Automatic Detection of Individual Trees from VHR Satellite Images Using Scale-Space Methods.

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10-20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.

Earth Observation: Investigating Noncommunicable Diseases from Space.

The United Nations has called on all nations to take immediate actions to fight noncommunicable diseases (NCDs), which have become an increasingly significant burden to public health systems around the world. NCDs tend to be more common in developed countries but are also becoming of growing concern in low- and middle-income countries. Earth observation (EO) technologies have been used in many infectious disease studies but have been less commonly employed in NCD studies. This review discusses the roles that EO data and technologies can play in NCD research, including ( a) integrating natural and built environment factors into NCD research, ( b) explaining individual-environment interactions, ( c) scaling up local studies and interventions, ( d) providing repeated measurements for longitudinal studies including cohorts, and ( e) advancing methodologies in NCD research. Such extensions hold great potential for overcoming the challenges of inaccurate and infrequent measurements of environmental exposure at the level of both the individual and the population, which is of great importance to NCD research, practice, and policy.

A spatial-temporal statistical analysis of health seasonality: explaining HFMD infections within a children population along the Vietnamese south central coast.

BACKGROUND: Various neglected tropical diseases show spatially changing seasonality at small areas. This phenomenon has received little scientific attention so far. Our study contributes to advancing the understanding of its drivers. This study focuses on the effects of the seasonality of increasing social contacts on the incidence proportions at multiple district level of the childhood hand-foot-mouth disease in Da Nang city, Viet Nam from 2012 to 2016. METHODS: We decomposed the nonstationary time series of the incidence proportions for the nine spatial-temporal (S-T) strata in the study area, where S indicates the spatial and T the temporal stratum. The long-term trends and the seasonality are presented by the Fourier series. To study the effects of the monthly average ambient temperature and the period of preschooling, we developed a spatial-temporal autoregressive model. RESULTS: Seasonality of childhood hand-foot-mouth disease incidence proportions shows two peaks in all spatial strata annually: large peaks synchronously in April and small ones asynchronously during the preschooling period. The peaks of the average temperature are asynchronous with the seasonal peaks of the childhood hand-foot-mouth disease incidence proportions in the period between January and May, with the negative values of the regression coefficients for all spatial strata, respectively: [Formula: see text]. The increasingly cumulative preschooling period and the seasonal component of the incidence proportions are negatively correlated in the period between August and December, with the negative values of the regression coefficients for all temporal strata, respectively: [Formula: see text]. CONCLUSIONS: The study shows that social contact amongst children under five years of age is the important driving factor of the dynamics of the childhood hand-foot-mouth disease outbreaks in the study area. The preschooling season when children's contact with each other increases stimulates the geographical variation of the seasonality of childhood hand-foot-mouth disease infections at small areas in the study area.

Spatial variation and hot-spots of district level diarrhea incidences in Ghana: 2010-2014.

BACKGROUND: Diarrhea is a public health menace, especially in developing countries. Knowledge of the biological and anthropogenic characteristics is abundant. However, little is known about its spatial patterns especially in developing countries like Ghana. This study aims to map and explore the spatial variation and hot-spots of district level diarrhea incidences in Ghana. METHODS: Data on district level incidences of diarrhea from 2010 to 2014 were compiled together with population data. We mapped the relative risks using empirical Bayesian smoothing. The spatial scan statistics was used to detect and map spatial and space-time clusters. Logistic regression was used to explore the relationship between space-time clustering and urbanization strata, i.e. rural, peri-urban, and urban districts. RESULTS: We observed substantial variation in the spatial distribution of the relative risk. There was evidence of significant spatial clusters with most of the excess incidences being long-term with only a few being emerging clusters. Space-time clustering was found to be more likely to occur in peri-urban districts than in rural and urban districts. CONCLUSION: This study has revealed that the excess incidences of diarrhea is spatially clustered with peri-urban districts showing the greatest risk of space-time clustering. More attention should therefore be paid to diarrhea in peri-urban districts. These findings also prompt public health officials to integrate disease mapping and cluster analyses in developing location specific interventions for reducing diarrhea.

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.

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.

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.

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.

Bayesian structured additive regression modeling of epidemic data: application to cholera.

