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Ferguson EA, Lugelo A, Czupryna A, Anderson D, Lankester F, Sikana L, Dushoff J, Hampson K. Reducing spatial heterogeneity in coverage improves the effectiveness of dog vaccination against rabies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.03.616420. [PMID: 39416172 PMCID: PMC11482771 DOI: 10.1101/2024.10.03.616420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Vaccination programs are the mainstay of control for many infectious diseases. Heterogeneous coverage is hypothesised to reduce vaccination effectiveness, but this impact has not been quantified in real systems. We address this gap using fine-scale data from two decades of rabies contact tracing and dog vaccination campaigns in Serengeti district, Tanzania. Using generalised linear mixed models, we find that current local (village-level) dog rabies incidence decreases with increasing recent local vaccination coverage. However, current local incidence is most dependent on recent incidence, both locally and in the wider district, consistent with high population connectivity. Removing the masking effects of prior non-local incidence shows that, for the same average prior vaccination coverage beyond the focal village, more spatial variation increases local incidence. These effects led to outbreaks following years when vaccination campaigns missed many villages, whereas when heterogeneity in coverage was reduced, incidence declined to low levels (<0.4 cases/1,000 dogs annually and no human deaths), such that short vaccination lapses thereafter did not lead to resurgence. We inferred ongoing rabies incursions into the district, suggesting regional connectivity as an important source of residual transmission. Overall, we provide an empirical demonstration of how the same average vaccination coverage can lead to differing outcomes based on its spatial distribution, highlighting the importance of fine-scale monitoring in managing vaccination programs.
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Affiliation(s)
- Elaine A Ferguson
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health & Veterinary Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
| | - Ahmed Lugelo
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Tanzania
- Global Animal Health Tanzania, Arusha, Tanzania
| | - Anna Czupryna
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health & Veterinary Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
| | - Danni Anderson
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health & Veterinary Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
| | - Felix Lankester
- Global Animal Health Tanzania, Arusha, Tanzania
- Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA
| | - Lwitiko Sikana
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Tanzania
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
| | - Katie Hampson
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health & Veterinary Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
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Zheng W, Chu J, Bambrick H, Wang N, Mengersen K, Guo X, Hu W. Temperature, relative humidity and elderly type 2 diabetes mortality: A spatiotemporal analysis in Shandong, China. Int J Hyg Environ Health 2024; 262:114442. [PMID: 39151320 DOI: 10.1016/j.ijheh.2024.114442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 08/10/2024] [Accepted: 08/12/2024] [Indexed: 08/19/2024]
Abstract
BACKGROUND The mortality of type 2 diabetes mellitus (T2DM) can be affected by environmental factors. However, few studies have explored the effects of environmental factors across diverse regions over time. Given the vulnerability observed in the elderly group in previous research, this research applied Bayesian spatiotemporal models to assess the associations in the elderly group. METHODS Data on T2DM death in the elderly group (aged over 60 years old) at the county level were collected from the National Death Surveillance System between 1st January 2013 and 31st December 2019 in Shandong Province, China. A Bayesian spatiotemporal model was employed with the integrated Nested Laplace Approach to explore the associations between socio-environmental factors (i.e., temperatures, relative humidity, the Normalized Difference Vegetation Index (NDVI), particulate matter with a diameter of 2.5 μm or less (PM2.5) and gross domestic product (GDP)) and T2DM mortality. RESULTS T2DM mortality in the elderly group was found to be associated with temperature and relative humidity (i.e., temperature: Relative Risk (RR) = 1.41, 95% Credible Interval (CI): 1.27-1.56; relative humidity: RR = 1.05, 95% CI:1.03-1.06), while no significant associations were found with NDVI, PM2.5 and GDP. In winter, significant impacts from temperature (RR = 1.18, 95% CI: 1.06-1.32) and relative humidity (RR = 0.94, 95% CI: 0.89-0.99) were found. Structured and unstructured spatial effects, temporal trends and space-time interactions were considered in the model. CONCLUSIONS Higher mean temperatures and relative humidities increased the risk of elderly T2DM mortality in Shandong Province. However, a higher humidity level decreased the T2DM mortality risk in winter in Shandong Province. This research indicated that the spatiotemporal method could be a useful tool to assess the impact of socio-environmental factors on health by combining the spatial and temporal effects.
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Affiliation(s)
- Wenxiu Zheng
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jie Chu
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China
| | - Hilary Bambrick
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia; National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Ning Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Kerrie Mengersen
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, Shandong, China
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia.
