Mapping disability-adjusted life years: a Bayesian hierarchical model framework for burden of disease and injury assessment

This paper presents a Bayesian disability-adjusted life year (DALY) methodology for spatial and spatiotemporal analyses of disease and/or injury burden. A Bayesian disease mapping model framework, which blends together spatial modelling, shared-component modelling (SCM), temporal modelling, ecological modelling, and non-linear modelling, is developed for small-area DALY estimation and inference. In particular, we develop a model framework that enables SCM as well as multivariate CAR modelling of non-fatal and fatal disease or injury rates and facilitates spline smoothing for non-linear modelling of temporal rate and risk trends. Using British Columbia (Canada) hospital admission-separation data and vital statistics mortality data on non-fatal and fatal road traffic injuries to male population age 20-39 for year 1991-2000 and for 84 local health areas and 16 health service delivery areas, spatial and spatiotemporal estimation and inference on years of life lost due to premature death, years lived with disability, and DALYs are presented. Fully Bayesian estimation and inference, with Markov chain Monte Carlo implementation, are illustrated. We present a methodological framework within which the DALY and the Bayesian disease mapping methodologies interface and intersect. Its development brings the relative importance of premature mortality and disability into the assessment of community health and health needs in order to provide reliable information and evidence for community-based public health surveillance and evaluation, disease and injury prevention, and resource provision.     

Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture

Routine surveillance of notifiable infectious diseases gives rise to daily or weekly counts of reported cases stratified by region and age group. From a public health perspective, forecasts of infectious disease spread are of central importance. We argue that such forecasts need to properly incorporate the attached uncertainty, so they should be probabilistic in nature. However, forecasts also need to take into account temporal dependencies inherent to communicable diseases, spatial dynamics through human travel and social contact patterns between age groups. We describe a multivariate time series model for weekly surveillance counts on norovirus gastroenteritis from the 12 city districts of Berlin, in six age groups, from week 2011/27 to week 2015/26. The following year (2015/27 to 2016/26) is used to assess the quality of the predictions. Probabilistic forecasts of the total number of cases can be derived through Monte Carlo simulation, but first and second moments are also available analytically. Final size forecasts as well as multivariate forecasts of the total number of cases by age group, by district and by week are compared across different models of varying complexity. This leads to a more general discussion of issues regarding modelling, prediction and evaluation of public health surveillance data. Copyright © 2017 John Wiley & Sons, Ltd.     

Modeling of under-detection of cases in disease surveillance

PURPOSE: Accurate epidemiological surveillance of leprosy is a matter of international public health concern. It often suffers, however, from potential problems of under-registration of reported cases, particularly in poorer and more socially deprived areas. Such problems also apply in the surveillance of many other communicable or transmissible diseases. We develop a Bayesian model for small-area disease rates that allows for censoring of case detection in suspect districts and can therefore be used to estimate under-reporting of cases in a given study region. METHODS: Such methods are applied to leprosy incidence in a municipality of Pernambuco State in North Eastern Brazil, using a social deprivation indicator as the basis for considering data from certain districts to be censored. The time period we consider was immediately prior to an extension of the coverage and efficacy of the control program and model predictions concerning under reporting can therefore be compared with more reliable data subsequently collected from the same region. RESULTS: The proposed method produces informative estimates of under detection of leprosy cases in the defined study region and these estimates compare well, both in size and in geographical location, with the numbers of cases subsequently detected. CONCLUSIONS: As illustrated by the application discussed in this article, the proposed model provides a general tool that may be used in spatial epidemiological surveillance situations where the available data is suspected to contain significant under-registrations of cases in certain geographical areas.     

Spatial aberration vs. geographical substance: representing place in public health surveillance

Public health surveillance involves the routine and ongoing collection, analysis and dissemination of health information for a variety of stakeholders—including both public health officials and the public. Much of the current focus of public health surveillance is on detecting aberrations in space—largely inspired by concerns about bioterrorism and newly emerging infectious diseases. We argue that the current focus on spatial aberrations has limited the development of public health surveillance by excluding a more explicit geographical understanding and representation of place. A more place-focused public health surveillance could represent geography in ways that are useful to a broader audience, provide information on the social and physical contexts related to health, facilitate a better understanding of health inequalities, and can benefit from local knowledge. Geographers can make important contributions to public health practice by contributing to more meaningful definitions of place in the design and operation of public health surveillance systems.     