BACKGROUND: A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. METHODS: We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. The model is applied to cholera epidemic data in the Kumasi Metropolis, Ghana. Proximity to refuse dumps, density of refuse dumps, and proximity to potential cholera reservoirs were modeled as continuous functions; presence of slum settlers and population density were modeled as fixed effects, whereas spatial references to the communities were modeled as structured and unstructured spatial effects. RESULTS: We observe that the risk of cholera is associated with slum settlements and high population density. The risk of cholera is equal and lower for communities with fewer refuse dumps, but variable and higher for communities with more refuse dumps. The risk is also lower for communities distant from refuse dumps and potential cholera reservoirs. The results also indicate distinct spatial variation in the risk of cholera infection. CONCLUSION: The study highlights the usefulness of Bayesian semi-parametric regression model analyzing public health data. These findings could serve as novel information to help health planners and policy makers in making effective decisions to control or prevent cholera epidemics.

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.

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.

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.

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India.

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment.

Kenyan school book knowledge for water, sanitation, hygiene and health education interventions: Disconnect, integration or opportunities?

BACKGROUND: Schools, depending on their access to and quality of water, sanitation and hygiene (WASH) and the implementation of healthy behaviours, can be critical for the control and spread of many infectious diseases, including COVID-19. Schools provide opportunities for pupils to learn about the importance of hygiene and WASH-related practice, and build healthy habits and skills, with beneficial medium- and long-term consequences particularly in low- and middle-income countries: reducing pupils' absenteeism due to diseases, promoting physical, mental and social health, and improving learning outcomes. WASH services alone are often not sufficient and need to be combined with educational programmes. As pupils disseminate their acquired health-promoting knowledge to their (extended) families, improved WASH provisions and education in schools have beneficial effects also on the community. International organisations frequently roll out interventions in schools to improve WASH services and, in some cases, train pupils and teachers on safe WASH behaviours. How such interventions relate to local school education on WASH, health promotion and disease prevention knowledge, whether and how such knowledge and school books are integrated into WASH education interventions in schools, are knowledge gaps we fill. METHODS: We analyzed how Kenyan primary school science text book content supports WASH and health education by a book review including books used from class 1 through class 8, covering the age range from 6 to 13 years. We then conducted a rapid literature review of combined WASH interventions that included a behaviour change or educational component, and a rapid review of international policy guidance documents to contextualise the results and understand the relevance of books and school education for WASH interventions implemented by international organisations. We conducted a content analysis based on five identified thematic categories, including drinking water, sanitation, hygiene, environmental hygiene & health promotion and disease risks, and mapped over time the knowledge about WASH and disease prevention. RESULTS: The books comprehensively address drinking water issues, including sources, quality, treatment, safe storage and water conservation; risks and transmission pathways of various waterborne (Cholera, Typhoid fever), water-based (Bilharzia), vector-related (Malaria) and other communicable diseases (Tuberculosis); and the importance of environmental hygiene and health promotion. The content is broadly in line with internationally recommended WASH topics and learning objectives. Gaps remain on personal hygiene and handwashing, including menstrual hygiene, sanitation education, and related health risks and disease exposures. The depth of content varies greatly over time and across the different classes. Such locally available education materials already used in schools were considered by none of the WASH education interventions in the considered intervention studies. CONCLUSIONS: The thematic gaps/under-representations in books that we identified, namely sanitation, hygiene and menstrual hygiene education, are all high on the international WASH agenda, and need to be filled especially now, in the context of the current COVID-19 pandemic. Disconnects exist between school book knowledge and WASH education interventions, between policy and implementation, and between theory and practice, revealing missed opportunities for effective and sustainable behaviour change, and underlining the need for better integration. Considering existing local educational materials and knowledge may facilitate the buy-in and involvement of teachers and school managers in strengthening education and implementing improvements. We suggest opportunities for future research, behaviour change interventions and decision-making to improve WASH in schools.

Geospatial analysis of HIV-Related social stigma: a study of tested females across mandals of Andhra Pradesh in India.

