Multiple Geographic Levels Research Articles

A synthesis of research on the sequestration of carbon in forests and the conservation of water

Forests help solve environmental issues by sequestering carbon and conserving water. Forest management's ultimate goal is to optimize dual functions to reduce greenhouse gas emissions and maintain the water cycle. Increased forest production and ecosystem water balance have costs and benefits. This study reviews forest carbon sequestration and hydrological principles for future research. The interaction between forest carbon sequestration and water conservation revealed information gaps and research needs. Previous research has helped comprehend forest carbon fixing and hydrological regulation. Many equipment and methods can quantify and monitor forest carbon and hydrological issues at multiple geographical and temporal levels. Afforestation programs that improve carbon sequestration and water maintenance ecosystem services lack knowledge. Top-down scheduling of afforestation in locations with uncertain water supplies must address how much and where to plant assumed existing land, ecological implications, and local progress and income. Planting decisions dominate local management. Cooperative research is needed to build and manage planted forests for carbon sequestration, water management, and other societal purposes. This study's integrated paradigm for forest management considers carbon sequestration and water conservation can assist future research.

Packaging code and data for reproducible research: A case study of journey time statistics

The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. However, open data is not enough in many cases: publicly available datasets need to be accessible in an analysis-ready form from popular data science tools, such as R and Python, for them to realise their full potential. This paper explores ways to maximise the impact of open data with reference to a case study of packaging code to facilitate reproducible analysis. We present the jtstats project, which consists of a main Python package, and a smaller R version, for importing, processing, and visualising large and complex datasets representing journey times, for many transport modes and trip purposes at multiple geographic levels, released by the UK Department for Transport (DfT). jtstats shows how domain specific packages can enable reproducible research within the public sector and beyond, saving duplicated effort and reducing the risks of errors from repeated analyses. We hope that the jtstats project inspires others, particularly those in the public sector, to add value to their data sets by making them more accessible.

FindSVI: an R package to calculate the Social Vulnerability Index at multiple geographical levels

findSVI: an R package to calculate the Social Vulnerability Index at multiple geographical levels

Multilevel analysis of determinants in postnatal care utilisation among mother-newborn pairs in India, 2019-21.

Postnatal care (PNC) utilisation within 24 hours of delivery is a critical component of health care services for mothers and newborns. While substantial geographic variations in various health outcomes have been documented in India, there remains a lack of understanding regarding PNC utilisation and underlying factors accounting for these geographic variations. In this study, we aimed to partition and explain the variation in PNC utilisation across multiple geographic levels in India. Using India's 5th National Family Health Survey (2019-21), we conducted four-level logistic regression analyses to partition the total geographic variation in PNC utilisation by state, district, and cluster levels, and to quantify how much of theses variations are explained by a set of 12 demographic, socioeconomic, and pregnancy-related factors. We also conducted analyses stratified by selected states/union territories. Among 149 622 mother-newborn pairs, 82.29% of mothers and 84.92% of newborns were reported to have received PNC within 24 hours of delivery. In the null model, more than half (56.64%) of the total geographic variation in mother's PNC utilisation was attributed to clusters, followed by 26.06% to states/union territories, and 17.30% to districts. Almost 30% of the between-state variation in mother's PNC utilisation was explained by the demographic, socioeconomic, and pregnancy-related factors (i.e. state level variance reduced from 0.486 (95% confidence interval (CI) = 0.238, 0.735) to 0.320 (95% CI = 0.152, 0.488)). We observed consistent results for newborn's PNC utilisation. State-specific analyses showed substantial geographic variation attributed to clusters across all selected states/union territories. Our findings highlight the consistently large cluster variation in PNC utilisation that remains unexplained by compositional effects. Future studies should explore contextual drivers of cluster variation in PNC utilisation to inform and design interventions aimed to improve maternal and child health.

