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Jones HS, Anderson RL, Cust H, McClelland RS, Richardson BA, Thirumurthy H, Malama K, Hensen B, Platt L, Rice B, Cowan FM, Imai-Eaton JW, Hargreaves JR, Stevens O. HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health 2024; 12:e1244-e1260. [PMID: 39030057 DOI: 10.1016/s2214-109x(24)00227-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 04/08/2024] [Accepted: 05/24/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Women who engage in sex work in sub-Saharan Africa have a high risk of acquiring HIV infection. HIV incidence has declined among all women in sub-Saharan Africa, but trends among women who engage in sex work are poorly characterised. We synthesised data on HIV incidence among women who engage in sex work in sub-Saharan Africa and compared these with the total female population to understand relative incidence and trends over time. METHODS We searched MEDLINE, Embase, Global Health, and Google Scholar from Jan 1, 1990, to Feb 28, 2024, and grey literature for studies that reported empirical estimates of HIV incidence among women who engage in sex work in any sub-Saharan Africa country. We calculated incidence rate ratios (IRRs) compared with total female population incidence estimates matched for age, district, and year, did a meta-analysis of IRRs, and used a continuous mixed-effects model to estimate changes in IRR over time. FINDINGS From 32 studies done between 1985 and 2020, 2194 new HIV infections were observed among women who engage in sex work over 51 490 person-years. Median HIV incidence was 4·3 per 100 person years (IQR 2·8-7·0 per 100 person-years). Incidence among women who engage in sex work was eight times higher than matched total population women (IRR 7·8 [95% CI 5·1-11·8]), with larger relative difference in western and central Africa (19·9 [9·6-41·0]) than in eastern and southern Africa (4·9 [3·4-7·1]). There was no evidence that IRRs changed over time (IRR per 5 years: 0·9 [0·7-1·2]). INTERPRETATION Across sub-Saharan Africa, HIV incidence among women who engage in sex work remains disproportionately high compared with the total female population. However, constant relative incidence over time indicates HIV incidence among women who engage in sex work has declined at a similar rate. Location-specific data for women who engage in sex work incidence are sparse, but improved surveillance and standardisation of incidence measurement approaches could fill these gaps. Sustained and enhanced HIV prevention for women who engage in sex work is crucial to address continuing inequalities and ensure declines in new HIV infections. FUNDING Bill & Melinda Gates Foundation, UK Research and Innovation, National Institutes of Health. TRANSLATION For the French translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Harriet S Jones
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
| | - Rebecca L Anderson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Henry Cust
- Institute of Global Health, University College London, London, UK
| | - R Scott McClelland
- Department of Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA; Department of Global Health, University of Washington, Seattle, WA, USA
| | - Barbra A Richardson
- Department of Biostatistics, University of Washington, Seattle, WA, USA; Department of Global Health, University of Washington, Seattle, WA, USA
| | - Harsha Thirumurthy
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA, USA
| | - Kalonde Malama
- Ingram School of Nursing, McGill University, Montréal, Quebec, QC, Canada
| | - Bernadette Hensen
- Sexual and Reproductive Health Group, Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
| | - Lucy Platt
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Brian Rice
- Sheffield Centre for Health and Related Research, School of Medicine and Population Health, University of Sheffield, UK
| | - Frances M Cowan
- Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, UK; Centre for Sexual Health and HIV/AIDS Research Zimbabwe, Harare, Zimbabwe
| | - Jeffrey W Imai-Eaton
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - James R Hargreaves
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Oliver Stevens
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
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Hosseini R, Chen Z, Goligher E, Fan E, Ferguson ND, Harhay MO, Sahetya S, Urner M, Yarnell CJ, Heath A. Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations. Contemp Clin Trials 2024; 142:107560. [PMID: 38735571 DOI: 10.1016/j.cct.2024.107560] [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: 12/16/2023] [Revised: 04/20/2024] [Accepted: 05/01/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference. METHODS We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design. RESULTS Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size. CONCLUSIONS We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.
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Affiliation(s)
- Reyhaneh Hosseini
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
| | - Ziming Chen
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
| | - Ewan Goligher
- Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada
| | - Eddy Fan
- Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada; Insititute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
| | - Niall D Ferguson
- Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada; Insititute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
| | - Michael O Harhay
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarina Sahetya
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Martin Urner
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
| | - Christopher J Yarnell
- Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada; Insititute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
| | - Anna Heath
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Statistical Science, University College London, London, UK.
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Otero J, Tabares A, Santos-Vega M. Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia. Viruses 2024; 16:906. [PMID: 38932198 PMCID: PMC11209037 DOI: 10.3390/v16060906] [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: 03/09/2024] [Revised: 04/23/2024] [Accepted: 04/23/2024] [Indexed: 06/28/2024] Open
Abstract
Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.
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Affiliation(s)
- Julian Otero
- Centro Para los Objetivos de Desarrollo Sostenible, Universidad de Los Andes, Bogotá 111711, Colombia
- Grupo Biología Matemática y Computacional (BIOMAC), Universidad de Los Andes, Bogotá 111711, Colombia;
| | - Alejandra Tabares
- Departamento de Ingeniería Industrial, Universidad de los Andes, Bogotá 111711, Colombia;
| | - Mauricio Santos-Vega
- Grupo Biología Matemática y Computacional (BIOMAC), Universidad de Los Andes, Bogotá 111711, Colombia;
- Departamento de Ciencias Biológicas, Universidad de Los Andes, Bogotá 111711, Colombia
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Abdul-Fattah E, Krainski E, Van Niekerk J, Rue H. Non-stationary Bayesian spatial model for disease mapping based on sub-regions. Stat Methods Med Res 2024; 33:1093-1111. [PMID: 38594934 DOI: 10.1177/09622802241244613] [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] [Indexed: 04/11/2024]
Abstract
This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model's ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior to the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package fbesag equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary. Additionally, we model different causes of death in Brazil, where we use the new model to investigate the spatial stationarity of these causes.
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Affiliation(s)
- Esmail Abdul-Fattah
- Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Elias Krainski
- Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Janet Van Niekerk
- Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Håvard Rue
- Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Lin K, Shao J, Cao Y, Lu L, Lei P, Chen X, Tong M, Lu Y, Yan Y, Zhang L, Pan X, Nong W. The trend of lymphoma incidence in China from 2005 to 2017 and lymphoma incidence trend prediction from 2018 to 2035: a log-linear regression and Bayesian age-period-cohort analysis. Front Oncol 2024; 14:1297405. [PMID: 38868533 PMCID: PMC11167089 DOI: 10.3389/fonc.2024.1297405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 04/29/2024] [Indexed: 06/14/2024] Open
Abstract
Objectives The aims of this study were to explore the incidence characteristics and trend prediction of lymphoma from 2005 to 2035, and to provide data basis for the prevention and control of lymphoma in China. Method The data on lymphoma incidence in China from 2005 to 2017 were obtained from the Chinese Cancer Registry Annual Report. The Joinpoint regression model was used to calculate annual percentage change (APC) and average annual percentage change (AAPC) to reflect time trends. Age-period-cohort models were conducted to estimate age, period, and cohort effects on the lymphoma incidence. A Bayesian age-period-cohort model was used to predict lymphoma incidence trends from 2018 to 2035. Results From 2005 to 2017, the incidence of lymphoma was 6.26/100,000, and the age-standardized incidence rate (ASIR) was 4.11/100,000, with an AAPC of 1.4% [95% confidence interval (CI): 0.3%, 2.5%]. The ASIR was higher in men and urban areas than in women and rural areas, respectively. The age effect showed that the incidence risk of lymphoma increased with age. In the period effect, the incidence risk of lymphoma in rural areas decreased first and then increased with 2010 as the cutoff point. The overall risk of lymphoma incidence was higher in the cohort before the 1970-1974 birth cohort than in the cohort after. From 2018 to 2035, the lymphoma incidence in men, women, and urban areas will show an upward trend. Conclusion From 2005 to 2017, the incidence of lymphoma showed an increasing trend, and was different in regions, genders, and age groups in China. It will show an upward trend from 2018 to 2035. These results are helpful for the formulation and adjustment of lymphoma prevention, control, and management strategies, and have important reference significance for the treatment of lymphoma in China.
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Affiliation(s)
- Kangqian Lin
- Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China
| | - Jianjiang Shao
- Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China
| | - Yuting Cao
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
| | - Lijun Lu
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
| | - Peng Lei
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
| | - Xiaohong Chen
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
| | - Mengwei Tong
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
| | - Yaping Lu
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
| | - Yizhong Yan
- Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China
| | - Lei Zhang
- Clinical Laboratory, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
| | - Xin Pan
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
- National Hematology Clinical Research Center Xinjiang Production and Construction Corps Branch Center, Shihezi, Xinjiang, China
| | - Weixia Nong
- Department of Hematology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China
- National Hematology Clinical Research Center Xinjiang Production and Construction Corps Branch Center, Shihezi, Xinjiang, China
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Hay EM, McGee MD, White CR, Chown SL. Body size shapes song in honeyeaters. Proc Biol Sci 2024; 291:20240339. [PMID: 38654649 PMCID: PMC11040244 DOI: 10.1098/rspb.2024.0339] [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: 02/08/2024] [Accepted: 03/22/2024] [Indexed: 04/26/2024] Open
Abstract
Birdsongs are among the most distinctive animal signals. Their evolution is thought to be shaped simultaneously by habitat structure and by the constraints of morphology. Habitat structure affects song transmission and detectability, thus influencing song (the acoustic adaptation hypothesis), while body size and beak size and shape necessarily constrain song characteristics (the morphological constraint hypothesis). Yet, support for the acoustic adaptation and morphological constraint hypotheses remains equivocal, and their simultaneous examination is infrequent. Using a phenotypically diverse Australasian bird clade, the honeyeaters (Aves: Meliphagidae), we compile a dataset consisting of song, environmental, and morphological variables for 163 species and jointly examine predictions of these two hypotheses. Overall, we find that body size constrains song frequency and pace in honeyeaters. Although habitat type and environmental temperature influence aspects of song, that influence is indirect, likely via effects of environmental variation on body size, with some evidence that elevation constrains the evolution of song peak frequency. Our results demonstrate that morphology has an overwhelming influence on birdsong, in support of the morphological constraint hypothesis, with the environment playing a secondary role generally via body size rather than habitat structure. These results suggest that changing body size (a consequence of both global effects such as climate change and local effects such as habitat transformation) will substantially influence the nature of birdsong.
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Affiliation(s)
- Eleanor M. Hay
- School of Biological Sciences, Monash University, Melbourne, Victoria 3800, Australia
| | - Matthew D. McGee
- School of Biological Sciences, Monash University, Melbourne, Victoria 3800, Australia
| | - Craig R. White
- School of Biological Sciences, Monash University, Melbourne, Victoria 3800, Australia
| | - Steven L. Chown
- School of Biological Sciences, Monash University, Melbourne, Victoria 3800, Australia
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Lan Y, Crudu V, Ciobanu N, Codreanu A, Chitwood MH, Sobkowiak B, Warren JL, Cohen T. Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model. EBioMedicine 2024; 102:105085. [PMID: 38531172 PMCID: PMC10987885 DOI: 10.1016/j.ebiom.2024.105085] [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: 01/12/2024] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Multidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). METHODS We developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FINDINGS We found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. INTERPRETATION Our results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option. FUNDING Funding for this work was provided by the National Institute of Allergy and Infectious Diseases at the US National Institutes of Health.
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Affiliation(s)
- Yu Lan
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Valeriu Crudu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | - Nelly Ciobanu
- Phthisiopneumology Institute, Chisinau, Republic of Moldova
| | | | - Melanie H Chitwood
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Benjamin Sobkowiak
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Joshua L Warren
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
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Zhang H, Sun R, Wu Z, Liu Y, Chen M, Huang J, Lv Y, Zhao F, Zhang Y, Li M, Jiang H, Zhan Y, Xu J, Xu Y, Yuan J, Zhao Y, Shen X, Yang C. Spatial pattern of isoniazid-resistant tuberculosis and its associated factors among a population with migrants in China: a retrospective population-based study. Front Public Health 2024; 12:1372146. [PMID: 38510351 PMCID: PMC10951094 DOI: 10.3389/fpubh.2024.1372146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 02/21/2024] [Indexed: 03/22/2024] Open
Abstract
Background Isoniazid-resistant, rifampicin-susceptible tuberculosis (Hr-TB) globally exhibits a high prevalence and serves as a potential precursor to multidrug-resistant tuberculosis (MDR-TB). Recognizing the spatial distribution of Hr-TB and identifying associated factors can provide strategic entry points for interventions aimed at early detection of Hr-TB and prevention of its progression to MDR-TB. This study aims to analyze spatial patterns and identify socioeconomic, demographic, and healthcare factors associated with Hr-TB in Shanghai at the county level. Method We conducted a retrospective study utilizing data from TB patients with available Drug Susceptible Test (DST) results in Shanghai from 2010 to 2016. Spatial autocorrelation was explored using Global Moran's I and Getis-Ord G i ∗ statistics. A Bayesian hierarchical model with spatial effects was developed using the INLA package in R software to identify potential factors associated with Hr-TB at the county level. Results A total of 8,865 TB patients with DST were included in this analysis. Among 758 Hr-TB patients, 622 (82.06%) were new cases without any previous treatment history. The drug-resistant rate of Hr-TB among new TB cases in Shanghai stood at 7.20% (622/8014), while for previously treated cases, the rate was 15.98% (136/851). Hotspot areas of Hr-TB were predominantly situated in southwestern Shanghai. Factors positively associated with Hr-TB included the percentage of older adult individuals (RR = 3.93, 95% Crl:1.93-8.03), the percentage of internal migrants (RR = 1.35, 95% Crl:1.15-1.35), and the number of healthcare institutions per 100 population (RR = 1.17, 95% Crl:1.02-1.34). Conclusion We observed a spatial heterogeneity of Hr-TB in Shanghai, with hotspots in the Songjiang and Minhang districts. Based on the results of the models, the internal migrant population and older adult individuals in Shanghai may be contributing factors to the emergence of areas with high Hr-TB notification rates. Given these insights, we advocate for targeted interventions, especially in identified high-risk hotspots and high-risk areas.
