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Traditional sampling methods may prove inadequate when dealing with spatially clustered populations or when studying rare events or traits that are not easily detectable across the target population. When both scenarios occur simultaneously, adaptive sampling strategies can represent a viable option to enhance the detectability of cases of interest. This paper delves into the application of a novel class of sequential adaptive sampling strategies to animal surveys. These strategies, originally proposed for human population tuberculosis prevalence surveys, allow oversampling of the rare interest variables while managing on-field constraints. This ensures that the unfixed sample size, typical of adaptive sampling, does not compromise overall cost-effectiveness. We explore a strategy within this class that integrates an adaptive component into a Poisson sequential selection. The aim is twofold: to intensify the detection of cases by exploiting the spatial clustering and to provide a flexible framework for managing logistics and budget constraints. To illustrate the strengths and weaknesses of this Poisson-based sequential adaptive sampling strategy compared to traditional sampling methods, a simulation study was conducted on a blue-winged teal population in Florida, USA. The results showcase the benefits of the proposed strategy and open avenues for future methodological and practical improvements.

In observational studies, one of the main difficulties consists in the comparison of treat- ment effects. In fact, receiving a treatment is not a “purely random” event, and there could be relevant differences between treatment groups. Propensity score is a popular tool to account for this source of bias. However, its use requires a careful modelization of the dependence relationships of the treatment on the covariates. In this work, we consider a general setting with multiple treatments and discrete multi-valued outcome. We propose to estimate the propensity score by using Bayesian Networks and, based on this, we develop an inferential methodology to evaluate the treatments effect. The performance of the pro- posed approach have been studied through a simulation study with very promising results.

This paper intends to contribute to an up-to-date discussion of dual frame designs in agricultural surveys. It starts by reviewing historical scenarios of applications to envision new perspectives, and ends by presenting a modern approach to the problem. A dual frame sampling design is proposed that has the appeal of relying on low-cost technological resources. The design has enough generality to allow for applications not only on agricultural but also on rural and environmental surveys, or any other survey related to the use of soil. Unbiased estimations based on domain and multiplicity approaches are presented and their major differences are discussed. Design parameters, design feasibility by different sample size allocations, as well as the statistical performance of several dual frame estimators are investigated using a Monte Carlo simulation study that is built on information from the Brazilian agricultural census of 2006 and FAO’s Global Strategy’s field experiences in the city of Goiana, Pernambuco. The results show dual frames present a gain in precision when compared to a single area frame survey. In addition, the choice of the best design and estimator depends upon scenarios with different types of allocation and different sizes of area frame segments.

Composite indicators are a common choice for synthesizing complex phenomena. Over the years, they have grown in popularity and are now applied in many social and environmental sciences. Among others, a subject of increasing interest is gender equality analysis. Gender composite indicators, even if easy to read, may provide a limited picture of the problem. Here we discuss the potentiality to integrate the use of composite indicators for gender gaps with Bayesian networks, powerful tools for explaining the complex association structure in the dataset and developing scenarios to orient policy-making. An example is carried out on Italian province-level data.

A resampling technique for probability-proportional-to size sampling designs is proposed. It is essentially based on a special form of variable probability, without replacement sampling applied directly to the sample data, yet according to the pseudo-population approach. From a theoretical point of view, it is asymptotically correct: as both the sample size and the population size increase, under mild regularity conditions the proposed resampling design tends to coincide with the original sampling design under which sample data were collected. From a computational point of view, the proposed methodology is easy to be implemented and efficient, because it neither requires the actual construction of the pseudo-population nor any form of randomization to ensure integer weights and sizes. Empirical evidence based on a simulation study1 indicates that the proposed resampling technique outperforms its two main competitors for confidence interval construction of various population parameters including quantiles.

