Transmissibility of the highly pathogenic avian influenza virus, subtype H5N1 in domestic poultry: a spatio-temporal estimation at the global scale

The highly pathogenic avian influenza virus (HPAIV), subtype H5N1 poses a serious threat not only to the poultry industry and wild birds but also to humans. Despite a large number of studies conducted on various aspects of this virus, its transmissibility is still poorly understood. This study quantifies the basic reproductive number (R0) of the global HPAIV H5N1 spread within domestic poultry during December 2003 to December 2009. Three different approaches were applied to estimate R0 for HPAIV H5N1: (i) epidemic doubling time; (ii) spatial distance-based nearest neighbour; and (iii) spatio-temporal distance-based nearest neighbour. These three approaches represent temporal (tR0), spatial (sR0) and spatio-temporal transmissibility (stR0), respectively. The joint application of these three approaches provides a more complete profile by characterising the transmissibility traits of infectious diseases from different perspectives. Estimates of tR0 gradually decreased over the six sequential epidemic waves (EWs) examined, suggesting that the implemented control measures were effective in reducing the number of outbreaks. However, sR0 and stR0 increased from EW1, peaked in EW3 and then gradually decreased during EW4-EW6, reflecting different aspects of disease transmissibility compared to tR0. The application of all three methods in the final EW6 showed R0 >1, suggesting that the control measures implemented did not completely interrupt the transmission cycle, and hence were insufficient to eliminate HPAIV H5N1. Close monitoring of HPAIV H5N1 outbreaks and enhanced control policies is advised.

[Analysis of the spatial and temporal characteristics of measles patients younger than 1 year old in Shandong province]

OBJECTIVE

To explore the spatial and temporal characteristics of measles patients younger than 1 year old in Shandong province.

METHODS

A total of 5309 cases of measles, whose patients were younger than 1 year old in Shandong province between year 1999 and 2008 were collected. The epidemic features of measles were described, and the annual infant incidence was calculated. Software ArcGIS9.3 was applied to draw the spatial map of the disease, and the software GeoDa0.95i-beta was adopted to analyze the spatial autocorrelation.

RESULTS

The incidence among infants younger than 1 year old reported in Shandong province rose from 23.45/100 000 (206 cases) in 1999 to 269.60/100 000 (2791 cases) in 2008.5309 cases covered all month-aged infants under 1 year old, except 12 months old. Most patients (3494 cases) aged between 6 - 9 months old; especially the infants around 8 months old, accounting for 20.7% (1100/5309). The epidemic peak was between March and May, accounting for 45.5% (2414/5309). The spatial and temporal distribution features showed an up and down temporal trend and an increase from east to west in spatial trend. The global Moran's I values of measles incidence among infants in Shandong province were 0.346, 0.150, 0.396, 0.213, 0.477, 0.354 and 0.331 in year 1999, 2001 - 2002, 2005 - 2008 (P < 0.01) and 0.076 in year 2004 (P < 0.05). The local spatial autocorrelation analysis showed that southwest and northwest districts of Shandong were highly clustered districts of measles.

CONCLUSION

In Shandong, the measles incidence among infants younger than 1 year old rose obviously; especially the infants aged between 6-9 months age. The epidemic peak was between March and May. A positive spatial correlation was found, the disease showed a distinct regional distribution feature, and a cluster district was found.

[Spatial distribution and risk factors of tuberculosis in Songjiang district, Shanghai during 2006 and 2009]

OBJECTIVE

To identify spatial distribution and risk factors among tuberculosis (TB) cases in Songjiang district, Shanghai, 2006 - 2009.

METHODS

All active TB cases and all bacteriologically confirmed TB cases diagnosed during the period from 2006 to 2009 were recruited into the study. Spatial scan statistics were used to identify spatial clusters. Using logistic regression, we compared the demographic and clinical characteristics of TB cases in spatial clusters versus TB cases not in spatial clusters.

