Discussion on On the role of data, statistics and decisions in a pandemic

The authors make an important contribution presenting a comprehensive and thoughtful overview about the many different aspects of data, statistics and data analyses in times of the recent COVID-19 pandemic discussing all relevant topics. The paper certainly provides a very valuable reflection of what has been done, what could have been done and what needs to be done. We contribute here with a few comments and some additional issues. We do not discuss all chapters of Jahn et al. (AStA Adv Stat Anal, 2022. 10.1007/s10182-022-00439-7), but focus on those where our personal views and experiences might add some additional aspects

Essential Indicators Identifying Chronic Inorganic Mercury Intoxication: Pooled Analysis across Multiple Cross-Sectional Studies

The continuous exposure to inorganic mercury vapour in artisanal small-scale gold mining (ASGM) areas leads to chronic health problems. It is therefore essential to have a quick, but reliable risk assessing tool to diagnose chronic inorganic mercury intoxication. This study re-evaluates the state-of-the-art toolkit to diagnose chronic inorganic mercury intoxication by analysing data from multiple pooled cross-sectional studies. The primary research question aims to reduce the currently used set of indicators without affecting essentially the capability to diagnose chronic inorganic mercury intoxication. In addition, a sensitivity analysis is performed on established biomonitoring exposure limits for mercury in blood, hair, urine and urine adjusted by creatinine, where the biomonitoring exposure limits are compared to thresholds most associated with chronic inorganic mercury intoxication in artisanal small-scale gold mining.Health data from miners and community members in Indonesia, Tanzania and Zimbabwe were obtained as part of the Global Mercury Project and pooled into one dataset together with their biomarkers mercury in urine, blood and hair. The individual prognostic impact of the indicators on the diagnosis of mercury intoxication is quantified using logistic regression models. The selection is performed by a stepwise forward/backward selection. Different models are compared based on the Bayesian information criterion (BIC) and Cohen`s kappa is used to evaluate the level of agreement between the diagnosis of mercury intoxication based on the currently used set of indicators and the result based on our reduced set of indicators. The sensitivity analysis of biomarker exposure limits of mercury is based on a sequence of chi square tests.The variable selection in logistic regression reduced the number of medical indicators from thirteen to ten in addition to the biomarkers. The estimated level of agreement using ten of thirteen medical indicators and all four biomarkers to diagnose chronic inorganic mercury intoxication yields a Cohen`s Kappa of 0.87. While in an additional stepwise selection the biomarker blood was not selected, the level of agreement based on ten medical indicators and only the three biomarkers urine, urine/creatinine and hair reduced Cohen`s Kappa to 0.46. The optimal cut-point for the biomarkers blood, hair, urine and urine/creatinine were estimated at 11. 6 μg/l, 3.84 μg/g, 24.4 μg/l and 4.26 μg/g, respectively.The results show that a reduction down to only ten indicators still allows a reliable diagnosis of chronic inorganic mercury intoxication. This reduction of indicators will simplify health assessments in artisanal small-scale gold mining areas

On the use of Fractional Polynomials in Dynamic Cox Models

Despite a sophisticated research on modelling of survival data in the last years, the most popular model used in practice is still the proportional hazards regression model proposed by Cox (1972). This is mainly due to its exceptional simplicity. Nevertheless the fundamental assumption of the Cox model is the proportionality of the hazards, which particularly implies that the covariate effects are constant over time. For many applications this assumption is, however, doubtful. Other, more flexible approaches, which are able to cope with non-proportional hazards usually require non-standard estimation techniques, which are often rather complex and thus not favoured in application. Moreover, the selection of an appropriate test-statistic, to examine the improvement of the fit, is not obvious. In this paper we propose a flexible, yet simple method for modelling dynamic effects in survival data within the Cox framework. The method is based on Fractional Polynomials as introduced by Royston and Altman (1994). This allows for a transformation of the dynamic predictor which leads back to the conventional Cox model and hence fitting is straightforward using standard estimation techniques. In addition, it offers the possibility to easily verify the existence of time-variation. We describe a model selection algorithm which enables to include time-varying effects only when evidence is given in the data, in order to construct a model, which is just as complex as needed. We illustrate the properties of the approach in a simulation study and an application to gastric carcinoma data and compare it with other methods (e.g. the residual score test and smoothed Schoenfeld residuals of Grambsch and Therneau, 1994; natural smoothing splines of Hastie and Tibshirani, 1993)

Using Geographically Referenced Data on Environmental Exposures for Public Health Research: A Feasibility Study Based on the German Socio-Economic Panel Study (SOEP)

Background: In panel datasets information on environmental exposures is scarce. Thus, our goal was to probe the use of area-wide geographically referenced data for air pollution from an external data source in the analysis of physical health. Methods: The study population comprised SOEP respondents in 2004 merged with exposures for NO2, PM10 and O3 based on a multi-year reanalysis of the EURopean Air pollution Dispersion-Inverse Model (EURAD-IM). Apart from bivariate analyses with subjective air pollution we estimated cross-sectional multilevel regression models for physical health as assessed by the SF-12. Results: The variation of average exposure to NO2, PM10 and O3 was small with the interquartile range being less than 10µg/m3 for all pollutants. There was no correlation between subjective air pollution and average exposure to PM10 and O3, while there was a very small positive correlation between the first and NO2. Inclusion of objective air pollution in regression models did not improve the model fit. Conclusions: It is feasible to merge environmental exposures to a nationally representative panel study like the SOEP. However, in our study the spatial resolution of the specific air pollutants has been too little, yet.SOEP, Geographically Referenced Data, Feasibility Study, Air Pollution, EURAD-IM, Physical Health