Adaptive Co-Management for Regional Tourism Governance Under the COVID-19 Pandemic in the Greater Bay Area, China

ABSTRACT This study examined the process of adaptive co-management (ACM) among actors and institutions in the Greater Bay Area (GBA) in China to rejuvenate the region’s tourism recovery from the COVID-19 pandemic. Qualitative in-depth interviews were conducted with 19 key informants from various government departments within the region. Results demonstrated how actors and flexible institutions at multiple geographical scales and levels had joined efforts to cope with the various phases of uncertainty through a learning-by-doing process. Several essential factors leading to the success of the learning process within the region, its positive outcomes, and barriers encountered. This study contributed to understanding how the region, through ACM, was able to balance the pandemic control measures and restoration of its tourism industry during the crisis. Practical suggestions were also offered.

Inequality in Chile's Philanthropic Ecosystem: Evidence and Implications.

Philanthropy seeks to address deep-rooted social issues and assume responsibility for the creation of public goods not provided by the public sector-and in this way help reduce inequality. Yet philanthropy has also been criticized for bypassing democratic mechanisms for the public determination of how to invest in society-and thus may perpetuate other inequities. In both cases, inequality, defined as asymmetries of resources and power, plays a critical role in public goods creation and in the legitimacy of a country's philanthropic ecosystem. However, little empirical research examines the existence and role of inequality in country-level donation systems. To fill this gap, this study provides evidence of growing donation concentration in Chile's philanthropic ecosystem, with a focus on the culture sector, characterizes it by mapping systematic differences in ecosystem perceptions by actor type, and identifies and tests statistically structural and organizational factors associated with these perceptions. Inequality in Chile's donation system operates at multiple geographical, legal, and organizational levels, all of which are reflected in objective donation amounts and subjective ecosystem perceptions. We conclude that in Chile resource asymmetries and power imbalances hinder the fulfillment of philanthropy's promise and call for further research to identify policies that address inequities in emerging philanthropic ecosystems in Chile, Latin America, and beyond.

Measuring conservation priorities: A simple tool for conservation planning in poorly sampled areas

Global conservation resources are limited and as a result donors and funders are forced to make difficult decisions as to which areas are in most urgent need of support. Biodiversity can play a key role in these choices, but many other factors must be considered. In order to assist with such decisions we present a simple index that can be employed by non-scientists in poorly sampled countries, using the reptile fauna of 55 protected and two unprotected areas of Paraguay as a case study. This index can be applied at multiple taxonomic and geographic levels to minimize biases generated by uneven sampling. We offer words of caution on unsupported claims of high biodiversity, and highlight how the use of inaccurate terminology, although well-intentioned, can be detrimental to national or local conservation efforts. Results show that the top two areas of conservation priority in Paraguay are currently unprotected, and current investment of resources is ineffective and insufficient for long term protection of Paraguay’s globally and nationally threatened reptiles.

Small-area assessment of temperature-related mortality risks in England and Wales: a case time series analysis

Epidemiological literature on the health risks associated with non-optimal temperature has mostly reported average estimates across large areas or specific population groups. However, the heterogeneous distribution of drivers of vulnerability can result in local differences in health risks associated with heat and cold. We aimed to analyse the association between ambient air temperature and all-cause mortality across England and Wales and characterise small scale patterns in temperature-related mortality risks and impacts. We performed a country-wide small-area analysis using data on all-cause mortality and air temperature for 34 753 lower super output areas (LSOAs) within 348 local authority districts (LADs) across England and Wales between Jan 1, 2000, and Dec 31, 2019. We first performed a case time series analysis of LSOA-specific and age-specific mortality series matched with 1 × 1 km gridded temperature data using distributed lag non-linear models, and then a repeated-measure multivariate meta-regression to pool LAD-specific estimates using area-level climatological, socioeconomic, and topographical predictors. The final analysis included 10 716 879 deaths from all causes. The small-area assessment estimated that each year in England and Wales, there was on average 791 excess deaths (empirical 95% CI 611-957) attributable to heat and 60 573 (55 796-65 145) attributable to cold, corresponding to standardised excess mortality rates of 1·57 deaths (empirical 95% CI 1·21-1·90) per 100 000 person-years for heat and 122·34 deaths (112·90-131·52) per 100 000 person-years for cold. The risks increased with age and were highly heterogeneous geographically, with the minimum mortality temperature ranging from 14·9°C to 22·6°C. Heat-related mortality was higher in urban areas, whereas cold-related mortality showed a more nuanced geographical pattern and increased risk in areas with greater socioeconomic deprivation. This study provides a comprehensive assessment of excess mortality related to non-optimal outdoor temperature, with several risk indicators reported by age and multiple geographical levels. The analysis provides detailed risk maps that are useful for designing effective public health and climate policies at both local and national levels. Medical Research Council, Natural Environment Research Council, EU Horizon 2020 Programme, National Institute of Health Research.