RESULTS

A total of 1815 active TB cases and 730 bacteriologically confirmed TB cases were recruited during 2006 - 2009. Chedun township and Xinqiao township was detected to be a spatial cluste (RR = 1.38, LLR = 16.78, P < 0.01), which was the location of the municipal industrial zone. No spatial cluster was found during 2006 - 2007, while during 2008 - 2009 Chedun township was detected to be a spatial cluster (RR = 1.70, LLR = 15.06, P < 0.01). Among resident population, the spatial cluster of TB cases was located in the southwestern part of Songjiang district, which included five townships Xinbang, Shihudang, Xiaokunshan, Maogang and Yongfeng (RR = 1.49, LLR = 10.52, P < 0.01); while among migrant population, the spatial cluster of TB cases was located in Chedun township (RR = 1.55, LLR = 15.64, P < 0.01). There were higher proportions of resident TB cases who were farmers (AOR = 4.9, 95%CI: 1.9 - 12.3) or had other occupations (AOR = 2.6, 95%CI: 1.1 - 5.9) in the spatial cluster. There were higher proportions of migrant TB cases who lived here for less than 5 years (< 1 year: AOR = 5.9, 95%CI: 1.8 - 19.5; 1 - 5 years: AOR = 3.2, 95%CI: 1.0 - 9.9) or worked at other occupations (AOR = 2.8, 95%CI: 1.5 - 5.1) and lower proportions of migrant TB cases who came from Eastern region (AOR = 0.3, 95%CI: 0.1 - 0.8) or Middle region (AOR = 0.5, 95%CI: 0.3 - 0.9) in the spatial cluster.

CONCLUSION

In Songjiang district there was a spatial cluster in TB cases, which was Chedun township. Local residents with TB who were farmers or had other occupations were more likely to be in the spatial cluster. Migrants with TB who lived here for less than 5 years or came from Western region were more likely to be in the spatial cluster.

[Temporal and spatial variation of shorebirds in Barra de Navidad lagoon, Jalisco, during three non-breeding seasons]

Resident and migratory shorebirds inhabit different kinds of wetlands such as lagoons, rivers and seashores among others. In recent years, these areas have been importantly affected by urban, agriculture and touristic activities, such as the Barra de Navidad lagoon, for which little information is available to support conservation programs. The aim of this work was to describe shorebirds temporal and spatial distribution in Barra de Navidad lagoon during three non-breeding seasons (1999-2000, 2006-2007 and 2008-2009). For this, monthly censuses were performed from November-April with the purpose of registering all the shorebirds species. We were able to identify 19 shorebirds species (three residents and 16 winter visitors), of which Charadrius wilsonia, Limosa fedoa and Tringa semipalmata were the most abundant. The greater number of species was registered for November, December and March of the first and third seasons. The greater number of individuals was registered when birds were feeding during low tides, mainly in December, January and February of the first and third seasons. At low tide, there was a great number of species and individuals in zone C. This area had muddy substrates that were exposed during low tides and were used to feed. Barra de Navidad lagoon provided suitable habitats for feeding and resting for resident and migratory birds. Twelve of the 19 species were considered as priority within the Mexican bird conservation strategy. However, these habitats are threatened by human activities performed in the nearby areas of the lagoon that may have negative consequences for the distribution, abundance and conservation of these species.

Spatiotemporal analysis of indigenous and imported dengue fever cases in Guangdong province, China

BACKGROUND

Dengue fever has been a major public health concern in China since it re-emerged in Guangdong province in 1978. This study aimed to explore spatiotemporal characteristics of dengue fever cases for both indigenous and imported cases during recent years in Guangdong province, so as to identify high-risk areas of the province and thereby help plan resource allocation for dengue interventions.

METHODS

Notifiable cases of dengue fever were collected from all 123 counties of Guangdong province from 2005 to 2010. Descriptive temporal and spatial analysis were conducted, including plotting of seasonal distribution of cases, and creating choropleth maps of cumulative incidence by county. The space-time scan statistic was used to determine space-time clusters of dengue fever cases at the county level, and a geographical information system was used to visualize the location of the clusters. Analysis were stratified by imported and indigenous origin.

RESULTS

1658 dengue fever cases were recorded in Guangdong province during the study period, including 94 imported cases and 1564 indigenous cases. Both imported and indigenous cases occurred more frequently in autumn. The areas affected by the indigenous and imported cases presented a geographically expanding trend over the study period. The results showed that the most likely cluster of imported cases (relative risk = 7.52, p < 0.001) and indigenous cases (relative risk = 153.56, p < 0.001) occurred in the Pearl River Delta Area; while a secondary cluster of indigenous cases occurred in one district of the Chao Shan Area (relative risk = 471.25, p < 0.001).

CONCLUSIONS

This study demonstrated that the geographic range of imported and indigenous dengue fever cases has expanded over recent years, and cases were significantly clustered in two heavily urbanised areas of Guangdong province. This provides the foundation for further investigation of risk factors and interventions in these high-risk areas.

A review of spatial methods in epidemiology, 2000-2010

Understanding the impact of place on health is a key element of epidemiologic investigation, and numerous tools are being employed for analysis of spatial health-related data. This review documents the huge growth in spatial epidemiology, summarizes the tools that have been employed, and provides in-depth discussion of several methods. Relevant research articles for 2000-2010 from seven epidemiology journals were included if the study utilized a spatial analysis method in primary analysis (n = 207). Results summarized frequency of spatial methods and substantive focus; graphs explored trends over time. The most common spatial methods were distance calculations, spatial aggregation, clustering, spatial smoothing and interpolation, and spatial regression. Proximity measures were predominant and were applied primarily to air quality and climate science and resource access studies. The review concludes by noting emerging areas that are likely to be important to future spatial analysis in public health.