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Jean Baptiste AE, Wagai J, Hahné S, Adeniran A, Koko RI, de Vos S, Shibeshi M, Sanders EAM, Masresha B, Hak E. High-Resolution Geospatial Mapping of Zero-Dose and Underimmunized Children Following Nigeria's 2021 Multiple Indicator Cluster Survey/National Immunization Coverage Survey. J Infect Dis 2024; 230:e131-e138. [PMID: 39052714 PMCID: PMC11272093 DOI: 10.1093/infdis/jiad476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 10/06/2023] [Accepted: 10/27/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND "Zero-dose" children are those who are without any routine vaccination or are lacking the first dose of the diphtheria, tetanus, and pertussis-containing vaccine. Based on global estimates from the World Health Organization/United Nations Children's Fund in 2022, Nigeria has the highest number of zero-dose children, with >2.3 million unvaccinated. METHODS We used data from the 2021 Nigeria Multiple Indicator Cluster Survey/National Immunization Coverage Survey to identify zero-dose and underimmunized children. Geospatial modeling techniques were employed to determine the prevalence of zero-dose children and predict risk areas with underimmunized children at a high resolution (1 × 1 km). RESULTS Zero-dose and underimmunized children are more prevalent in socially deprived groups. Univariate and multivariate bayesian analyses showed positive correlations between the prevalence of zero-dose and underimmunized children and factors such as stunting, contraceptive prevalence, and literacy. The prevalence of zero-dose and underimmunized children varies significantly by region and ethnicity, with higher rates observed in the country's northern parts. Significant heterogeneity in the distribution of undervaccinated children was observed. CONCLUSIONS Nigeria needs to enhance its immunization system and coverage. Geospatial modeling can help deliver vaccines effectively to underserved communities. By adopting this approach, countries can ensure equitable vaccine access and contribute to global vaccination objectives.
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Affiliation(s)
| | - John Wagai
- Country Office, World Health Organization, Abuja, Nigeria
| | - Susan Hahné
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | | | | | - Stijn de Vos
- Groningen Research Institute of Pharmacy, University of Groningen, the Netherlands
| | - Messeret Shibeshi
- African Regional Office, World Health Organization, Brazzaville, Congo
| | - E A M Sanders
- Department of Paediatric Immunology and Infectious Diseases, University Medical Centre Utrecht, the Netherlands
| | - Balcha Masresha
- African Regional Office, World Health Organization, Brazzaville, Congo
| | - Eelko Hak
- Groningen Research Institute of Pharmacy, University of Groningen, the Netherlands
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Kawakatsu Y, Mosser JF, Adolph C, Baffoe P, Cheshi F, Aiga H, Watkins DA, Sherr KH. High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach. BMJ Open 2024; 14:e080135. [PMID: 38858137 PMCID: PMC11168136 DOI: 10.1136/bmjopen-2023-080135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 05/12/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Nigeria using machine learning techniques. METHODS Essential MCH services in this study included antenatal care, facility-based delivery, childhood vaccinations and treatments of childhood illnesses. We estimated generalised additive models (GAMs) and gradient boosting regressions (GB) for each essential MCH service using data from five national representative cross-sectional surveys in Nigeria from 2003 to 2018 and geospatial socioeconomic, environmental and physical characteristics. Using the best-performed model for each service, we map predicted coverage at 1 km2 and 5 km2 spatial resolutions in urban and rural areas, respectively. RESULTS GAMs consistently outperformed GB models across a range of essential MCH services, demonstrating low systematic prediction errors. High-resolution maps revealed stark geographic disparities in MCH service coverage, especially between rural and urban areas and among different states and service types. Temporal trends indicated an overall increase in MCH service coverage from 2003 to 2018, although with variations by service type and location. Priority areas with lower coverage of both maternal and vaccination services were identified, mostly located in the northern parts of Nigeria. CONCLUSION High-resolution spatial estimates can guide geographic prioritisation and help develop better strategies for implementation plans, allowing limited resources to be targeted to areas with lower coverage of essential MCH services.
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Affiliation(s)
- Yoshito Kawakatsu
- Department of Global Health, University of Washington, Seattle, Washington, USA
| | - Jonathan F Mosser
- Health Metrics Sciences, University of Washington, Seattle, Washington, USA
| | - Christopher Adolph
- Department of Political Science, University of Washington, Seattle, Washington, USA
| | | | | | - Hirotsugu Aiga
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Global Health, George Washington University School of Public Health and Health Services, Washington, DC, USA
| | - D A Watkins
- Department of Medicine, University of Washington, Seattle, Seattle, Washington, USA
| | - Kenneth H Sherr
- Department of Global Health, University of Washington, Seattle, Washington, USA
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Orozco-Acosta E, Riebler A, Adin A, Ugarte MD. A scalable approach for short-term disease forecasting in high spatial resolution areal data. Biom J 2023; 65:e2300096. [PMID: 37890279 DOI: 10.1002/bimj.202300096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/29/2023]
Abstract
Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed "divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatiotemporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error, and interval score when forecasting cancer mortality 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace estimation technique.