Assessing the Impact of Differential Privacy on Measures of Population and Racial Residential Segregation

The U.S. Census Bureau plans to use a new disclosure avoidance technique based on differential privacy to protect respondent confidentiality for the 2020 Decennial Census of Population and Housing. Their new technique injects noise based on a number of parameters into published statistics. While the noise injection does protect respondent confidentiality, it achieves the protection at the cost of less accurate data. To better understand the impact that differential privacy has on accuracy, we compare data from the complete-count 1940 Census with multiple differentially private versions of the same data set. We examine the absolute and relative accuracy of population counts in total and by race for multiple geographic levels, and we compare commonly used measures of residential segregation computed from these data sets. We find that accuracy varies by the global privacy-loss budget and the allocation of the privacy-loss budget to geographic levels (e.g., states, counties, enumeration district) and queries. For measures of segregation, we observe situations where the differentially private data indicate less segregation than the original data and situations where the differentially private data indicate more segregation than the original data. The sensitivity of accuracy to the overall global privacy-loss budget and its allocation highlight the fundamental importance of these policy decisions. Data producers like the U.S. Census Bureau must collaborate with users not only to determine the most useful set of parameters to receive allocations of the privacy-loss budget, but also to provide documentation and tools for users to gauge the reliability and validity of statistics from publicly released data products. If they do not, producers may create statistics that are unusable or misleading for the wide variety of use cases that rely on those statistics.

Extended two-stage designs for environmental research

BackgroundThe two-stage design has become a standard tool in environmental epidemiology to model multi-location data. However, its standard form is rather inflexible and poses important limitations for modelling complex risks associated with environmental factors. In this contribution, we illustrate multiple design extensions of the classical two-stage method, all implemented within a unified analytic framework.MethodsWe extended standard two-stage meta-analytic models along the lines of linear mixed-effects models, by allowing location-specific estimates to be pooled through flexible fixed and random-effects structures. This permits the analysis of associations characterised by combinations of multivariate outcomes, hierarchical geographical structures, repeated measures, and/or longitudinal settings. The analytic framework and inferential procedures are implemented in the R package mixmeta.ResultsThe design extensions are illustrated in examples using multi-city time series data collected as part of the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Specifically, four case studies demonstrate applications for modelling complex associations with air pollution and temperature, including non-linear exposure–response relationships, effects clustered at multiple geographical levels, differential risks by age, and effect modification by air conditioning in a longitudinal analysis.ConclusionsThe definition of several design extensions of the classical two-stage design within a unified framework, along with its implementation in freely-available software, will provide researchers with a flexible tool to address novel research questions in two-stage analyses of environmental health risks.

An integrated modelling methodology for estimating global incidence and prevalence of hereditary spastic paraplegia subtypes SPG4, SPG7, SPG11, and SPG15