Regression B-spline smoothing in Bayesian disease mapping: with an application to patient safety surveillance

In the context of Bayesian disease mapping, recent literature presents generalized linear mixed models that engender spatial smoothing. The methods assume spatially varying random effects as a route to partially pooling data and 'borrowing strength' in small-area estimation. When spatiotemporal disease rates are available for sequential risk mapping of several time periods, the 'smoothing' issue may be explored by considering spatial smoothing, temporal smoothing and spatiotemporal interaction. In this paper, these considerations are motivated and explored through development of a Bayesian semiparametric disease mapping model framework which facilitates temporal smoothing of rates and relative risks via regression B-splines with mixed-effect representation of coefficients. Specifically, we develop spatial priors such as multivariate Gaussian Markov random fields and non-spatial priors such as unstructured multivariate Gaussian distributions and illustrate how time trends in small-area relative risks may be explored by splines which vary in either a spatially structured or unstructured manner. In particular, we show that with suitable prior specifications for the random effects ensemble, small-area relative risk trends may be fit by 'spatially varying' or randomly varying B-splines. A recently developed Bayesian hierarchical model selection criterion, the deviance information criterion, is used to assess the trade-off between goodness-of-fit and smoothness and to select the number of knots. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risks and to quantify uncertainty. The study offers a disease and health outcome surveillance methodology for flexible and efficient exploration and assessment of emerging risk trends and clustering. The methods are motivated and illustrated through a Bayesian analysis of adverse medical events (also known as iatrogenic injuries) among hospitalized elderly patients in British Columbia, Canada.     

Bayesian空间泊松模型对小区域非传染病患病率的估计

目的为地理小区域非传染病患病率的估计及疾病地理分布情况的探讨提供方法学上的理论依据。方法以四川省2000年8～10岁儿童甲状腺肿大率为例，为克服甲状腺肿大的空间自相关性和异质性，构建Bayesian空间泊松模型，用Gibbs抽样的MCMC模型技术估计各县（区）的甲状腺肿大率。结果用Bayesian空间泊松模型得到的估计率与粗率相比，前者得到的非病区、中等病区及重病区数目比后者要少，轻病区数目要多。结论利用Bayesian空间泊松模型有利于消除抽样引起的极端值，使得其估计值比由原样本得到的粗率稳健。     

On Bayesian shared component disease mapping and ecological regression with errors in covariates

Recent literature on Bayesian disease mapping presents shared component models (SCMs) for joint spatial modeling of two or more diseases with common risk factors. In this study, Bayesian hierarchical formulations of shared component disease mapping and ecological models are explored and developed in the context of ecological regression, taking into consideration errors in covariates. A review of multivariate disease mapping models (MultiVMs) such as the multivariate conditional autoregressive models that are also part of the more recent Bayesian disease mapping literature is presented. Some insights into the connections and distinctions between the SCM and MultiVM procedures are communicated. Important issues surrounding (appropriate) formulation of shared‐ and disease‐specific components, consideration/choice of spatial or non‐spatial random effects priors, and identification of model parameters in SCMs are explored and discussed in the context of spatial and ecological analysis of small area multivariate disease or health outcome rates and associated ecological risk factors. The methods are illustrated through an in‐depth analysis of four‐variate road traffic accident injury (RTAI) data: gender‐specific fatal and non‐fatal RTAI rates in 84 local health areas in British Columbia (Canada). Fully Bayesian inference via Markov chain Monte Carlo simulations is presented. Copyright © 2010 John Wiley & Sons, Ltd.     