[Study on the influence of landscape elements regarding on the transmission of hemorrhagic fever with renal syndrome in Changsha]

OBJECTIVE

To explore the influence of landscape elements on the transmission of hemorrhagic fever with renal syndrome (HFRS) in Changsha.

METHODS

A total of 327 cases of HFRS diagnosed between year 2005 - 2009 were recruited in the study. Based on the demographic data, meteorological data and the data of second national land survey during the same period, a GIS landscape elements database of HFRS at the township scale of Changsha was established. Spatial-temporal cluster analysis methods were adopted to explore the influence of landscape elements on the spatial-temporal distribution of HFRS in Changsha during the year of 2005 - 2009.

RESULTS

The annual incidences of HFRS in Changsha between year 2005 - 2009 were 1.16/100 000 (70 cases), 0.95/100 000 (58 cases), 1.40/100 000(87 cases), 0.75/100 000(47 cases) and 1.02/100 000(65 cases) respectively. The results of poisson regression model analysis of principal component showed that the incidence of HFRS was positively correlated with farmland area (M = 29.00 km2) and urban and rural area (M = 6.12 km2; incidence rate ratios (IRR) = 1.34, 95% CI: 1.27 - 1.41); but negatively correlated with forestland area (M = 39.00 km2; IRR = 0.67, 95% CI: 0.55 - 0.81) and garden plot area (M = 0.99 km2; IRR = 0.74, 95% CI: 0.63 - 0.86). A significant cluster of the spatial-temporal distribution of HFRS cases was found in the study. The primary cluster (28.9 N, 113.37 E, radius at 22.22 km, RR = 5.23, log likelihood ratio (LLR) = 51.61, P <0.01, 67 cases of HFRS and incidence at 4.4/100 000) was found between year 2006 and 2007; and the secondary cluster (28.2 N, 113.6 E, RR = 10.77, LLR = 16.01, P < 0.01, 11 cases of HFRS and the incidence at 10.6/100 000) was found between year 2008 and 2009.

CONCLUSION

The landscape elements were found to be closely related to the prevalence and transmission of HFRS.

Space-time clustering of childhood central nervous system tumours in Yorkshire, UK

BACKGROUND

We specifically tested the aetiological hypothesis that a factor influencing geographical or temporal heterogeneity of childhood central nervous system (CNS) tumour incidence was related to exposure to a transient environmental agent.

METHODS

Information was extracted on individuals aged 0-14 years, diagnosed with a CNS tumour between the 1st January 1974 and 31st December 2006 from the Yorkshire Specialist Register of Cancer in Children and Young People. Ordnance Survey eight-digit grid references were allocated to each case with respect to addresses at the time of birth and the time of diagnosis, locating each address to within 0.1 km. The following diagnostic groups were specified a priori for analysis: ependymoma; astrocytoma; primitive neuroectodermal tumours (PNETs); other gliomas; total CNS tumours. We applied the K-function method for testing global space-time clustering using fixed geographical distance thresholds. Tests were repeated using variable nearest neighbour (NN) thresholds.

RESULTS

There was statistically significant global space-time clustering for PNETs only, based on time and place of diagnosis (P = 0.03 and 0.01 using the fixed geographical distance and the variable NN threshold versions of the K-function method respectively).

CONCLUSIONS

There was some evidence for a transient environmental component to the aetiology of PNETs. However, a possible role for chance cannot be excluded.

Suicide clusters: introducing a novel type of categorization

A suicide cluster within a given community may be defined as a group of suicides or suicide attempts, or both, that happen closer together in time and space than would generally be expected. However, since the perception of clustering may itself be a risk factor for suicide, suicide clusters differ almost from all other event clusters. The aim of this article, therefore, is to discuss the unique pattern of suicide cluster and introduce a novel type of categorization taking into account varieties of studies, which investigate suicide clusters within diverse settings. This article concludes with challenging areas in suicide cluster studies and emphasizing that each community must deal rapidly and appropriately with any perceived suicide clusters.