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Affiliation(s)
- Hongyin Zhang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Ruoyao Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Zheyuan Wu
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
- Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Yueting Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Meiru Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jinrong Huang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yixiao Lv
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Fei Zhao
- Department of Pharmacy, Beijing Hospital, National Center of Gerontology, Beijing, China
- Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, China
| | - Yangyi Zhang
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
- Shanghai Institutes of Preventive Medicine, Shanghai, China
- Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China
| | - Minjuan Li
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Hongbing Jiang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yiqiang Zhan
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jimin Xu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yanzi Xu
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Jianhui Yuan
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Yang Zhao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xin Shen
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
- Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Chongguang Yang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong, China
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
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Li Z, Yue Y(R, Bruce SA. ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS. Ann Appl Stat 2024; 18:328-349. [PMID: 38435672 PMCID: PMC10906746 DOI: 10.1214/23-aoas1791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramér spectral representation, the proposed model can be used to compare the second-order time-varying frequency patterns among different groups of time series and to estimate group effects as functions of both time and frequency. Formulated in a Bayesian framework, independent two-dimensional second-order random walk (RW2D) priors are assumed on each of the time-varying functional effects for flexible and adaptive smoothing. A piecewise stationary approximation of the nonstationary time series is used to obtain localized estimates of time-varying spectra. Posterior distributions of the time-varying functional group effects are then obtained via integrated nested Laplace approximations (INLA) at a low computational cost. The large-sample distribution of local periodograms can be appropriately utilized to improve estimation accuracy since INLA allows modeling of data with various types of distributions. The usefulness of the proposed model is illustrated through two real data applications: analyses of seismic signals and pupil diameter time series in children with attention deficit hyperactivity disorder. Simulation studies, Supplementary Materials (Li, Yue and Bruce, 2023a), and R code (Li, Yue and Bruce, 2023b) for this article are also available.
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Affiliation(s)
- Zeda Li
- Baruch College, The City University of New York
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10
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Desjardins MR, Davis BJK, Curriero FC. Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:255. [PMID: 38345642 DOI: 10.1007/s10661-024-12401-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/29/2024] [Indexed: 02/15/2024]
Abstract
The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g., salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods produce surface predictions of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Bayesian approaches for geostatistical modelling are becoming increasingly popular and can be preferred over frequentist approaches because full and exact inference can be computed, along with more accurate characterization of uncertainty. Traditional geostatistical prediction methods assume a Euclidean distance between two points when characterizing spatial dependence as a function of distance. However, Euclidean approaches may not be appropriate in estuarine environments when water-land boundaries are crossed during the modelling process. In this study, we compare stationary and barrier INLA geostatistical models with a classic kriging geostatistical model to predict salinity in the Chesapeake Bay during 4 months in 2019. Cross-validation is conducted for each approach to evaluate model performance based on prediction accuracy and precision. The results provide evidence that the two Bayesian-based models outperformed ordinary kriging, especially when examining prediction accuracy (most notably in the tributaries). We also suggest that the non-Euclidean model accounts for the appropriate water-based distances between sampling locations and is likely better at characterizing the uncertainty. However, more complex bodies of water may better showcase the capabilities and efficacy of the physical barrier INLA model.
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Affiliation(s)
- M R Desjardins
- Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - B J K Davis
- Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center for Chemical Regulation and Food Safety, Exponent Inc., Washington, D.C, USA
| | - F C Curriero
- Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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11
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Alhasan DM, Larson G, Lohman MC, Cai B, LaPorte FB, Miller MC, Jackson WB, MacNell NS, Hirsch JA, Jackson CL. Features of the Physical and Social Neighborhood Environment and Neighborhood-Level Alzheimer's Disease and Related Dementia in South Carolina. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:27013. [PMID: 38416540 PMCID: PMC10901285 DOI: 10.1289/ehp13183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 01/16/2024] [Accepted: 01/26/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Studies are increasingly examining the relationship between the neighborhood environment and cognitive decline; yet, few have investigated associations between multiple neighborhood features and Alzheimer's disease and related dementias (ADRD). OBJECTIVE We investigated the relationship between neighborhood features and ADRD cumulative incidence from 2010 to 2014 in the South Carolina Alzheimer's Disease Registry (SCADR). METHODS Diagnosed ADRD cases ≥ 50 years of age were ascertained from the SCADR by ZIP code and census tract. Neighborhood features from multiple secondary sources included poverty, air pollution [particulate matter with a diameter of 2.5 micrometers or less (PM 2.5 )], and rurality at the census-tract level and access to healthy food, recreation facilities, and diabetes screening at the county level. In addition to using Poisson generalized linear regression to estimate ADRD incident rate ratios (IRR) with 95% confidence intervals (CIs), we applied integrated nested Laplace approximations and stochastic partial differential equations (INLA-SPDE) to address disparate spatial scales. We estimated associations between neighborhood features and ADRD cumulative incidence. RESULTS The average annual ADRD cumulative incidence was 690 per 100,000 people per census tract (95% CI: 660, 710). The analysis was limited to 98% of census tracts with a population ≥ 50 years old (i.e., 1,081 of 1,103). The average percent of families living below the federal poverty line per census tract was 18.8%, and ∼ 20 % of census tracts were considered rural. The average percent of households with limited access to healthy food was 6.4%. In adjusted models, every 5 μ g / m 3 ) increase of PM 2.5 was associated with 65% higher ADRD cumulative incidence (IRR = 1.65 ; 95% CI: 1.30, 2.09), where PM 2.5 at or below 12 μ g / m 3 is considered healthy. Compared to large urban census tracts, rural and small urban tracts had 10% (IRR = 1.10 ; 95% CI: 1.00, 1.23) and 5% (IRR = 1.05 ; 95% CI: 0.96, 1.16) higher ADRD, respectively. For every percent increase of the county population with limited access to healthy food, ADRD was 2% higher (IRR = 1.02 ; 95% CI: 1.01, 1.04). CONCLUSIONS Neighborhood environment features, such as higher air pollution levels, were associated with higher neighborhood ADRD incidence. The INLA-SPDE method could have broad applicability to data collected across disparate spatial scales. https://doi.org/10.1289/EHP13183.
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Affiliation(s)
- Dana M. Alhasan
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
| | - Gary Larson
- Social & Scientific Systems, Inc., a DLH Holdings Company, Durham, North Carolina, USA
| | - Matthew C. Lohman
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Bo Cai
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Frankie B. LaPorte
- Social & Scientific Systems, Inc., a DLH Holdings Company, Durham, North Carolina, USA
| | - Maggi C. Miller
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - W. Braxton Jackson
- Social & Scientific Systems, Inc., a DLH Holdings Company, Durham, North Carolina, USA
| | - Nathaniel S. MacNell
- Social & Scientific Systems, Inc., a DLH Holdings Company, Durham, North Carolina, USA
| | - Jana A. Hirsch
- Urban Health Collaborative, Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA
| | - Chandra L. Jackson
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
- Intramural Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
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12
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Branco P, Mascarenhas AM, Duarte G, Romão F, Quaresma A, Amaral SD, Ferreira MT, Pinheiro AN, Santos JM. Vertical Slot Fishways: Incremental Knowledge to Define the Best Solution. BIOLOGY 2023; 12:1431. [PMID: 37998030 PMCID: PMC10669019 DOI: 10.3390/biology12111431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/25/2023]
Abstract
River artificial fragmentation is arguably the most imperilling threat for freshwater-dependent fish species. Fish need to be able to freely move along river networks as not only spawning grounds but also refuge and feeding areas may be spatially and temporally separated. This incapacity of free displacement may result in genetic depletion of some populations, density reduction and even community changes, which may in turn affect how meta-community balances are regulated, potentially resulting in functional resilience reduction and ecosystem processes' malfunction. Fishways are the most common and widely used method to improve connectivity for fish species. These structures allow fish to negotiate full barriers, thus reducing their connectivity impairment. Among all technical fishway types, vertical slot fishways (VSF) are considered to be the best solution, as they remain operational even with fluctuating water discharges and allow fish to negotiate each cross-wall at their desired depth. In the present study, we collected both published and original data on fish experiments within VSF, to address two questions, (1) What variables affect fish passage during experimental fishway studies? and (2) What is the best VSF configuration? We used Bayesian Generalized Mixed Models accounting for random effects of non-controlled factors, limiting inherent data dependencies, that may influence the model outcome. Results highlight that fish size, regardless of the species, is a good predictor of fishway negotiation success. Generally, multiple slot fishways with one orifice proved to be the best solution. Future work should be focused on small-sized fish to further improve the design of holistic fishways.
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Affiliation(s)
- Paulo Branco
- Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal; (A.M.M.); (G.D.); (S.D.A.); (M.T.F.); (J.M.S.)
| | - Ana Margarida Mascarenhas
- Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal; (A.M.M.); (G.D.); (S.D.A.); (M.T.F.); (J.M.S.)
| | - Gonçalo Duarte
- Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal; (A.M.M.); (G.D.); (S.D.A.); (M.T.F.); (J.M.S.)
| | - Filipe Romão
- Civil Engineering for Research and Innovation for Sustainability, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; (F.R.); (A.Q.); (A.N.P.)
| | - Ana Quaresma
- Civil Engineering for Research and Innovation for Sustainability, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; (F.R.); (A.Q.); (A.N.P.)
| | - Susana Dias Amaral
- Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal; (A.M.M.); (G.D.); (S.D.A.); (M.T.F.); (J.M.S.)
| | - Maria Teresa Ferreira
- Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal; (A.M.M.); (G.D.); (S.D.A.); (M.T.F.); (J.M.S.)
| | - António N. Pinheiro
- Civil Engineering for Research and Innovation for Sustainability, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal; (F.R.); (A.Q.); (A.N.P.)
| | - José Maria Santos
- Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal; (A.M.M.); (G.D.); (S.D.A.); (M.T.F.); (J.M.S.)
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13
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Reis DJ, Yen P, Tizenberg B, Gottipati A, Postolache SY, De Riggs D, Nance M, Dagdag A, Plater L, Federline A, Grassmeyer R, Dagdag A, Akram F, Ozorio Dutra SV, Gragnoli C, RachBeisel JA, Volkov J, Bahraini NH, Stiller JW, Brenner LA, Postolache TT. Longitude-based time zone partitions and rates of suicide. J Affect Disord 2023; 339:933-942. [PMID: 37481129 PMCID: PMC10870927 DOI: 10.1016/j.jad.2023.07.080] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 06/19/2023] [Accepted: 07/14/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Increasing evidence suggests that conditions with decreased morning and increased evening light exposure, including shift work, daylight-saving time, and eveningness, are associated with elevated mortality and suicide risk. Given that the alignment between the astronomical, biological, and social time varies across a time zone, with later-shifted daylight exposure in the western partition, we hypothesized that western time zone partitions would have higher suicide rates than eastern partitions. METHODS United States (U.S.) county-level suicide and demographic data, from 2010 to 2018, were obtained from a Centers for Disease Control database. Using longitude and latitude, counties were sorted into the western, middle, or eastern partition of their respective time zones, as well as the northern and southern halves of the U.S. Linear regressions were used to estimate the associations between suicide rates and time zone partitions, adjusting for gender, race, ethnicity, age group, and unemployment rates. RESULTS Data were available for 2872 counties. Across the U.S., western partitions had statistically significantly higher rates of suicide compared to eastern partitions and averaged up to two additional yearly deaths per 100,000 people (p < .001). LIMITATIONS Ecological design and limited adjustment for socioeconomic factors. CONCLUSIONS To our knowledge, this is the first study of the relationship between longitude-based time zone partitions and suicide. The results were consistent with the hypothesized elevated suicide rates in the western partitions, and concordant with previous reports on cancer mortality and transportation fatalities. The next step is to retest the hypothesis with individual-level data, accounting for latitude, photoperiodic changes, daylight-saving time, geoclimatic variables, physical and mental health indicators, as well as socioeconomic adversity and protection.