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Affiliation(s)
- Erick Orozco-Acosta
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics, InaMat2, Public University of Navarre, Pamplona, Spain
| | - Andrea Riebler
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Aritz Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics, InaMat2, Public University of Navarre, Pamplona, Spain
| | - Maria D Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics, InaMat2, Public University of Navarre, Pamplona, Spain
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Qiu M, Hu T. Bayesian transformation model for spatial partly interval-censored data. J Appl Stat 2023; 51:2139-2156. [PMID: 39157272 PMCID: PMC11328804 DOI: 10.1080/02664763.2023.2263819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
Abstract
The transformation model with partly interval-censored data offers a highly flexible modeling framework that can simultaneously support multiple common survival models and a wide variety of censored data types. However, the real data may contain unexplained heterogeneity that cannot be entirely explained by covariates and may be brought on by a variety of unmeasured regional characteristics. Due to this, we introduce the conditionally autoregressive prior into the transformation model with partly interval-censored data and take the spatial frailty into account. An efficient Markov chain Monte Carlo method is proposed to handle the posterior sampling and model inference. The approach is simple to use and does not include any challenging Metropolis steps owing to four-stage data augmentation. Through several simulations, the suggested method's empirical performance is assessed and then the method is used in a leukemia study.
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Affiliation(s)
- Mingyue Qiu
- School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China
| | - Tao Hu
- School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China
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Chen X, Porter A, Abdur Rehman N, Morris SK, Saif U, Chunara R. Area-based determinants of outreach vaccination for reaching vulnerable populations: A cross-sectional study in Pakistan. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001703. [PMID: 37756308 PMCID: PMC10529552 DOI: 10.1371/journal.pgph.0001703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 08/16/2023] [Indexed: 09/29/2023]
Abstract
The objective of this study is to gain a comparative understanding of spatial determinants for outreach and clinic vaccination, which is critical for operationalizing efforts and breaking down structural biases; particularly relevant in countries where resources are low, and sub-region variance is high. Leveraging a massive effort to digitize public system reporting by Lady and Community Health Workers (CHWs) with geo-located data on over 4 million public-sector vaccinations from September 2017 through 2019, understanding health service operations in relation to vulnerable spatial determinants were made feasible. Location and type of vaccinations (clinic or outreach) were compared to regional spatial attributes where they were performed. Important spatial attributes were assessed using three modeling approaches (ridge regression, gradient boosting, and a generalized additive model). Consistent predictors for outreach, clinic, and proportion of third dose pentavalent vaccinations by region were identified. Of all Penta-3 vaccination records, 86.3% were performed by outreach efforts. At the tehsil level (fourth-order administrative unit), controlling for child population, population density, proportion of population in urban areas, distance to cities, average maternal education, and other relevant factors, increased poverty was significantly associated with more in-clinic vaccinations (β = 0.077), and lower proportion of outreach vaccinations by region (β = -0.083). Analyses at the union council level (fifth-administrative unit) showed consistent results for the differential importance of poverty for outreach versus clinic vaccination. Relevant predictors for each type of vaccination (outreach vs. in-clinic) show how design of outreach vaccination can effectively augment vaccination efforts beyond healthcare services through clinics. As Pakistan is third among countries with the most unvaccinated and under-vaccinated children, understanding barriers and factors associated with vaccination can be demonstrative for other national and sub-national regions facing challenges and also inform guidelines on supporting CHWs in health systems.
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Affiliation(s)
- Xiaoting Chen
- Department of Biostatistics, New York University, New York, New York, United States of America
| | - Allan Porter
- Department of Computer Science Engineering, New York University, Brooklyn, New York, United States of America
| | - Nabeel Abdur Rehman
- Department of Computer Science Engineering, New York University, Brooklyn, New York, United States of America
| | - Shaun K. Morris
- Division of Infectious Diseases and Centre for Global Child Health, The Hospital for Sick Children, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Department of Paediatrics, University of Toronto, Toronto, Canada
| | - Umar Saif
- UNESCO Chair for ICTD, Lahore, Pakistan
| | - Rumi Chunara
- Department of Biostatistics, New York University, New York, New York, United States of America
- Department of Computer Science Engineering, New York University, Brooklyn, New York, United States of America
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8
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Fendrich AN, Matthews F, Van Eynde E, Carozzi M, Li Z, d'Andrimont R, Lugato E, Martin P, Ciais P, Panagos P. From regional to parcel scale: A high-resolution map of cover crops across Europe combining satellite data with statistical surveys. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 873:162300. [PMID: 36828062 DOI: 10.1016/j.scitotenv.2023.162300] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
The reformed Common Agricultural Policy of 2023-2027 aims to promote a more sustainable and fair agricultural system in the European Union. Among the proposed measures, the incentivized adoption of cover crops to cover the soil during winter provides numerous benefits such as improved soil structure and reduced nutrient leaching and erosion. Despite this recognized importance, the availability of spatial data on cover crops is scarce. The increasing availability of field parcel declarations in the European Union has not yet filled this data gap due to its insufficient information content, limited public availability and a lack of standardization at continental scale. At present, the best information available is regionally aggregated survey data, which although indicative, hinders the development of spatially accurate studies. In this work, we propose a statistical model relating Sentinel-1 data to the existence of cover crops at the 100-m spatial resolution over the entirety of the European Union and United Kingdom and estimate its parameters using the spatially aggregated survey data. To validate the method in a spatially-explicit way, predictions were compared against farmers' registered declarations in France, where the adoption of cover crops is widespread. The results indicate a good agreement between predictions and parcel-level data. When interpreted as a binary classifier, the model yielded an Area Under the Curve (AUC) of 0.74 for the whole country. When the country was divided into five regions for the evaluation of regional biases, the AUC values were 0.77, 0.75, 0.74, 0.70, and 0.65 for the North, Center, West, East, and South regions respectively. Despite limitations such as the lack of data for validation outside France, and the non-standardized nomenclature for cover crops among Member States, this work constitutes the first effort to obtain a relevant cover crop map at a European scale for researchers and practitioners.