Space-time confounding adjusted determinants of child HIV/TB mortality for large zero-inflated data in rural South Africa

South Africa is experiencing a major burden of HIV/TB. We used longitudinal data from the Agincourt sub-district in rural northeast South Africa over the years 2000 to 2005. A total of 187 HIV/TB deaths were observed among 16,844 children aged 1-5 years coming from 8,863 households. In this paper we used Bayesian models to assess risk factors for child HIV/TB mortality taking into account the presence of spatial correlation. Bayesian zero inflated spatiotemporal models were able to detect hidden patterns within the data. Our main finding was that maternal orphans experienced a threefold greater risk of HIV/TB death compared to those with living mothers (AHR=2.93, 95% CI[1.29;6.93]). Risk factor analyses which adjust for person, place and time provide evidence for policy makers that includes a spatial distribution of risk. Child survival is dependent on the mother's survival; hence programs that promote maternal survival are critical.

Space-time clustering in childhood nervous system tumors in the Region of Murcia, Spain, 1998-2009

AIMS

The aims of this study are the following: first, to analyze incidence, trends, and survival of nervous system tumors in children under the age of 15 in the Region of Murcia, Spain, during the years 1998-2009 and second, to evaluate if certain environmental exposures may be involved in the etiology of childhood nervous system tumors. The study was performed on the spatial and temporo-spatial distribution of the observed cases.

METHODS

The Environment and Pediatric Cancer in the Region of Murcia is an ongoing research project aimed at carefully collecting pediatric environmental history (PEH) and to use geographical information systems to map the incidence and to analyze the geographical distribution of pediatric cancer incidence in our region. Between 1998 and 2009, 125 patients were diagnosed with nervous system tumors. The spatial and temporal space clusters were evaluated using Kulldorff's statistics. Address at diagnosis was the main feature evaluated.

RESULTS

The incidence (cases/million children) for central nervous system (CNS) tumors was 34.2, that for sympathetic nervous system tumors was 10.9, and that for retinoblastoma was 1.9. There was evidence of space clustering for medulloblastoma and space-time clustering for all tumors, CNS tumors, astrocytoma, and neuroblastoma.

CONCLUSIONS

The incidence and survival for each type and subtype of nervous system tumors were within the reported values for the European region. There is evidence that spatial and spatial-temporal distribution in these cases is not random. The development of a careful PEH in these patients will help to reinforce geographical information system studies and to ascertain the importance of associated risk factors.

Risk factors for colonization of E. coli in Atlantic Bottlenose Dolphins (Tursiops truncatus) in the Indian River Lagoon, Florida

Opportunistic pathogens related to degradation in water quality are of concern to both wildlife and public health. The objective of this study was to identify spatial, temporal, and environmental risk factors for E. coli colonization among Atlantic bottlenose dolphins (Tursiops truncatus) inhabiting the Indian River Lagoon (IRL), FL between 2003 and 2007. Age, gender, capture location, coastal human population density, proximity of sewage treatment plants, number of septic tanks, cumulative precipitation 48 hrs and 30 days prior to capture, salinity, and water temperature were analyzed as potential risk factors. Highest E. coli colonization rates occurred in the northern segments of the IRL. The risk of E. coli colonization was the highest among the youngest individuals, in counties with the highest cumulative rainfall 48 hrs and in counties with the highest number of septic systems during the year of capture. The prevalence of colonization was the highest during 2004, a year during which multiple hurricanes hit the coast of Florida. Septic tanks, in combination with weather-related events suggest a possible pathway for introduction of fecal coliforms into estuarine ecosystems. The ability of E. coli and related bacteria to act as primary pathogens or cause opportunistic infections adds importance of these findings.

Spread of E. coli O157 infection among Scottish cattle farms: stochastic models and model selection

Identifying risk factors for the presence of Escherichia coli O157 infection on cattle farms is important for understanding the epidemiology of this zoonotic infection in its main reservoir and for informing the design of interventions to reduce the public health risk. Here, we use data from a large-scale field study carried out in Scotland to fit both "SIS"-type dynamical models and statistical risk factor models. By comparing the fit (assessed using maximum likelihood) of different dynamical models we are able to identify the most parsimonious model (using the AIC statistic) and compare it with the model suggested by risk factor analysis. Both approaches identify 2 key risk factors: the movement of cattle onto the farm and the number of cattle on the farm. There was no evidence for a role of other livestock species or seasonality, or of significant risk of local spread. However, the most parsimonious dynamical model does predict that farms can infect other farms through routes other than cattle movement, and that there is a nonlinear relationship between the force of infection and the number of infected farms. An important prediction from the most parsimonious model is that although only approximately 20% farms may harbour E. coli O157 infection at any given time approximately 80% may harbour infection at some point during the course of a year.