Modelling the geographical distribution of co-infection risk from single-disease surveys

Background: The need to deliver interventions targeting multiple diseases in a cost‐effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co‐infection is particularly high. Co‐infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. Methods: Bayesian geostatistical shared component models (allowing for covariates, disease‐specific and shared spatial and non‐spatial random effects) are proposed to model the geographical distribution and burden of co‐infection risk from single‐disease surveys. The ability of the models to capture co‐infection risk is assessed on simulated data sets based on multinomial distributions assuming light‐ and heavy‐dependent diseases, and a real data set of Schistosoma mansoni –hookworm co‐infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single‐disease surveys. The estimated results of co‐infection risk, together with independent and multinomial model results, were compared via different validation techniques. Results: The results showed that shared component models result in more accurate estimates of co‐infection risk than models assuming independence in settings of heavy‐dependent diseases. The shared spatial random effects are similar to the spatial co‐infection random effects of the multinomial model for heavy‐dependent data. Conclusions: In the absence of true co‐infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co‐infection risk from single‐disease survey data, especially in settings of heavy‐dependent diseases. Copyright © 2011 John Wiley & Sons, Ltd.     

Clustering of childhood mortality in rural Burkina Faso.

BACKGROUND: Childhood mortality is a major public health problem in sub-Saharan Africa. For the implementation of efficient public health systems, knowledge of the spatial distribution of mortality is required. METHODS: Data from a demographic surveillance research project were analysed which comprised information obtained for about 30 000 individuals from 39 villages in northwest Burkina Faso (West Africa) in the period 1993--1998. Total childhood mortality rates were calculated and the geographical distribution of total childhood mortality was investigated. In addition, data from a cohort of 686 children sampled from 16/39 of the villages followed up during a randomized controlled trial in 1999 were also used to validate the results from the surveillance data. A spatial scan statistic was used to test for clusters of total childhood mortality in both space and time. RESULTS: Several statistically significant clusters of higher childhood mortality rates comprising different sets of villages were identified; one specific village was consistently identified in both study populations indicating non-random distribution of childhood mortality. Potential risk factors which were available in the database (ethnicity, religion, distance to nearest health centre) did not explain the spatial pattern. CONCLUSION: The findings indicate non-random clustering of total childhood mortality in the study area. The study may be regarded as a first step in prioritizing areas for follow-up public health efforts.     

Relative risk estimation of dengue disease at small spatial scale

Background

Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach.

Methods

We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1–20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008–2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen’s Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation.

Results

We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair.

Conclusion

The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.

     

Public health surveillance and assessment of emerging infectious threats: method and criteria for risk analysis

One of the objectives of the surveillance systems implemented by the French National Institute for Public Health Surveillance is to detect communicable diseases and to reduce their impact. For emerging infections, the detection and risk analysis pose specific challenges due to lack of documented criteria for the event. The surveillance systems detect a variety of events, or “signals” which represent a potential risk, such as a novel germ, a pathogen which may disseminate in a non-endemic area, or an abnormal number of cases for a well-known disease. These signals are first verified and analyzed, then classified as: potential public health threat, event to follow-up, or absence of threat. Through various examples, we illustrate the method and criteria which are used to analyze and classify these events considered to be emerging. The examples highlight the importance of host characteristics and exposure in groups at particular risk, such as professionals in veterinarian services, health care workers, travelers, immunodepressed patients, etc. The described method should allow us to identify future needs in terms of surveillance and to improve timeliness, quality of expertise, and feedback information regarding the public health risk posed by events which are insufficiently documented.     

Estimation of incidences of infectious diseases based on antibody measurements

Owing to underascertainment it is difficult if not impossible to determine the incidence of a given disease based on cases notified to routine public health surveillance. This is especially true for diseases that are often present in mild forms as for example diarrhoea caused by foodborne bacterial infections. This study presents a Bayesian approach for obtaining incidence estimates by use of measurements of serum antibodies against Salmonella from a cross‐sectional study. By comparing these measurements with antibody measurements from a follow‐up study of infected individuals it was possible to estimate the time since last infection for each individual in the cross‐sectional study. These time estimates were then converted into incidence estimates. Information about the incidence of Salmonella infections in Denmark was obtained by using blood samples from 1780 persons. The estimated incidence was about 0.094 infections per person year. This number corresponds to 325 infections per culture‐confirmed case captured in the Danish national surveillance system. We present a novel approach, termed as seroincidence, that has potentials to compare the sensitivity of public health surveillance between different populations, countries and over time. Copyright © 2009 John Wiley & Sons, Ltd.     

