This study analyzed the association between individual and household factors and the incidence of trachoma among a population aged between 1 and 9 years in the state of Pernambuco. This was a population-based household study conducted using a population-based sample of residents from 96 census sectors of the 1778 sectors considered to be at social risk in the state. The estimated odds ratio of the univariate analysis presented a confidence interval of 95%. Weights and clusters were adjusted through the Generalized Linear and Latent Mixed Model (GLLAM) method. Trachoma cases were the dependent variable in the multivariate analysis. The independent variables were selected through the stepwise forward method, with an input criterion of 20% (p < 0.20) and an output criterion of 10% (p < 0.10). The prevalence was 6.65%. Trachoma was associated with a female sex, age of 5–9 years, either the absence of use or infrequent use of soap to wash the hands and face, the presence of nasal secretion, a lack of piped water from a public supply system, a greater number of rooms used for sleeping, a greater number of people living in the same household, and a family income of up to one minimum monthly wage. The prevalence of follicular trachoma in Pernambuco was higher than what is recommended by the World Health Organization (WHO).

To estimate the prevalence of cases of trachoma in the population at social risk aged 1-9 years in Pernambuco State and its mesoregions. This was a cross-sectional study in which secondary data from the National Household Survey on Trachoma (2014-2015) were analyzed. The study covered 96 census tracts distributed across the mesoregions of Pernambuco State (Metropolitan Region of Recife, Zona da Mata, Agreste, Sertao do Sao Francisco and Sertao Pernambucano). The study included all homes with children aged 1-9 years, and all of them were examined for trachoma, using the WHO criteria. Among the total of 7,423 children aged 1-9 years who participated in this survey, 446 presented with active trachoma. Thus, the prevalence of trachoma in Pernambuco State, in children between 1-9 years old was 6.65%, and it was higher among children aged 5-9 years (7.12%) and among girls (7.23%). This result was seen in the majority of the mesoregions studied, except in the Zona da Mata and Sertao do Sao Francisco, where there were higher prevalences in the age group of 1-4 years (5.55%) and among boys (5.90%). A tendency towards a higher prevalence between 1 and 6 years of age was observed. The follicular trachoma continues to be an important public health problem in Pernambuco State. It particularly affects poor people, including those living in urban areas of the State.

This study investigates howspatially constrained clustering can reveal actionable crime patterns to support public–security planning. Using neighbourhood–level crime records for Recife, Brazil (2007–2015), we provide new versions of two established methods —Ward–like hierarchical clustering and the graph-based spatial kluster analysis by tree edge removal (SKATER) algorithm– so that they: (i) enforce geographical contiguity and (ii) handle mixed (qualitative and quantitative) variable types through the Gower dissimilarity. Validation with the Calinski–Harabasz, Dunn, and Davies–Bouldin indices shows that the new Ward–like method achieves the strongest compactness and inter–cluster separation, whereas the Gower– based SKATER method achieves the highest variance-based discrimination. The resulting clusters partly, but not fully, coincide with Recife’s official Integrated Security Areas (or Áreas Integradas de Segurança, AIS, in Portuguese), suggesting that a data-driven redrawing of boundaries could improve police resource allocation and the monitoring of local safety policies. All computations were performed in the R software and both the code and methods are readily transferable to other cities facing similar crime-mapping challenges.

Crop area estimates based on counting pixels over classified satellite images are a promising application of remote sensing to agriculture. However, such area estimates are biased, and their variance is a function of the error rates of the classification rule. To redress the bias, estimators (direct and inverse) relying on the so-called confusion matrix have been proposed, but analytic estimators for variances can be tricky to derive. This article proposes a bootstrap method for assessing statistical properties of such estimators based on information from a sample confusion matrix. The proposed method can be applied to any other type of estimator that is built upon confusion matrix information. The resampling procedure is illustrated in a small study to assess the biases and variances of estimates using purely pixel counting and estimates provided by both direct and inverse estimators. The method has the advantage of being simple to implement even when the sample confusion matrix is generated under unequal probability sample design. The results show the limitations of estimates based solely on pixel counting as well as respective advantages and drawbacks of the direct and inverse estimators with respect to their feasibility, unbiasedness, and variance.

Sequential identification of epsilon-Pareto-optimal alternatives among a finite set with noisy, possibly correlated objectives. Simultaneous anytime-valid confidence sequences for every objective of every alternative (Waudby-Smith and Ramdas, 2024, ) certify, at any time, which alternatives are epsilon-dominated and which are not, report the estimated frontier, the plausible Pareto set and the certified-dominated set, propose the next alternative to evaluate, and record an auditable result contract.