Evidence of spread of the emerging infectious disease, finch trichomonosis, by migrating birds

Finch trichomonosis emerged in Great Britain in 2005 and led to epidemic mortality and a significant population decline of greenfinches, Carduelis chloris and chaffinches, Fringilla coelebs, in the central and western counties of England and Wales in the autumn of 2006. In this article, we show continued epidemic spread of the disease with a pronounced shift in geographical distribution towards eastern England in 2007. This was followed by international spread to southern Fennoscandia where cases were confirmed at multiple sites in the summer of 2008. Sequence data of the ITS1/5.8S/ITS2 ribosomal region and part of the small subunit (SSU) rRNA gene showed no variation between the British and Fennoscandian parasite strains of Trichomonas gallinae. Epidemiological and historical ring return data support bird migration as a plausible mechanism for the observed pattern of disease spread, and suggest the chaffinch as the most likely primary vector. This finding is novel since, although intuitive, confirmed disease spread by migratory birds is very rare and, when it has been recognised, this has generally been for diseases caused by viral pathogens. We believe this to be the first documented case of the spread of a protozoal emerging infectious disease by migrating birds.

The use of remotely sensed environmental data in the study of malaria

Mapping and anticipating risk is a major issue in the fight against malaria, a disease causing an estimated one million deaths each year. Approximately half the world's population is at risk and it is of prime importance to evaluate the burden of malaria at the spatial as well as the temporal level. The role of the environment with regard to the determinants of transmission and burden of the disease are described followed by a discussion of special issues such as urban malaria, human population mapping and the detection of changes at the temporal scale. Risk maps at appropriate scales can provide valuable information for targeted control and the present review discusses the essentials of principles, methods, advantages and limitations of remote sensing along with a presentation of ecological, meteorological and climatologic data which rule the distribution of malaria. The panel of commonly used analytic methods is examined and the methodological limitations are highlighted. A review of the literature details the increasing interest in the use of remotely sensed data in the study of malaria, by mapping or modeling several malariometric indices such as prevalence, morbidity and mortality, which are discussed with reference to vector breeding, vector density and entomological inoculation rate, estimates of which constitute the foundation for understanding endemicity and epidemics.

[Typhoid and paratyphoid fever in Yunnan province: distributional patterns and the related meteorological factors]

OBJECTIVE

To characterize the spatial distribution of typhoid and paratyphoid fever (TPF) in Yunnan province, China and to determine the effectiveness of meteorological factors on the epidemics of TPF.

METHODS

Data of reported TPF cases in Yunnan province (2001 - 2007) from the China Information System for Diseases Control and Prevention was applied to GIS-based spatial analyses to detect their spatial distribution and clustering of TPF incidence at the county level. Panel data analysis was used to identify the relationships between the TPF incidence and meteorological factors including monthly average temperature, monthly cumulative precipitation and monthly average relative humidity.

RESULTS

During the study period, the average incidence of TPF in Yunnan province was 23.11/100 000, with majority of the TPF cases emerged in summer and autumn. Although widely distributed, two TPF clusters were detected in Yunnan province based on the spatial analysis: one area around Yuxi city with the average annual incidence as 207.45/100 000 and another at the junctions of Yunnan province with Burma and Laos. Based on results from panel data analysis, the incidence of TFP was shown to be associated with meteorological factors such as temperature, precipitation, relative humidity and one month lag of temperature increase [10°C increase in the monthly average temperature: IRR = 1.30 (95%CI: 1.24 - 1.36); 10% increase in monthly average relative humidity: IRR = 1.07 (95%CI: 1.05 - 1.09); 100 mm rise in monthly cumulative precipitation: IRR = 1.02 (95%CI: 1.00 - 1.03); and 10°C average temperature increase, the last month: IRR = 1.73 (95%CI: 1.64 - 1.82)].

CONCLUSION

Areas with high TPF incidence were detected in this study, which indicated the key areas for TPF control in Yunnan province. Meteorological factors such as temperature, precipitation and humidity played a role in the incidence of TPF.

Survived infancy but still vulnerable: spatial-temporal trends and risk factors for child mortality in the Agincourt rural sub-district, South Africa, 1992-2007

