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Tyagi R, Mittal S, Madan K, Pandey RM, Pandey A, Mohan A, Hadda V, Tiwari P, Guleria R. Association of air pollution and COVID-19 in India. Monaldi Arch Chest Dis 2023; 94. [PMID: 37325971 DOI: 10.4081/monaldi.2023.2537] [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: 01/29/2023] [Accepted: 06/06/2023] [Indexed: 06/17/2023] Open
Abstract
The COVID-19 pandemic has affected the world, leading to significant morbidity and mortality. Various meteorological parameters are considered essential for the viability and transmission of the virus. Multiple reports from various parts of the world suggest a correlation between the disease spread and air pollution severity. This study was carried out to identify the relationship between meteorological parameters, air pollution, and COVID-19 in New Delhi, one of the worst-affected states in India. We studied air pollution and meteorological parameters in New Delhi, India. We obtained data about COVID-19 occurrence, meteorological parameters, and air pollution indicators from various sources from April 1, 2020, until November 12, 2020. We performed correlational analysis and employed autoregressive distributed lag models to identify the relationship between COVID-19 cases, air pollution and meteorological parameters. We found a significant impact of particulate matter (PM) 2.5, PM10, and meteorological parameters on COVID-19. There was a significant positive correlation between daily COVID-19 cases and COVID-19-related deaths with PM2.5 and PM10 levels. Increasing temperature and wind speed were associated with a reduction in the number of cases, while increasing humidity was associated with increased cases. This study demonstrated a significant association between PM2.5 and PM10 and daily COVID-19 cases and COVID-19-related mortality. This knowledge will likely help us prepare well for the future and implement air pollution control measures for other airborne disease epidemics.
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Affiliation(s)
- Rahul Tyagi
- Department of Pulmonary, Critical Care, and Sleep Medicine, All India Institute of Medical Sciences, New Delhi; Department of Pulmonary Medicine, Army Institute of Cardiothoracic Sciences, Pune.
| | - Saurabh Mittal
- Department of Pulmonary, Critical Care, and Sleep Medicine, All India Institute of Medical Sciences, New Delhi.
| | - Karan Madan
- Department of Pulmonary, Critical Care, and Sleep Medicine, All India Institute of Medical Sciences, New Delhi.
| | | | - Anjali Pandey
- Department of Biostatistics, All India Institute of Medical Sciences, New Delhi.
| | - Anant Mohan
- Department of Pulmonary, Critical Care, and Sleep Medicine, All India Institute of Medical Sciences, New Delhi.
| | - Vijay Hadda
- Department of Pulmonary, Critical Care, and Sleep Medicine, All India Institute of Medical Sciences, New Delhi.
| | - Pawan Tiwari
- Department of Pulmonary, Critical Care, and Sleep Medicine, All India Institute of Medical Sciences, New Delhi.
| | - Randeep Guleria
- Department of Pulmonary, Critical Care, and Sleep Medicine, All India Institute of Medical Sciences, New Delhi.
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Šulc L, Gregor P, Kalina J, Mikeš O, Janoš T, Čupr P. City-scale assessment of long-term air quality impacts on the respiratory and cardiovascular health. Front Public Health 2022; 10:1006536. [PMID: 36438287 PMCID: PMC9687097 DOI: 10.3389/fpubh.2022.1006536] [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: 07/29/2022] [Accepted: 10/17/2022] [Indexed: 11/12/2022] Open
Abstract
Background The impact of the urban environment on human health is a contemporary subject of environmental research. Air pollution is often considered a leading environmental driver. However, a plethora of other factors within the urban exposome may be involved. At the same time, the resolution of spatial data is also an important facet to consider. Generally, systematic tools for accurate health risk assessment in the urban environment are missing or are not implemented. Methods The long-term impact of air quality (PM10, PM2.5, NO2, benzene, and SO2) on respiratory and cardiovascular health was assessed with a log-linear model. We used the most accurate health data in high city scale spatial resolution over the period 2010 to 2018. Selected external exposome parameters were also included in the analysis. Results Statistically significant associations between air pollution and the health of the urban population were found. The strongest association was between benzene and the incidence of bronchitis in the adult population [RR 1.552 95% CI (1.415-1.704) per 0.5 μg/m3 change in benzene concentration]. A similar relation was observed between NO2 and the same health condition [RR 1.483 95% CI (1.227-1.792) per 8.9 μg/m3 of change in NO2]. Other weaker associations were also found between asthma in children and PMs, NO2, or benzene. Cardiovascular-related hospitalizations in the general population were linked with NO2 [RR 1.218 95% CI (1.119-1.325) per 9.7 μg/m3 change in NO2]. The remaining pollutants were slightly less but still significantly associated with cardiovascular-related hospitalizations. Conclusion Our findings are mostly highly statistically significant (p ≤ 0.001) and are in line with current literature on the adverse effects of air pollution on the human population. The results highlight the need for continual improvements in air quality. We propose the implementation of this approach as a systematic tool for the investigation of possible health risks over a long period of time. However, further research involving other variables is an essential step toward understanding the complex urban exposome and its implications for human health. An increase in data spatial resolution is especially important in this respect as well as for improving city health risk management.
