Multivariate Poisson cokriging: A geostatistical model for health count data.

Multivariate disease mapping is important for public health research, as it provides insights into spatial patterns of health outcomes. Geostatistical methods that are widely used for mapping spatially correlated health data encounter challenges when dealing with spatial count data. These include heterogeneity, zero-inflated distributions and unreliable estimation, and lead to difficulties when estimating spatial dependence and poor predictions. Variability in population sizes further complicates risk estimation from the counts. This study introduces multivariate Poisson cokriging for predicting and filtering out disease risk. Pairwise correlations between the target variable and multiple ancillary variables are included. By means of a simulation experiment and an application to human immunodeficiency virus incidence and sexually transmitted diseases data in Pennsylvania, we demonstrate accurate disease risk estimation that captures fine-scale variation. This method is compared with ordinary Poisson kriging in prediction and smoothing. Results of the simulation study show a reduction in the mean square prediction error when utilizing auxiliary correlated variables, with mean square prediction error values decreasing by up to 50%. This gain is further evident in the real data analysis, where Poisson cokriging yields a 74% drop in mean square prediction error relative to Poisson kriging, underscoring the value of incorporating secondary information. The findings of this work stress on the potential of Poisson cokriging in disease mapping and surveillance, offering richer risk predictions, better representation of spatial interdependencies, and identification of high-risk and low-risk areas.

Examining energy and nutrient production across the different agroecological zones in rural Ethiopia using statistical methods.

Poor-quality diets are of huge concern in areas where consumption is dominated by locally sourced foods that provide inadequate nutrients. In agroecologically diverse countries like Ethiopia, food production is also likely to vary spatially. Yet, little is known about how nutrient production varies by agroecology. Our study looked at the adequacy of essential nutrients from local production in the midland, highland, and upper highland agroecological zones (AEZs). Data were collected at the village level from the kebele agriculture office and at the farm and household levels through surveys in rural districts of the South Wollo zone, Ethiopia. Household data were acquired from 478 households, and crop samples were collected from 120 plots during the 2020 production year. Annual crop and livestock production across the three AEZs was converted into energy and nutrient supply using locally developed crops' energy and nutrient composition data. The total produced energy (kcal) met significant proportions of per capita energy demand in the highland and upper highland, while the supply had a 50% energy deficit in the midland. Shortfalls in per capita vitamin A supply decreased across the agroecological gradient from midland (46%) to upper highland (31%). The estimated shortfall in folate supply was significantly higher in the upper highlands (63%) and negligible in the highlands (2%). The risk of deficient iron and zinc supply was relatively low across all AEZs (<10%), but the deficiency risk of calcium was unacceptably high. Agroecology determines the choice of crop produced and, in this way, affects the available supply of energy and nutrients. Therefore, agroecological variations should be a key consideration when designing food system interventions dedicated to improving diets.

Causal inference from cross-sectional earth system data with geographical convergent cross mapping.

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect.

Proof of evidence of changes in global terrestrial biomes using historic and recent NDVI time series.

Climate change affects plant dynamics and functioning of terrestrial ecosystems. This study aims to investigate temporal changes in global vegetation coverage and biomes during the past three decades. We compared historic annual NDVI time series (1982, 1983, 1984 and 1985) with recent ones (2015, 2016, 2017 and 2018), captured from NOAA-AVHRR satellite observations. To correct the NDVI time series for missing data and outliers, we applied the Harmonic Analysis of Time Series (HANTS) algorithm. The NDVI time series were decomposed in their significant amplitude and phase given their periodic fluctuation, except for ever green vegetation. Our findings show that the average NDVI values in most biomes have increased significantly (F-value<0.01) by 0.05 ndvi units over during the past three decades, except in tundra, and deserts and xeric shrublands. The highest rates of change in the harmonic components were observed in the northern hemisphere, mainly above 30° latitude. Worldwide, the mean annual phase reduced by 9° corresponding to a 9 days shift in the beginning of the growing season. Annual phases in the recent time series reduced significantly as compared to the historic time series in the five major global biomes: by 14.1, 14.8, 10.6, 9.5, and 22.8 days in boreal forests/taiga; Mediterranean forests, woodlands, and scrubs; temperate conifer forests; temperate grasslands, savannas, and shrublands; and deserts, and xeric shrublands, respectively. In tropical and subtropical biomes, however, changes in the annual phase of vegetation coverage were not statistically significant. The decrease in the level of phases and acceleration of growth and changes in plant phenology indicate the increase in temperature and climate changes of the planet.