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Affiliation(s)
- Daniel J Reis
- VA Rocky Mountain Mental Illness Research, Education, and Clinical Center for Veteran Suicide Prevention, Aurora, CO, USA; Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Poyu Yen
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Boris Tizenberg
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Anurag Gottipati
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Sonia Y Postolache
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Demitria De Riggs
- VISN 5 Capitol Health Care Network Mental Illness Research Education and Clinical Center, Baltimore, MD, USA
| | - Morgan Nance
- VA Rocky Mountain Mental Illness Research, Education, and Clinical Center for Veteran Suicide Prevention, Aurora, CO, USA; Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Alexandra Dagdag
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Lynn Plater
- VISN 5 Capitol Health Care Network Mental Illness Research Education and Clinical Center, Baltimore, MD, USA
| | - Amanda Federline
- VISN 5 Capitol Health Care Network Mental Illness Research Education and Clinical Center, Baltimore, MD, USA
| | - Riley Grassmeyer
- VA Rocky Mountain Mental Illness Research, Education, and Clinical Center for Veteran Suicide Prevention, Aurora, CO, USA
| | - Aline Dagdag
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Faisal Akram
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Psychiatry Residency Training, Saint Elizabeth's Hospital, Department of Behavioral Health, Washington, DC, USA
| | | | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA; Division of Endocrinology, Department of Medicine, Creighton University School of Medicine, Omaha, NE, USA
| | - Jill A RachBeisel
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Janna Volkov
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Psychiatry Residency Training, Saint Elizabeth's Hospital, Department of Behavioral Health, Washington, DC, USA
| | - Nazanin H Bahraini
- VA Rocky Mountain Mental Illness Research, Education, and Clinical Center for Veteran Suicide Prevention, Aurora, CO, USA; Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - John W Stiller
- Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Saint Elizabeth's Hospital, Neurology Consultation Service, Washington, DC, USA; Maryland State Athletic Commission, Baltimore, MD, USA
| | - Lisa A Brenner
- VA Rocky Mountain Mental Illness Research, Education, and Clinical Center for Veteran Suicide Prevention, Aurora, CO, USA; Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Military and Veteran Microbiome: Consortium for Research and Education (MVM-CoRE), Denver, CO, USA
| | - Teodor T Postolache
- VA Rocky Mountain Mental Illness Research, Education, and Clinical Center for Veteran Suicide Prevention, Aurora, CO, USA; Mood and Anxiety Program, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; VISN 5 Capitol Health Care Network Mental Illness Research Education and Clinical Center, Baltimore, MD, USA; Military and Veteran Microbiome: Consortium for Research and Education (MVM-CoRE), Denver, CO, USA
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14
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Trächsel B, Rousson V, Bulliard JL, Locatelli I. Comparison of statistical models to predict age-standardized cancer incidence in Switzerland. Biom J 2023; 65:e2200046. [PMID: 37078835 DOI: 10.1002/bimj.202200046] [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: 02/16/2022] [Revised: 02/07/2023] [Accepted: 03/01/2023] [Indexed: 04/21/2023]
Abstract
This study compares the performance of statistical methods for predicting age-standardized cancer incidence, including Poisson generalized linear models, age-period-cohort (APC) and Bayesian age-period-cohort (BAPC) models, autoregressive integrated moving average (ARIMA) time series, and simple linear models. The methods are evaluated via leave-future-out cross-validation, and performance is assessed using the normalized root mean square error, interval score, and coverage of prediction intervals. Methods were applied to cancer incidence from the three Swiss cancer registries of Geneva, Neuchatel, and Vaud combined, considering the five most frequent cancer sites: breast, colorectal, lung, prostate, and skin melanoma and bringing all other sites together in a final group. Best overall performance was achieved by ARIMA models, followed by linear regression models. Prediction methods based on model selection using the Akaike information criterion resulted in overfitting. The widely used APC and BAPC models were found to be suboptimal for prediction, particularly in the case of a trend reversal in incidence, as it was observed for prostate cancer. In general, we do not recommend predicting cancer incidence for periods far into the future but rather updating predictions regularly.
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Affiliation(s)
- Bastien Trächsel
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Valentin Rousson
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Jean-Luc Bulliard
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Isabella Locatelli
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
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15
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Redondo-Sánchez D, Fernández-Navarro P, Rodríguez-Barranco M, Nuñez O, Petrova D, García-Torrecillas JM, Jiménez-Moleón JJ, Sánchez MJ. Socio-economic inequalities in lung cancer mortality in Spain: a nation-wide study using area-based deprivation. Int J Equity Health 2023; 22:145. [PMID: 37533035 PMCID: PMC10399030 DOI: 10.1186/s12939-023-01970-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/19/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND Lung cancer is the main cause of cancer mortality worldwide and in Spain. Several previous studies have documented socio-economic inequalities in lung cancer mortality but these have focused on specific provinces or cities. The goal of this study was to describe lung cancer mortality in Spain by sex as a function of socio-economic deprivation. METHODS We analysed all registered deaths from lung cancer during the period 2011-2017 in Spain. Mortality data was obtained from the National Institute of Statistics, and socio-economic level was measured with the small-area deprivation index developed by the Spanish Society of Epidemiology, with the census tract of residence at the time of death as the unit of analysis. We computed crude and age-standardized rates per 100,000 inhabitants by sex, deprivation quintile, and type of municipality (rural, semi-rural, urban) considering the 2013 European standard population (ASR-E). We further calculated ASR-E ratios between the most deprived (Q5) and the least deprived (Q1) areas and mapped census tract smoothed standardized lung cancer mortality ratios by sex. RESULTS We observed 148,425 lung cancer deaths (80.7% in men), with 73.5 deaths per 100,000 men and 17.1 deaths per 100,000 women. Deaths from lung cancer in men were five times more frequent than in women (ASR-E ratio = 5.3). Women residing in the least deprived areas had higher mortality from lung cancer (ASR-E = 22.2), compared to women residing in the most deprived areas (ASR-E = 13.2), with a clear gradient among the quintiles of deprivation. For men, this pattern was reversed, with the highest mortality occurring in areas of lower socio-economic level (ASR-E = 99.0 in Q5 vs. ASR-E = 86.6 in Q1). These socio-economic inequalities remained fairly stable over time and across urban and rural areas. CONCLUSIONS Socio-economic status is strongly related to lung cancer mortality, showing opposite patterns in men and women, such that mortality is highest in women residing in the least deprived areas and men residing in the most deprived areas. Systematic surveillance of lung cancer mortality by socio-economic status may facilitate the assessment of public health interventions aimed at mitigating cancer inequalities in Spain.
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Grants
- PROYE20023SÁNC High Resolution Study of Social Inequalities in Cancer (HiReSIC), Asociación Española Contra el Cáncer (AECC)
- PROYE20023SÁNC High Resolution Study of Social Inequalities in Cancer (HiReSIC), Asociación Española Contra el Cáncer (AECC)
- PROYE20023SÁNC High Resolution Study of Social Inequalities in Cancer (HiReSIC), Asociación Española Contra el Cáncer (AECC)
- PROYE20023SÁNC High Resolution Study of Social Inequalities in Cancer (HiReSIC), Asociación Española Contra el Cáncer (AECC)
- PROYE20023SÁNC High Resolution Study of Social Inequalities in Cancer (HiReSIC), Asociación Española Contra el Cáncer (AECC)
- PROYE20023SÁNC High Resolution Study of Social Inequalities in Cancer (HiReSIC), Asociación Española Contra el Cáncer (AECC)
- PROYE20023SÁNC High Resolution Study of Social Inequalities in Cancer (HiReSIC), Asociación Española Contra el Cáncer (AECC)
- Not applicable Subprograma de Vigilancia Epidemiológica del Cáncer (VICA), del CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII)
- Not applicable Subprograma de Vigilancia Epidemiológica del Cáncer (VICA), del CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII)
- Not applicable Subprograma de Vigilancia Epidemiológica del Cáncer (VICA), del CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII)
- Not applicable Subprograma de Vigilancia Epidemiológica del Cáncer (VICA), del CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII)
- Not applicable Subprograma de Vigilancia Epidemiológica del Cáncer (VICA), del CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII)
- Not applicable Subprograma de Vigilancia Epidemiológica del Cáncer (VICA), del CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII)
- PI18/01593 EU/FEDER Instituto de Salud Carlos III
- PI18/01593 EU/FEDER Instituto de Salud Carlos III
- PI18/01593 EU/FEDER Instituto de Salud Carlos III
- Not applicable Acciones de Movilidad CIBERESP, 2022
- JC2019-039691-I Juan de la Cierva Fellowship from the Ministry of Science and the National Research Agency of Spain
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Affiliation(s)
- Daniel Redondo-Sánchez
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, 18012, Spain.
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, 28029, Spain.
- Escuela Andaluza de Salud Pública, Granada, 18080, Spain.
| | - Pablo Fernández-Navarro
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, 28029, Spain
- Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, 28029, Spain
| | - Miguel Rodríguez-Barranco
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, 18012, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, 28029, Spain
- Escuela Andaluza de Salud Pública, Granada, 18080, Spain
| | - Olivier Nuñez
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, 28029, Spain
- Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, 28029, Spain
| | - Dafina Petrova
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, 18012, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, 28029, Spain
- Escuela Andaluza de Salud Pública, Granada, 18080, Spain
| | - Juan Manuel García-Torrecillas
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, 18012, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, 28029, Spain
- Emergency and Research Unit, Torrecárdenas University Hospital, Almería, 04009, Spain
| | - Jose Juan Jiménez-Moleón
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, 18012, Spain
- Department of Preventive Medicine and Public Health, University of Granada, Granada, 18071, Spain
| | - María-José Sánchez
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, 18012, Spain
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, 28029, Spain
- Escuela Andaluza de Salud Pública, Granada, 18080, Spain
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16
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Thawer SG, Golumbeanu M, Lazaro S, Chacky F, Munisi K, Aaron S, Molteni F, Lengeler C, Pothin E, Snow RW, Alegana VA. Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania. Sci Rep 2023; 13:10600. [PMID: 37391538 PMCID: PMC10313820 DOI: 10.1038/s41598-023-37669-x] [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: 10/25/2022] [Accepted: 06/26/2023] [Indexed: 07/02/2023] Open
Abstract
As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.
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Affiliation(s)
- Sumaiyya G Thawer
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
| | - Monica Golumbeanu
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Samwel Lazaro
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Frank Chacky
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Khalifa Munisi
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Sijenunu Aaron
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Fabrizio Molteni
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- National Malaria Control Programme, Dodoma, Tanzania
| | - Christian Lengeler
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Clinton Health Access Initiative, New York, USA
| | - Robert W Snow
- Population Health Unit, KEMRI-Welcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Victor A Alegana
- World Health Organization, Regional Office for Africa, Brazzaville, Republic of Congo
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17
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Saint-Jacques N, Brown PE, Purcell J, Rainham DG, Terashima M, Dummer TJB. The Nova Scotia Community Cancer Matrix: A geospatial tool to support cancer prevention. Soc Sci Med 2023; 330:116038. [PMID: 37390806 DOI: 10.1016/j.socscimed.2023.116038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/26/2023] [Accepted: 06/16/2023] [Indexed: 07/02/2023]
Abstract
Globally, cancer is a leading cause of death and morbidity and its burden is increasing worldwide. It is established that medical approaches alone will not solve this cancer crisis. Moreover, while cancer treatment can be effective, it is costly and access to treatment and health care is vastly inequitable. However, almost 50% of cancers are caused by potentially avoidable risk factors and are thus preventable. Cancer prevention represents the most cost-effective, feasible and sustainable pathway towards global cancer control. While much is known about cancer risk factors, prevention programs often lack consideration of how place impacts cancer risk over time. Maximizing cancer prevention investment requires an understanding of the geographic context for why some people develop cancer while others do not. Data on how community and individual level risk factors interact is therefore required. The Nova Scotia Community Cancer Matrix (NS-Matrix) study was established in Nova Scotia (NS), a small province in Eastern Canada with a population of 1 million. The study integrates small-area profiles of cancer incidence with cancer risk factors and socioeconomic conditions, to inform locally relevant and equitable cancer prevention strategies. The NS-Matrix Study includes over 99,000 incident cancers diagnosed in NS between 2001 and 2017, georeferenced to small-area communities. In this analysis we used Bayesian inference to identify communities with high and low risk for lung and bladder cancer: two highly preventable cancers with rates in NS exceeding the Canadian average, and for which key risk factors are high. We report significant spatial heterogeneity in lung and bladder cancer risk. The identification of spatial disparities relating to a community's socioeconomic profile and other spatially varying factors, such as environmental exposures, can inform prevention. Adopting Bayesian spatial analysis methods and utilizing high quality cancer registry data provides a model to support geographically-focused cancer prevention efforts, tailored to local community needs.