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Huang G, Brown PE, Fu SH, Shin HH. Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12525] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Guowen Huang
- Department of Statistical Sciences University of Toronto Toronto Ontario Canada
- Centre for Global Health Research St Michael’s Hospital Toronto Ontario Canada
| | - Patrick E. Brown
- Department of Statistical Sciences University of Toronto Toronto Ontario Canada
- Centre for Global Health Research St Michael’s Hospital Toronto Ontario Canada
| | - Sze Hang Fu
- Centre for Global Health Research St Michael’s Hospital Toronto Ontario Canada
| | - Hwashin Hyun Shin
- Environmental Health Science and Research Bureau Health Canada Ottawa Ontario Canada
- Department of Mathematics and Statistics Queen’s University Kingston Ontario Canada
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Okunlola OA, Oyeyemi OT, Lukman AF. Modeling the relationship between malaria prevalence and insecticide-treated bed net coverage in Nigeria using a Bayesian spatial generalized linear mixed model with a Leroux prior. Epidemiol Health 2021; 43:e2021041. [PMID: 34098626 PMCID: PMC8510838 DOI: 10.4178/epih.e2021041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/04/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES To evaluate malaria transmission in relation to insecticide-treated net (ITN) coverage in Nigeria. METHODS We used an exploratory analysis approach to evaluate variation in malaria transmission in relation to ITN distribution in 1,325 Demographic and Health Survey clusters in Nigeria. A Bayesian spatial generalized linear mixed model with a Leroux conditional autoregressive prior for the random effects was used to model the spatial and contextual variation in malaria prevalence and ITN distribution after adjusting for environmental variables. RESULTS Spatial smoothed maps showed the nationwide distribution of malaria and ITN. The distribution of ITN varied significantly across the 6 geopolitical zones (p<0.05). The North-East had the least ITN distribution (0.196±0.071), while ITN distribution was highest in the South-South (0.309±0.075). ITN coverage was also higher in rural areas (0.281±0.074) than in urban areas (0.240±0.096, p<0.05). The Bayesian hierarchical regression results showed a non-significant negative relationship between malaria prevalence and ITN coverage, but a significant spatial structured random effect and unstructured random effect. The correlates of malaria transmission included rainfall, maximum temperature, and proximity to water. CONCLUSIONS Reduction in malaria transmission was not significantly related to ITN coverage, although much could be achieved in attempts to curtail malaria transmission through enhanced ITN coverage. A multifaceted and integrated approach to malaria control is strongly advocated.
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Affiliation(s)
- Oluyemi A Okunlola
- Department of Mathematics, University of Medical Sciences, Ondo, Nigeria
| | - Oyetunde T Oyeyemi
- Department of Biological Sciences, University of Medical Sciences, Ondo, Nigeria
| | - Adewale F Lukman
- Department of Physical Sciences, Landmark University, Omu-Aran, Nigeria
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5
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Lubinda J, Haque U, Bi Y, Shad MY, Keellings D, Hamainza B, Moore AJ. Climate change and the dynamics of age-related malaria incidence in Southern Africa. ENVIRONMENTAL RESEARCH 2021; 197:111017. [PMID: 33766570 DOI: 10.1016/j.envres.2021.111017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 02/27/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
In the last decade, many malaria-endemic countries, like Zambia, have achieved significant reductions in malaria incidence among children <5 years old but face ongoing challenges in achieving similar progress against malaria in older age groups. In parts of Zambia, changing climatic and environmental factors are among those suspectedly behind high malaria incidence. Changes and variations in these factors potentially interfere with intervention program effectiveness and alter the distribution and incidence patterns of malaria differentially between young children and the rest of the population. We used parametric and non-parametric statistics to model the effects of climatic and socio-demographic variables on age-specific malaria incidence vis-à-vis control interventions. Linear regressions, mixed models, and Mann-Kendall tests were implemented to explore trends, changes in trends, and regress malaria incidence against environmental and intervention variables. Our study shows that while climate parameters affect the whole population, their impacts are felt most by people aged ≥5 years. Climate variables influenced malaria substantially more than mosquito nets and indoor residual spraying interventions. We establish that climate parameters negatively impact malaria control efforts by exacerbating the transmission conditions via more conducive temperature and rainfall environments, which are augmented by cultural and socioeconomic exposure mechanisms. We argue that an intensified communications and education intervention strategy for behavioural change specifically targeted at ≥5 aged population where incidence rates are increasing, is urgently required and call for further malaria stratification among the ≥5 age groups in the routine collection, analysis and reporting of malaria mortality and incidence data.
