UNASSIGNED: To evaluate bias and precision of exposure-outcome effect estimates from three control sampling strategies in a case-crossover study.
UNASSIGNED: Online case-crossover study investigating eight physical activity-related triggers for acute flares in knee osteoarthritis. Exposures were measured in hazard periods (≤24 hours before self-declared flare onset). Control period exposure was measured in three ways: (1) four scheduled questionnaires over 13-weeks, (2) \"usual\" physical activity levels ascertained at baseline, (3) over three days before flare onset. Derived odds ratios, 95% confidence intervals and standard errors were compared.
UNASSIGNED: Of 744 participants (mean age 62.1 [SD 10.2] years; 61% female), 493 reported 714 flares. Selecting controls from scheduled questionnaires, independent of hazard periods, yielded predominantly odds ratios in the expected direction (exposure \"a lot\" versus exposure \"not at all\", range: 0.57-3.22). When controls were sampled at baseline (range: 0.01-1.42) or immediately before a flare (range: 0.30-1.27) most odds ratio estimates were inverted. Standard errors of the log odds ratios were smallest when controls were sampled from scheduled questionnaires (range: 0.264-0.473) compared to controls sampled at baseline (range: 0.267-0.589) or immediately before a flare (range: 0.319-0.621).
UNASSIGNED: Our findings are sensitive to control sample selection. Under certain conditions, different patterns could be attributed to over reporting and social desirability bias, where people may want to present themselves more positively about their \"usual\" physical activity levels, at baseline. Exposure measurement at the time of a flare may be less precise and more susceptible to recall bias due to systematically reporting exposures differently during a flare, compared to control measurement independent of flares.

Historical data on food safety monitoring often serve as an information source in designing monitoring plans. However, such data are often unbalanced: a small fraction of the dataset refers to food safety hazards that are present in high concentrations (representing commodity batches with a high risk of being contaminated, the positives) and a high fraction of the dataset refers to food safety hazards that are present in low concentrations (representing commodity batches with a low risk of being contaminated, the negatives). Such unbalanced datasets complicate modeling to predict the probability of contamination of commodity batches. This study proposes a weighted Bayesian network (WBN) classifier to improve the model prediction accuracy for the presence of food and feed safety hazards using unbalanced monitoring data, specifically for the presence of heavy metals in feed. Applying different weight values resulted in different classification accuracies for each involved class; the optimal weight value was defined as the value that yielded the most effective monitoring plan, that is, identifying the highest percentage of contaminated feed batches. Results showed that the Bayesian network classifier resulted in a large difference between the classification accuracy of positive samples (20%) and negative samples (99%). With the WBN approach, the classification accuracy of positive samples and negative samples were both around 80%, and the monitoring effectiveness increased from 31% to 80% for pre-set sample size of 3000. Results of this study can be used to improve the effectiveness of monitoring various food safety hazards in food and feed.

The scope of this study consists of setting up of an integrated cost-effective sampling & laboratory analyses procedure which delineates sampling, sub-sampling and analytical uncertainties in case of fine-grained extractive waste deposits. This procedure is designed to support the decision makers towards fine-grained waste deposits upcycling and land reclamation. This procedure consists of a balanced replicated sampling design (BRSD) coupled with a three split levels ANOVA data processing. The paper provides the readership with the mathematical backgrounds of the three split level ANOVA analysis (3L-ANOVA) and an Excel algorithm for its implementation. Also, the paper presents an example of implementation of the developed methods in the case of a Romanian iron ore tailings (IOT) old pond. The findings of the paper consist of: a) argues, based on OM, SEM-EDS, XRFS and XRD observations, that classical TOS is ineffective for fine-grained waste deposits; b) BRSD in conjunction with 3L-ANOVA analysis is the only approach fit for reliable characterization of the fine-grained stockpiles; c) sampling uncertainty is the critical factor of the uncertainty budget of the analyte concentration; d) Lilliefors approach is adequate for the hypothesis testing where or not the measurand is normal distributed; e) The outcomes of the BRDSD&3L-ANOVA investigations carried on Teliuc tailings, estimated at circa 5.5* 106 m3, consist mainly of mineral quantification at lot level i.e. quartz ∼54% (±7%), hematite ∼15% (±3%), calcite ∼11% (±3%), MgO 3% (±1%), Al2O3 9% (±2%). The concentrations of some CRMs like Ti, V, Ba, Y, W were found at ACE limits and their associated relative expanded uncertainties overpass 50%. Thus, the expanded uncertainties clearly depict the reliability of acquired data for the decision makers regarding waste valorization. f) The IOT into Teliuc can be upcycled as minerals for cement and ceramic industries as well as for geopolymer manufacture. Also, IOT can be downcycles as filler in road construction and mine closure. Finally, the Teliuc yard can be rehabilitated with zero-waste left behind. The data exactness provided by this procedure can be increased to any desirable level through increasing the number of collected items, but the cost of sampling and analyses increases proportionally. In such circumstances, the posted approach can be tailored at the stakeholder request as to safely underpin the decision to turn finegrained by-products into valuable secondary resources, facilitating a greater circularity of the mining industry.