Comparison of the association between different ozone indicators and daily respiratory hospitalization in Guangzhou, China.

Background: Epidemiological studies have widely proven the impact of ozone (O3) on respiratory mortality, while only a few studies compared the association between different O3 indicators and health. Methods: This study explores the relationship between daily respiratory hospitalization and multiple ozone indicators in Guangzhou, China, from 2014 to 2018. It uses a time-stratified case-crossover design. Sensitivities of different age and gender groups were analyzed for the whole year, the warm and the cold periods. We compared the results from the single-day lag model and the moving average lag model. Results: The results showed that the maximum daily 8 h average ozone concentration (MDA8 O3) had a significant effect on the daily respiratory hospitalization. This effect was stronger than for the maximum daily 1 h average ozone concentration (MDA1 O3). The results further showed that O3 was positively associated with daily respiratory hospitalization in the warm season, while there was a significantly negative association in the cold season. Specifically, in the warm season, O3 has the most significant effect at lag 4 day, with the odds ratio (OR) equal to 1.0096 [95% confidence intervals (CI): 1.0032, 1.0161]. Moreover, at the lag 5 day, the effect of O3 on the 15-60 age group was less than that on people older than 60 years, with the OR value of 1.0135 (95% CI: 1.0041, 1.0231) for the 60+ age group; women were more sensitive than men to O3 exposure, with an OR value equal to 1.0094 (95% CI: 0.9992, 1.0196) for the female group. Conclusion: These results show that different O3 indicators measure different impacts on respiratory hospitalization admission. Their comparative analysis provided a more comprehensive insight into exploring associations between O3 exposure and respiratory health.

A sensitive soil biological indicator to changes in land-use in regions with Mediterranean climate.

The demand for reliable indicators to quantify soil health has increased recently. We propose and test the use of soil microbial functional diversity as an indicator of multifunctional performance in agriculturally important areas. Agricultural fields in the Mediterranean and semiarid regions of Israel were selected as test sites and measured in Spring and Autumn seasons. Measurements included microbial parameters, basic soil abiotic properties and biological responses to agricultural management relative to measures of a natural ecosystem. Using a canonical correlation analysis we found that soil moisture was the most important basic soil property with different responses in Spring and Autumn. In Spring, it had a strongly negative relation with microbial biomass (MB), community level physiological profiling (CLPP) and the Shannon-Weaver index H', while in Autumn it had a strong relation with CLPP. We further show a significant interaction between CLPP and climate for land-use type "orchards". CLPP measured in the autumn season was thus identified as a useful and rapid biological soil health indicator, recommended for application in semiarid and Mediterranean agricultural regions. Apart from obtaining a better understanding of CLPP as the soil indicator, the study concludes that CLPP is well suited to differentiate between soils in different climates, seasons and land use types. The study shows a promising direction for further research on characterizing soil health under a larger variety of conditions.

Short-term health impacts related to ozone in China before and after implementation of policy measures: A systematic review and meta-analysis.

This paper presents a meta-analysis of the impacts of short-term exposure to ozone (O3) on three health endpoints: all-cause, cardiovascular, and respiratory mortality in China. All relevant studies from January 1990 to December 2021 were searched from four databases. After screening, 30 studies were included for the meta-analysis. The results showed that a significant rise of 0.41 % (95 % confidence interval (CI): 0.35 %-0.48 %) in all-cause, 0.60 % (95 % CI: 0.51 %-0.68 %) in cardiovascular and 0.45 % (95 % CI: 0.28 %-0.62 %) in respiratory mortality for each 10 µg m-3 increase in the maximum daily 8 h average O3 concentration (MDA8 O3). Moreover, results stratified by heterogeneous time periods before and after implementing a policy measure in 2013, showed that the pooled effects for all-cause and respiratory mortality before were greater than those after, while the pooled effects for cardiovascular mortality before 2013 were slightly smaller than those after. The finding that short-term exposure to O3 was positively related to the three health endpoints was validated by means of a sensitivity analysis. Furthermore, we did not observe any publication bias. Our results present an updated and better understanding of the relationship between short-term exposure to O3 and the three health endpoints, while providing a reference for further assessment of the impact of short-term O3 exposure on human health.

Mapping twenty years of antimicrobial resistance research trends.

OBJECTIVE: Antimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses. METHODS: Structural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space. RESULTS: In total, 88 topics were identified in 158,616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor. CONCLUSION: This study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research.