Risk assessment of vector-borne diseases for public health governance

ObjectivesIn the context of public health, risk governance (or risk analysis) is a framework for the assessment and subsequent management and/or control of the danger posed by an identified disease threat. Generic frameworks in which to carry out risk assessment have been developed by various agencies. These include monitoring, data collection, statistical analysis and dissemination. Due to the inherent complexity of disease systems, however, the generic approach must be modified for individual, disease-specific risk assessment frameworks.Study designThe analysis was based on the review of the current risk assessments of vector-borne diseases adopted by the main Public Health organisations (OIE, WHO, ECDC, FAO, CDC etc…).MethodsLiterature, legislation and statistical assessment of the risk analysis frameworks.ResultsThis review outlines the need for the development of a general public health risk assessment method for vector-borne diseases, in order to guarantee that sufficient information is gathered to apply robust models of risk assessment. Stochastic (especially spatial) methods, often in Bayesian frameworks are now gaining prominence in standard risk assessment procedures because of their ability to assess accurately model uncertainties.ConclusionsRisk assessment needs to be addressed quantitatively wherever possible, and submitted with its quality assessment in order to enable successful public health measures to be adopted. In terms of current practice, often a series of different models and analyses are applied to the same problem, with results and outcomes that are difficult to compare because of the unknown model and data uncertainties. Therefore, the risk assessment areas in need of further research are identified in this article.     

Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging

Background  

Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers) under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided.     

Using a geographical information system to plan a malaria control programme in South Africa

INTRODUCTION: Sustainable control of malaria in sub-Saharan Africa is jeopardized by dwindling public health resources resulting from competing health priorities that include an overwhelming acquired immunodeficiency syndrome (AIDS) epidemic. In Mpumalanga province, South Africa, rational planning has historically been hampered by a case surveillance system for malaria that only provided estimates of risk at the magisterial district level (a subdivision of a province). METHODS: To better map control programme activities to their geographical location, the malaria notification system was overhauled and a geographical information system implemented. The introduction of a simplified notification form used only for malaria and a carefully monitored notification system provided the good quality data necessary to support an effective geographical information system. RESULTS: The geographical information system displays data on malaria cases at a village or town level and has proved valuable in stratifying malaria risk within those magisterial districts at highest risk, Barberton and Nkomazi. The conspicuous west-to-east gradient, in which the risk rises sharply towards the Mozambican border (relative risk = 4.12, 95% confidence interval = 3.88-4.46 when the malaria risk within 5 km of the border was compared with the remaining areas in these two districts), allowed development of a targeted approach to control. DISCUSSION: The geographical information system for malaria was enormously valuable in enabling malaria risk at town and village level to be shown. Matching malaria control measures to specific strata of endemic malaria has provided the opportunity for more efficient malaria control in Mpumalanga province.     

Point pattern analysis with spatially varying covariate effects,applied to the study of cerebrovascular deaths

This article proposes a modeling approach for handling spatial heterogeneity present in the study of the geographical pattern of deaths due to cerebrovascular disease.The framework involvesa point pattern analysis with components exhibiting spatial variation. Preliminary studies indicate that mortality of this disease and the effect of relevant covariates do not exhibit uniform geographic distribution. Our model extends a previously proposed model in the literature that uses spatial and non‐spatial variables by allowing for spatial variation of the effect of non‐spatial covariates. A number of relative risk indicators are derived by comparing different covariate levels, different geographic locations, or both. The methodology is applied to the study of the geographical death pattern of cerebrovascular deaths in the city of Rio de Janeiro. The results compare well against existing alternatives, including fixed covariate effects. Our model is able to capture and highlight important data information that would not be noticed otherwise, providing information that is required for appropriate health decision‐making. Copyright © 2014 John Wiley & Sons, Ltd.     