Nowcasting fatal COVID-19 infections on a regional level in Germany

We analyse the temporal and regional structure in mortality rates related to COVID‐19 infections, making use of the openly available data on registered cases in Germany published by the Robert Koch Institute on a daily basis. Estimates for the number of present‐day infections that will, at a later date, prove to be fatal are derived through a nowcasting model, which relates the day of death of each deceased patient to the corresponding day of registration of the infection. Our district‐level modelling approach for fatal infections disentangles spatial variation into a global pattern for Germany, district‐specific long‐term effects and short‐term dynamics, while also taking the age and gender structure of the regional population into account. This enables to highlight areas with unexpectedly high disease activity. The analysis of death counts contributes to a better understanding of the spread of the disease while being, to some extent, less dependent on testing strategy and capacity in comparison to infection counts. The proposed approach and the presented results thus provide reliable insight into the state and the dynamics of the pandemic during the early phases of the infection wave in spring 2020 in Germany, when little was known about the disease and limited data were available

Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections

Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context, it is crucial for policymakers to have a firm grasp on what the current state of the pandemic is, and to envision how the number of infections is going to evolve over the next days. However, as in many other situations involving compulsory registration of sensitive data, cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. We provide a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level. We accomplish this through nowcasting of cases that have not yet been reported as well as through predictions of future infections. We apply our model to German data, for which our focus lies in predicting and explain infectious behavior by district. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10182-021-00433-5

Prevalence and sociodemographic determinants of adult obesity: A large representative household survey in a resource-constrained African setting with double burden of undernutrition and overnutrition

BACKGROUND The obesity epidemic has continued to spread across the globe involving even poor nations of the world. METHOD Household population survey of adults aged 20-60 years. Multistage stratified cluster randomised sampling involving both urban and rural statewide representative population samples. Anthropometric measurements were taken using standard methods. Prevalences were weighted and multinomial regression analyses were done. RESULTS A total of 6628 individuals from 2843 households were surveyed. The weighted overall prevalence for underweight was 9.1% (95% CI 8.1 to 10.1), 65.1% (95% CI 63.6 to 66.6) for normal weight, 19.0% (95% CI 17.8 to 20.3) for overweight and 6.8% (95% CI 6.0 to 7.5) for obese. Men were less likely to be overweight (adjusted OR (AOR) 0.79; 95% CI 0.68 to 0.92) and obese (AOR 0.24; 95% CI 0.19 to 0.31) than women. Urban residents were more likely to be overweight (AOR 1.42; 95% CI 1.18 to 1.71) and obese (AOR 2.09; 95% CI 1.58 to 2.76) than rural residents. Each additional 1-year increase in age increased the risk of overweight by 1.012 (AOR 1.012; 95% CI 1.005 to 1.018) and that of obesity by 1.03 (AOR 1.03; 95% CI 1.02 to 1.04). The low-income class was less likely to be overweight (AOR 0.694; 95% CI 0.507 to 0.951) and obese (AOR 0.44; 95% CI 0.28 to 0.67). CONCLUSION The prevalence of obesity and overweight in Enugu Nigeria is high and fast approaching that of underweight. Women, urban dwellers, older adults and high-income earners are at higher risk for obesity and overweight. The study provides robust information for public health policies towards the prevention of obesity in Nigeria

Skin cancer risk and shade: comparing the risk of foresters with other outdoor workers

Background: Keratinocyte carcinoma (KC) is an increasingly important public health problem with an especially high prevalence in outdoor workers. In contrast to other occupations, foresters spend most of their outdoor time under the shade of trees. Objectives: We aimed to compare the unique sun exposure patterns and sun protection behaviour of foresters with those of other outdoor workers and their relation to the KC risk. Methods: In July 2018, a cross‐sectional study was conducted at an international forestry fair using a questionnaire about health awareness and skin cancer screening by dermatologists to assess the prevalence of KC. Results: A total of 591 participants (78.7% male; mean age 46.8 ± 16.2 years) including 193 foresters were enrolled. Of all foresters, 72% experienced sunburns (solar erythema) within the past year and 50% of them experienced the worst sunburn during work. Foresters were most likely to often/always wear protective clothes (29.0%) but were least likely to often/always avoid midday sun (23.8%) and stay in the shade (31.1%). Having an outdoor profession or spending hours outside for leisure was negatively associated with sun protection. Skin examination revealed an overall KC prevalence of 16.7%, with 16.5% of foresters being affected. Conclusion: Despite being protected by trees, the risk of KC for foresters is comparable to that of other professional groups. Shade alone may not provide sufficient protection. Additional sun protection measures are necessary