Study of the impact of available healthcare resources on the population’s mortality rate using the hierarchical linear modelling

Introduction. The uneven development of the medical material and technical base and resources is observed worldwide. At the same time, healthcare resource availability is associated with the territorial characteristics of the population’s mortality rate. In order to reduce mortality, a better understanding of this relationship is needed. The purpose of the study is to assess the impact of healthcare resource availability on mortality, taking into account the hierarchical nesting of municipalities in subjects of the Russian Federation with further funding for health care and demographic indicators. Material and methods. For these purposes, hierarchical linear modelling is used. The assessment was carried out on the data of 265 municipalities attributed to 6 constituent entities of the Russian Federation. The data sources are the Territorial Bodies of the Federal State Statistics Service and the Unified Interdepartmental Information and Statistical System (www.fedstat.ru). Results. As a result of modelling, the health care resources (doctors, medical personnel, beds) at the municipal level were determined to reduce the population mortality rate positively. At the same time, an ambiguous influence of the actual cost of the territorial compulsory medical insurance program was revealed at the regional level. Conclusion. The results obtained correspond to studies devoted to the regional diversity of the population mortality rate and the available healthcare resources. However, they make it possible to determine the influence of factors taking into account the level of their formation (regional, municipal). The proposed models make it possible to improve the quality of managerial decision-making in the health care system since, taking into account the hierarchical nesting, they share the influence of regional and local factors on the variation of municipalities in terms of the mortality rate of the population.

Modelling and Incorporating the Variable Demand Patterns to the Calibration of Water Distribution System Hydraulic Model

Calibration of a water distribution system (WDS) hydraulic model requires adjusting several parameters including hourly or sub-hourly demand multipliers, pipe roughness and settings of various hydraulic components. The water usage patterns or demand patterns in a 24-h cycle varies with the customer types and can be related to many factors including spatial and temporal factors. The demand patterns can also vary on a daily basis. For an extended period of hydraulic simulation, the modelling tools allows modelling of the variable demand patterns using daily multiplication factors. In this study, a linear modelling approach was used to handle the variable demand patterns. The parameters of the linear model allow modelling of the variable demand patterns with respect to the baseline values, and they were optimised to maximise the association with the observed data. This procedure was applied to calibrate the hydraulic model developed in EPANET of a large drinking water distribution system in regional South Australia. Local and global optimisation techniques were used to find the optimal values of the linear modelling parameters. The result suggests that the approach has the potential to model the variable demand patterns in a WDS hydraulic model and it improves the objective function of calibration.

Influence of the Choice of Cultivar and Soil Fertilization on PTE Concentrations in Lactuca sativa L. in the Framework of the Regenerative Agriculture Revolution

Evaluating the relative weight of the choice of cultivar and soil fertilization on potentially toxic elements (PTEs) accumulation is crucial in promoting informed decisions in the framework of regenerative agriculture. To this end, 11 PTEs (Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Zn) were determined in both leaves and roots of six cultivars (Stylist, Xanadu, Aljeva, Bacio, Analena, Vincenzo) of lettuce (Lactuca sativa L.) grown side by side on mineral fertilized or biowaste compost amended soils, under greenhouse conditions. The use of multivariate and linear modelling approaches indicated that the organ and cultivar primarily account for the variability in PTE concentrations. In terms of PTE partitioning between organs, Cd and Mg were preferentially accumulated in leaves, whereas Cu, Pb, K and Zn in roots. As for the cultivar, Xanadu showed the highest concentrations of several PTEs, with Cd reaching concerning levels. Fertilization had a detectable contribution only on Cd accumulation, slightly increased in leaves by compost. Findings highlight the key role of cultivar choice in guaranteeing food safety and grant the possibility to adopt biowaste compost in regenerative agriculture without concerns about PTE accumulation enhancements in lettuce, but demand a cautionary approach in the case of Cd.

A General Framework for Multiple-Recapture Estimation that Incorporates Linkage Error Correction

The size of a partly observed population is often estimated with the capture-recapture model. An important assumption of this chat model is that sources can be perfectly linked. This assumption is of relevance if the identification of records is not obtained by some perfect identifier (such as an id code) but by indirect identifiers (such as name and address). In that case, the perfect linkage assumption is often violated, which in general leads to biased population size estimates. Initial suggestions to solve this use record linkage probabilities to correct the capture-recapture model. In this article we provide a general framework, based on the standard log-linear modelling approach, that generalises this work towards the inclusion of additional sources and covariates. We show that the method performs well in a simulation study.

The multivariate analysis of variance as a powerful approach for circular data

A broad range of scientific studies involve taking measurements on a circular rather than linear scale (often times or orientations). For linear measures there is a well-established statistical toolkit based on linear modelling to explore the associations between this focal variable and potentially several explanatory factors and covariates. However, most statistical testing of circular statistics is much simpler: often involving either testing whether variation in the focal measurements departs from circular uniformity, or whether a single explanatory factor with two levels is supported. Here we demonstrate that a MANOVA approach based on the sines and cosines of the circular data is as effective as the most-commonly used tests in these simple situations, while additionally it offers extension to multi-factorial modelling that these conventional tests do not. This, in combination with recent developments in Bayesian approaches, offers a substantial broadening of the scientific questions that can be addressed statistically with circular data.