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Affiliation(s)
- Jailos Lubinda
- School of Geography and Environmental Sciences, Ulster University, Coleraine, UK; School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom; School of Nursing, Faculty of Life & Health Sciences, Jordanstown, Newtownabbey, United Kingdom.
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Centre, Fort Worth, TX, 76107, USA; Department of Geography, University of Florida, Gainesville, FL, USA; Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Yaxin Bi
- School of Computing, Ulster University, Jordanstown, Newtownabbey, UK
| | | | - David Keellings
- Department of Geography, University of Alabama, Tuscaloosa, AL, USA
| | - Busiku Hamainza
- Ministry of Health, National Malaria Elimination Center, Lusaka, Zambia
| | - Adrian J Moore
- School of Geography and Environmental Sciences, Ulster University, Coleraine, UK
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Huang G, Brown PE. Population-weighted exposure to air pollution and COVID-19 incidence in Germany. SPATIAL STATISTICS 2021; 41:100480. [PMID: 33163351 PMCID: PMC7606077 DOI: 10.1016/j.spasta.2020.100480] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 10/23/2020] [Accepted: 10/26/2020] [Indexed: 05/20/2023]
Abstract
Many countries have enforced social distancing to stop the spread of COVID-19. Within countries, although the measures taken by governments are similar, the incidence rate varies among areas (e.g., counties, cities). One potential explanation is that people in some areas are more vulnerable to the coronavirus disease because of their worsened health conditions caused by long-term exposure to poor air quality. In this study, we investigate whether long-term exposure to air pollution increases the risk of COVID-19 infection in Germany. The results show that nitrogen dioxide (NO 2 ) is significantly associated with COVID-19 incidence, with a 1 μ g m - 3 increase in long-term exposure to NO 2 increasing the COVID-19 incidence rate by 5.58% (95% credible interval [CI]: 3.35%, 7.86%). This result is consistent across various models. The analyses can be reproduced and updated routinely using public data sources and shared R code.
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Affiliation(s)
- Guowen Huang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Global Health Research, St Michael's Hospital, Toronto, ON, Canada
| | - Patrick E Brown
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Global Health Research, St Michael's Hospital, Toronto, ON, Canada
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Lee D, Robertson C, Ramsay C, Gillespie C, Napier G. Estimating the health impact of air pollution in Scotland, and the resulting benefits of reducing concentrations in city centres. Spat Spatiotemporal Epidemiol 2019; 29:85-96. [PMID: 31128634 DOI: 10.1016/j.sste.2019.02.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 02/19/2019] [Accepted: 02/23/2019] [Indexed: 11/18/2022]
Abstract
Air pollution continues to be a key health issue in Scotland, despite recent improvements in concentrations. The Scottish Government published the Cleaner Air For Scotland strategy in 2015, and will introduce Low Emission Zones (LEZs) in the four major cities (Aberdeen, Dundee, Edinburgh and Glasgow) by 2020. However, there is no epidemiological evidence quantifying the current health impact of air pollution in Scotland, which this paper addresses. Additionally, we estimate the health benefits of reducing concentrations in city centres where most LEZs are located. We focus on cardio-respiratory disease and total non-accidental mortality outcomes, linking them to concentrations of both particulate (PM10 and PM2.5) and gaseous (NO2 and NOx) pollutants. Our two main findings are that: (i) all pollutants exhibit significant associations with respiratory disease but not cardiovascular disease; and (ii) reducing concentrations in city centres with low resident populations only provides a small health benefit.