Targeting of health interventions to poor children at highest risk of mortality are promising approaches for enhancing equity. Methods have emerged to accurately quantify excess risk and identify space-time disparities. This provides useful and detailed information for guiding policy. A spatio-temporal analysis was performed to identify risk factors associated with child (1-4 years) mortality in the Agincourt sub-district, South Africa, to assess temporal changes in child mortality patterns within the study site between 1992 and 2007, and to produce all-cause and cause-specific mortality maps to identify high risk areas. Demographic, maternal, paternal and fertility-related factors, household mortality experience, distance to health care facility and socio-economic status were among the examined risk factors. The analysis was carried out by fitting a Bayesian discrete time Bernoulli survival geostatistical model using Markov chain Monte Carlo simulation. Bayesian kriging was used to produce mortality risk maps. Significant temporal increase in child mortality was observed due to the HIV epidemic. A distinct spatial risk pattern was observed with higher risk areas being concentrated in poorer settlements on the eastern part of the study area, largely inhabited by former Mozambican refugees. The major risk factors for childhood mortality, following multivariate adjustment, were mother's death (especially when due to HIV and tuberculosis), greater number of children under 5 years living in the same household and winter season. This study demonstrates the use of Bayesian geostatistical models for accurately quantifying risk factors and producing maps of child mortality risk in a health and demographic surveillance system. According to the space-time analysis, the southeast and upper central regions of the site appear to have the highest mortality risk. The results inform policies to address health inequalities in the Agincourt sub-district and to improve access to health services. Targeted efforts to prevent vertical transmission of HIV in specific settings need to be undertaken as well as ensuring the survival of the mother and father in childhood.

Spatial analysis of bluetongue cases and vaccination of Swiss cattle in 2008 and 2009

Bluetongue (BT) is a vector-borne viral disease of ruminants. The infection is widespread globally with major implications for international animal trade and production. In 2006, BT virus serotype 8 (BTV-8) was encountered in Europe for the first time, causing extensive production losses and death in susceptible livestock. Following the appearance of BTV-8 in Switzerland in 2007, a compulsory vaccination programme was launched in the subsequent year. Due to social factors and difficulties to reach animals on high pasture, the regional vaccination coverage varied across the country in both 2008 and 2009. In this study, the effect of vaccination on the spatial occurrence of BTV-8 and the associated relative disease risk in Switzerland in 2008 and 2009 were investigated by a spatial Bayesian hierarchical approach. Bayesian posterior distributions were obtained by integrated nested Laplace approximations, a promising alternative to commonly used Markov chain Monte Carlo methods. The number of observed BTV-8 outbreaks in Switzerland decreased notably from 2008 to 2009. However, only a non-significant association between vaccination coverage and the probability of a spatial unit being infected with BTV-8 was identified using the model developed for this study. The relative disease risk varied significantly across the country, with a higher relative risk of BTV-8 infection in western and north-western Switzerland where environmental conditions are more suitable for vector presence and viral transmission. Examination of the spatial correlation between disease occurrence, control measures and associated ecological factors can be valuable in the evaluation and development of disease control programmes, allowing prioritisation of areas with a high relative risk of disease.

Spatiotemporal spread of cucurbit downy mildew in the eastern United States

The dynamics of cucurbit downy mildew, caused by Pseudoperonospora cubensis, in the eastern United States in 2008 and 2009 were investigated based on disease records collected in 24 states as part of the Cucurbit downy mildew ipmPIPE monitoring program. The mean season-long rate of temporal disease progress across the 2 years was 1.4 new cases per day. Although cucurbit downy mildew was detected in mid-February and early March in southern Florida, the disease progressed slowly during the spring and early summer and did not enter its exponential phase until mid-June. The median nearest-neighbor distance of spread of new disease cases was ≈110 km in both years, with ≈15% of the distances being >240 km. Considering disease epidemics on all cucurbits, the epidemic expanded at a rate of 9.2 and 10.5 km per day in 2008 and 2009, respectively. These rates of spatial spread are at the lower range of those reported for the annual spread of tobacco blue mold in the southeastern United States, a disease that is also aerially dispersed over long distances. These results suggest that regional spread of cucurbit downy mildew may be limited by opportunities for establishment in the first half of the year, when fewer cucurbit hosts are available for infection. The O-ring statistic was used to determine the spatial pattern of cucurbit downy mildew outbreaks using complete spatial randomness as the null model for hypothesis testing. Disease outbreaks in both years were spatially aggregated and the extent of spatial dependence was up to 1,000 km. Results from the spatial analysis suggests that disease outbreaks in the Great Lakes and mid-Atlantic regions may be due to the spread of P. cubensis sporangia from outbreaks of the disease near the Georgia/South Carolina/North Carolina border rather than from overwintering sites in southern Florida. Space-time point pattern analysis indicated strong (P < 0.001) evidence for a space-time interaction and a space-time risk window of ≈3 to 5 months after first disease outbreak and 300 to 600 km was detected in both years. Results of this study support the hypothesis that infection of cucurbits by P. cubensis appears to be an outcome of a contagion process, and the relative large space-time window suggests that factors occurring on a large spatial scale (≈1,000 km) facilitate the spread of cucurbit downy mildew in the eastern United States.