BACKGROUND: In Geographical Information Systems issues of scale are of an increasing interest in storing health data and using these in policy support. National and international policies on treating HIV (Human Immunodeficiency Virus) positive women in India are based on case counts at Voluntary Counseling and Testing Centers (VCTCs). In this study, carried out in the Indian state of Andhra Pradesh, these centers are located in subdistricts called mandals, serving for both registration and health facility policies. This study hypothesizes that people may move to a mandal different than their place of residence for being tested for reasons of stigma. Counts of a single mandal therefore may include cases from inside and outside a mandal. HIV counts were analyzed on the presence of outside cases and the most likely explanations for movement. Counts of women being tested on a practitioners' referral (REFs) and those directly walking-in at testing centers (DWs) were compared and with counts of pregnant women. RESULTS: At the mandal level incidence among REFs is on the average higher than among DWs. For both groups incidence is higher in the South-Eastern coastal zones, being an area with a dense highway network and active port business. A pattern on the incidence maps was statistically confirmed by a cluster analysis. A spatial regression analysis to explain the differences in incidence among pregnant women and REFs shows a negative relation with the number of facilities and a positive relation with the number of roads in a mandal. Differences in incidence among pregnant women and DWs are explained by the same variables, and by a negative relation with the number of neighboring mandals. Based on the assumption that pregnant women are tested in their home mandal, this provides a clear indication that women move for testing as well as clues for explanations why. CONCLUSIONS: The spatial analysis shows that women in India move towards a different mandal for getting tested on HIV. Given the scale of study and different types of movements involved, it is difficult to say where they move to and what the precise effect is on HIV registration. Better recording the addresses of tested women may help to relate HIV incidence to population present within a mandal. This in turn may lead to a better incidence count and therefore add to more reliable policy making, e.g. for locating or expanding health facilities.

Bayesian analysis of the short-term association of NO2 exposure with local burden of asthmatic symptoms in children.

Short-term exposure to air pollution has been associated with exacerbation of respiratory diseases such as asthma. Substantial heterogeneity in effect estimates has been observed between previous studies. This study aims to quantify the local burden of daily asthma symptoms in asthmatic children in a medium-sized city. Air pollution exposure was estimated using the nearest sensor in a fine resolution urban air quality sensor network in the city of Eindhoven, the Netherlands. Bayesian estimates of the exposure response function were obtained by updating a priori information from a meta-analysis with data from a panel study using a daily diary. Five children participated in the panel study, resulting in a total of 400 daily diary records. Positive associations between NO2 and lower respiratory symptoms and medication use were observed. The odds ratio for any lower respiratory symptoms was 1.07 (95% C.I. 0.92, 1.28) expressed per 10 µg m-3 for current day NO2 concentration, using data from the panel study only (uninformative prior). Odds ratios for dry cough and phlegm were close to unity. The pattern of associations agreed well with the updated meta-analysis. The meta-analytic random effects summary estimate was 1.05 (1.02, 1.07) for LRS. Credible intervals substantially narrowed when adding prior information from the meta-analysis. The odds ratio for lower respiratory symptoms with an informative prior was 1.06 (0.99, 1.14). Burden of disease maps showed a strong spatial variability in the number of asthmatic symptoms associated with ambient NO2 derived from a regression kriging model. In total, 70 cases of asthmatic symptoms can daily be associated with NO2 exposure in the city of Eindhoven. We conclude that Bayesian estimates are useful in estimation of specific local air pollution effect estimates and subsequent local burden of disease calculations. With the fine resolution air quality network, neighborhood-specific burden of asthmatic symptoms was assessed.

Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence.

BACKGROUND: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. METHODS: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. RESULTS: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. CONCLUSIONS: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programmes by providing reliable parameter estimates at the same spatial support and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement.

Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the critical need for public health and policy decisions to be based on the strongest science possible, transparency and clarity in reporting in spatial lifecourse epidemiologic studies is essential. A task force supported by the International Initiative on Spatial Lifecourse Epidemiology (ISLE) identified a need for guidance in this area and developed a Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) Statement. The aim is to provide a checklist of recommendations to improve and make more consistent reporting of spatial lifecourse epidemiologic studies. The STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies was identified as an appropriate starting point to provide initial items to consider for inclusion. Reporting standards for spatial data and methods were then integrated to form a single comprehensive checklist of reporting recommendations. The strength of our approach has been our international and multidisciplinary team of content experts and contributors who represent a wide range of relevant scientific conventions, and our adherence to international norms for the development of reporting guidelines. As spatial, location-based, and artificial intelligence technologies used in spatial lifecourse epidemiology continue to evolve at a rapid pace, it will be necessary to revisit and adapt the ISLE-ReSt at least every 2-3 years from its release.