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Affiliation(s)
- Nathalie Saint-Jacques
- NSH Cancer Care Program, Bethune Building, 1276 South Park St, Halifax, NS, Canada; Healthy Populations Institute, Dalhousie University, 1318 Robie St., Halifax, NS, Canada.
| | - Patrick E Brown
- Department of Statistical Science, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON, Canada.
| | - Judy Purcell
- NSH Cancer Care Program, Bethune Building, 1276 South Park St, Halifax, NS, Canada.
| | - Daniel G Rainham
- School of Health and Human Performance, Dalhousie University, 5981 University Avenue, Halifax, NS, Canada; Healthy Populations Institute, Dalhousie University, 1318 Robie St., Halifax, NS, Canada.
| | - Mikiko Terashima
- School of Planning, Dalhousie University, O'Brien Hall, 5217 Morris St., Halifax, NS, Canada.
| | - Trevor J B Dummer
- School of Population and Public Health, University of British Columbia, 226 East Mall, Vancouver, BC, Canada.
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18
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Rotejanaprasert C, Lawpoolsri S, Sa-Angchai P, Khamsiriwatchara A, Padungtod C, Tipmontree R, Menezes L, Sattabongkot J, Cui L, Kaewkungwal J. Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models. Sci Rep 2023; 13:7799. [PMID: 37179429 PMCID: PMC10182757 DOI: 10.1038/s41598-023-35007-9] [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: 10/24/2022] [Accepted: 05/11/2023] [Indexed: 05/15/2023] Open
Abstract
Thailand has set a goal of eliminating malaria by 2024 in its national strategic plan. In this study, we used the Thailand malaria surveillance database to develop hierarchical spatiotemporal models to analyze retrospective patterns and predict Plasmodium falciparum and Plasmodium vivax malaria incidences at the provincial level. We first describe the available data, explain the hierarchical spatiotemporal framework underlying the analysis, and then display the results of fitting various space-time formulations to the malaria data with the different model selection metrics. The Bayesian model selection process assessed the sensitivity of different specifications to obtain the optimal models. To assess whether malaria could be eliminated by 2024 per Thailand's National Malaria Elimination Strategy, 2017-2026, we used the best-fitted model to project the estimated cases for 2022-2028. The study results based on the models revealed different predicted estimates between both species. The model for P. falciparum suggested that zero P. falciparum cases might be possible by 2024, in contrast to the model for P. vivax, wherein zero P. vivax cases might not be reached. Innovative approaches in the P. vivax-specific control and elimination plans must be implemented to reach zero P. vivax and consequently declare Thailand as a malaria-free country.
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Affiliation(s)
- Chawarat Rotejanaprasert
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Saranath Lawpoolsri
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Patiwat Sa-Angchai
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Amnat Khamsiriwatchara
- Center of Excellence for Biomedical and Public Health Informatics (BIOPHICS), Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Chantana Padungtod
- Division of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | - Rungrawee Tipmontree
- Division of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | - Lynette Menezes
- Division of Infectious Diseases and Internal Medicine, Department of Internal Medicine, University of South Florida, Tampa, USA
| | - Jetsumon Sattabongkot
- Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Liwang Cui
- Division of Infectious Diseases and Internal Medicine, Department of Internal Medicine, University of South Florida, Tampa, USA
| | - Jaranit Kaewkungwal
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
- Center of Excellence for Biomedical and Public Health Informatics (BIOPHICS), Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
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19
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de Graaf M, Langeveld J, Post J, Carrizosa C, Franz E, Izquierdo-Lara RW, Elsinga G, Heijnen L, Been F, van Beek J, Schilperoort R, Vriend R, Fanoy E, de Schepper EIT, Koopmans MPG, Medema G. Capturing the SARS-CoV-2 infection pyramid within the municipality of Rotterdam using longitudinal sewage surveillance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 883:163599. [PMID: 37100150 PMCID: PMC10125208 DOI: 10.1016/j.scitotenv.2023.163599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 04/07/2023] [Accepted: 04/15/2023] [Indexed: 05/03/2023]
Abstract
Despite high vaccination rates in the Netherlands, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate. Longitudinal sewage surveillance was implemented along with the notification of cases as two parts of the surveillance pyramid to validate the use of sewage for surveillance, as an early warning tool, and to measure the effect of interventions. Sewage samples were collected from nine neighborhoods between September 2020 and November 2021. Comparative analysis and modeling were performed to understand the correlation between wastewater and case trends. Using high resolution sampling, normalization of wastewater SARS-CoV-2 concentrations, and 'normalization' of reported positive tests for testing delay and intensity, the incidence of reported positive tests could be modeled based on sewage data, and trends in both surveillance systems coincided. The high collinearity implied that high levels of viral shedding around the onset of disease largely determined SARS-CoV-2 levels in wastewater, and that the observed relationship was independent of variants of concern and vaccination levels. Sewage surveillance alongside a large-scale testing effort where 58 % of a municipality was tested, indicated a five-fold difference in the number of SARS-CoV-2-positive individuals and reported cases through standard testing. Where trends in reported positive cases were biased due to testing delay and testing behavior, wastewater surveillance can objectively display SARS-CoV-2 dynamics for both small and large locations and is sensitive enough to measure small variations in the number of infected individuals within or between neighborhoods. With the transition to a post-acute phase of the pandemic, sewage surveillance can help to keep track of re-emergence, but continued validation studies are needed to assess the predictive value of sewage surveillance with new variants. Our findings and model aid in interpreting SARS-CoV-2 surveillance data for public health decision-making and show its potential as one of the pillars of future surveillance of (re)emerging viruses.
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Affiliation(s)
- Miranda de Graaf
- Department of Viroscience, Erasmus University Medical Center, Rotterdam, the Netherlands; Pandemic and Disaster Preparedness Centre Rotterdam and Delft, the Netherlands.
| | - Jeroen Langeveld
- Partners4urbanwater, Nijmegen, the Netherlands; Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
| | - Johan Post
- Partners4urbanwater, Nijmegen, the Netherlands
| | - Christian Carrizosa
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Eelco Franz
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Ray W Izquierdo-Lara
- Department of Viroscience, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Goffe Elsinga
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Leo Heijnen
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Frederic Been
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Janko van Beek
- Department of Viroscience, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Rianne Vriend
- Regional Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
| | - Ewout Fanoy
- Regional Public Health Service Rotterdam-Rijnmond, Rotterdam, the Netherlands
| | - Evelien I T de Schepper
- Department of General Practice, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marion P G Koopmans
- Department of Viroscience, Erasmus University Medical Center, Rotterdam, the Netherlands; Pandemic and Disaster Preparedness Centre Rotterdam and Delft, the Netherlands
| | - Gertjan Medema
- Pandemic and Disaster Preparedness Centre Rotterdam and Delft, the Netherlands; KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands; Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
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20
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Skirgård H, Haynie HJ, Blasi DE, Hammarström H, Collins J, Latarche JJ, Lesage J, Weber T, Witzlack-Makarevich A, Passmore S, Chira A, Maurits L, Dinnage R, Dunn M, Reesink G, Singer R, Bowern C, Epps P, Hill J, Vesakoski O, Robbeets M, Abbas NK, Auer D, Bakker NA, Barbos G, Borges RD, Danielsen S, Dorenbusch L, Dorn E, Elliott J, Falcone G, Fischer J, Ghanggo Ate Y, Gibson H, Göbel HP, Goodall JA, Gruner V, Harvey A, Hayes R, Heer L, Herrera Miranda RE, Hübler N, Huntington-Rainey B, Ivani JK, Johns M, Just E, Kashima E, Kipf C, Klingenberg JV, König N, Koti A, Kowalik RG, Krasnoukhova O, Lindvall NL, Lorenzen M, Lutzenberger H, Martins TR, Mata German C, van der Meer S, Montoya Samamé J, Müller M, Muradoglu S, Neely K, Nickel J, Norvik M, Oluoch CA, Peacock J, Pearey IO, Peck N, Petit S, Pieper S, Poblete M, Prestipino D, Raabe L, Raja A, Reimringer J, Rey SC, Rizaew J, Ruppert E, Salmon KK, Sammet J, Schembri R, Schlabbach L, Schmidt FW, Skilton A, Smith WD, de Sousa H, Sverredal K, Valle D, Vera J, Voß J, Witte T, Wu H, Yam S, Ye J, Yong M, Yuditha T, Zariquiey R, Forkel R, Evans N, Levinson SC, Haspelmath M, Greenhill SJ, Atkinson QD, Gray RD. Grambank reveals the importance of genealogical constraints on linguistic diversity and highlights the impact of language loss. SCIENCE ADVANCES 2023; 9:eadg6175. [PMID: 37075104 PMCID: PMC10115409 DOI: 10.1126/sciadv.adg6175] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
While global patterns of human genetic diversity are increasingly well characterized, the diversity of human languages remains less systematically described. Here, we outline the Grambank database. With over 400,000 data points and 2400 languages, Grambank is the largest comparative grammatical database available. The comprehensiveness of Grambank allows us to quantify the relative effects of genealogical inheritance and geographic proximity on the structural diversity of the world's languages, evaluate constraints on linguistic diversity, and identify the world's most unusual languages. An analysis of the consequences of language loss reveals that the reduction in diversity will be strikingly uneven across the major linguistic regions of the world. Without sustained efforts to document and revitalize endangered languages, our linguistic window into human history, cognition, and culture will be seriously fragmented.
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Affiliation(s)
- Hedvig Skirgård
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Department of Linguistics, School of Culture, History and Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Corresponding author. (H.S.); (R.D.G.)