Use of spatiotemporal analysis of laboratory submission data to identify potential outbreaks of new or emerging diseases in cattle in Great Britain

BACKGROUND

New and emerging diseases of livestock may impact animal welfare, trade and public health. Early detection of outbreaks can reduce the impact of these diseases by triggering control measures that limit the number of cases that occur. The aim of this study was to investigate whether prospective spatiotemporal methods could be used to identify outbreaks of new and emerging diseases in scanning surveillance data. SaTScan was used to identify clusters of unusually high levels of submissions where a diagnosis could not be reached (DNR) using different probability models and baselines. The clusters detected were subjected to a further selection process to reduce the number of false positives and a more detailed epidemiological analysis to ascertain whether they were likely to represent real outbreaks.

RESULTS

187,925 submissions of clinical material from cattle were made to the Regional Laboratory of the Veterinary Laboratories Agency (VLA) between 2002 and 2007, and the results were stored on the VLA FarmFile database. 16,925 of these were classified as DNRs and included in the analyses. Variation in the number and proportion of DNRs was found between syndromes and regions, so a spatiotemporal analysis for each DNR syndrome was done. Six clusters were identified using the Bernoulli model after applying selection criteria (e.g. size of cluster). The further epidemiological analysis revealed that one of the systemic clusters could plausibly have been due to Johne's disease. The remainder were either due to misclassification or not consistent with a single diagnosis.

CONCLUSIONS

Our analyses have demonstrated that spatiotemporal methods can be used to detect clusters of new or emerging diseases, identify clusters of known diseases that may not have been diagnosed and identify misclassification in the data, and highlighted the impact of data quality on the ability to detect outbreaks. Spatiotemporal methods should be used alongside current temporal methods for analysis of scanning surveillance data. These statistical analyses should be followed by further investigation of possible outbreaks to determine whether cases have common features suggesting that these are likely to represent real outbreaks, or whether issues with the collection or processing of information have resulted in false positives.

Health and income: a robust comparison of Canada and the US

This paper uses sequential stochastic dominance procedures to compare the joint distribution of health and income across space and time. It is the first application of which we are aware of methods to compare multidimensional distributions of income and health using procedures that are robust to aggregation techniques. The paper's approach is more general than comparisons of health gradients and does not require the estimation of health equivalent incomes. We illustrate the approach by contrasting Canada and the US using comparable data. Canada dominates the US over the bottom part of the bi-dimensional distribution of health and income, though not generally over the uni-dimensional distributions of health or income. The paper also finds that welfare for both Canadians and Americans has not unambiguously improved during the last decade over the joint distribution of income and health, in spite of the fact that the uni-dimensional distributions of income have clearly improved during that period.

Part 1. Short-term effects of air pollution on mortality: results from a time-series analysis in Chennai, India

This report describes the results of a time-series analysis of the effect of short-term exposure to particulate matter with an aerodynamic diameter < or = 10 pm (PM10) on mortality in metropolitan Chennai, India (formerly Madras). This was one of three sites in India chosen by HEI as part of its Public Health and Air Pollution in Asia (PAPA) initiative. The study involved integration and analysis of retrospective data for the years 2002 through 2004. The data were obtained from relevant government agencies in charge of routine data collection. Data on meteorologic confounders (including temperature, relative humidity, and dew point) were available on all days of the study period. Data on mortality were also available on all days, but information on cause-of-death (including accidental deaths) could not be reliably ascertained. Hence, only all-cause daily mortality was used as the major outcome for the time-series analyses. Data on PM10, nitrogen dioxide (NO2), and sulfur dioxide (SO2) were limited to a much smaller number of days, but spanned the full study period. Data limitations resulting from low sensitivity of gaseous pollutant measurements led to using only PM10 in the main analysis. Of the eight operational ambient air quality monitor (AQM) stations in the city, seven met the selection criteria set forth in the common protocol developed for the three PAPA studies in India. In addition, all raw data used in the analysis were subjected to additional quality assurance (QA) and quality control (QC) criteria to ensure the validity of the measurements. Two salient features of the PM10 data set in Chennai were a high percentage of missing readings and a low correlation among daily data recorded by the AQMs. The latter resulted partly because each AQM had a small footprint (approximate area over which the air pollutant measurements recorded in the AQM are considered valid), and partly because of differences in source profiles among the 10 zones within the city. The zones were defined by the Chennai Corporation based on population density. Alternative exposure series were developed to control for these data features. We first developed exposure series based on data from single AQMs and multiple AQMs. Because neither was found to satisfactorily represent population exposures, we subsequently developed an exposure series that disaggregated pollutant data to individual zones within the city boundary. The zonal series, despite some uncertainties, was found to best represent population exposures among other available choices. The core model was thus a zonal model developed using disaggregated mortality and pollutant data from individual zones. We used quasi-Poisson generalized additive models (GAMs) with smooth functions of time, temperature, and relative humidity modeled using penalized splines. The degrees of freedom (df) for these confounders were selected to maximize the precision with which the relative risk for PM10 was estimated. This is a deviation from the traditional approaches to degrees of freedom selection, which usually aim to optimize overall model fit. Our approach led to the use of 8 df/year for time, 6 df/year for temperature, and 5 df/year for relative humidity. The core model estimated a 0.44% (95% confidence interval [CI] = 0.17 to 0.71) increase in daily all-cause mortality per 10-pg/m3 increase in daily average PM10 concentrations. Extensive sensitivity analyses compared models constructed using alternative exposure series and contributions of model parameters to the core model with regard to confounder degrees of freedom, alternative lags for exposure and meteorologic confounders, inclusion of outliers, seasonality, inclusion of multiple pollutants, and stratification by sex and age. The sensitivity analyses showed that our estimates were robust to a range of specifications and were also comparable to estimates reported in previous time-series studies: PAPA, the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), Air Pollution and Health: A European Approach (APHEA), and Air Pollution and Health: A European and North American Approach (APHENA). While the approaches developed in previous studies served as the basis for our model development, the present study has new refinements that have allowed us to address specific data limitations (such as missing measurements and small footprints of air pollution monitors). The methods developed in the study may allow better use of routine data for time-series analysis in a broad range of settings where similar exposure and data-related issues prevail. We hope that the estimates derived in this study, although somewhat tentative, will facilitate local environmental management initiatives and spur future studies.