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Affiliation(s)
- Arthur Nicolaus Fendrich
- European Commission, Joint Research Centre (JRC), Ispra 21027, Italy; Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ-UPSACLAY, Gif sur Yvette 91190, France; Université Paris-Saclay, INRAE, AgroParisTech, UMR SAD-APT, 91120, Palaiseau, France.
| | - Francis Matthews
- European Commission, Joint Research Centre (JRC), Ispra 21027, Italy; KU Leuven, Unit of Geography and Tourism, Celestijnenlaan 200e, Leuven 3001, Belgium
| | - Elise Van Eynde
- European Commission, Joint Research Centre (JRC), Ispra 21027, Italy
| | - Marco Carozzi
- Université Paris-Saclay, INRAE, AgroParisTech, UMR SAD-APT, 91120, Palaiseau, France
| | - Zheyuan Li
- School of Mathematics and Statistics, Henan University, Kaifeng 475001, China; Department of Statistics and Actuarial Science, Simon Fraser University, University Dr W, 8888, Burnaby, BC V5A 1S6, Canada
| | | | - Emanuele Lugato
- European Commission, Joint Research Centre (JRC), Ispra 21027, Italy
| | - Philippe Martin
- Université Paris-Saclay, INRAE, AgroParisTech, UMR SAD-APT, 91120, Palaiseau, France
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ-UPSACLAY, Gif sur Yvette 91190, France
| | - Panos Panagos
- European Commission, Joint Research Centre (JRC), Ispra 21027, Italy
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9
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Liu Y, Goudie RJB. Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework. BAYESIAN ANALYSIS 2023; -1:1-36. [PMID: 36714467 PMCID: PMC7614111 DOI: 10.1214/22-ba1357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference models accommodate partial misspecification from a single component of the model. We extend these models to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence.
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Affiliation(s)
- Yang Liu
- MRC Biostatistics Unit, University of Cambridge, UK
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10
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Wigley A, Lorin J, Hogan D, Utazi CE, Hagedorn B, Dansereau E, Tatem AJ, Tejedor-Garavito N. Estimates of the number and distribution of zero-dose and under-immunised children across remote-rural, urban, and conflict-affected settings in low and middle-income countries. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0001126. [PMID: 36962682 PMCID: PMC10021885 DOI: 10.1371/journal.pgph.0001126] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/05/2022] [Indexed: 02/11/2023]
Abstract
While there has been great success in increasing the coverage of new childhood vaccines globally, expanding routine immunization to reliably reach all children and communities has proven more challenging in many low- and middle-income countries. Achieving this requires vaccination strategies and interventions that identify and target those unvaccinated, guided by the most current and detailed data regarding their size and spatial distribution. Through the integration and harmonisation of a range of geospatial data sets, including population, vaccination coverage, travel-time, settlement type, and conflict locations. We estimated the numbers of children un- or under-vaccinated for measles and diphtheria-tetanus-pertussis, within remote-rural, urban, and conflict-affected locations. We explored how these numbers vary both nationally and sub-nationally, and assessed what proportions of children these categories captured, for 99 lower- and middle-income countries, for which data was available. We found that substantial heterogeneities exist both between and within countries. Of the total 14,030,486 children unvaccinated for DTP1, over 11% (1,656,757) of un- or under-vaccinated children were in remote-rural areas, more than 28% (2,849,671 and 1,129,915) in urban and peri-urban areas, and up to 60% in other settings, with nearly 40% found to be within 1-hour of the nearest town or city (though outside of urban/peri-urban areas). Of the total number of those unvaccinated, we estimated between 6% and 15% (826,976 to 2,068,785) to be in conflict-affected locations, based on either broad or narrow definitions of conflict. Our estimates provide insights into the inequalities in vaccination coverage, with the distributions of those unvaccinated varying significantly by country, region, and district. We demonstrate the need for further inquiry and characterisation of those unvaccinated, the thresholds used to define these, and for more country-specific and targeted approaches to defining such populations in the strategies and interventions used to reach them.