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Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, Scotland, United Kingdom.
| | - Chris Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Scotland, United Kingdom
| | - Colin Ramsay
- Health Protection Scotland, Scotland, United Kingdom
| | - Colin Gillespie
- Scottish Environment Protection Agency Scotland, Scotland, United Kingdom
| | - Gary Napier
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, Scotland, United Kingdom
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8
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Spatial Relationships between Urban Structures and Air Pollution in Korea. SUSTAINABILITY 2019. [DOI: 10.3390/su11020476] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Urban structures facilitate human activities and interactions but are also a main source of air pollutants; hence, investigating the relationship between urban structures and air pollution is crucial. The lack of an acceptable general model poses significant challenges to investigations on the underlying mechanisms, and this gap fuels our motivation to analyze the relationships between urban structures and the emissions of four air pollutants, including nitrogen oxides, sulfur oxides, and two types of particulate matter, in Korea. We first conduct exploratory data analysis to detect the global and local spatial dependencies of air pollutants and apply Bayesian spatial regression models to examine the spatial relationship between each air pollutant and urban structure covariates. In particular, we use population, commercial area, industrial area, park area, road length, total land surface, and gross regional domestic product per person as spatial covariates of interest. Except for park area and road length, most covariates have significant positive relationships with air pollutants ranging from 0 to 1, which indicates that urbanization does not result in a one-to-one negative influence on air pollution. Findings suggest that the government should consider the degree of urban structures and air pollutants by region to achieve sustainable development.
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Huang G, Lee D, Scott EM. Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty. Stat Med 2018; 37:1134-1148. [PMID: 29205447 PMCID: PMC5888175 DOI: 10.1002/sim.7570] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 08/15/2017] [Accepted: 11/02/2017] [Indexed: 01/07/2023]
Abstract
The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects.
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Affiliation(s)
- Guowen Huang
- School of Mathematics and StatisticsUniversity of GlasgowGlasgow G12 8SQUK
| | - Duncan Lee
- School of Mathematics and StatisticsUniversity of GlasgowGlasgow G12 8SQUK
| | - E. Marian Scott
- School of Mathematics and StatisticsUniversity of GlasgowGlasgow G12 8SQUK
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10
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Lin H, Liu T, Xiao J, Zeng W, Guo L, Li X, Xu Y, Zhang Y, Chang JJ, Vaughn MG, Qian ZM, Ma W. Hourly peak PM 2.5 concentration associated with increased cardiovascular mortality in Guangzhou, China. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2017; 27:333-338. [PMID: 27805624 DOI: 10.1038/jes.2016.63] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 08/23/2016] [Indexed: 06/06/2023]
Abstract
Hourly peak concentration may capture health effects of ambient fine particulate matter pollution (PM2.5) better than daily averages. We examined the associations of hourly peak concentration of PM2.5 with cardiovascular mortality in Guangzhou, China. We obtained daily data on cardiovascular mortality and hourly PM2.5 concentrations in Guangzhou from 19 January 2013 through 30 June 2015. Generalized additive models were applied to evaluate the associations with adjustment for potential confounding factors. Significant associations were found between hourly peak concentrations of PM2.5 and cardiovascular mortality, particularly from ischemic heart diseases (IHD) and cerebrovascular diseases (CBD). Every 10 μg/m3 increment of hourly peak PM2.5 at lag 03 day was associated with a 1.15% (95% CI: 0.67%, 1.63%); 1.02% (95% CI: 0.30%, 1.74%) and 1.09% (95% CI: 0.27%, 1.91%) increase in mortalities from total cardiovascular diseases, IHD and CBD, respectively. The effects remained after adjustment for daily mean PM2.5 and gaseous air pollutants, though there was a high correlation between PM2.5 peak and PM2.5 mean (correlation coefficient=0.95). No significant association was observed for acute myocardial infarction (AMI). In addition to daily mean concentration of PM2.5, hourly peak concentration of PM2.5 might be one important risk factor of cardiovascular mortality and should be considered as an important air pollution indicator when assessing the possible cardiovascular effects of PM2.5.
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Affiliation(s)
- Hualiang Lin
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Weilin Zeng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Lingchuan Guo
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yanjun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yonghui Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jen Jen Chang
- College for Public Health and Social Justice, Saint Louis University, Saint Louis, Missouri, USA
| | - Michael G Vaughn
- College for Public Health and Social Justice, Saint Louis University, Saint Louis, Missouri, USA
| | - Zhengmin Min Qian
- College for Public Health and Social Justice, Saint Louis University, Saint Louis, Missouri, USA
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
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Lee D, Mukhopadhyay S, Rushworth A, Sahu SK. A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health. Biostatistics 2017; 18:370-385. [PMID: 28025181 DOI: 10.1093/biostatistics/kxw048] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 10/11/2016] [Indexed: 11/14/2022] Open
Abstract
In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale.
