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Fisher L, Wakefield J, Bauer C, Self S. Time series modeling of pathogen-specific disease probabilities with subsampled data. Biometrics 2017; 73:283-293. [PMID: 27378138 PMCID: PMC5224700 DOI: 10.1111/biom.12560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 04/01/2016] [Accepted: 05/01/2016] [Indexed: 11/26/2022]
Abstract
Many diseases arise due to exposure to one of multiple possible pathogens. We consider the situation in which disease counts are available over time from a study region, along with a measure of clinical disease severity, for example, mild or severe. In addition, we suppose a subset of the cases are lab tested in order to determine the pathogen responsible for disease. In such a context, we focus interest on modeling the probabilities of disease incidence given pathogen type. The time course of these probabilities is of great interest as is the association with time-varying covariates such as meteorological variables. In this set up, a natural Bayesian approach would be based on imputation of the unsampled pathogen information using Markov Chain Monte Carlo but this is computationally challenging. We describe a practical approach to inference that is easy to implement. We use an empirical Bayes procedure in a first step to estimate summary statistics. We then treat these summary statistics as the observed data and develop a Bayesian generalized additive model. We analyze data on hand, foot, and mouth disease (HFMD) in China in which there are two pathogens of primary interest, enterovirus 71 (EV71) and Coxackie A16 (CA16). We find that both EV71 and CA16 are associated with temperature, relative humidity, and wind speed, with reasonably similar functional forms for both pathogens. The important issue of confounding by time is modeled using a penalized B-spline model with a random effects representation. The level of smoothing is addressed by a careful choice of the prior on the tuning variance.
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Affiliation(s)
- Leigh Fisher
- Department of Biostatistics, University of Washington,
Seattle, WA
| | - Jon Wakefield
- Department of Biostatistics, University of Washington,
Seattle, WA
- Department of Statistics, University of Washington,
Seattle, WA
| | - Cici Bauer
- Department of Biostatistics, Brown University, Providence,
Rhode Island, USA
| | - Steve Self
- Vaccine and Infectious Disease Division, Fred Hutchinson
Cancer Research Center, Seattle, WA
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Koh WM, Bogich T, Siegel K, Jin J, Chong EY, Tan CY, Chen MIC, Horby P, Cook AR. The Epidemiology of Hand, Foot and Mouth Disease in Asia: A Systematic Review and Analysis. Pediatr Infect Dis J 2016; 35:e285-300. [PMID: 27273688 PMCID: PMC5130063 DOI: 10.1097/inf.0000000000001242] [Citation(s) in RCA: 159] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/22/2016] [Indexed: 12/22/2022]
Abstract
CONTEXT Hand, foot and mouth disease (HFMD) is a widespread pediatric disease caused primarily by human enterovirus 71 (EV-A71) and Coxsackievirus A16 (CV-A16). OBJECTIVE This study reports a systematic review of the epidemiology of HFMD in Asia. DATA SOURCES PubMed, Web of Science and Google Scholar were searched up to December 2014. STUDY SELECTION Two reviewers independently assessed studies for epidemiologic and serologic information about prevalence and incidence of HFMD against predetermined inclusion/exclusion criteria. DATA EXTRACTION Two reviewers extracted answers for 8 specific research questions on HFMD epidemiology. The results are checked by 3 others. RESULTS HFMD is found to be seasonal in temperate Asia with a summer peak and in subtropical Asia with spring and fall peaks, but not in tropical Asia; evidence of a climatic role was identified for temperate Japan. Risk factors for HFMD include hygiene, age, gender and social contacts, but most studies were underpowered to adjust rigorously for confounding variables. Both community-level and school-level transmission have been implicated, but their relative importance for HFMD is inconclusive. Epidemiologic indices are poorly understood: No supporting quantitative evidence was found for the incubation period of EV-A71; the symptomatic rate of EV-A71/Coxsackievirus A16 infection was from 10% to 71% in 4 studies; while the basic reproduction number was between 1.1 and 5.5 in 3 studies. The uncertainty in these estimates inhibits their use for further analysis. LIMITATIONS Diversity of study designs complicates attempts to identify features of HFMD epidemiology. CONCLUSIONS Knowledge on HFMD remains insufficient to guide interventions such as the incorporation of an EV-A71 vaccine in pediatric vaccination schedules. Research is urgently needed to fill these gaps.