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Affiliation(s)
- Adelle Wigley
- WorldPop, Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, United Kingdom
| | - Josh Lorin
- Gavi, The Vaccine Alliance, Geneva, Switzerland
| | - Dan Hogan
- Gavi, The Vaccine Alliance, Geneva, Switzerland
| | - C Edson Utazi
- WorldPop, Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, United Kingdom
| | - Brittany Hagedorn
- Institute for Disease Modelling, Bill & Melinda Gates Foundation, Seattle, Washington, WA, United States of America
| | - Emily Dansereau
- Institute for Disease Modelling, Bill & Melinda Gates Foundation, Seattle, Washington, WA, United States of America
| | - Andrew J Tatem
- WorldPop, Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, United Kingdom
| | - Natalia Tejedor-Garavito
- WorldPop, Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, United Kingdom
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11
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MacNab YC. Bayesian disease mapping: Past, present, and future. SPATIAL STATISTICS 2022; 50:100593. [PMID: 35075407 PMCID: PMC8769562 DOI: 10.1016/j.spasta.2022.100593] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analysing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.
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Affiliation(s)
- Ying C MacNab
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
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12
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Muchiri SK, Muthee R, Kiarie H, Sitienei J, Agweyu A, Atkinson PM, Edson Utazi C, Tatem AJ, Alegana VA. Unmet need for COVID-19 vaccination coverage in Kenya. Vaccine 2022; 40:2011-2019. [PMID: 35184925 PMCID: PMC8841160 DOI: 10.1016/j.vaccine.2022.02.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/30/2022] [Accepted: 02/07/2022] [Indexed: 11/30/2022]
Abstract
COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.
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Affiliation(s)
- Samuel K Muchiri
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.
| | - Rose Muthee
- Department of Health Informatics, Monitoring and Evaluation, Ministry of Health, Nairobi, Kenya
| | - Hellen Kiarie
- Department of Health Informatics, Monitoring and Evaluation, Ministry of Health, Nairobi, Kenya
| | - Joseph Sitienei
- Department of Health Informatics, Monitoring and Evaluation, Ministry of Health, Nairobi, Kenya
| | - Ambrose Agweyu
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme Nairobi, Kenya
| | - Peter M Atkinson
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK; Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK; Institute of Geographic Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China
| | - C Edson Utazi
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Victor A Alegana
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya; Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
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13
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Jaya IGNM, Folmer H. Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease. JOURNAL OF GEOGRAPHICAL SYSTEMS 2022; 24:527-581. [PMID: 35221792 PMCID: PMC8857957 DOI: 10.1007/s10109-021-00368-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 10/08/2021] [Indexed: 05/16/2023]
Abstract
Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the "big n" problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January-July and a decrease in the period August-December. Supplementary Information The online version contains supplementary material available at 10.1007/s10109-021-00368-0.
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Affiliation(s)
- I. Gede Nyoman Mindra Jaya
- Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands
- Statistics Department, Padjadjaran University, Bandung, Indonesia
| | - Henk Folmer
- Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands
- Statistics Department, Padjadjaran University, Bandung, Indonesia
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14
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Ochoa C, Pittavino M, Babo Martins S, Alcoba G, Bolon I, Ruiz de Castañeda R, Joost S, Sharma SK, Chappuis F, Ray N. Estimating and predicting snakebite risk in the Terai region of Nepal through a high-resolution geospatial and One Health approach. Sci Rep 2021; 11:23868. [PMID: 34903803 PMCID: PMC8668914 DOI: 10.1038/s41598-021-03301-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 12/01/2021] [Indexed: 11/09/2022] Open
Abstract
Most efforts to understand snakebite burden in Nepal have been localized to relatively small areas and focused on humans through epidemiological studies. We present the outcomes of a geospatial analysis of the factors influencing snakebite risk in humans and animals, based on both a national-scale multi-cluster random survey and, environmental, climatic, and socio-economic gridded data for the Terai region of Nepal. The resulting Integrated Nested Laplace Approximation models highlight the importance of poverty as a fundamental risk-increasing factor, augmenting the snakebite odds in humans by 63.9 times. For animals, the minimum temperature of the coldest month was the most influential covariate, increasing the snakebite odds 23.4 times. Several risk hotspots were identified along the Terai, helping to visualize at multiple administrative levels the estimated population numbers exposed to different probability risk thresholds in 1 year. These analyses and findings could be replicable in other countries and for other diseases.
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Affiliation(s)
- Carlos Ochoa
- Institute of Global Health (IGH), Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Chemin des Mines 9, 1202, Geneva, Switzerland.