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Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QW,
| | - Sabyasachi Mukhopadhyay
- School of Mathematics, University of Southampton, Building 54, Salisbury Road, Southampton SO17 1BJ, UK
| | - Alastair Rushworth
- Department of Mathematics and Statistics, University of Strathclyde, Livingston Tower, 26 Richmond Street, Glasgow G1 1XH, UK
| | - Sujit K Sahu
- School of Mathematics, University of Southampton, Building 54, Salisbury Road, Southampton SO17 1BJ, UK
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12
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Pannullo F, Lee D, Neal L, Dalvi M, Agnew P, O’Connor FM, Mukhopadhyay S, Sahu S, Sarran C. Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England. Environ Health 2017; 16:29. [PMID: 28347336 PMCID: PMC5368918 DOI: 10.1186/s12940-017-0237-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2016] [Accepted: 03/20/2017] [Indexed: 05/21/2023]
Abstract
BACKGROUND Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future. METHODS We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP). RESULTS NO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations. CONCLUSIONS NO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.
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Affiliation(s)
- Francesca Pannullo
- School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QW UK
| | - Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QW UK
| | - Lucy Neal
- Met Office, FitzRoy Road, Exeter, EX1 3PB UK
| | - Mohit Dalvi
- Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB UK
| | - Paul Agnew
- Met Office, FitzRoy Road, Exeter, EX1 3PB UK
| | | | | | - Sujit Sahu
- Mathematical Sciences, University of Southampton, Highfield, Southampton, SO17 1BJ UK
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How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging. Spat Spatiotemporal Epidemiol 2016; 18:53-62. [PMID: 27494960 PMCID: PMC4985538 DOI: 10.1016/j.sste.2016.04.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 02/16/2016] [Accepted: 04/01/2016] [Indexed: 11/22/2022]
Abstract
We explored the sensitivity of the pollution-health effect to three factors. Estimation of NO2, choice of deprivation and choice of spatial autocorrelation model. Choice of these factors leads to a wide variation in pollution-health effects. BMA is utilised to estimate an overall effect while accounting for model uncertainty. Overall, a positive but borderline pollution-health effect was obtained.
The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012.
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14
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Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia. Spat Spatiotemporal Epidemiol 2016; 18:13-23. [PMID: 27494956 DOI: 10.1016/j.sste.2016.02.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Revised: 02/16/2016] [Accepted: 02/23/2016] [Indexed: 11/24/2022]
Abstract
BACKGROUND Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality. METHODS We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics. RESULTS We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution. CONCLUSION Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.
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Lee D, Sarran C. Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies. ENVIRONMETRICS 2015; 26:477-487. [PMID: 27547047 PMCID: PMC4975605 DOI: 10.1002/env.2348] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Revised: 03/25/2015] [Accepted: 06/04/2015] [Indexed: 05/22/2023]
Abstract
The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, owing to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the functional form of the model to account for the spatial misalignment between the pollution and disease data, which causes within-area variation in the pollution data. These challenges have largely been ignored in existing long-term spatial air pollution and health studies, so here we propose a novel Bayesian hierarchical model that addresses both challenges and provide software to allow others to apply our model to their own data. The effectiveness of the proposed model is compared by simulation against a number of state-of-the-art alternatives proposed in the literature and is then used to estimate the impact of nitrogen dioxide and particulate matter concentrations on respiratory hospital admissions in a new epidemiological study in England in 2010 at the local authority level. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.
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Affiliation(s)
- Duncan Lee
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowU.K.
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16
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Pannullo F, Lee D, Waclawski E, Leyland AH. Improving spatial nitrogen dioxide prediction using diffusion tubes: A case study in West Central Scotland. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2015; 118:227-235. [PMID: 26435684 PMCID: PMC4567077 DOI: 10.1016/j.atmosenv.2015.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 07/09/2015] [Accepted: 08/03/2015] [Indexed: 05/30/2023]
Abstract
It has been well documented that air pollution adversely affects health, and epidemiological pollution-health studies utilise pollution data from automatic monitors. However, these automatic monitors are small in number and hence spatially sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for these epidemiological health studies. Nitrogen dioxide (NO2) diffusion tubes are also used to measure concentrations, and due to their lower cost compared to automatic monitors are much more prevalent. However, even combining both data sets still does not provide sufficient spatial coverage of NO2 for epidemiological studies, and modelled concentrations on a regular grid from atmospheric dispersion models are also available. This paper proposes the first modelling approach to using all three sources of NO2 data to make fine scale spatial predictions for use in epidemiological health studies. We propose a geostatistical fusion model that regresses combined NO2 concentrations from both automatic monitors and diffusion tubes against modelled NO2 concentrations from an atmospheric dispersion model in order to predict fine scale NO2 concentrations across our West Central Scotland study region. Our model exhibits a 47% improvement in fine scale spatial prediction of NO2 compared to using the automatic monitors alone, and we use it to predict NO2 concentrations across West Central Scotland in 2006.
