-fold [95% CI 0.9--8.1] in patients with the homozygous GSTP1 val105 genotype. Furthermore, individuals with this latter genotype were over-represented in the p53 mutation group (p = 0.05). After storage duration and hemolysis adjustment, a significantly lower plasmatic GSTP1 level was observed in complete responders compared with partial and non-responders (mean: 4.4 +/- 0.06 microg/l, 4.7 +/- 0.06 microg/l and 4.7 +/- 0.07 microg/l; p = 0.05), respectively. The prevalence of p53-mutated tumors was significantly higher in the group of non-responders (81%) compared with partial (60%) and complete responders (64%) (p = 0.05). Two types of multivariate analysis were performed including parameters that have been shown to influence response to chemotherapy significantly in univariate analysis. p53 mutations and high tumor stage are independent factors of non-response to chemotherapy, whereas plasmatic GSTP1 levels and low tumor stage are independent factors of complete response. Our data suggest that GST enzymes are associated with larynx cancer and that their use as predictive factors and treatment targets should be further explored., (Copyright 2001 Wiley-Liss, Inc.)