- Institute for Environmental Sciences (ISE), University of Geneva, Geneva, Switzerland.
| | - Marta Pittavino
- Research Center for Statistics (RCS), Geneva School of Economics and Management (GSEM), University of Geneva, Geneva, Switzerland
| | - Sara Babo Martins
- Institute of Global Health (IGH), Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Chemin des Mines 9, 1202, Geneva, Switzerland
| | - Gabriel Alcoba
- Institute of Global Health (IGH), Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Chemin des Mines 9, 1202, Geneva, Switzerland
- Médecins Sans Frontières (MSF), Geneva, Switzerland
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals (HUG), Geneva, Switzerland
| | - Isabelle Bolon
- Institute of Global Health (IGH), Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Chemin des Mines 9, 1202, Geneva, Switzerland
| | - Rafael Ruiz de Castañeda
- Institute of Global Health (IGH), Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Chemin des Mines 9, 1202, Geneva, Switzerland
| | - Stéphane Joost
- Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - François Chappuis
- Division of Tropical and Humanitarian Medicine, Geneva University Hospitals (HUG), Geneva, Switzerland
- Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicolas Ray
- Institute of Global Health (IGH), Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Chemin des Mines 9, 1202, Geneva, Switzerland
- Institute for Environmental Sciences (ISE), University of Geneva, Geneva, Switzerland
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15
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Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health. J 2021. [DOI: 10.3390/j4020015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.
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16
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Modeling and presentation of vaccination coverage estimates using data from household surveys. Vaccine 2021; 39:2584-2594. [PMID: 33824039 DOI: 10.1016/j.vaccine.2021.03.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 02/23/2021] [Accepted: 03/02/2021] [Indexed: 11/23/2022]
Abstract
It is becoming increasingly popular to produce high-resolution maps of vaccination coverage by fitting Bayesian geostatistical models to data from household surveys. Usually, the surveys adopt a stratified cluster sampling design. We discuss a number of crucial choices with respect to two key aspects of the map production process: the acknowledgement of the survey design in modeling, and the appropriate presentation of estimates and their uncertainties. Specifically, we consider the importance of accounting for urban/rural stratification and cluster-level non-spatial excess variation in survey outcomes, when fitting geostatistical models. We also discuss the trade-off between the geographical scale and precision of model-based estimates, and demonstrate visualization methods for mapping and ranking that emphasize the probabilistic interpretation of results. A novel approach to coverage map presentation is proposed to allow comparison and control of the overall map uncertainty. We use measles vaccination coverage in Nigeria as a motivating example and illustrate the different issues using data from the 2018 Nigeria Demographic and Health Survey.
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17
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Marquez N, Wakefield J. Harmonizing child mortality data at disparate geographic levels. Stat Methods Med Res 2021; 30:1187-1210. [PMID: 33525965 DOI: 10.1177/0962280220988742] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
There is an increasing focus on reducing inequalities in health outcomes in developing countries. Subnational variation is of particular interest, with geographically-indexed data being used to understand the spatial risk of detrimental outcomes and to identify who is at greatest risk. While some health surveys provide observations with associated geographic coordinates (point data), many others provide data that have their locations masked and instead only report the strata (polygon information) within which the data resides (masked data). How to harmonize these data sources for spatial analysis has been previously considered although only ad hoc methods and comparison of methods is lacking. In this paper, we present a new method for analyzing masked survey data, using a method that is consistent with the data-generating process. In addition, we critique two previously proposed approaches to analyzing masked data and illustrate that they are fundamentally flawed methodologically. To validate our method, we compare our approach with previously formulated solutions in several realistic simulation environments in which the underlying structure of the risk field is known. We simulate samples from spatiotemporal fields in a way that mimics the sampling frame implemented in the most common health surveys in low- and middle-income countries, the Demographic and Health Surveys and Multiple Indicator Cluster Surveys. In simulations, the newly proposed approach outperforms previously proposed approaches in terms of minimizing error while increasing the precision of estimates. The approaches are subsequently compared using child mortality data from the Dominican Republic where our findings are reinforced. The ability to accurately increase precision of child mortality estimates, and health outcomes in general, by leveraging various types of data, improves our ability to implement precision public health initiatives and better understand the landscape of geographic health inequalities.
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Affiliation(s)
- Neal Marquez
- Department of Sociology, University of Washington, Seattle, WA, USA
| | - Jon Wakefield
- Department of Statistics, University of Washington, Seattle, WA, USA
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18
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Zhao TT, Feng YJ, Doanh PN, Sayasone S, Khieu V, Nithikathkul C, Qian MB, Hao YT, Lai YS. Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia. eLife 2021; 10:59755. [PMID: 33432926 PMCID: PMC7870142 DOI: 10.7554/elife.59755] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 01/11/2021] [Indexed: 12/29/2022] Open
Abstract
Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10–15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions.