BackgroundHereditary spastic paraplegias (HSPs) are progressively debilitating neurodegenerative disorders that follow heterogenous patterns of Mendelian inheritance. Available epidemiological evidence provides limited incidence and prevalence data, especially at the genetic subtype level, preventing a realistic estimation of the true social burden of the disease. The objectives of this study were to (1) review the literature on epidemiology of HSPs; and (2) develop an epidemiological model of the prevalence of HSP, focusing on four common HSP genetic subtypes at the country and region-level.MethodsA model was constructed estimating the incidence at birth, survival, and prevalence of four genetic subtypes of HSP based on the most appropriate published literature. The key model parameters were assessed by HSP clinical experts, who provided feedback on the validity of assumptions. A model was then finalized and validated through comparison of outputs against available evidence. The global, regional, and national prevalence and patient pool were calculated per geographic region and per genetic subtype.ResultsThe HSP global prevalence was estimated to be 3.6 per 100,000 for all HSP forms, whilst the estimated global prevalence per genetic subtype was 0.90 (SPG4), 0.22 (SPG7), 0.34 (SPG11), and 0.13 (SPG15), respectively. This equates to an estimated 3365 (SPG4) and 872 (SPG11) symptomatic patients, respectively, in the USA.ConclusionsThis is the first epidemiological model of HSP prevalence at the genetic subtype-level reported at multiple geographic levels. This study offers additional data to better capture the burden of illness due to mutations in common genes causing HSP, that can inform public health policy and healthcare service planning, especially in regions with higher estimated prevalence of HSP.

Geographic variation in caesarean delivery in India.

BackgroundThe rate of caesarean delivery has increased markedly both globally and within India. However, there is considerable variation within countries. No previous studies have examined the relative importance of multiple geographic levels in shaping the distribution of caesarean delivery and to what extent they can be explained by individual‐level risk factors.ObjectivesTo describe geographic variation in caesarean delivery and quantify the contribution of individual‐level risk factors to the variation in India.MethodsWe conducted four‐level logistic regression analysis to partition total variation in caesarean delivery to three geographic levels (states, districts and communities) and quantify the extent to which variance at each level was explained by a set of 20 sociodemographic, medical and institutional risk factors. Stratified analyses were conducted by the type of delivery facility (public/private).ResultsOverall prevalence of caesarean delivery was 19.3% in India in 2016. Most geographic variation was attributable to states (44%), followed by communities (32%), and lastly districts (24%). Adjustment for all risk factors explained 44%, 52% and 46% of variance for states, districts and communities, respectively. The proportion explained by individual risk factors was larger in public facilities than in private facilities at all three levels. A substantial proportion of between‐population variation still existed even after clustering of individual risk factors was comprehensively adjusted for.ConclusionsDiverse contextual factors driving high or low rate of caesarean delivery at each geographic level should be explored in future studies so that tailored intervention can be implemented to reduce the overall variation in caesarean delivery.

Trends in bird abundance differ among protected forests but not bird guilds.

Improved monitoring and associated inferential tools to efficiently identify declining bird populations, particularly of rare or sparsely distributed species, is key to informed conservation and management across large spatiotemporal regions. We assess abundance trends for 106 bird species in a network of eight forested national parks located within the northeast United States from 2006 to 2019 using a novel hierarchical model. We develop a multispecies, multiregion, removal-sampling model that shares information across species and parks to enable inference on rare species and sparsely sampled parks and to evaluate the effects of local forest structure. Trends in bird abundance over time varied widely across parks, but species showed similar trends within parks. Three parks (Acadia National Park and Marsh-Billings-Rockefeller and Morristown National Historical Parks [NHP]) decreased in bird abundance across all species, while three parks (Saratoga NHP and Roosevelt-Vanderbilt and Weir-Farm National Historic Sites) increased in abundance. Bird abundance peaked at medium levels of basal area and high levels of percent forest and forest regeneration, with percent forest having the largest effect. Variation in these effects across parks could be a result of differences in forest structural stage and diversity. By sharing information across both communities and parks, our novel hierarchical model enables uncertainty-quantified estimates of abundance across multiple geographical (i.e., network, park) and taxonomic (i.e., community, guild, species) levels over a large spatiotemporal region. We found large variation in abundance trends across parks but not across bird guilds, suggesting that local forest condition might have a broad and consistent effect on the entire bird community within a given park. Research should target the three parks with overall decreasing trends in bird abundance to further identify what specific factors are driving observed declines across the bird community. Understanding how bird communities respond to local forest structure and other stressors (e.g., pest outbreaks, climate change) is crucial for informed and lasting management.