A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at hand. It is based on integrating an adaptive component into a sequential selection, which aims both to intensify the detection of positive cases, upon exploiting the spatial clustering, and to provide a flexible framework to manage logistics and budget constraints. A class of estimators is also proposed to account for the selection bias, that are proved unbiased for the population mean (prevalence) as well as consistent and asymptotically Normal distributed. Unbiased variance estimation is also provided. A ready-to-implement weighting system is developed for estimation purposes. Two special strategies included in the proposed class are presented, that are based on the Poisson sampling and proved more efficient. The selection of primary sampling units is also illustrated for tuberculosis prevalence surveys, which are recommended in many countries and supported by the World Health Organisation as an emblematic example of the need for an improved sampling design. Simulation results are given in the tuberculosis application to illustrate the strengths and weaknesses of the proposed sequential adaptive sampling strategies with respect to traditional cross-sectional non-informative sampling as currently suggested by World Health Organisation guidelines.

The pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to reflect women’s and men’s lives. In pursuing the 2030 Sustainability Development Goals (SDGs), the availability of high- quality gender-sensitive data has generated the current informative outburst. In the process, gender-sensitive data collection has departed from a mere dis- aggregation between men and women towards an unprecedented multifaceted informational spectrum. Methods for full exploiting gender-sensitive statis- tics, both standard and big data, though, faces some levels of criticality. The traditional descriptive linear combinations of a collection of simple indica- tors yields contradictory order results, whereas inference has so far privileged latent modelling only, holding several constraints. A novel statistical perspec- tive stems from recent developments of Multivariate Latent Markov Models (MLMMs), suitable to express a latent characteristic both in time and space. In addition to introducing covariates, on any measurement scales, not only in the structural model bul also the measurement one, MLMMs are innovative in so that they can handle a vast mass of data from very different sources. Thus they lead the way to an extensive investigation of the gender gap, account- ing for apparent contradictions in rankings and hence highlighting different paths, or “transition”, toward a more equitable society.

Item does not contain fulltext, Pakistan's national tuberculosis control programme (NTP) is among the many programmes worldwide that value the importance of subnational tuberculosis (TB) burden estimates to support disease control efforts, but do not have reliable estimates. A hackathon was thus organised to solicit the development and comparison of several models for small area estimation of TB. The TB hackathon was launched in April 2019. Participating teams were requested to produce district-level estimates of bacteriologically positive TB prevalence among adults (over 15 years of age) for 2018. The NTP provided case-based data from their 2010-2011 TB prevalence survey, along with data relating to TB screening, testing and treatment for the period between 2010-2011 and 2018. Five teams submitted district-level TB prevalence estimates, methodological details and programming code. Although the geographical distribution of TB prevalence varied considerably across models, we identified several districts with consistently low notification-to-prevalence ratios. The hackathon highlighted the challenges of generating granular spatiotemporal TB prevalence forecasts based on a cross-sectional prevalence survey data and other data sources. Nevertheless, it provided a range of approaches to subnational disease modelling. The NTP's use and plans for these outputs shows that, limitations notwithstanding, they can be valuable for programme planning.

In the present paper, resampling for finite populations under an iid sampling design is reviewed. Our attention is mainly focused on pseudo-population-based resampling due to its properties. A principled appraisal of the main theoretical foundations and results is given and discussed, together with important computational aspects. Finally, a discussion on open problems and research perspectives is provided.

Gender-sensitive statistics can highlight gender gaps, but current measurement tools have serious limitations Here, Fulvia Mecatti, Paola Vicard, Flaminia Musella and Lorenzo Giammei explore how Bayesian networks could help improve the measurement, monitoring and prediction of gender equality.