Nested case-control sampled event time data under a highly stratified proportional hazards model, in which the number of strata increases proportional to sample size, is described and analyzed. The data can be characterized as stratified sampling from the event time risk sets and the analysis approach of Borgan et al. (Ann Stat 23:1749-1778, 1995) is adapted to accommodate both the stratification and case-control sampling from the stratified risk sets. Conditions for the consistency and asymptotic normality of the maximum partial likelihood estimator are provided and the results are used to compare the efficiency of the stratified analysis to an unstratified analysis when the baseline hazards can be semi-parametrically modeled in two special cases. Using the stratified sampling representation of the stratified analysis, methods for absolute risk estimation described by Borgan et al. (1995) for nested case-control data are used to develop methods for absolute risk estimation under the stratified model. The methods are illustrated by a year of birth stratified analysis of radon exposure and lung cancer mortality in a cohort of uranium miners from the Colorado Plateau.

The application of serial principled sampling designs for diagnostic testing is often viewed as an ideal approach to monitoring prevalence and case counts of infectious or chronic diseases. Considering logistics and the need for timeliness and conservation of resources, surveillance efforts can generally benefit from creative designs and accompanying statistical methods to improve the precision of sampling-based estimates and reduce the size of the necessary sample. One option is to augment the analysis with available data from other surveillance streams that identify cases from the population of interest over the same timeframe, but may do so in a highly nonrepresentative manner. We consider monitoring a closed population (e.g., a long-term care facility, patient registry, or community), and encourage the use of capture-recapture methodology to produce an alternative case total estimate to the one obtained by principled sampling. With care in its implementation, even a relatively small simple or stratified random sample not only provides its own valid estimate, but provides the only fully defensible means of justifying a second estimate based on classical capture-recapture methods. We initially propose weighted averaging of the two estimators to achieve greater precision than can be obtained using either alone, and then show how a novel single capture-recapture estimator provides a unified and preferable alternative. We develop a variant on a Dirichlet-multinomial-based credible interval to accompany our hybrid design-based case count estimates, with a view toward improved coverage properties. Finally, we demonstrate the benefits of the approach through simulations designed to mimic an acute infectious disease daily monitoring program or an annual surveillance program to quantify new cases within a fixed patient registry.

Efficient food safety monitoring should achieve optimal resource allocation. In this article, a methodology is presented to optimize the use of resources for food safety monitoring aimed at identifying noncompliant samples and estimating background level of hazards in food products. A Bayesian network (BN) model and an optimization model were combined in a single framework. The framework was applied to monitoring dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs) in primary animal-derived food products in the Netherlands. The BN model was built using a national dataset with monitoring results of dioxins and DL-PCBs in animal-derived food products over a 10-year period (2008-2017). These data were used to estimate the probability of detecting suspect samples with dioxins and DL-PCBs levels above preset thresholds, given certain sample conditions. The results of the BN model were then inserted into the optimization model to compute an optimal monitoring scheme. Model estimates showed that the probability of dioxins and DL-PCBs exceeding threshold limits was higher in laying hen eggs and sheep meat than in other animal-derived food (except deer meat). Compared with the monitoring scheme used in the Netherlands in 2018, the optimal monitoring scheme would save around 10,000 EUR per year. This could be obtained by reallocating monitoring resources from products with lower probability of dioxin and DL-PCBs exceeding threshold limits (e.g., pig meat) to products with higher probability (e.g., bovine animal meat), and by shifting sample collection from the last quarter of the year toward the first three quarters of the year.