Bioinformatic Algorithm and Prediction to Control SARS-Cov-2 Spike Protein Variant on COVID-19 Pandemic Progression

Recently severe SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global COVID-19 pandemic. Systemic tracking algorithms of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographical levels, the shift occurred even in narrow scope of local epidemics. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have fitness advantage. Based on findings that the G614 variant grows to a higher titer as pseudo-typed virions. These findings illuminate changes important for mechanistic understanding of the virus and support continuing surveillance of Spike mutations to aid with development of immunological interventions. Furthermore, predictive stewardship would be conducted for protection and limitation of transmissibility.

Analysis of COVID-19 Epidemics on a Timescale of Burden

The 2020 COVID-19 pandemic produced thousands of well-quantified epidemics in counties, states, and countries around the world. Comparing the dynamics and outcomes of these nested epidemics could improve our understanding of the impact of non-pharmaceutical interventions (NPIs) and help public health officials, political leaders, and other managers with real-time risk assessment across multiple geographic levels. Cross-outbreak comparisons are challenging due to their variable dates of introduction of the SARS-CoV-2 virus, rates of transmission, case detection rates, and asynchronous and diverse management interventions. Here, we present a new graphical method for comparing ongoing COVID-19 epidemics by using disease burden as a natural timescale for comparison. Trajectories of growth rates of cases over the timescale of lagged deaths per-capita produces coherent visual comparisons of epidemics that are otherwise incoherent and asynchronous in the timescale of calendar dates or incomparable using non-stationary measures of burden such as cases. Applied to US COVID-19 outbreaks, this approach intuitively depicts multiple findings including early lockdowns reducing transmission at fewer deaths per-capita early in the epidemic, reopenings coinciding clearly with resurgent summer epidemics, and peaks in unmitigated epidemics that, while separated in time and place, converge to a population fatality rate lower than current, commonly used estimates based on infection fatality rates and herd immunity thresholds. Our method uses early and minimally mitigated epidemics, like that in NYC in March-April 2020 and Sweden in later 2020, to define what we call “epidemic resistance lines” (ERLs) or hypothesized upper bounds of epidemic speed and burden. ERLs from less-mitigated epidemics allow cross-outbreak benchmarking, for example of resurgent summer epidemics in the US. In particular, the unmitigated NYC epidemic resistance line appears to bound the growth rates of 3,000 US counties and funnel growth rates across counties to their peaks where growth rates equal zero in the fall and winter of 2020 before vaccines could be widely administered. Corroboration of upper-bounds on epidemic trajectories allowed early predictions of mortality burden for unmitigated COVID-19 epidemics in these populations, predictions that were more accurate for counties in states without mask-wearing mandates. We discuss how this method could be used for future epidemics, including seasonal epidemics caused by influenza or ongoing epidemics caused by new SARS-CoV-2 variants.

Quantifying stakeholder learning in climate change adaptation across multiple relational and participatory networks

Responding to accelerating climate change impacts requires broad and effective engagement with stakeholders, at multiple geographic and governance levels. Stakeholder participation has been hailed as a facilitated approach in climate change adaptation that supports social learning, depolarization of perceptions, and fosters collective action. But stakeholder participation remains loosely interpreted and evaluating measures are limited. This study employs social network analysis (SNA) to investigate how social relations among stakeholders, which emerge as a result of participation, are associated with stakeholder learning, as changes in perceptions of climate change. We hypothesized that reciprocal ties of understanding, respect, and influence can predict changes in perceptions of climate change. This approach was applied to a case study in Deal Island Peninsula, Maryland (USA) where local residents, scientists, and government officials met from 2016 to 2018 to collaboratively manage the impacts of sea-level rise in their communities. We found that social relations based on mutual understanding, respect,and influence are positively associated with perceptions of climate change. We provide a detailed conceptualization and implementation of a network-based approach that may serve as a potential quantitative performance measure of stakeholder participation processes in climate change adaptation. Overall, this study provides empirical evidence of the role that emerging social relations have on enhancing or constraining social learning among stakeholders in the Deal Island Peninsula project.