| | - Hannah J. Haynie
- Department of Linguistics, University of Colorado Boulder, Boulder, CO, USA
| | - Damián E. Blasi
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Human Relation Area Files, Yale University, New Haven, CT, USA
| | - Harald Hammarström
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden
| | - Jeremy Collins
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Department of Linguistics, Faculty of Arts, Radboud University, Nijmegen, Netherlands
| | - Jay J. Latarche
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Jakob Lesage
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Langage, Langues et Cultures d'Afrique (LLACAN), Centre National de la Recherche Scientifique (CNRS), Villejuif, France
- Institut National des Langues et Civilisations Orientales (INALCO), Paris, France
- Department of Asian and African Studies, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tobias Weber
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Alena Witzlack-Makarevich
- Department of Linguistics, Faculty of Humanities, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Sam Passmore
- Evolution of Cultural Diversity Initiative, School of Culture, History and Language, College of Asia and the Pacific, The Australian National University, Canberra, ACT, Australia
- Faculty of Environment and Information Studies, Keio University SFC (Shonan Fujisawa Campus), Tokyo, Japan
- Department of Anthropology and Archaeology, Faculty of Arts, University of Bristol, Bristol, UK
| | - Angela Chira
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Luke Maurits
- Department of Comparative Cultural Psychology, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Russell Dinnage
- Department of Biological Sciences, Institute of Environment, Florida International University, Miami, FL, USA
| | - Michael Dunn
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden
| | - Ger Reesink
- Department of Linguistics, Faculty of Arts, Radboud University, Nijmegen, Netherlands
| | - Ruth Singer
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Research Unit for Indigenous Language, School of Languages and Linguistics, University of Melbourne, Melbourne, Australia
| | - Claire Bowern
- Department of Linguistics, Yale University, New Haven, CT, USA
| | - Patience Epps
- Department of Linguistics, University of Texas at Austin, Austin, TX, USA
| | - Jane Hill
- School of Anthropology, University of Arizona, Tucson, AZ, USA
| | - Outi Vesakoski
- Department of Biology, Turku University, Turku, Finland
- Department of Finnish and Finno-Ugric languages, University of Turku, Turku, Finland
| | - Martine Robbeets
- Department of Archaeology, Max Planck Institute for the Science of Human History, Jena, Germany
| | - Noor Karolin Abbas
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Daniel Auer
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Nancy A. Bakker
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Giulia Barbos
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Robert D. Borges
- Institute of Slavic Studies, Polish Academy of Sciences, Warsaw, Poland
| | - Swintha Danielsen
- Zentrum für Kleine und Regionale Sprachen, Friesisches Seminar, Europa-Universität Flensburg, Flensburg, Germany
- Centro de Investigaciones Históricas y Antropológicas (CIHA), Santa Cruz de la Sierra, Bolivia
- Europa-Universität Flensburg (EUF), Flensburg, Germany
| | - Luise Dorenbusch
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Institute of Linguistics, Leipzig University, Leipzig, Germany
| | - Ella Dorn
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - John Elliott
- Department of Linguistics, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Giada Falcone
- Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden
| | - Jana Fischer
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Yustinus Ghanggo Ate
- Department of Linguistics, School of Culture, History and Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Universitas Katolik Weetebula, Sumba Island, Indonesia
| | - Hannah Gibson
- Department of Languages and Linguistics, University of Essex, Essex, UK
| | - Hans-Philipp Göbel
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
- Department of Linguistics, University of Cologne, Cologne, Germany
| | - Jemima A. Goodall
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Victoria Gruner
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Andrew Harvey
- Faculty of Languages and Literatures, University of Bayreuth, Bayreuth, Germany
| | - Rebekah Hayes
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Leonard Heer
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Roberto E. Herrera Miranda
- Institut National des Langues et Civilisations Orientales (INALCO), Paris, France
- Institute of Linguistics, Leipzig University, Leipzig, Germany
- Structure et Dynamique des Langues (SeDyl), Centre National de la Recherche Scientifique (CNRS), Villejuif, France
- Sprachwissenschaftliches Seminar, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Nataliia Hübler
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Biu Huntington-Rainey
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
- Division of Psychology and Language Sciences, Faculty of Brain Sciences, University College London (UCL), University of London, London, UK
- Institutt for Filosofi, ide- og Kunsthistorie og Klassiske Språk (IFIKK), Det Humanistisk Fakultet, Universitet i Oslo, Oslo, Norway
| | - Jessica K. Ivani
- Department of Comparative Linguistics, University of Zürich, Zürich, Switzerland
| | - Marilen Johns
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Erika Just
- Department of Comparative Linguistics, University of Zürich, Zürich, Switzerland
| | - Eri Kashima
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Department of Linguistics, School of Culture, History and Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
| | - Carolina Kipf
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Janina V. Klingenberg
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Nikita König
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
- Department of Linguistics, European University Viadrina, Frankfur an der Oder, Germany
| | - Aikaterina Koti
- Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden
| | | | - Olga Krasnoukhova
- Centre for Linguistics, Leiden University, Leiden, Netherlands
- Department of Linguistics, University of Antwerpen, Antwerpen, Belgium
| | - Nora L. M. Lindvall
- Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden
| | - Mandy Lorenzen
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Hannah Lutzenberger
- Department of Linguistics, Faculty of Arts, Radboud University, Nijmegen, Netherlands
- Department of English Language and Linguistics, University of Birmingham, Birmingham, UK
| | - Tânia R. A. Martins
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Celia Mata German
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Suzanne van der Meer
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Jaime Montoya Samamé
- Facultad de Letras y Ciencias Humanas, Pontificia Universidad Católica del Perú, Lima, Perú
| | - Michael Müller
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Saliha Muradoglu
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
| | - Kelsey Neely
- Department of Linguistics, University of Texas at Austin, Austin, TX, USA
| | - Johanna Nickel
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Miina Norvik
- Institute of Estonian and General Linguistics, University of Tartu, Tartu, Estonia
- Department of Modern Languages, Uppsala University, Uppsala, Sweden
| | - Cheryl Akinyi Oluoch
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Jesse Peacock
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Department of Linguistics, Faculty of Arts, Radboud University, Nijmegen, Netherlands
| | - India O. C. Pearey
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Naomi Peck
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- University of Freiburg, Freiburg, Germany
| | - Stephanie Petit
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Sören Pieper
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Mariana Poblete
- Facultad de Letras y Ciencias Humanas, Pontificia Universidad Católica del Perú, Lima, Perú
- Universidad de Chile, Santiago, Chile
| | - Daniel Prestipino
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
| | - Linda Raabe
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Amna Raja
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Janis Reimringer
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Sydney C. Rey
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
- The Language Conservancy, Bloomington, IN, USA
| | - Julia Rizaew
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Eloisa Ruppert
- Department of Linguistics, Quantitative Lexicology and Variational Linguistics (QLVL), KU Leuven, Leuven, Belgium
| | - Kim K. Salmon
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Jill Sammet
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Rhiannon Schembri
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Division of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - Lars Schlabbach
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | | | - Amalia Skilton
- Department of Linguistics, Cornell University, Ithaca, NY, USA
| | | | - Hilário de Sousa
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Centre de Recherches Linguistiques sur l'Asie Orientale (CRLAO), École des Hautes Études en Sciences Sociales (EHESS), Aubervilliers, France
| | - Kristin Sverredal
- Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden
| | - Daniel Valle
- Department of Modern Languages, University of Mississippi, Oxford, MS, USA
| | - Javier Vera
- Facultad de Letras y Ciencias Humanas, Pontificia Universidad Católica del Perú, Lima, Perú
| | - Judith Voß
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Tim Witte
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Frisian and General Linguistics, Department of General Linguistics, Institute for Scandinavian Studies, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Henry Wu
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- International College for Postgraduate Buddhist Studies, Tokyo, Japan
| | - Stephanie Yam
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Institute for General Linguistics, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Jingting Ye
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Department of Chinese Language and Literature, Fudan University, Shanghai, China
| | - Maisie Yong
- Department of Linguistics, School of Languages, Cultures and Linguistics, School of Oriental and African Studies (SOAS), University of London, London, UK
| | - Tessa Yuditha
- Department of Linguistics, Faculty of Arts, Radboud University, Nijmegen, Netherlands
- Department of Spanish, Linguistics, and Theory of Literature (Linguistics), Faculty of Philology, University of Seville, Seville, Spain
| | - Roberto Zariquiey
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Facultad de Letras y Ciencias Humanas, Pontificia Universidad Católica del Perú, Lima, Perú
| | - Robert Forkel
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Nicholas Evans
- ARC Centre of Excellence for the Dynamics of Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
- Department of Linguistics, School of Culture, History and Language, College of Asia and the Pacific, Australian National University, Canberra, Australia
| | - Stephen C. Levinson
- Department of Language and Cognition, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Martin Haspelmath
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Simon J. Greenhill
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- School of Psychology, University of Auckland, Auckland, New Zealand
| | | | - Russell D. Gray
- Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- School of Psychology, University of Auckland, Auckland, New Zealand
- Corresponding author. (H.S.); (R.D.G.)
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21
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Howes A, Risher KA, Nguyen VK, Stevens O, Jia KM, Wolock TM, Esra RT, Zembe L, Wanyeki I, Mahy M, Benedikt C, Flaxman SR, Eaton JW. Spatio-temporal estimates of HIV risk group proportions for adolescent girls and young women across 13 priority countries in sub-Saharan Africa. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001731. [PMID: 37075002 PMCID: PMC10115274 DOI: 10.1371/journal.pgph.0001731] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/23/2023] [Indexed: 04/20/2023]
Abstract
The Global AIDS Strategy 2021-2026 identifies adolescent girls and young women (AGYW) as a priority population for HIV prevention, and recommends differentiating intervention portfolios geographically based on local HIV incidence and individual risk behaviours. We estimated prevalence of HIV risk behaviours and associated HIV incidence at health district level among AGYW living in 13 countries in sub-Saharan Africa. We analysed 46 geospatially-referenced national household surveys conducted between 1999-2018 across 13 high HIV burden countries in sub-Saharan Africa. Female survey respondents aged 15-29 years were classified into four risk groups (not sexually active, cohabiting, non-regular or multiple partner[s] and female sex workers [FSW]) based on reported sexual behaviour. We used a Bayesian spatio-temporal multinomial regression model to estimate the proportion of AGYW in each risk group stratified by district, year, and five-year age group. Using subnational estimates of HIV prevalence and incidence produced by countries with support from UNAIDS, we estimated new HIV infections in each risk group by district and age group. We then assessed the efficiency of prioritising interventions according to risk group. Data consisted of 274,970 female survey respondents aged 15-29. Among women aged 20-29, cohabiting (63.1%) was more common in eastern Africa than non-regular or multiple partner(s) (21.3%), while in southern countries non-regular or multiple partner(s) (58.9%) were more common than cohabiting (23.4%). Risk group proportions varied substantially across age groups (65.9% of total variation explained), countries (20.9%), and between districts within each country (11.3%), but changed little over time (0.9%). Prioritisation based on behavioural risk, in combination with location- and age-based prioritisation, reduced the proportion of population required to be reached in order to find half of all expected new infections from 19.4% to 10.6%. FSW were 1.3% of the population but 10.6% of all expected new infections. Our risk group estimates provide data for HIV programmes to set targets and implement differentiated prevention strategies outlined in the Global AIDS Strategy. Successfully implementing this approach would result in more efficiently reaching substantially more of those at risk for infections.
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Affiliation(s)
- Adam Howes
- Department of Mathematics, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Kathryn A. Risher
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Heidelberg Institute for Global Health, Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Van Kính Nguyen
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, United States of America
| | - Oliver Stevens
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Katherine M. Jia
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Timothy M. Wolock
- Department of Mathematics, Imperial College London, London, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Rachel T. Esra
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
| | - Lycias Zembe
- Joint United Nations Programme on HIV/AIDS, Geneva, Switzerland
| | - Ian Wanyeki
- Joint United Nations Programme on HIV/AIDS, Geneva, Switzerland
| | - Mary Mahy
- Joint United Nations Programme on HIV/AIDS, Geneva, Switzerland
| | | | - Seth R. Flaxman
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Jeffrey W. Eaton
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
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22
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Hernandez HG, Brown GD, Lima ID, Coutinho JF, Wilson ME, Nascimento ELT, Jeronimo SMB, Petersen CA, Oleson JJ. Hierarchical spatiotemporal modeling of human visceral leishmaniasis in Rio Grande do Norte, Brazil. PLoS Negl Trop Dis 2023; 17:e0011206. [PMID: 37011128 PMCID: PMC10101641 DOI: 10.1371/journal.pntd.0011206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 04/13/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Visceral leishmaniasis (VL) is a neglected tropical disease that is globally distributed and has the potential to cause very serious illness. Prior literature highlights the emergence and spread of VL is influenced by multiple factors, such as socioeconomic status, sanitation levels or animal and human reservoirs. The study aimed to retrospectively investigate the presence and infectiousness of VL in Rio Grande do Norte (RN), Brazil between 2007 and 2020. We applied a hierarchical Bayesian approach to estimate municipality-specific relative risk of VL across space and time. The results show evidence that lower socioeconomic status is connected to higher municipality-specific VL risk. Overall, estimates reveal spatially heterogeneous VL risks in RN, with a high probability that VL risk for municipalities within the West Potiguar mesoregion are more than double the expected VL risk. Additionally, given the data available, results indicate there is a high probability of increasing VL risk in the municipalities of Natal, Patu and Pau dos Ferros. These findings demonstrate opportunities for municipality-specific public health policy interventions and warrant future research on identifying epidemiological drivers in at-risk regions.
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Affiliation(s)
- Helin G Hernandez
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, United States of America
| | - Grant D Brown
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, United States of America
| | - Iraci D Lima
- State Health Secretariat, State Government of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - José F Coutinho
- Institute of Tropical Medicine of Rio Grande do Norte, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Mary E Wilson
- Departments of Internal Medicine and Microbiology & Immunology, University of Iowa, Iowa City, Iowa, United States of America
- Iowa City VA Medical Center, Iowa City, Iowa, United States of America
| | - Eliana L T Nascimento
- Institute of Tropical Medicine of Rio Grande do Norte, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Department of Infectious Diseases, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Selma M B Jeronimo
- Institute of Tropical Medicine of Rio Grande do Norte, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Department of Biochemistry, Federal University of Rio Grande do Norte, Natal, Brazil
- National Institute of Sciences and Technology of Tropical Disease, Natal, Rio Grande do Norte, Brazil
| | - Christine A Petersen
- Department of Epidemiology, University of Iowa, Iowa City, Iowa, United States of America
| | - Jacob J Oleson
- Department of Biostatistics, University of Iowa, Iowa City, Iowa, United States of America
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23
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Ament JM, Carbone C, Crees JJ, Freeman R, Turvey ST. Anthropogenic predictors of varying Holocene occurrence for Europe's large mammal fauna. Biol Lett 2023; 19:20220578. [PMID: 37073526 PMCID: PMC10114012 DOI: 10.1098/rsbl.2022.0578] [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: 12/03/2022] [Accepted: 03/27/2023] [Indexed: 04/20/2023] Open
Abstract
Understanding how species respond to different anthropogenic pressures is essential for conservation planning. The archaeological record has great potential to inform extinction risk assessment by providing evidence on past human-caused biodiversity loss, but identifying specific drivers of past declines from environmental archives has proved challenging. We used 17 684 Holocene zooarchaeological records for 15 European large mammal species together with data on past environmental conditions and anthropogenic activities across Europe, to assess the ability of environmental archives to determine the relative importance of different human pressures in shaping faunal distributions through time. Site occupancy probability showed differing significant relationships with environmental covariates for all species, and nine species also showed significant relationships with anthropogenic covariates (human population density, % cropland, % grazing land). Across-species differences in negative relationships with covariates provide ecological insights for understanding extinction dynamics: some mammals (red deer, aurochs, wolf, wildcat, lynx, pine marten and beech marten) were more vulnerable to past human-environmental interactions, and differing single and synergistic anthropogenic factors influenced likelihood of past occurrence across species. Our results provide new evidence for pre-industrial population fragmentation and depletion in European mammals, and demonstrate the usefulness of historical baselines for understanding species' varying long-term sensitivity to multiple threats.