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Affiliation(s)
- Wee Ming Koh
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Tiffany Bogich
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Karen Siegel
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Jing Jin
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Elizabeth Y. Chong
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Chong Yew Tan
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Mark IC Chen
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Peter Horby
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
| | - Alex R. Cook
- From the Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore; Standard Analytics, New York, New York; Rollins School of Public Health, Emory University, Atlanta, Georgia; Duke-NUS Graduate Medical School, Singapore; Communicable Disease Centre, Tan Tock Seng Hospital, Singapore; Nuffield Department of Medicine, University of Oxford, United Kingdom; and Yale-NUS College, National University of Singapore, Singapore
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Bauer C, Wakefield J, Rue H, Self S, Feng Z, Wang Y. Bayesian penalized spline models for the analysis of spatio-temporal count data. Stat Med 2015; 35:1848-65. [PMID: 26530705 DOI: 10.1002/sim.6785] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 10/02/2015] [Accepted: 10/10/2015] [Indexed: 11/11/2022]
Abstract
In recent years, the availability of infectious disease counts in time and space has increased, and consequently, there has been renewed interest in model formulation for such data. In this paper, we describe a model that was motivated by the need to analyze hand, foot, and mouth disease surveillance data in China. The data are aggregated by geographical areas and by week, with the aims of the analysis being to gain insight into the space-time dynamics and to make short-term predictions, which will aid in the implementation of public health campaigns in those areas with a large predicted disease burden. The model we develop decomposes disease-risk into marginal spatial and temporal components and a space-time interaction piece. The latter is the crucial element, and we use a tensor product spline model with a Markov random field prior on the coefficients of the basis functions. The model can be formulated as a Gaussian Markov random field and so fast computation can be carried out using the integrated nested Laplace approximation approach. A simulation study shows that the model can pick up complex space-time structure and our analysis of hand, foot, and mouth disease data in the central north region of China provides new insights into the dynamics of the disease.
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Affiliation(s)
- Cici Bauer
- Department of Biostatistics, Brown University, Providence, RI, U.S.A
| | - Jon Wakefield
- Department of Statistics, University of Washington, Seattle, WA, U.S.A
| | - Håvard Rue
- Norwegian University of Science and Technology, Trondheim, Norway
| | - Steve Self
- Fred Hutchinson Cancer Research Center, Seattle, WA, U.S.A
| | - Zijian Feng
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yu Wang
- Chinese Center for Disease Control and Prevention, Beijing, China
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Determinants of the incidence of hand, foot and mouth disease in China using geographically weighted regression models. PLoS One 2012; 7:e38978. [PMID: 22723913 PMCID: PMC3377651 DOI: 10.1371/journal.pone.0038978] [Citation(s) in RCA: 89] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2011] [Accepted: 05/15/2012] [Indexed: 11/29/2022] Open
Abstract
Background Over the past two decades, major epidemics of hand, foot, and mouth disease (HFMD) have occurred throughout most of the West-Pacific Region countries, causing thousands of deaths among children. However, few studies have examined potential determinants of the incidence of HFMD. Methods Reported HFMD cases from 2912 counties in China were obtained for May 2008. The monthly HFMD cumulative incidence was calculated for children aged 9 years and younger. Child population density (CPD) and six climate factors (average-temperature [AT], average-minimum-temperature [ATmin], average-maximum-temperature [ATmax], average-temperature-difference [ATdiff], average-relative-humidity [ARH], and monthly precipitation [MP]) were selected as potential explanatory variables for the study. Geographically weighted regression (GWR) models were used to explore the associations between the selected factors and HFMD incidence at county level. Results There were 176,111 HFMD cases reported in the studied counties. The adjusted monthly cumulative incidence by county ranged from 0.26 cases per 100,000 children to 2549.00 per 100,000 children. For local univariate GWR models, the percentage of counties with statistical significance (p<0.05) between HFMD incidence and each of the seven factors were: CPD 84.3%, ATmax 54.9%, AT 57.8%, ATmin 61.2%, ARH 54.4%, MP 50.3%, and ATdiff 51.6%. The R2 for the seven factors’ univariate GWR models are CPD 0.56, ATmax 0.53, AT 0.52, MP 0.51, ATmin 0.52, ARH 0.51, and ATdiff 0.51, respectively. CPD, MP, AT, ARH and ATdiff were further included in the multivariate GWR model, with R2 0.62, and all counties show statistically significant relationship. Conclusion Child population density and climate factors are potential determinants of the HFMD incidence in most areas in China. The strength and direction of association between these factors and the incidence of HFDM is spatially heterogeneous at the local geographic level, and child population density has a greater influence on the incidence of HFMD than the climate factors.