The FENStatS Covid19 WR is a free spontaneous association of statistical experts from 14 European countries, united by concerns related to the current pan-info-demic and statistical challenges revealed by the Covid-19 havoc. The present shortpaper tracks when, how and why the WG has formed, illustrates our mission,aims and scope, describes the steps taken so far and outlines perspectives for futurework

Pakistan’s national tuberculosis control programme (NTP) is among the many programmes worldwide that value the importance of subnational tuberculosis (TB) burden estimates to support disease control efforts, but do not have reliable estimates. A hackathon was thus organised to solicit the development and comparison of several models for small area estimation of TB. The TB hackathon was launched in April 2019. Participating teams were requested to produce district-level estimates of bacteriologically positive TB prevalence among adults (over 15 years of age) for 2018. The NTP provided case-based data from their 2010–2011 TB prevalence survey, along with data relating to TB screening, testing and treatment for the period between 2010–2011 and 2018. Five teams submitted district-level TB prevalence estimates, methodological details and programming code. Although the geographical distribution of TB prevalence varied considerably across models, we identified several districts with consistently low notification-to-prevalence ratios. The hackathon highlighted the challenges of generating granular spatiotemporal TB prevalence forecasts based on a cross-sectional prevalence survey data and other data sources. Nevertheless, it provided a range of approaches to subnational disease modelling. The NTP’s use and plans for these outputs shows that, limitations notwithstanding, they can be valuable for programme planning.

When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.

Background: The aim of the study was to investigate if a parents’ training program on nutritional and oral health behaviours held by health professionals can influence children habits in a sample of kindergartens in Northern Italy. Methods: The study designed was a longitudinal study. The study population were children aged 6–36 months attending four kindergartens. Parents were invited to participate filling out a self-administered questionnaire, and after having returned the informed consent form they were asked to participate to a training meeting. Parents had to fill out a questionnaire at baseline and after 3 months from the training meeting. The questionnaire included information on socio-demographics about parents, oral hygiene habits of parents and child, and eating habits of child. Wald test was used to analyse data collected. Results: After the training program, almost all children were able to use a toothbrush suitable for kids (from 91% at the baseline to 99% after the 3-months). The analysis shows that the given training significantly increased the number of children who use toothpaste from 86% at baseline (95%CI: 85%-88%) to 96% (95%CI: 94%-98%). The use of fluoride toothpaste significantly increased after the training intervention as the baseline measure of 59% proportion increased to 80%. The intervention study showed a positive impact on the number of pupils who wash their tooth more than twice per week and on the timing of oral hygiene as both night and morning time proportion increased after the 3-month. Regarding the feeding habits the given consultation resulted in a statistically significant increase regarding the importance of morning snack from 94% at baseline (95%CI: 92%-96%) to 97% after 3-month (95%CI: 94%-99%). Another promising effect of the education is the decrease from 47% at baseline (95%CI: 46%-49%) to 42% after the study (95%CI:41%-43%) of the bad habit of having a snack after dinner. Conclusions: Results of our study have shown tha

In observational studies evaluating the treatment effect on a given out- come, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to esti- mate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.

The number of samples is a major issue when estimating the mean herbage mass of grazed paddocks. The aim of this study was to assess the number of samples required for accurate visual estimation of mean herbage mass in relation to the herbage mass heterogeneity and size of paddocks. Data were collected across scales of space and time (273 sampling events) from paddocks on Campos grasslands in Uruguay, using the visual estimation technique. The mean herbage mass of the paddocks ranged from 270 to 6350 kg of dry matter (DM) per hectare with coefficient of variation (CV) of 0.13 to 1.26. Twenty-four events representing four levels of herbage mass hetero- geneity (CV = 0.3, 0.5, 0.7 and 1.0) × three levels of paddock size (small, 5–13 ha; medium, 41–67 ha; large, 100–140 ha) were chosen (two replicates per group), and analyzed for the probability that the estimation error exceeded 10% of the mean (10% error probability) using the bootstrap technique. The number of samples required for controlling the 10% error probability below 0.1 increased gradually from 50 to 150 per paddock as the CV increased from 0.3 to 0.7, then sharply to 350 until the CV increased to 1.0, with no effect of paddock size. Taking account of the distribution of CV (< 0.7 in nearly 80% of the events), we propose a general recommendation to take a minimum of 150 samples per paddock for accurate estimation of mean herbage mass in Campos grasslands irrespective of the size of paddocks.