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Affiliation(s)
- Ting-Ting Zhao
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yi-Jing Feng
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Pham Ngoc Doanh
- Department of Parasitology, Institute of Ecology and Biological Resources, Graduate University of Science and Technology, Vietnam Academy of Sciences and Technology, Cau Giay, Hanoi, Viet Nam
| | - Somphou Sayasone
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People's Democratic Republic
| | - Virak Khieu
- National Center for Parasitology, Entomology and Malaria Control, Ministry of Health, Phnom Penh, Cambodia
| | - Choosak Nithikathkul
- Tropical and Parasitic Diseases Research Unit, Faculty of Medicine, Mahasarakham University, Mahasarakham, Thailand
| | - Men-Bao Qian
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.,WHO Collaborating Centre for Tropical Diseases, Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
| | - Yuan-Tao Hao
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.,Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Ying-Si Lai
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.,Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
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19
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Parsaeian M, Jafari Khaledi M, Farzadfar F, Mahdavi M, Zeraati H, Mahmoudi M, Khosravi A, Mohammad K. Spatio-temporal analysis of misaligned burden of disease data using a geo-statistical approach. Stat Med 2020; 40:1021-1033. [PMID: 33283326 DOI: 10.1002/sim.8817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 10/06/2020] [Accepted: 11/04/2020] [Indexed: 11/06/2022]
Abstract
Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo-statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We applied the model to combine data from nine different sources of body mass index in a national and sub-national BOD study. The cross-validation results confirmed a high out-of-sample predictive ability in sparse and noisy data. The proposed model can be used by other BOD studies especially at the sub-national level when the areal units are subject to misalignment.
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Affiliation(s)
- Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Majid Jafari Khaledi
- Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Farshad Farzadfar
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Mahdavi
- National Institute of Health Research (NIHR), Tehran University of Medical Sciences, Tehran, Iran.,Erasmus School of Health Policy and Management (ESHPM), Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahmood Mahmoudi
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Ardeshir Khosravi
- Deputy for Public Health, Ministry of Health and Medical Education, Tehran, Iran
| | - Kazem Mohammad
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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20
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Ferreira LZ, Blumenberg C, Utazi CE, Nilsen K, Hartwig FP, Tatem AJ, Barros AJD. Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys. Int J Health Geogr 2020; 19:41. [PMID: 33050935 PMCID: PMC7552506 DOI: 10.1186/s12942-020-00239-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/05/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies. METHODS Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded. RESULTS We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure. CONCLUSIONS The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.
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Affiliation(s)
- Leonardo Z Ferreira
- International Center for Equity in Health, Universidade Federal de Pelotas, Pelotas, Brazil.
- Post-Graduate Program in Epidemiology, Universidade Federal de Pelotas, Pelotas, Brazil.
| | - Cauane Blumenberg
- International Center for Equity in Health, Universidade Federal de Pelotas, Pelotas, Brazil
| | - C Edson Utazi
- WorldPop, Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Kristine Nilsen
- WorldPop, Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Fernando P Hartwig
- Post-Graduate Program in Epidemiology, Universidade Federal de Pelotas, Pelotas, Brazil
| | - Andrew J Tatem
- WorldPop, Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Aluisio J D Barros
- International Center for Equity in Health, Universidade Federal de Pelotas, Pelotas, Brazil
- Post-Graduate Program in Epidemiology, Universidade Federal de Pelotas, Pelotas, Brazil
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21
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Utazi CE, Wagai J, Pannell O, Cutts FT, Rhoda DA, Ferrari MJ, Dieng B, Oteri J, Danovaro-Holliday MC, Adeniran A, Tatem AJ. Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys. Vaccine 2020; 38:3062-3071. [PMID: 32122718 PMCID: PMC7079337 DOI: 10.1016/j.vaccine.2020.02.070] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/31/2020] [Accepted: 02/03/2020] [Indexed: 11/21/2022]
Abstract
Measles vaccination campaigns are conducted regularly in many low- and middle-income countries to boost measles control efforts and accelerate progress towards elimination. National and sometimes first-level administrative division campaign coverage may be estimated through post-campaign coverage surveys (PCCS). However, these large-area estimates mask significant geographic inequities in coverage at more granular levels. Here, we undertake a geospatial analysis of the Nigeria 2017-18 PCCS data to produce coverage estimates at 1 × 1 km resolution and the district level using binomial spatial regression models built on a suite of geospatial covariates and implemented in a Bayesian framework via the INLA-SPDE approach. We investigate the individual and combined performance of the campaign and routine immunization (RI) by mapping various indicators of coverage for children aged 9-59 months. Additionally, we compare estimated coverage before the campaign at 1 × 1 km and the district level with predicted coverage maps produced using other surveys conducted in 2013 and 2016-17. Coverage during the campaign was generally higher and more homogeneous than RI coverage but geospatial differences in the campaign's reach of previously unvaccinated children are shown. Persistent areas of low coverage highlight the need for improved RI performance. The results can help to guide the conduct of future campaigns, improve vaccination monitoring and measles elimination efforts. Moreover, the approaches used here can be readily extended to other countries.
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Affiliation(s)
- C Edson Utazi
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton SO17 1BJ, UK.
| | - John Wagai
- World Health Organization Consultant, Abuja, Nigeria
| | - Oliver Pannell
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Felicity T Cutts
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Matthew J Ferrari
- Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA, 16802, USA
| | | | - Joseph Oteri
- National Primary Health Care Development Agency, Abuja, Nigeria
| | | | | | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
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22
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Ruktanonchai CW, Nieves JJ, Ruktanonchai NW, Nilsen K, Steele JE, Matthews Z, Tatem AJ. Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania. BMJ Glob Health 2020; 4:e002092. [PMID: 32154032 PMCID: PMC7044704 DOI: 10.1136/bmjgh-2019-002092] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 01/02/2020] [Accepted: 01/09/2020] [Indexed: 11/03/2022] Open
Abstract
Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.