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Affiliation(s)
- Judith M. Ament
- Centre for Biodiversity and Environment Research, University College London, London WC1E 6BT, UK
- Institute of Zoology, Zoological Society of London, London NW1 4RY, UK
| | - Chris Carbone
- Institute of Zoology, Zoological Society of London, London NW1 4RY, UK
| | - Jennifer J. Crees
- Department of Earth Sciences, Natural History Museum, London SW7 5BD, UK
| | - Robin Freeman
- Institute of Zoology, Zoological Society of London, London NW1 4RY, UK
| | - Samuel T. Turvey
- Institute of Zoology, Zoological Society of London, London NW1 4RY, UK
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24
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Mostert PS, O'Hara RB. PointedSDMs:
An R package to help facilitate the construction of integrated species distribution models. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Affiliation(s)
- Philip S. Mostert
- Department of Mathematical sciences Norwegian University of Science and Technology Trondheim Norway
- Centre for Biodiversity Dynamics Norwegian University of Science and Technology Trondheim Norway
| | - Robert B. O'Hara
- Department of Mathematical sciences Norwegian University of Science and Technology Trondheim Norway
- Centre for Biodiversity Dynamics Norwegian University of Science and Technology Trondheim Norway
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25
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Rustand D, van Niekerk J, Rue H, Tournigand C, Rondeau V, Briollais L. Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation. Biom J 2023; 65:e2100322. [PMID: 36846925 DOI: 10.1002/bimj.202100322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/16/2022] [Accepted: 12/30/2022] [Indexed: 03/01/2023]
Abstract
Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.
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Affiliation(s)
- Denis Rustand
- Biostatistic Team, Bordeaux Population Health Center, ISPED, Centre INSERM U1219, Bordeaux, France.,Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Janet van Niekerk
- Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Håvard Rue
- Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | | | - Virginie Rondeau
- Biostatistic Team, Bordeaux Population Health Center, ISPED, Centre INSERM U1219, Bordeaux, France
| | - Laurent Briollais
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Dalla Lana School of Public Health (Biostatistics), University of Toronto, Toronto, Ontario, Canada
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26
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Teng J, Ding S, Zhang H, Wang K, Hu X. Bayesian spatiotemporal modelling analysis of hemorrhagic fever with renal syndrome outbreaks in China using R-INLA. Zoonoses Public Health 2023; 70:46-57. [PMID: 36093577 DOI: 10.1111/zph.12999] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 07/09/2022] [Accepted: 08/06/2022] [Indexed: 01/07/2023]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a category B infectious disease caused by Hantavirus infection, which can cause acute kidney injury and has a high mortality rate. At present, China is the country most severely afflicted by HFRS in the world, and it is critical to carry out efficient HFRS prevention and management in a scientific and accurate manner. The study used data on the incidence of HFRS in mainland China from 2015 to 2018, built a Bayesian hierarchical spatiotemporal distribution model, and applied the Integrated Nested Laplace Approximation algorithm to analyse the factors influencing the development of HFRS, the spatial and temporal distribution characteristics, and the threshold exceedance locations. The results revealed that the woodland and grassland area (RR = 1.357, 95% CI: 1.005-1.791), economic level (RR = 1.299, 95% CI: 1.007-1.649), and traffic level (RR = 2.442, 95% CI: 1.825-3.199) were all significantly and positively associated with the development of HFRS, with traffic level having the strongest promoting effect. The seasonal cycle was obvious in time, with peaks in May-June and October-December each year, most notably in November. Spatially, there was a south-heavy north-light trend, with a high risk of incidence largely in places rich in mountain and forest vegetation, of which Guizhou, Guangxi, Guangdong, and Jiangxi provinces continuing to have a high incidence in recent years, and the evolution of the epidemic in Hubei and Hunan was becoming more serious. When the early warning threshold was set at 0.2, the detection impact was best, and Guizhou, Guangxi, Guangdong, Jiangxi, Hainan, and Tianjin were positioned near the critical point of the exceedance threshold with the highest risk of incidence. It is recommended that the relevant managers call for active vaccination of outdoor workers, such as those working in agriculture and construction sites, implement rat prevention and extermination before winter arrives, and warn high-risk and medium-high-risk areas to conduct early outbreak surveillance. Move the prevention and control gates forward based on the exceedance threshold for doing preventive and control detection and epidemic research and judgement work.
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Affiliation(s)
- Jiaqi Teng
- Department of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang, China
| | - Shuzhen Ding
- Department of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang, China
| | - Huiguo Zhang
- Department of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang, China
| | - Kai Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xijian Hu
- Department of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang, China
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27
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Klim H, William T, Chua TH, Rajahram GS, Drakeley CJ, Carroll MW, Fornace KM. Quantifying human-animal contact rates in Malaysian Borneo: Influence of agricultural landscapes on contact with potential zoonotic disease reservoirs. FRONTIERS IN EPIDEMIOLOGY 2023; 2:1057047. [PMID: 38455308 PMCID: PMC10910987 DOI: 10.3389/fepid.2022.1057047] [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] [Received: 09/29/2022] [Accepted: 12/28/2022] [Indexed: 03/09/2024]
Abstract
Changing landscapes across the globe, but particularly in Southeast Asia, are pushing humans and animals closer together and may increase the likelihood of zoonotic spillover events. Malaysian Borneo is hypothesized to be at high risk of spillover events due to proximity between reservoir species and humans caused by recent deforestation in the region. However, the relationship between landscape and human-animal contact rates has yet to be quantified. An environmentally stratified cross-sectional survey was conducted in Sabah, Malaysia in 2015, collecting geolocated questionnaire data on potential risk factors for contact with animals for 10,100 individuals. 51% of individuals reported contact with poultry, 46% with NHPs, 30% with bats, and 2% with swine. Generalised linear mixed models identified occupational and demographic factors associated with increased contact with these species, which varied when comparing wildlife to domesticated animals. Reported contact rates with each animal group were integrated with remote sensing-derived environmental data within a Bayesian framework to identify regions with high probabilities of contact with animal reservoirs. We have identified high spatial heterogeneity of contact with animals and clear associations between agricultural practices and high animal rates. This approach will help inform public health campaigns in at-risk populations and can improve pathogen surveillance efforts on Malaysian Borneo. This method can additionally serve as a framework for researchers looking to identify targets for future pathogen detection in a chosen region of study.
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Affiliation(s)
- Hannah Klim
- Wellcome Centre for Human Genetics and Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Future of Humanity Institute, Faculty of Philosophy, University of Oxford, Oxford, United Kingdom
| | - Timothy William
- Infectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit, Kota Kinabalu, Malaysia
- Gleneagles Hospital, Kota Kinabalu, Malaysia
- Clinical Research Centre, Queen Elizabeth II Hospital, Kota Kinabalu, Malaysia
| | - Tock H. Chua
- Faculty of Medicine and Health Sciences, University of Malaysia Sabah, Kota Kinabalu, Malaysia
| | - Giri S. Rajahram
- Clinical Research Centre, Queen Elizabeth II Hospital, Kota Kinabalu, Malaysia
| | - Chris J. Drakeley
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Miles W. Carroll
- Wellcome Centre for Human Genetics and Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Kimberly M. Fornace
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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28
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Beechey T. Ordinal Pattern Analysis: A Tutorial on Assessing the Fit of Hypotheses to Individual Repeated Measures Data. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2023; 66:347-364. [PMID: 36542850 DOI: 10.1044/2022_jslhr-22-00133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
PURPOSE This article provides a tutorial introduction to ordinal pattern analysis, a statistical analysis method designed to quantify the extent to which hypotheses of relative change across experimental conditions match observed data at the level of individuals. This method may be a useful addition to familiar parametric statistical methods including repeated measures analysis of variance and generalized linear mixed-effects models, particularly when analyzing inherently individual characteristics, such as perceptual processes, and where experimental effects are usefully modeled in relative rather than absolute terms. METHOD Three analyses of increasing complexity are demonstrated using ordinal pattern analysis. An initial analysis of a very small data set is designed to explicate the simple mathematical calculations that make up ordinal pattern analysis, which can be performed without the aid of a computer. Analyses of slightly larger data sets are used to demonstrate familiar concepts, including comparison of competing hypotheses, handling missing data, group comparisons, and pairwise tests. All analyses can be reproduced using provided code and data. RESULTS Ordinal pattern analysis results are presented, along with an analogous linear mixed-effects analysis, to illustrate the similarities and differences in information provided by ordinal pattern analysis in comparison to familiar parametric methods. CONCLUSION Although ordinal pattern analysis does not produce familiar numerical effect sizes, it does provide highly interpretable results in terms of the proportion of individuals whose results are consistent with a hypothesis, along with individual and group-level statistics, which quantify hypothesis performance.
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Affiliation(s)
- Timothy Beechey
- Hearing Sciences-Scottish Section, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Glasgow, United Kingdom
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Delatouche L, de Lapeyre de Bellaire L, Tixier P. Disentangling the Factors Affecting the Dynamic of Pseudocercospora fijiensis: Quantification of Weather, Fungicide, and Landscape Effects. PHYTOPATHOLOGY 2023; 113:31-43. [PMID: 35939624 DOI: 10.1094/phyto-04-22-0132-r] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Quantifying the effect of landscape composition on disease dynamics remains challenging because it depends on many factors. In this study, we used a hybrid process-based/statistical modeling approach to separate the effect of the landscape composition on the epidemiology of banana leaf streak disease (BLSD) from weather and fungicide effects. We parameterized our model with a 5-year dataset, including weekly measures of BLSD on 83 plots in Martinique. After estimating the intrinsic growth parameters of the stage evolution of the disease (SED), we evaluated the dynamic effect of five fungicides. Then, we added the intra- and inter-annual effect on disease dynamics using a generalized linear model. Finally, the whole model was used to assess the annual effect of the landscape on the SED for 11 plots. We evaluated the significance of the landscape composition (proportions of landscape elements in 200-, 500-, 800-, 1,000-m-radius buffer zones) on the landscape effect evaluated with the model. The percentage of hedgerows in a 200-m-radius buffer zone was negatively correlated to the landscape effect, i.e., it acted as a constraint against BLSD spreading and development. The proportion of managed-banana-plants in a 1,000-m-radius buffer zone was negatively correlated to the landscape effect, probably due to a mass effect of fungicide treatments. Inversely, the proportions of forest and the proportion of unmanaged-banana-plants, both in 1,000-m-radius buffer zones, were positively correlated with the landscape effect. Our study provides a holistic approach of the role biotic and abiotic factors play on the dynamics of BLSD.
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Affiliation(s)
- Lucile Delatouche
- CIRAD, UPR GECO, F-97285 Le Lamentin, Martinique, France
- CIRAD, UPR GECO, F-34398 Montpellier, France
- GECO, University of Montpellier, CIRAD, Montpellier, France
| | | | - Philippe Tixier
- CIRAD, UPR GECO, F-34398 Montpellier, France
- GECO, University of Montpellier, CIRAD, Montpellier, France
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Ertl HA, Hill MS, Wittkopp PJ. Differential Grainy head binding correlates with variation in chromatin structure and gene expression in Drosophila melanogaster. BMC Genomics 2022; 23:854. [PMID: 36575386 PMCID: PMC9795675 DOI: 10.1186/s12864-022-09082-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
Phenotypic evolution is often caused by variation in gene expression resulting from altered gene regulatory mechanisms. Genetic variation affecting chromatin remodeling has been identified as a potential source of variable gene expression; however, the roles of specific chromatin remodeling factors remain unclear. Here, we address this knowledge gap by examining the relationship between variation in gene expression, variation in chromatin structure, and variation in binding of the pioneer factor Grainy head between imaginal wing discs of two divergent strains of Drosophila melanogaster and their F1 hybrid. We find that (1) variation in Grainy head binding is mostly due to sequence changes that act in cis but are located outside of the canonical Grainy head binding motif, (2) variation in Grainy head binding correlates with changes in chromatin accessibility, and (3) this variation in chromatin accessibility, coupled with variation in Grainy head binding, correlates with variation in gene expression in some cases but not others. Interactions among these three molecular layers is complex, but these results suggest that genetic variation affecting the binding of pioneer factors contributes to variation in chromatin remodeling and the evolution of gene expression.