Part 2. Time-series study on air pollution and mortality in Delhi

INTRODUCTION

Air pollution concentrations in most of the megacities in India exceed the air quality guidelines recommended by the World Health Organization and may adversely affect human health in these cities. Particulate matter (PM) is the pollutant of concern in many Indian cities, particularly in the capital city of Delhi, In recent years, several actions have been taken to address the growing air pollution problem in Delhi and other Indian cities; however, few studies have been designed to assess the health effects of air pollution in Indian cities. To bridge the gap in scientific knowledge and add evidence to the ongoing studies in other Asian cities, a retrospective time-series study on air pollution and mortality in Delhi was initiated under the HEI Public Health and Air Pollution in Asia (PAPA) program.

APPROACH

The study used retrospective time-series data of air quality and of naturally-occurring deaths recorded in Delhi to identify changes in the daily all-natural-cause mortality rate that could be attributed to changes in air quality. The 3-year study period included the years 2002 through 2004. The methodology involved: (1) collecting data on ambient air quality for major pollutants from all monitoring stations in Delhi; (2) collecting meteorologic data (temperature, humidity, and visibility); (3) collecting daily mortality records from the Registrar of Births and Deaths; (4) statistically analyzing the data using the common protocol for Indian PAPA studies, which included city-specific modifications.

RESULTS AND IMPLICATIONS

The study findings showed that increased concentrations of PM with an aerodynamic diameter < or = 10 microg/m3 (PM10) and of nitrogen dioxide (NO2) were associated with increased all-natural-cause mortality. It was found that every 10-microg/m3 change in PM10 was associated with only a 0.15% increase in total all-natural-cause mortality. When NO2 alone was considered in the model, daily all-natural-cause mortality increased 0.84% for every 10-microg/m3 increase in NO2 concentration. No significant effect was observed for changes in sulfur dioxide (SO2) concentrations. The study provides insight into the link between air pollution and mortality in local populations and contributes information to the existing body of knowledge.

Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data

Timely detection of clusters of localized influenza activity in excess of background seasonal levels could improve situational awareness for public health officials and health systems. However, no single data type may capture influenza activity with optimal sensitivity, specificity, and timeliness, and it is unknown which data types could be most useful for surveillance. We compared the performance of 10 types of electronic clinical data for timely detection of influenza clusters throughout the 2007/08 influenza season in northern California. Kaiser Permanente Northern California generated zip code-specific daily episode counts for: influenza-like illness (ILI) diagnoses in ambulatory care (AC) and emergency departments (ED), both with and without regard to fever; hospital admissions and discharges for pneumonia and influenza; antiviral drugs dispensed (Rx); influenza laboratory tests ordered (Tests); and tests positive for influenza type A (FluA) and type B (FluB). Four credible events of localized excess illness were identified. Prospective surveillance was mimicked within each data stream using a space-time permutation scan statistic, analyzing only data available as of each day, to evaluate the ability and timeliness to detect the credible events. AC without fever and Tests signaled during all four events and, along with Rx, had the most timely signals. FluA had less timely signals. ED, hospitalizations, and FluB did not signal reliably. When fever was included in the ILI definition, signals were either delayed or missed. Although limited to one health plan, location, and year, these results can inform the choice of data streams for public health surveillance of influenza.