Open-Source 3D Printable GPS Tracker to Characterize the Role of Human Population Movement on Malaria Epidemiology in River Networks: A Proof-of-Concept Study in the Peruvian Amazon.

Human movement affects malaria epidemiology at multiple geographical levels; however, few studies measure the role of human movement in the Amazon Region due to the challenging conditions and cost of movement tracking technologies. We developed an open-source low-cost 3D printable GPS-tracker and used this technology in a cohort study to characterize the role of human population movement in malaria epidemiology in a rural riverine village in the Peruvian Amazon. In this pilot study of 20 participants (mean age = 40 years old), 45,980 GPS coordinates were recorded over 1 month. Characteristic movement patterns were observed relative to the infection status and occupation of the participants. Applying two analytical animal movement ecology methods, utilization distributions (UDs) and integrated step selection functions (iSSF), we showed contrasting environmental selection and space use patterns according to infection status. These data suggested an important role of human movement in the epidemiology of malaria in the Peruvian Amazon due to high connectivity between villages of the same riverine network, suggesting limitations of current community-based control strategies. We additionally demonstrate the utility of this low-cost technology with movement ecology analysis to characterize human movement in resource-poor environments.

Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus

SummaryA SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional, and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to a higher titer as pseudotyped virions. In infected individuals, G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, but not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus and support continuing surveillance of Spike mutations to aid with development of immunological interventions.

Administrative hierarchy, housing market inequality, and multilevel determinants: a cross-level analysis of housing prices in China

Numerous studies have examined the determinants of housing prices in Chinese cities. However, most of them ignored the hierarchical structural characteristics of housing prices because of the use of ordinary least squares and hedonic pricing to model the housing prices for a single city. Therefore, this study explores the multi-level determinants of housing prices and their interactions at different levels. To this end, it proposes a three-level hierarchical linear model (HLM) using observations from 146,099 communities nested in 1120 counties of 31 provinces in China as a case study. The results of the hierarchical linear regression indicate significant variances in average housing prices. This finding suggests HLM to be appropriate when dealing with housing prices inherently nested at multiple geographic levels. Overall, housing values in China are not only determined by accessibility factors but are also driven by multi-level socioeconomic aspects. Among the selected variables, high-speed railway shows a significant positive effect, while ordinary railway shows a significant negative effect on housing values at the community level. At the county level, rural–urban migration and per capita living space have significant positive impacts on housing value. At the province level, the relationship between rural–urban migration and housing prices depends on economic development and urban employment. Similarly, the average wages of urban employment influence the relationship between per capita living space and housing prices. These results suggest that contextual effects exist between the determinants of housing prices at county and province levels.

Cross-sector collaboration on Safe Routes to School policy advocacy and implementation: A mixed methods evaluation from Minnesota

Abstract Cross-sector collaboration has been a crucial element of planning and implementing large-scale obesity prevention-related policies and programs in the United States, including Safe Routes to School (SRTS), which promotes walking and bicycling through physical infrastructure changes and programmatic efforts. Minnesota provides a unique opportunity to evaluate a collaborative partnership that successfully implemented and institutionalized one of the first state-funded SRTS initiatives in the country and included close collaboration between transportation and health officials and advocates at both the state and local levels. This theoretically informed mixed methods case study identified and described the roles of both central and peripheral members of a collaborative SRTS partnership in Minnesota. Qualitative analyses of 18 stakeholder interviews identified key aspects of success, including passionate and skilled individuals with a common objective, structures that facilitated work across partnership functions and geographies, strong interpersonal and inter-organizational relationships, capacity for implementation and advocacy, and information and knowledge sharing. Findings indicate that contributors to partnership success may differ across multiple geographic levels and partnership functions. Contextual factors external to the partnership are also likely to affect whether partnerships achieve their goals.