The inverse propensity score weighting (IPSW) has been often used to es- timate causal effects of treatments for observational data. However, IPSW requires strong assumptions, in which their misspecifications may severely bias estimated treatment effect. We present a bootstrap based bias-correction to adjust the propen- sity score weights in case of misspecifications of one of the main assumption. We showed, using simulation, the approach performs well in correcting biases due to model misspecifications in various contexts.The method was also illustrated using a real data based on end-stage renal disease.

In this paper, a new resampling technique for sampling designs with unequal inclusion probabilities is proposed. The basic idea is to use a resampling design based on ppswor. Its main properties are studied, and its relationships with other resampling methodologies are discussed.

Resampling methods are widely studied and increasinglyemployed in applied research and practice. When deal-ing with complex sampling designs, common resamplingtechniques require adjusting noninteger sampling weightsin order to construct the so called “pseudopopulation” inorder to perform the actual resampling. The practice ofrounding, however, has been empirically shown to beharmful under general designs. In this paper, we presentasymptotic results concerning, in particular, the practiceof rounding resampling weights to the nearest integer, anapproach that is commonly adopted by virtue of its reducedcomputational burden, as opposed to randomization-basedalternatives. We prove that such approach leads to noncon-sistent estimation of the distribution function of the surveyvariable; we provide empirical evidence of the practicalconsequences of the nonconsistency when the point esti-mation of the variance of complex estimators is of interest

Sampling a rare and clustered trait in a finite population is challenging: traditional sampling designs usually require a large sample size in order to obtain reasonably accurate estimates, resulting in a considerable investment of resources in front of the detection of a small number of cases. A notable example is the case of WHO’s tubercoulosis (TB) prevalence surveys, crucial for countries that bear a high TB burden, the prevalence of cases being still less than 1%. In the latestWHOguidelines, spatial patterns are not explicitly accounted for, with the risk of missing a large number of cases; moreover, cost and logistic constraints can pose further problems. After reviewing the methodology in use by WHO, the use of adaptive and sequential approaches is discussed as natural alternatives to overcome the limitations of the current practice. A small simulation study is presented to highlight possible advantages and limitations of these alternatives, and an integrated approach, combining both adaptive and sequential features in a single sampling strategy is discussed as a promising methodological perspective

This paper concerns a Multivariate Latent Markov Model recently introduced in the literature for estimating latent traits in social sciences. Based on its ability of simultaneously dealing with longitudinal and spacial data, the model is proposed when the latent response variable is expected to have a time and space dynamic of its own, as an innovative alternative to popular methodologies such as the construction of composite indicators and structural equation modeling. The potentials of the proposed model and the added value with respect to the traditional weighted composition methodology, are illustrated via an empirical Gender Statistics exercise, focused on gender gap as the latent status to be measured and based on supranational ocial statistics for 30 European countries in the period 2010-2015.

Sampling a rare and clustered trait in a finite population is challenging: traditional sampling designs usually require a large sample size in order to obtain reasonably accurate estimates, resulting in a considerable investment of resources in front of the detection of a small number of cases. A notable example is the case of WHO’s tuberculosis (TB) prevalence surveys, crucial for countries that bear a high TB burden, the prevalence of cases being still less than 1%. In the latest WHO guidelines, spatial patterns are not explicitly accounted for, with the risk of missing a large number of cases; moreover, cost and logistic constraints can pose further problems. After reviewing the methodology in use by WHO, the use of adaptive and sequential approaches is discussed as natural alternatives to improve over the limits of the current practice. A simulation study is presented to highlight possible advantages and limitations of these alternatives, and an integrated approach, combining both adaptive and sequential features in a single sampling strategy is advocated as a promising methodological perspective.