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Affiliation(s)
- Henry A. Ertl
- grid.214458.e0000000086837370Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109 USA
| | - Mark S. Hill
- grid.214458.e0000000086837370Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109 USA ,grid.83440.3b0000000121901201Present address: Cancer Evolution and Genome Instability Laboratory, University College London Cancer Institute and The Francis Crick Institute, London, UK
| | - Patricia J. Wittkopp
- grid.214458.e0000000086837370Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109 USA ,grid.214458.e0000000086837370Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109 USA
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Osgood‐Zimmerman A, Wakefield J. A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling. Int Stat Rev 2022. [DOI: 10.1111/insr.12534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
| | - Jon Wakefield
- Departments of Statistics and Biostatistics University of Washington Seattle Washington USA
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Reiner RC, Hay SI. The overlapping burden of the three leading causes of disability and death in sub-Saharan African children. Nat Commun 2022; 13:7457. [PMID: 36473841 PMCID: PMC9726883 DOI: 10.1038/s41467-022-34240-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 10/19/2022] [Indexed: 12/12/2022] Open
Abstract
Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival.
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Affiliation(s)
- Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
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Löbel LM, Kröger H, Tibubos AN. How Migration Status Shapes Susceptibility of Individuals' Loneliness to Social Isolation. Int J Public Health 2022; 67:1604576. [PMID: 36561278 PMCID: PMC9763294 DOI: 10.3389/ijph.2022.1604576] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Objectives: Our research provides competing hypotheses and empirical evidence how associations between objectively social isolation and subjective loneliness differ between host populations, migrants, and refugees. Methods: The analysis uses data of 25,171 participants from a random sample of the German population (SOEP v.35). We estimate regression models for the host population, migrants, and refugees and test five hypotheses on the association between social isolation and loneliness using a Bayesian approach in a multiverse framework. Results: We find the strongest relative support for an increased need for social inclusion among refugees, indicated by a higher Bayes factor compared to the hosts and migrants. However, all theoretically developed hypotheses perform poorly in explaining the major pattern in our data: The association of social isolation and loneliness is persistently lower for migrants (0.15 SD-0.29 SD), with similar sizes of associations for refugees and the host population (0.38 SD-0.67 SD). Conclusion: The migration history must be actively considered in health service provision and support programs to better cater to the needs of the different groups.
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Affiliation(s)
- Lea-Maria Löbel
- German Institute for Economic Research (DIW), Berlin, Germany,Berlin Graduate School of Social Sciences, Humboldt University of Berlin, Berlin, Germany,*Correspondence: Lea-Maria Löbel,
| | - Hannes Kröger
- German Institute for Economic Research (DIW), Berlin, Germany
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Egbon OA, Bogoni MA, Babalola BT, Louzada F. Under age five children survival times in Nigeria: a Bayesian spatial modeling approach. BMC Public Health 2022; 22:2207. [PMID: 36443732 PMCID: PMC9706907 DOI: 10.1186/s12889-022-14660-1] [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: 11/02/2021] [Accepted: 11/17/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Nigeria is among the top five countries in the world with the highest under-five mortality rates. In addition to the general leading causes of under-five mortality, studies have shown that disparity in sociocultural values and practices across ethnic groups in Nigeria influence child survival, thus there is a need for scientific validation. This study quantified the survival probabilities and the impact of socioeconomic and demographic factors, proximate and biological determinants, and environmental factors on the risk of under-five mortality in Nigeria. METHODS The Kaplan-Meier survival curve, Nelson Aalen hazard curve, and components survival probabilities were estimated. The Exponential, Gamma, Log-normal, Weibull, and Cox hazard models in a Bayesian mixed effect hierarchical hazard modeling framework with spatial components were considered, and the Deviance and Watanabe Akaike information criteria were used to select the best model for inference. A [Formula: see text] level of significance was assumed throughout this work. The 2018 Nigeria Demographic and Health Survey dataset was used, and the outcome variable was the time between birth and death or birth and the date of interview for children who were alive on the day of the interview. RESULTS Findings show that the probability of a child dying within the first two months is 0.04, and the probability of a boy child dying before attaining age five is 0.106, while a girl child is 0.094 probability. Gender, maternal education, household wealth status, source of water and toilet facility, residence, mass media, frequency of antenatal and postnatal visits, marital status, place of delivery, multiple births, who decide healthcare use, use of bednet are significant risk factors of child mortality in Nigeria. The mortality risk is high among the maternal age group below 24 and above 44years, and birth weight below 2.5Kg and above 4.5Kg. The under-five mortality risk is severe in Kebbi, Kaduna, Jigawa, Adamawa, Gombe, Kano, Kogi, Nasarawa, Plateau, and Sokoto states in Nigeria. CONCLUSION This study accentuates the need for special attention for the first two months after childbirth as it is the age group with the highest expected mortality. A practicable way to minimize death in the early life of children is to improve maternal healthcare service, promote maternal education, encourage delivery in healthcare facilities, positive parental attitude to support multiple births, poverty alleviation programs for the less privileged, and a prioritized intervention to Northern Nigeria.
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Affiliation(s)
- Osafu Augustine Egbon
- Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Statistics, Universidade Federal de São Carlos, São Carlos, Brazil
| | - Mariella Ananias Bogoni
- Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Statistics, Universidade Federal de São Carlos, São Carlos, Brazil
| | | | - Francisco Louzada
- Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil
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Morales-Navarrete D, Bevilacqua M, Caamaño-Carrillo C, Castro LM. Modelling Point Referenced Spatial Count Data: A Poisson Process Approach. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2140053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Affiliation(s)
- Diego Morales-Navarrete
- Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus Center for the Discovery of Structures in Complex Data, Chile
| | - Moreno Bevilacqua
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Viña del Mar, Chile
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Ca’ Foscari University of Venice, Italy
| | | | - Luis M. Castro
- Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus Center for the Discovery of Structures in Complex Data, Chile
- Centro de Riesgos y Seguros UC, Pontificia Universidad Católica de Chile, Santiago, Chile
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Simkin J, Dummer TJB, Erickson AC, Otterstatter MC, Woods RR, Ogilvie G. Small area disease mapping of cancer incidence in British Columbia using Bayesian spatial models and the smallareamapp R Package. Front Oncol 2022; 12:833265. [PMID: 36338766 PMCID: PMC9627310 DOI: 10.3389/fonc.2022.833265] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 09/26/2022] [Indexed: 09/28/2023] Open
Abstract
INTRODUCTION There is an increasing interest in small area analyses in cancer surveillance; however, technical capacity is limited and accessible analytical approaches remain to be determined. This study demonstrates an accessible approach for small area cancer risk estimation using Bayesian hierarchical models and data visualization through the smallareamapp R package. MATERIALS AND METHODS Incident lung (N = 26,448), female breast (N = 28,466), cervical (N = 1,478), and colorectal (N = 25,457) cancers diagnosed among British Columbia (BC) residents between 2011 and 2018 were obtained from the BC Cancer Registry. Indirect age-standardization was used to derive age-adjusted expected counts and standardized incidence ratios (SIRs) relative to provincial rates. Moran's I was used to assess the strength and direction of spatial autocorrelation. A modified Besag, York and Mollie model (BYM2) was used for model incidence counts to calculate posterior median relative risks (RR) by Community Health Service Areas (CHSA; N = 218), adjusting for spatial dependencies. Integrated Nested Laplace Approximation (INLA) was used for Bayesian model implementation. Areas with exceedance probabilities (above a threshold RR = 1.1) greater or equal to 80% were considered to have an elevated risk. The posterior median and 95% credible intervals (CrI) for the spatially structured effect were reported. Predictive posterior checks were conducted through predictive integral transformation values and observed versus fitted values. RESULTS The proportion of variance in the RR explained by a spatial effect ranged from 4.4% (male colorectal) to 19.2% (female breast). Lung cancer showed the greatest number of CHSAs with elevated risk (Nwomen = 50/218, Nmen = 44/218), representing 2357 total excess cases. The largest lung cancer RRs were 1.67 (95% CrI = 1.06-2.50; exceedance probability = 96%; cases = 13) among women and 2.49 (95% CrI = 2.14-2.88; exceedance probability = 100%; cases = 174) among men. Areas with small population sizes and extreme SIRs were generally smoothed towards the null (RR = 1.0). DISCUSSION We present a ready-to-use approach for small area cancer risk estimation and disease mapping using BYM2 and exceedance probabilities. We developed the smallareamapp R package, which provides a user-friendly interface through an R-Shiny application, for epidemiologists and surveillance experts to examine geographic variation in risk. These methods and tools can be used to estimate risk, generate hypotheses, and examine ecologic associations while adjusting for spatial dependency.
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Affiliation(s)
- Jonathan Simkin
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Trevor J. B. Dummer
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Anders C. Erickson
- Office of the Provincial Health Officer, Government of British Columbia, Victoria, BC, Canada
| | - Michael C. Otterstatter
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Ryan R. Woods
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Gina Ogilvie
- Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Women’s Health Research Institute, BC Women’s Hospital + Health Centre, Vancouver, BC, Canada
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Chiuchiolo C, van Niekerk J, Rue H. Joint posterior inference for latent Gaussian models with R-INLA. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2117813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Cristian Chiuchiolo
- CEMSE Division, Department of Statistics, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Janet van Niekerk
- CEMSE Division, Department of Statistics, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Håvard Rue
- CEMSE Division, Department of Statistics, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Humphreys JM, Srygley RB, Lawton D, Hudson AR, Branson DH. Grasshoppers exhibit asynchrony and spatial non-stationarity in response to the El Niño/Southern and Pacific Decadal Oscillations. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Sonnet V, Guidi L, Mouw CB, Puggioni G, Ayata S. Length, width, shape regularity, and chain structure: time series analysis of phytoplankton morphology from imagery. LIMNOLOGY AND OCEANOGRAPHY 2022; 67:1850-1864. [PMID: 36247385 PMCID: PMC9546331 DOI: 10.1002/lno.12171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/13/2021] [Accepted: 05/22/2022] [Indexed: 06/16/2023]
Abstract
Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time-series station. We analyzed a 2-yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year-round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter-annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.
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Affiliation(s)
- Virginie Sonnet
- CNRS, Laboratoire d'Océanographie de VillefrancheSorbonne UniversitéVillefranche‐sur‐MerFrance
- Graduate School of OceanographyUniversity of Rhode IslandNarragansettRhode Island
| | - Lionel Guidi
- CNRS, Laboratoire d'Océanographie de VillefrancheSorbonne UniversitéVillefranche‐sur‐MerFrance
| | - Colleen B. Mouw
- Graduate School of OceanographyUniversity of Rhode IslandNarragansettRhode Island
| | - Gavino Puggioni
- Department of Computer Science and StatisticsUniversity of Rhode IslandKingstonRhode Island
| | - Sakina‐Dorothée Ayata
- CNRS, Laboratoire d'Océanographie de VillefrancheSorbonne UniversitéVillefranche‐sur‐MerFrance
- Laboratoire d'Océanographie et du Climat, Institut Pierre Simon Laplace (LOCEAN, SU/CNRS/IRD/MNHN)Sorbonne UniversitéParisFrance
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Chaudhuri S, Juan P, Mateu J. Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation. J Appl Stat 2022; 50:3229-3250. [PMID: 37969892 PMCID: PMC10637209 DOI: 10.1080/02664763.2022.2104822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 07/09/2022] [Indexed: 10/16/2022]
Abstract
Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network. A Bayesian methodology using Integrated nested Laplace approximations with stochastic partial differential equations (SPDE) has been applied in the modeling process. As a novelty, we have introduced SPDE network triangulation to estimate the spatial autocorrelation restricted to the linear network. The resulting risk maps provide information to identify safe routes between source and destination points, and can be useful for accident prevention and multi-disciplinary road safety measures.
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Affiliation(s)
- Somnath Chaudhuri
- Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain
| | - Pablo Juan
- Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain
- IMAC, University Jaume I, Castellón, Spain
| | - Jorge Mateu
- Department of Mathematics, University Jaume I, Castellón, Spain
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Ogunsakin RE, Ginindza TG. Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19158886. [PMID: 35897258 PMCID: PMC9331550 DOI: 10.3390/ijerph19158886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/04/2022] [Accepted: 07/18/2022] [Indexed: 02/06/2023]
Abstract
Determining spatial links between disease risk and socio-demographic characteristics is vital in disease management and policymaking. However, data are subject to complexities caused by heterogeneity across host classes and space epidemic processes. This study aims to implement a spatially varying coefficient (SVC) model to account for non-stationarity in the effect of covariates. Using the South Africa general household survey, we study the provincial variation of people living with diabetes and hypertension risk through the SVC model. The people living with diabetes and hypertension risk are modeled using a logistic model that includes spatially unstructured and spatially structured random effects. Spatial smoothness priors for the spatially structured component are employed in modeling, namely, a Gaussian Markov random field (GMRF), a second-order random walk (RW2), and a conditional autoregressive (CAR) model. The SVC model is used to relax the stationarity assumption in which non-linear effects of age are captured through the RW2 and allow the mean effect to vary spatially using a CAR model. Results highlight a non-linear relationship between age and people living with diabetes and hypertension. The SVC models outperform the stationary models. The results suggest significant provincial differences, and the maps provided can guide policymakers in carefully exploiting the available resources for more cost-effective interventions.