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Affiliation(s)
- Francesca Pannullo
- MRC∣CSO Social and Public Health Science Unit, University of Glasgow, 200 Renfield Street, Glasgow, G2 3QB, UK
| | - Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QW, UK
| | | | - Alastair H. Leyland
- MRC∣CSO Social and Public Health Science Unit, University of Glasgow, 200 Renfield Street, Glasgow, G2 3QB, UK
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Huang G, Lee D, Scott M. An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: A case study of nitrogen dioxide concentrations in Scotland. Spat Spatiotemporal Epidemiol 2015; 14-15:63-74. [DOI: 10.1016/j.sste.2015.09.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Revised: 07/11/2015] [Accepted: 09/23/2015] [Indexed: 11/24/2022]
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18
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Rushworth A, Lee D, Mitchell R. A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London. Spat Spatiotemporal Epidemiol 2014; 10:29-38. [PMID: 25113589 DOI: 10.1016/j.sste.2014.05.001] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Revised: 02/05/2014] [Accepted: 05/06/2014] [Indexed: 11/30/2022]
Abstract
It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices.
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Affiliation(s)
- Alastair Rushworth
- School of Mathematics and Statistics, University Gardens, University of Glasgow, Glasgow G12 8QW, UK.
| | - Duncan Lee
- School of Mathematics and Statistics, University Gardens, University of Glasgow, Glasgow G12 8QW, UK
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19
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Testing constancy in monotone response models. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2013.10.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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20
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Morrison S, Fordyce FM, Scott EM. An initial assessment of spatial relationships between respiratory cases, soil metal content, air quality and deprivation indicators in Glasgow, Scotland, UK: relevance to the environmental justice agenda. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2014; 36:319-332. [PMID: 24203260 PMCID: PMC3938858 DOI: 10.1007/s10653-013-9565-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2012] [Accepted: 07/17/2013] [Indexed: 05/30/2023]
Abstract
There is growing interest in links between poor health and socio-environmental inequalities (e.g. inferior housing, crime and industrial emissions) under the environmental justice agenda. The current project assessed associations between soil metal content, air pollution (NO2/PM10) and deprivation and health (respiratory case incidence) across Glasgow. This is the first time that both chemical land quality and air pollution have been assessed citywide in the context of deprivation and health for a major UK conurbation. Based on the dataset 'averages' for intermediate geography areas, generalised linear modelling of respiratory cases showed significant associations with overall soil metal concentration (p = 0.0367) and with deprivation (p < 0.0448). Of the individual soil metals, only nickel showed a significant relationship with respiratory cases (p = 0.0056). Whilst these associations could simply represent concordant lower soil metal concentrations and fewer respiratory cases in the rural versus the urban environment, they are interesting given (1) possible contributions from soil to air particulate loading and (2) known associations between airborne metals like nickel and health. This study also demonstrated a statistically significant correlation (-0.213; p < 0.05) between soil metal concentration and deprivation across Glasgow. This highlights the fact that despite numerous regeneration programmes, the legacy of environmental pollution remains in post-industrial areas of Glasgow many decades after heavy industry has declined. Further epidemiological investigations would be required to determine whether there are any causal links between soil quality and population health/well-being. However, the results of this study suggest that poor soil quality warrants greater consideration in future health and socio-environmental inequality assessments.