Gender inequality - both in space and time - is a latent trait, namely only indirectly measurable through a collection of observable variables and indicators purposively selected. Even if composite indicators are normally used by social scientists, when measuring gender-gap they are known to have case-specific technical limitations. In this paper we propose an innovative approach based on a multivariate Latent Markov model (LMM) for the analysis of gender inequalities as measured by the aforementioned indicators

Gender inequality - both in space and time - is a latent trait, namely only indirectly measurable through a collection of observable variables and indicators purposively selected. Even if composite indicators are normally used by social scientists, when measuring gender-gap they are known to have case-specific technical limitations. In this paper we propose an innovative approach based on a multivariate Latent Markov model (LMM) for the analysis of gender inequalities as measured by the aforementioned indicators

A new sampling design is derived for sampling a rare and clustered population under both cost and logistic constraints. It is motivated based on the example of national TB prevalence surveys, sponsored by WHO for high TB-burden countries and usually located in the poorest parts of the world. A Poisson-type sampling design named Poisson Sequential Adaptive (PoSA) is proposed with a twofold purpose: (i) to increase the detection rate of positive cases; and (ii) to reduce survey costs by accounting for logistic constraints at the design level of the survey. PoSA is derived by integrating both an adaptive component able to enhace detectability and a sequential component for dealing with costs and logistic constraints. An unbiased HT-type estimator for the population prevalence (mean) is derived by adjusting for both the over-selection bias and for the conditional structure induced by the sequential selection. Unbiased variance estimation in a closed form is also provided. Simulation results are presented and show a significant pontential of PoSA in improving the sampling methodology currently suggested by WHO guidelines.

Multiple-frame surveys are commonly used to deal with under-coverage bias. The use of more than one frame introduces the possibility that frames overlap leading to increased inclusion probabilities of units that appear in multiple lists. Following the guide example of a dual frame set-up (DF) in telephone surveys, this contribution presents an extensive simulation study where different types of screenings to deal with the overlap issue are compared with the proper DF approach. We empirically show that the efforts of screening do not guarantee estimators more efficient than the DF estimators that do not need screening. Moreover simulation results show that screening at sample level does not correct for the increased inclusion probability of units in both frames produced by the DF set-up nor improve efficiency. The different estimation options will be compared with regards to survey costs, amount of information required and statistical properties of the final estimates

Recent gender literature shows a growing demand of sound statistical methods for analysing gender gaps, for capturing their complexity and for exploring the pattern of relationships among a collection of observable variables selected in order to disentangle the latent trait of gender equity. In this paper we consider parametric Hierarchical Marginal Models applying to binary and categorical data, as a promising statistical tool for gender studies. We explore the potential of Chain Graphical Models, by focusing on a special smooth sub-class of models known as Graphical Models of type II as recently developed (Nicolussi in Marginal parameterizations for conditional independence models and graphical models for categorical data, 2013) , i.e. an advanced methodology for untangling and highlighting any dependence/independence pattern between gender and a set of covariates of mixed nature, either categorical, ordinal or quantitative. With respect to traditional methodologies for treating categorical variables, such as Logistic Regression and Chi-Squared test for contingency table, the proposed model lead to a full multivariate analysis, allowing for isolating the effect of each dependent variable from all other response variables. At the same time, the resulting graph offers an immediate visual idea of the association pattern in the entire set of study variables. The empirical performance of the method is tested by using data from a recent survey about sexual harassment issues inside university, granted by the Equal Opportunities Committee of the University of Milano-Bicocca (Italy).

Multiple-frame surveys are commonly used to deal with under-coverage bias. The use of more than one frame introduces the possibility that frames overlap leading to increased inclusion probabilities of units that appear in multiple lists. Following the guide example of a dual frame set-up (DF) in telephone surveys, this contribution presents an extensive simulation study where different types of screenings to deal with the overlap issue are compared with the proper DF approach. We empirically show that the efforts of screening do not guarantee estimators more efficient than the DF estimators that do not need screening. Moreover simulation results show that screening at sample level does not correct for the increased inclusion probability of units in both frames produced by the DF set-up nor improve efficiency. The different estimation options will be compared with regards to survey costs, amount of information required and statistical properties of the final estimates