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Affiliation(s)
| | - Jeremiah J Nieves
- School of Geography & Environmental Science, University of Southampton, Southampton, UK
| | - Nick W Ruktanonchai
- School of Geography & Environmental Science, University of Southampton, Southampton, UK
| | - Kristine Nilsen
- School of Geography & Environmental Science, University of Southampton, Southampton, UK
| | - Jessica E Steele
- School of Geography & Environmental Science, University of Southampton, Southampton, UK
| | - Zoe Matthews
- Department of Social Statistics & Demography, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- School of Geography & Environmental Science, University of Southampton, Southampton, UK
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23
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Cutts FT, Dansereau E, Ferrari MJ, Hanson M, McCarthy KA, Metcalf CJE, Takahashi S, Tatem AJ, Thakkar N, Truelove S, Utazi E, Wesolowski A, Winter AK. Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications. Vaccine 2020; 38:979-992. [PMID: 31787412 PMCID: PMC6996156 DOI: 10.1016/j.vaccine.2019.11.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 01/30/2023]
Abstract
After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.
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Affiliation(s)
- F T Cutts
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - E Dansereau
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - M J Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - M Hanson
- Vaccine Delivery, Global Development, The Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - K A McCarthy
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - S Takahashi
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
| | - A J Tatem
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - N Thakkar
- Institute for Disease Modeling, 3150 139th Ave SE, Bellevue, WA 98005, USA
| | - S Truelove
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - E Utazi
- WorldPop, Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK
| | - A Wesolowski
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - A K Winter
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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24
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Da Re D, Gilbert M, Chaiban C, Bourguignon P, Thanapongtharm W, Robinson TP, Vanwambeke SO. Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem. PLoS One 2020; 15:e0221070. [PMID: 31986146 PMCID: PMC6984718 DOI: 10.1371/journal.pone.0221070] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/18/2019] [Indexed: 01/18/2023] Open
Abstract
The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson's r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson's r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products.
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Affiliation(s)
- Daniele Da Re
- George Lemaitre Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
| | - Celia Chaiban
- George Lemaitre Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium
| | - Pierre Bourguignon
- George Lemaitre Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
| | | | - Timothy P. Robinson
- Policies, Institutions and Livelihoods (PIL), International Livestock Research Institute (ILRI), Nairobi, Kenya
- Livestock Information, Sector Analysis and Policy Branch (AGAL), Food and Agriculture Organisation of the United Nations (FAO), Rome, Italy
| | - Sophie O. Vanwambeke
- George Lemaitre Centre for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, Belgium
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25
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Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information. REMOTE SENSING 2019. [DOI: 10.3390/rs11212543] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A systematic and precise understanding of urban socio-economic spatial inequalities in developing regions is needed to address global sustainability goals. At the intra-urban scale, access to detailed databases (i.e., a census) is often a difficult exercise. Geolocated surveys such as the Demographic and Health Surveys (DHS) are a rich alternative source of such information but can be challenging to interpolate at such a fine scale due to their spatial displacement, survey design and the lack of very high-resolution (VHR) predictor variables in these regions. In this paper, we employ satellite-derived VHR land-use/land-cover (LULC) datasets and couple them with the DHS Wealth Index (WI), a robust household wealth indicator, in order to provide city-scale wealth maps. We undertake several modelling approaches using a random forest regressor as the underlying algorithm and predict in several geographic administrative scales. We validate against an exhaustive census database available for the city of Dakar, Senegal. Our results show that the WI was modelled to a satisfactory degree when compared against census data even at very fine resolutions. These findings might assist local authorities and stakeholders in rigorous evidence-based decision making and facilitate the allocation of resources towards the most disadvantaged populations. Good practices for further developments are discussed with the aim of upscaling these findings at the global scale.
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Geospatial Disaggregation of Population Data in Supporting SDG Assessments: A Case Study from Deqing County, China. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8080356] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative units, and it is difficult to reflect true distribution and uniformity in space. To solve this problem, based on fine building information, a geospatial disaggregation method of population data for supporting SDG assessments is presented in this paper. First, Deqing County in China, which was divided into residential areas and nonresidential areas according to the idea of dasymetric mapping, was selected as the study area. Then, the town administrative areas were taken as control units, building area and number of floors were used as weighting factors to establish the disaggregation model, and population data with a resolution of 30 m in Deqing County in 2016 were obtained. After analyzing the statistical population of 160 villages and the disaggregation results, we found that the global average accuracy was 87.08%. Finally, by using the disaggregation population data, indicators 3.8.1, 4.a.1, and 9.1.1 were selected to conduct an accessibility analysis and a buffer analysis in a quantitative assessment of the SDGs. The results showed that the SDG measurement and assessment results based on the disaggregated population data were more accurate and effective than the results obtained using the traditional method.
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