Moreau () has raised concerns about the use of DNA data obtained from vulnerable populations, such as the Uighurs in China. We discuss another case, situated in Europe and with a research history dating back 100 years: genetic investigations of Roma. In our article, we focus on problems surrounding representativity in these studies. We claim that many of the circa 440 publications in our sample neglect the methodological and conceptual challenges of representativity. Moreover, authors do not account for problematic misrepresentations of Roma resulting from the conceptual frameworks and sampling schemes they use. We question the representation of Roma as a \"genetic isolate\" and the underlying rationales, with a strong focus on sampling strategies. We discuss our results against the optimistic prognosis that the \"new genetics\" could help to overcome essentialist understandings of groups.

The objective of this article is to advocate a new way of sampling controls in the case-time-control design in a cohort of drug users when the studied outcome prevents further treatment.
Mathematically we demonstrate how a standard sampling of controls, where controls are sampled among all subjects without an event at end-of-study, leads to a biased effect estimate. We propose to add the requirement that controls initiate treatment before the calendar time of event of their matched case to circumvent this. The standard and proposed sampling methods are compared in a simulation study and in an empirical data example examining the effect of nonsteroidal anti-inflammatory drug usage on the risk of upper gastrointestinal bleeding.
When the controls are sampled the standard way, the case-time-control design confers a bias because cases and controls have a different time-trend of exposure. The bias has been upwards in all the scenarios we have investigated. The requirement we add to be a potential control ensures that cases and controls have the same time-trend of exposure when treatment and outcome are independent. The simulation study confirms that the proposed sampling method removes the bias between treatment and outcome. The proposed sampling method lowered the odds-ratio estimate from 3.72 to 3.26 in the data example.
The proposed sampling method makes it possible to use the case-time-control design in a cohort of subjects with registered use of a drug when outcome prevents further treatment.

Because of a growing older population, the sexual health (SH) of older adults, including sexual violence (SV), is becoming an increasingly important public health concern. Yet, reliable SV prevalence rates and risk factors are lacking, due to methodological shortcomings in current studies. SV research involves challenges regarding safety and disclosure, especially in older adults. In this paper, we reflect on the methods used in a sexual health and violence (SH&V) study in older adults balancing between privacy rules imposed by the General Data Protection Regulation (GDPR) and ethical and safety guidelines.To ensure the acceptability of the questionnaire, it was tested in a two-phase pilot study. To maximize SV disclosure, the questionnaire built up gradually towards the more sensitive SV modules. Interviewers were trained to approach participants in a non-judgmental manner. Due to GDPR, our data collection method was changed from a random sampling via the National Register to a cluster random probability sampling with a random walk finding approach.Older adults were willing to discuss SH&V during a structured face-to-face interview with trained interviewers. Following strict safety guidelines, no major incidents were reported. The cluster random probability sampling with random walk finding approach provided an adequate sampling frame, but was inefficient and time-consuming.Doing research on SH&V in older adults is feasible but requires a substantial investment of time and the challenges involved may incur greater costs. In order to guarantee further research on sensitive topics in older adults, we recommend that an interdisciplinary expert group consisting of researchers, donors, and policymakers investigates how GDPR and public health research in hard-to-reach populations can be better matched.

Food safety monitoring faces the challenge of tackling multiple chemicals along the various stages of the food supply chain. Our study developed a methodology for optimizing sampling for monitoring multiple chemicals along the dairy supply chain. We used a mixed integer nonlinear programming approach to maximize the performance of the sampling in terms of reducing the risk of the potential disability adjusted life years (DALYs) in the population. Decision variables are the number of samples collected and analyzed at each stage of the food chain (feed mills, dairy farms, milk trucks, and dairy processing plants) for each chemical, given a predefined budget. The model was applied to the case of monitoring for aflatoxin B1 /M1 (AFB1 /M1 ) and dioxins in a hypothetical Dutch dairy supply chain, and results were calculated for various contamination scenarios defined in terms of contamination fraction and concentrations. Considering various monitoring budgets for both chemicals, monitoring for AFB1 /M1 showed to be more effective than for dioxins in most of the considered scenarios, because AFB1 /M1 could result into more DALYs than dioxins when both chemicals are in same contamination fraction, and costs for analyzing one AFB1 /M1 sample are lower than for one dioxins sample. The results suggest that relatively more resources be spent on monitoring AFB1 /M1 when both chemicals\' contamination fractions are low; when both contamination fractions are higher, relatively more budget should be addressed to monitoring dioxins.