This fifth edition of the Guidelines for surveillance of drug resistance in tuberculosis is an updated version of earlier editions published in 1994 (1), 1997 (2), 2003 (3) and 2009 (4). The document takes into account recent advancements in laboratory diagnostics and subsequent WHO guidance, including Molecular line probe assays for rapid screening of patients at risk of multidrug-resistant tuberculosis (5), Xpert MTB/RIF assay for the diagnosis of pulmonary and extrapulmonary TB in adults and children (6) and Xpert MTB/RIF implementation manual (7). These updated guidelines also incorporate experience gained from 20 years of the Global Project on Anti-Tuberculosis Drug Resistance Surveillance (hereafter referred to as the Global Project), a project initiated by WHO and the International Union Against Tuberculosis and Lung Disease (The Union). Acknowledgements This updated edition was written by Anna Dean and Matteo Zignol. Development of this document was guided by the following panel of external experts and staff of the World Health Organization (WHO): External experts: Daniela Cirillo (San Raffaele Scientific Institute, Milan, Italy), Frank Cobelens (KNCV Tuberculosis Foundation, The Hague, The Netherlands), Ted Cohen (Yale School of Public Health, New Haven, United States), Charlotte Colvin (United States Agency for International Development (USAID), Washington DC, United States), Julia Ershova (Centers for Disease Prevention and Control, Atlanta, United States), Aleksandr Golubkov (USAID, Washington DC, United States), Arax Hovhannesyan (World Vision Armenia, Yerevan, Armenia), Maeve Lalor (Public Health England, London, United Kingdom), Fulvia Mecatti (University of Milan-Bicocca, Milan, Italy), Patrick Moonan (Centers for Disease Prevention and Control, Atlanta, United States), Amy Pietak (USAID, Washington DC, United States), Michael Selgelid (Australian National University, Canberra, Australia), Susan van den Hof (KNCV Tuberculosis Foundation, The Hague, T

Multiple frame surveys are powerful tools for dealing with a number of non-standard sampling designs, especially for improving population coverage and response rates by controlling survey costs. In an overlap design, the probability for units in the overlap domain to be included in the final sample is increased, and as a consequence this multiplicity must be accounted for at the estimation stage. In this paper we focus on an application issue tipical of modern telephone surveys. We assume a Dual Frame set up as extensively applied in large scale surveys and official statistics and assume that the variable of interest has a Bernuolli distribu- tion. The two frames available are the frame of landline telephone numbers (frame A) and the frame of the cellular telephone numbers (frame B) which are typically overlapping. A first sample is selected from frame A and then an indipendent sample is selected from B. It is a popular device in practice to overcome the overlap issue by dropping from interviewing any unit eventually re-selected from B. However since that would regard selected units only, it appears as not sufficient in order to deal with the actual dual frame design configured. In this paper empirical evidence will be provided of the bias and increased estimators instability/loss of accuracy that may result from the practical device above of actually ignoring the overlap versus the aknowledging of the overlap between frames as in a dual frames design implying a correction to the estimators. Empirical results include a comparison of bias and efficiency with special attention to the multiplicity dual frame estimator. Different estimators will be also considered, requiring more information at the estimation stage to be collected besides the study variable(s). Simulations are carried out at different overlapping rate ranging up to the special case where one frame is a sub- set of the other. The simulation is conducted with different success probabilities and assuming u

For agricultural surveys, a Dual Frame design combining the customary area frame with an additional list frame has been suggested in the 70s (Hartley, Sankhya 1974) for reducing costs and improving estimate accuracy. The availability of 2 or more partial list frames supporting the area frame is often the case in modern applications so that a more general Multiple Frame design appears as a promising tool for agricul- tural surveys. Some major issues will be discussed such as: i) the selection from overlapping frames under a sampling design able to exploit all the available auxiliary information including sub-area sizes; and ii) a weighting system at the estimation stage, feasible for dealing with the increased probability to be selected in the final sample for farms also included in one or more lists and, at the same time, to be selected more than once, from different lists, even if this have not occurred in practice.