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Wang Y, Feng Z, Yang Y, Self S, Gao Y, Longini IM, Wakefield J, Zhang J, Wang L, Chen X, Yao L, Stanaway JD, Wang Z, Yang W. Hand, foot, and mouth disease in China: patterns of spread and transmissibility. Epidemiology 2011; 22:781-92. [PMID: 21968769 PMCID: PMC3246273 DOI: 10.1097/ede.0b013e318231d67a] [Citation(s) in RCA: 177] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND There were large outbreaks of hand, foot, and mouth disease in both 2008 and 2009 in China. METHODS Using the national surveillance data since 2 May 2008, we summarized the epidemiologic characteristics of the recent outbreaks. Using a susceptible-infectious-recovered transmission model, we evaluated the transmissibility of the disease and potential risk factors. RESULTS Children ages 1.0 to 2.9 years were the most susceptible to hand, foot, and mouth disease (odds ratios [OR] >2.3 as compared with other age-groups). Infant cases had the highest incidences of severe disease (ORs >1.4) and death (ORs >2.4), as well as the longest delay from symptom onset to diagnosis (2.3 days). Boys were more susceptible than girls (OR = 1.56 [95% confidence interval = 1.56-1.57]). A 1-day delay in diagnosis was associated with increases in the odds of severe disease by 40% (39%-42%) and in the odds of death by 54% (44%-65%). Compared with Coxsackie A16, enterovirus 71 is more strongly associated with severe disease (OR = 16 [13-18]) and death (OR = 40 [13-127]). The estimated local effective reproductive numbers among prefectures ranged from 1.4 to 1.6 (median = 1.4) in spring and stayed below 1.2 in other seasons. A higher risk of transmission was associated with temperatures in the range of 70° F to 80°F, higher relative humidity, higher [corrected] wind speed, more precipitation, greater population density, and [corrected] periods during which schools were open. CONCLUSION Hand, foot, and mouth disease is a moderately transmittable infectious disease, mainly among preschool children. Enterovirus 71 was responsible for most severe cases and fatalities. Mixing of asymptomatically infected children in schools might have contributed to spread the of infection. Timely diagnosis may be [corrected] key to reducing the high mortality rate in infants.
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Affiliation(s)
- Yu Wang
- Chinese Center for Disease Control and Prevention
| | - Zijian Feng
- Chinese Center for Disease Control and Prevention
| | - Yang Yang
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
| | - Steve Self
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
| | - Yongjun Gao
- Chinese Center for Disease Control and Prevention
| | - Ira M. Longini
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
| | | | - Jing Zhang
- Chinese Center for Disease Control and Prevention
| | - Liping Wang
- Chinese Center for Disease Control and Prevention
| | - Xi Chen
- Department of Statistics, University of Washington
| | - Lena Yao
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
| | | | - Zijun Wang
- Chinese Center for Disease Control and Prevention
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Mao LX, Wu B, Bao WX, Han FA, Xu L, Ge QJ, Yang J, Yuan ZH, Miao CH, Huang XX, Zhang C, Xu H. Epidemiology of hand, foot, and mouth disease and genotype characterization of Enterovirus 71 in Jiangsu, China. J Clin Virol 2010; 49:100-4. [PMID: 20719557 DOI: 10.1016/j.jcv.2010.07.009] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2010] [Revised: 07/06/2010] [Accepted: 07/20/2010] [Indexed: 11/25/2022]
Abstract
BACKGROUND In the spring of 2008, an EV71-caused hand, foot, and mouth disease (HFMD) outbreak occurred in Fuyang city, Anhui Province, China. Jiangsu Province that borders Auhui to the east is presumed as a key station for the spread of EV71 to other regions of the Yangtze River Delta. OBJECTIVES To investigate the HFMD prevalence in Zhenjiang city of Jiangsu from May 2008 to October 2009, and the epidemic origin of EV71 circulating in Jiangsu. STUDY DESIGN During May 2008 and October 2009, a total of 6324 HFMD cases in Zhenjiang, Jiangsu, were investigated. Sixty throat specimens were randomly selected from different patients, and 28 nucleotide sequences of EV71 VP1 regions were successfully determined by RT-nested-PCR and sequencing. EV71 genotypes were characterized by phylogenetic analyses. RESULTS The incidence rate of HFMD was highest in the period of March-July and in the 1-4 years old age groups. Intriguingly, there was a slight predominance for boys and for children living in rural areas in HFMD infection. Phylogenetic analyses indicated that all Jiangsu EV71 strains and most China strains belonged to subgenotype C4a. CONCLUSION The C4a was the most prominent EV71 subgenotype circulating in China. Routine HFMD surveillance should be focused on the period of March-July, and more prevention efforts should be aimed at 1-4 years old children. Moreover, government efforts are urgently needed to improve public health condition and medical service quality in rural areas.
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Affiliation(s)
- Ling-Xiang Mao
- School of Medical Science and Laboratory Medicine, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu 212013, China
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