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Affiliation(s)
- S. Morrison
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow, G12 8QW Scotland, UK
| | - F. M. Fordyce
- British Geological Survey, West Mains Road, Edinburgh, EH9 3LA Scotland, UK
| | - E. Marian Scott
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow, G12 8QW Scotland, UK
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Lee D, Mitchell R. Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies. Stat Methods Med Res 2014; 23:488-506. [PMID: 24648100 PMCID: PMC4272194 DOI: 10.1177/0962280214527384] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Estimating the long-term health impact of air pollution using an ecological spatio-temporal study design is a challenging task, due to the presence of residual spatio-temporal autocorrelation in the health counts after adjusting for the covariate effects. This autocorrelation is commonly modelled by a set of random effects represented by a Gaussian Markov random field (GMRF) prior distribution, as part of a hierarchical Bayesian model. However, GMRF models typically assume the random effects are globally smooth in space and time, and thus are likely to be collinear to any spatially and temporally smooth covariates such as air pollution. Such collinearity leads to poor estimation performance of the estimated fixed effects, and motivated by this epidemiological problem, this paper proposes new GMRF methodology to allow for localised spatio-temporal smoothing. This means random effects that are either geographically or temporally adjacent are allowed to be autocorrelated or conditionally independent, which allows more flexible autocorrelation structures to be represented. This increased flexibility results in improved fixed effects estimation compared with global smoothing models, which is evidenced by our simulation study. The methodology is then applied to the motivating study investigating the long-term effects of air pollution on respiratory ill health in Greater Glasgow, Scotland between 2007 and 2011.
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Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Richard Mitchell
- Institute for Health and Wellbeing, University of Glasgow, Glasgow, UK
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22
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Lee D, Rushworth A, Sahu SK. A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution. Biometrics 2014; 70:419-29. [PMID: 24571082 PMCID: PMC4282098 DOI: 10.1111/biom.12156] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 01/01/2014] [Accepted: 01/01/2014] [Indexed: 11/28/2022]
Abstract
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models.
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Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QW, UK
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Modeling respiratory illnesses with change point: A lesson from the SARS epidemic in Hong Kong. Comput Stat Data Anal 2013; 57:589-599. [PMID: 32362698 PMCID: PMC7185837 DOI: 10.1016/j.csda.2012.07.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Revised: 05/24/2012] [Accepted: 07/31/2012] [Indexed: 11/23/2022]
Abstract
It is generally agreed that respiratory disease is closely related to ambient air quality and weather conditions. Besides, hygiene related factors such as the public health measures by the government and possible personal awareness in the community can also affect the spread of infectious respiratory diseases. However, there is no quantitative support for this conclusion, because of lack of quality data. The severe acute respiratory syndrome (or SARS) outbreak in 2003 triggered strict public health measures and personal awareness in the prevention of infectious respiratory diseases, providing us an opportunity to quantify the impact of hygiene related factors in the spread of the disease. In this paper, we model the number of the respiratory illnesses by a semiparametric model which models the environmental and weather impacts using a multiple index model and the impact of other public health measures and possible personal awareness using a growth curve with jump. Using data from Hong Kong, we found that public health measures contributed to about 39% of reduction in the number of respiratory illnesses during the SARS period. However, the impact of hygienically related factors eventually fades as time passes. The results provide indirect quantitative support to the usefulness of governmental campaigns to arouse the awareness of the public in staying away from transmission of respiratory diseases during the full outbreak of the disease. The results also show the fast fading of alertness of Hong Kong people towards the epidemic. Furthermore, our model also offers a way to model the impacts of environmental factors on respiratory diseases, when the data contains the effect of human intervention, by introducing the change point and growth curve to remove such an effect.
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24
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Lee D. Using spline models to estimate the varying health risks from air pollution across Scotland. Stat Med 2012; 31:3366-78. [DOI: 10.1002/sim.5420] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2011] [Revised: 03/20/2012] [Accepted: 03/24/2012] [Indexed: 11/06/2022]
Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University Gardens; University of Glasgow; Glasgow U.K. G12 8QQ
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25
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Milewski I. Identifying at-risk communities for action on cancer prevention: a case study in new brunswick (Canada) communities. New Solut 2012; 22:79-107. [PMID: 22436208 DOI: 10.2190/ns.22.1.f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Health statistics reported by large geographic area such as province, state, county or health region offer little insight into disease conditions at the community level where people live and work, where occupational and environmental exposures occur, and where industrial emissions are often concentrated. This study investigated overall patterns of cancer incidence and socioeconomic status (SES) among 14 communities in the province of New Brunswick (Canada). A multivariate ordination technique, hierarchical clustering, and permutation procedures were used to identify and test significance of community clusters and whether the overall pattern of SES was correlated with patterns of cancer among communities. Communities with significantly high or significantly low overall rates of cancers were identified, patterns that were not related to SES. The potential influence of age, small populations, diagnostic screening, smoking and environmental risk factors contributing to locally elevated cancer rates are discussed. Cancer incidence reported at smaller spatial scales provides health officials and researchers with a basis for identifying communities potentially at-risk and aids in the development of appropriate community-based risk reduction actions and cancer prevention.