Hierarchical Bayesian spatial modelling of small-area rates of non-rare disease

We present Bayesian hierarchical spatial models for the analysis of the geographical distribution of a non-rare disease or event. The work is motivated by the need for ascertaining regional variations in health services outcomes and resource use and for assessing the potential sources of these variations. The models discussed herein readily accommodate random spatial effects and covariate effects. We discuss Bayesian inferential framework and implementation of a hybrid Markov chain Monte Carlo method for full Bayesian model inference. The methods are illustrated through an analysis of regional variation in chronic lung disease (CLD) rates among neonatal intensive care unit (NICU) patients across Canada. Specifically, we first present a random effects binomial model for spatially correlated CLD rates, with random spatial effects accounting for latent or covariate effects. These random spatial effects depict regional or spatial variation in chronic lung disease occurrence. We then extend this model to include covariates. With this extension, we assess residual spatial effects and the extent to which risk factors such as illness severity at NICU admission, low birth weight, and very low birth weight influence the CLD rate variation.     

Patterns of variation in the infant mortality rate in Rio Grande do Sul State, Brazil: comparison of empirical Bayesian and fully Bayesian approaches

Infant mortality is considered a sensitive health indicator, and knowledge of its geographical profile is essential for formulating appropriate public health policies. Disease mapping aims to describe the geographical distribution of disease incidence and mortality rates. Due to the heavy instability of crude rates in small areas, methods involving Bayesian smoothing of rates are used, drawing on information for the whole area or neighborhood to estimate the event rate. The current study compares empirical Bayesian (EB) and fully Bayesian (FB) methods for infant mortality rates (accumulated data from 2001 to 2004) in Rio Grande do Sul State, Brazil. This study highlights the advantages of Bayesian estimators for viewing and interpreting maps. For the problem at hand, EB and FB methods showed quite similar results and had the great advantage of easy use by health professionals, since they evenly highlight the main spatial patterns in the mortality rate in the State during the target period.     

Assessing the utility of public health surveillance using specificity, sensitivity, and lives saved

In modern surveillance of public health, data may be reported in a timely fashion and include spatial data on cases in addition to the time of their occurrence. This has lead to many recent developments in statistical methods to detect events of public health importance. However, there has been relatively little work about how to compare such methods. One powerful rationale for performing surveillance is earlier detection of events of public health significance; previous evaluation tools have focused on metrics that include the timeliness of detection in addition to sensitivity and specificity. However, such metrics have not accounted for the number of persons affected by the events. We re-examine the rationale for this surveillance and conclude that earlier detection is preferred because it can prevent additional morbidity and mortality. On the basis this observation, we propose evaluating the number of cases prevented by each detection method, and include this information in assessing the value of different detection methods. Using this approach incorporates more information about the events and the detection and provides a sound basis for making decisions about which detection methods to employ.     

A Bayesian multinomial model to analyse spatial patterns of childhood co-morbidity in Malawi

Children in less developed countries die from relatively small number of infectious disease, some of which epidemiologically overlap. Using self-reported illness data from the 2000 Malawi Demographic and Health Survey, we applied a random effects multinomial model to assess risk factors of childhood co-morbidity of fever, diarrhoea and pneumonia, and quantify area-specific spatial effects. The spatial structure was modelled using the conditional autoregressive prior. Various models were fitted and compared using deviance information criterion. Inference was Bayesian and was based on Markov Chain Monte Carlo simulation techniques. We found spatial variation in childhood co-morbidity and determinants of each outcome category differed. Specifically, risk factors associated with child co-morbidity included age of the child, place of residence, undernutrition, bednet use and Vitamin A. Higher residual risk levels were identified in the central and southern–eastern regions, particularly for fever, diarrhoea and pneumonia; fever and pneumonia; and fever and diarrhoea combinations. This linkage between childhood health and geographical location warrants further research to assess local causes of these clusters. More generally, although each disease has its own mechanism, overlapping risk factors suggest that integrated disease control approach may be cost-effective and should be employed.