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Affiliation(s)
- Ropo E. Ogunsakin
- Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa;
- Correspondence:
| | - Themba G. Ginindza
- Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa;
- Cancer & Infectious Diseases Epidemiology Research Unit (CIDERU), College of Health Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa
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Stringer A, Brown P, Stafford J. Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2099403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Alex Stringer
- Department of Statistics and Actuarial Science, University of Waterloo
| | - Patrick Brown
- Department of Statistical Sciences, University of Toronto, Centre for Global Health Research
| | - Jamie Stafford
- Department of Statistical Sciences, University of Toronto
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Spatial variation and risk factors of malaria and anaemia among children aged 0 to 59 months: a cross-sectional study of 2010 and 2015 datasets. Sci Rep 2022; 12:11498. [PMID: 35798952 PMCID: PMC9262914 DOI: 10.1038/s41598-022-15561-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 06/27/2022] [Indexed: 11/30/2022] Open
Abstract
Malaria and anaemia are common diseases that affect children, particularly in Africa. Studies on the risk associated with these diseases and their synergy are scanty. This work aims to study the spatial pattern of malaria and anaemia in Nigeria and adjust for their risk factors using separate models for malaria and anaemia. This study used Bayesian spatial models within the Integrated Nested Laplace Approach (INLA) to establish the relationship between malaria and anaemia. We also adjust for risk factors of malaria and anaemia and map the estimated relative risks of these diseases to identify regions with a relatively high risk of the diseases under consideration. We used data obtained from the Nigeria malaria indicator survey (NMIS) of 2010 and 2015. The spatial variability distribution of both diseases was investigated using the convolution model, Conditional Auto-Regressive (CAR) model, generalized linear mixed model (GLMM) and generalized linear model (GLM) for each year. The convolution and generalized linear mixed models (GLMM) showed the least Deviance Information Criteria (DIC) in 2010 for malaria and anaemia, respectively. The Conditional Auto-Regressive (CAR) and convolution models had the least DIC in 2015 for malaria and anaemia, respectively. This study revealed that children in rural areas had strong and significant odds of malaria and anaemia infection [2010; malaria: AOR = 1.348, 95% CI = (1.117, 1.627), anaemia: AOR = 1.455, 95% CI = (1.201, 1.7623). 2015; malaria: AOR = 1.889, 95% CI = (1.568, 2.277), anaemia: AOR = 1.440, 95% CI = (1.205, 1.719)]. Controlling the prevalence of malaria and anaemia in Nigeria requires the identification of a child’s location and proper confrontation of some socio-economic factors which may lead to the reduction of childhood malaria and anaemia infection.
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Rohleder S, Costa DD, Bozorgmehr PK. Area-level socioeconomic deprivation, non-national residency, and Covid-19 incidence: A longitudinal spatiotemporal analysis in Germany. EClinicalMedicine 2022; 49:101485. [PMID: 35719293 PMCID: PMC9189383 DOI: 10.1016/j.eclinm.2022.101485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Background Socioeconomic conditions affect the dynamics of the Covid-19 pandemic. We analysed the association between area-level socioeconomic deprivation, proportion of non-nationals, and incidence of Covid-19 infections in Germany. Methods Using linked nationally representative data at the level of 401 German districts from three waves of infection (January-2020 to May-2021), we fitted Bayesian spatiotemporal models to assess the association between socioeconomic deprivation, and proportion of non-nationals with Covid-19 incidence, controlling for age, sex, vaccination coverage, settlement structure, and spatial and temporal effects. We estimated risk ratios (RR) and corresponding 95% credible intervals (95% CrI). We further examined the deprivation domains (education, income, occupation), interactions between deprivation, sex and the proportion of non-nationals, and explored potential pathways from deprivation to Covid-19 incidence. Findings Covid-19 incidence risk was 15% higher (RR=1·15, 95%-CrI=1·06-1·24) in areas classified with the highest deprivation quintile (Q5) compared to the least deprived areas (Q1). Medium-low (Q2), medium (Q3), and medium-high (Q4) deprived districts showed 6% (1·06, 1·00-1·12), 8% (1·08, 1·01-1·15), and 5% (1·05, 0·98-1·13) higher risk, respectively, compared to the least deprived. Districts with higher proportion of non-nationals showed higher incidence risk compared to districts with lowest proportion, but the association weakened across the three waves. During the first wave, an inverse association was observed with highest incidence risk in least deprived areas (Q1). Deprivation interacted with sex, but not with the proportion of non-nationals. Interpretation Socioeconomic deprivation, and proportion of non-nationals are independently associated with the incidence of Covid-19. Regional planning of non-pharmaceutical interventions and vaccination strategies would benefit from consideration of area-level deprivation and non-national residency. Funding The study was funded by the German Ministry of Health (ZMV I 1 - 25 20 COR 410).
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Affiliation(s)
- Sven Rohleder
- Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Universitätsstraße 25, 33501 Bielefeld, Bielefeld, Germany
- Section Health Equity Studies & Migration, Department of General Practice and Health Services Research, University Hospital Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Heidelberg, Germany
| | - Dr. Diogo Costa
- Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Universitätsstraße 25, 33501 Bielefeld, Bielefeld, Germany
| | - Prof Kayvan Bozorgmehr
- Department of Population Medicine and Health Services Research, School of Public Health, Bielefeld University, Universitätsstraße 25, 33501 Bielefeld, Bielefeld, Germany
- Section Health Equity Studies & Migration, Department of General Practice and Health Services Research, University Hospital Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Heidelberg, Germany
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45
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Hay EM, McGee MD, Chown SL. Geographic range size and speciation in honeyeaters. BMC Ecol Evol 2022; 22:86. [PMID: 35768772 PMCID: PMC9245323 DOI: 10.1186/s12862-022-02041-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Darwin and others proposed that a species' geographic range size positively influences speciation likelihood, with the relationship potentially dependent on the mode of speciation and other contributing factors, including geographic setting and species traits. Several alternative proposals for the influence of range size on speciation rate have also been made (e.g. negative or a unimodal relationship with speciation). To examine Darwin's proposal, we use a range of phylogenetic comparative methods, focusing on a large Australasian bird clade, the honeyeaters (Aves: Meliphagidae). RESULTS We consider the influence of range size, shape, and position (latitudinal and longitudinal midpoints, island or continental species), and consider two traits known to influence range size: dispersal ability and body size. Applying several analytical approaches, including phylogenetic Bayesian path analysis, spatiophylogenetic models, and state-dependent speciation and extinction models, we find support for both the positive relationship between range size and speciation rate and the influence of mode of speciation. CONCLUSIONS Honeyeater speciation rate differs considerably between islands and the continental setting across the clade's distribution, with range size contributing positively in the continental setting, while dispersal ability influences speciation regardless of setting. These outcomes support Darwin's original proposal for a positive relationship between range size and speciation likelihood, while extending the evidence for the contribution of dispersal ability to speciation.
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Affiliation(s)
- Eleanor M Hay
- School of Biological Sciences, Monash University, Melbourne, VIC, 3800, Australia.
| | - Matthew D McGee
- School of Biological Sciences, Monash University, Melbourne, VIC, 3800, Australia
| | - Steven L Chown
- School of Biological Sciences, Monash University, Melbourne, VIC, 3800, Australia
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Helton JJ, Nelson EJ, Boutwell BB, Lewis RD, Rosenfeld R, Seon J. Aggregate-level Lead Exposure and Child Maltreatment. JOURNAL OF INTERPERSONAL VIOLENCE 2022; 37:NP10418-NP10428. [PMID: 33300389 DOI: 10.1177/0886260520980390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The purpose of this study was to examine the possible ecological association between aggregate blood lead levels (BLL) and rates of child maltreatment. To this end, we employed an ecologic study design, analyzing results from 59,645 child BLL tests between the years 1996 and 2007, and 6,640 substantiated maltreatment investigations from 2006 to 2016 in a large Midwest city. Separate Bayesian spatial Poisson conditional autoregressive (CAR) and Bayesian spatial zero-inflated Poisson CAR models were used to predict the occurrence of maltreatment.Bivariate results showed that aggregate rates of maltreatment increased as aggregate BLL increased. Multivariate results showed that medium-exposure BLL census tracts (OR = 1.38) and high-exposure BLL tracts (OR = 1.38) had increased odds of substantiated investigations for any maltreatment compared to low BLL census tracts even after controlling for crime rates, age of the housing stock, and concentrated disadvantage. Our findings, considered with prior research, continue to reveal a confluence of deleterious outcomes in areas where exposure to lead seems elevated. In this case, child maltreatment also appears to represent a macro-level correlate of aggregate lead exposure. Yet our results preclude any causal inference, and further research on the intersection of child maltreatment with environmental toxins is needed to determine if contaminant abatement should be considered as a possible maltreatment prevention strategy.
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Affiliation(s)
| | - Erik J Nelson
- Indiana University School of Public Health-Bloomington, Bloomington, IN, USA
| | | | | | | | - Jisuk Seon
- Washington University in St. Louis, MO, USA
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47
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Semenova E, Xu Y, Howes A, Rashid T, Bhatt S, Mishra S, Flaxman S. PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation. J R Soc Interface 2022; 19:20220094. [PMID: 35673858 PMCID: PMC9174721 DOI: 10.1098/rsif.2022.0094] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/12/2022] [Indexed: 01/31/2023] Open
Abstract
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.
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Affiliation(s)
| | - Yidan Xu
- University of Michigan, Ann Arbor, MI, USA
| | | | | | - Samir Bhatt
- Imperial College London, London, UK
- University of Copenhagen, Kobenhavn, Denmark
| | - Swapnil Mishra
- Imperial College London, London, UK
- University of Copenhagen, Kobenhavn, Denmark
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48
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Lee EC, Arab A, Colizza V, Bansal S. Spatial aggregation choice in the era of digital and administrative surveillance data. PLOS DIGITAL HEALTH 2022; 1:e0000039. [PMID: 36812505 PMCID: PMC9931313 DOI: 10.1371/journal.pdig.0000039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 04/11/2022] [Indexed: 11/18/2022]
Abstract
Traditional disease surveillance is increasingly being complemented by data from non-traditional sources like medical claims, electronic health records, and participatory syndromic data platforms. As non-traditional data are often collected at the individual-level and are convenience samples from a population, choices must be made on the aggregation of these data for epidemiological inference. Our study seeks to understand the influence of spatial aggregation choice on our understanding of disease spread with a case study of influenza-like illness in the United States. Using U.S. medical claims data from 2002 to 2009, we examined the epidemic source location, onset and peak season timing, and epidemic duration of influenza seasons for data aggregated to the county and state scales. We also compared spatial autocorrelation and tested the relative magnitude of spatial aggregation differences between onset and peak measures of disease burden. We found discrepancies in the inferred epidemic source locations and estimated influenza season onsets and peaks when comparing county and state-level data. Spatial autocorrelation was detected across more expansive geographic ranges during the peak season as compared to the early flu season, and there were greater spatial aggregation differences in early season measures as well. Epidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Users of non-traditional disease surveillance should carefully consider how to extract accurate disease signals from finer-scaled data for early use in disease outbreaks.
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Affiliation(s)
- Elizabeth C. Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Ali Arab
- Department of Mathematics and Statistics, Georgetown University, Washington, District of Columbia, United States of America
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique, Paris, France
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, District of Columbia, United States of America
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49
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Egbon OA, Belachew AM, Bogoni MA. Risk factors of concurrent malnutrition among children in Ethiopia: a bivariate spatial modeling approach. ALL LIFE 2022. [DOI: 10.1080/26895293.2022.2067251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
- Osafu Augustine Egbon
- Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Statistics, Universidade Federal de São Carlos, São Carlos, Brazil
| | - Asrat Mekonnen Belachew
- Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Mathematics, Ambo University, Ambo, Ethiopia
| | - Mariella Ananias Bogoni
- Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Statistics, Universidade Federal de São Carlos, São Carlos, Brazil
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50
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Nightingale ES, Abbott S, Russell TW, Lowe R, Medley GF, Brady OJ. The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices. BMC Public Health 2022; 22:716. [PMID: 35410184 PMCID: PMC8996221 DOI: 10.1186/s12889-022-13069-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/14/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths. METHODS We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA. RESULTS A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%. CONCLUSIONS Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.
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Affiliation(s)
- Emily S Nightingale
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK.
| | - Sam Abbott
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Timothy W Russell
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Barcelona Supercomputing Centre (BSC), Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Graham F Medley
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
| | - Oliver J Brady
- Centre for Mathematical Modelling of Infectious Disease (CMMID), London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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