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Affiliation(s)
- Inka Milewski
- Conservation Council of New Brunswick, 180 St. John Street Fredericton, New Brunswick, E3B 4A9, Canada.
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Parenteau MP, Sawada MC. The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 and respiratory health. Int J Health Geogr 2011; 10:58. [PMID: 22040001 PMCID: PMC3245430 DOI: 10.1186/1476-072x-10-58] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2011] [Accepted: 10/31/2011] [Indexed: 11/24/2022] Open
Abstract
Background Many Canadian population health studies, including those focusing on the relationship between exposure to air pollution and health, have operationalized neighbourhoods at the census tract scale. At the same time, the conceptualization of place at the local scale is one of the weakest theoretical aspects in health geography. The modifiable areal unit problem (MAUP) raises issues when census tracts are used as neighbourhood proxies, and no other alternate spatial structure is used for sensitivity analysis. In the literature, conclusions on the relationship between NO2 and health outcomes are divided, and this situation may in part be due to the selection of an inappropriate spatial structure for analysis. Here, we undertake an analysis of NO2 and respiratory health in Ottawa, Canada using three different spatial structures in order to elucidate the effects that the spatial unit of analysis can have on analytical results. Results Using three different spatial structures to examine and quantify the relationship between NO2 and respiratory morbidity, we offer three main conclusions: 1) exploratory spatial analytical methods can serve as an indication of the potential effect of the MAUP; 2) OLS regression results differ significantly using different spatial representations, and this could be a contributing factor to the lack of consensus in studies that focus on the relation between NO2 and respiratory health at the area-level; and 3) the use of three spatial representations confirms no measured effect of NO2 exposure on respiratory health in Ottawa. Conclusions Area units used in population health studies should be delineated so as to represent the a priori scale of the expected scale interaction between neighbourhood processes and health. A thorough understanding of the role of the MAUP in the study of the relationship between NO2 and respiratory health is necessary for research into disease pathways based on statistical models, and for decision-makers to assess the scale at which interventions will have maximum benefit. In general, more research on the role of spatial representation in health studies is needed.
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Affiliation(s)
- Marie-Pierre Parenteau
- Laboratory for Applied Geomatics and GIS Science, Department of Geography, University of Ottawa, Canada
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Lee D. A comparison of conditional autoregressive models used in Bayesian disease mapping. Spat Spatiotemporal Epidemiol 2011; 2:79-89. [PMID: 22749587 DOI: 10.1016/j.sste.2011.03.001] [Citation(s) in RCA: 125] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2010] [Revised: 02/14/2011] [Accepted: 03/07/2011] [Indexed: 11/29/2022]
Abstract
Disease mapping is the area of epidemiology that estimates the spatial pattern in disease risk over an extended geographical region, so that areas with elevated risk levels can be identified. Bayesian hierarchical models are typically used in this context, which represent the risk surface using a combination of available covariate data and a set of spatial random effects. These random effects are included to model any overdispersion or spatial correlation in the disease data, that has not been accounted for by the available covariate information. The random effects are typically modelled by a conditional autoregressive (CAR) prior distribution, and a number of alternative specifications have been proposed. This paper critiques four of the most common models within the CAR class, and assesses their appropriateness via a simulation study. The four models are then applied to a new study mapping cancer incidence in Greater Glasgow, Scotland, between 2001 and 2005.
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Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University Gardens, University of Glasgow, Glasgow G12 8QW, United Kingdom.
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Lee D, Neocleous T. Bayesian quantile regression for count data with application to environmental epidemiology. J R Stat Soc Ser C Appl Stat 2010. [DOI: 10.1111/j.1467-9876.2010.00725.x] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Paciorek CJ. The importance of scale for spatial-confounding bias and precision of spatial regression estimators. Stat Sci 2010; 25:107-125. [PMID: 21528104 DOI: 10.1214/10-sts326] [Citation(s) in RCA: 120] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Residuals in regression models are often spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When unmeasured confounding introduces spatial structure into the residuals, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual: one can reduce bias by fitting a spatial model only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals are independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.
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Affiliation(s)
- Christopher J Paciorek
- Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115; Department of Statistics, 367 Evans Hall, University of California, Berkeley, California 94720, url: www.biostat.harvard.edu/~paciorek
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