Under-five mortality: spatial-temporal clusters in Ifakara HDSS in South-eastern Tanzania.

BACKGROUND\ud \ud Childhood mortality remains an important subject, particularly in sub-Saharan Africa where levels are still unacceptably high. To achieve the set Millennium Development Goals 4, calls for comprehensive application of the proven cost-effective interventions. Understanding spatial clustering of childhood mortality can provide a guide in targeting the interventions in a more strategic approach to the population where mortality is highest and the interventions are most likely to make an impact.\ud \ud METHODS\ud \ud Annual child mortality rates were calculated for each village, using person-years observed as the denominator. Kulldorff's spatial scan statistic was used for the identification and testing of childhood mortality clusters. All under-five deaths that occurred within a 10-year period from 1997 to 2006 were included in the analysis. Villages were used as units of clusters; all 25 health and demographic surveillance sites (HDSS) villages in the Ifakara health and demographic surveillance area were included.\ud \ud RESULTS\ud \ud Of the 10 years of analysis, statistically significant spatial clustering was identified in only 2 years (1998 and 2001). In 1998, the statistically significant cluster (p < 0.01) was composed of nine villages. A total of 106 childhood deaths were observed against an expected 77.3. The other statistically significant cluster (p < 0.05) identified in 2001 was composed of only one village. In this cluster, 36 childhood deaths were observed compared to 20.3 expected. Purely temporal analysis indicated that the year 2003 was a significant cluster (p < 0.05). Total deaths were 393 and expected were 335.8. Spatial-temporal analysis showed that nine villages were identified as statistically significant clusters (p < 0.05) for the period covering January 1997-December 1998. Total observed deaths in this cluster were 205 while 150.7 were expected.\ud \ud CONCLUSION\ud \ud There is evidence of spatial clustering in childhood mortality within the Ifakara HDSS. Further investigations are needed to explore the source of clustering and identify strategies of reaching the cluster population with the existing effective interventions. However, that should happen alongside delivery of interventions to the broader population

Automated Detection of Infectious Disease Outbreaks in Hospitals: A Retrospective Cohort Study

Susan Huang and colleagues describe an automated statistical software, WHONET-SaTScan, its application in a hospital, and the potential it has to identify hospital infection clusters that had escaped routine detection

Cluster detection methods applied to the Upper Cape Cod cancer data

BACKGROUND: A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. METHODS: We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. RESULTS: The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. CONCLUSION: The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component

Limits to Forecasting Precision for Outbreaks of Directly Transmitted Diseases

BACKGROUND: Early warning systems for outbreaks of infectious diseases are an important application of the ecological theory of epidemics. A key variable predicted by early warning systems is the final outbreak size. However, for directly transmitted diseases, the stochastic contact process by which outbreaks develop entails fundamental limits to the precision with which the final size can be predicted. METHODS AND FINDINGS: I studied how the expected final outbreak size and the coefficient of variation in the final size of outbreaks scale with control effectiveness and the rate of infectious contacts in the simple stochastic epidemic. As examples, I parameterized this model with data on observed ranges for the basic reproductive ratio (R (0)) of nine directly transmitted diseases. I also present results from a new model, the simple stochastic epidemic with delayed-onset intervention, in which an initially supercritical outbreak (R (0) > 1) is brought under control after a delay. CONCLUSION: The coefficient of variation of final outbreak size in the subcritical case (R (0) < 1) will be greater than one for any outbreak in which the removal rate is less than approximately 2.41 times the rate of infectious contacts, implying that for many transmissible diseases precise forecasts of the final outbreak size will be unattainable. In the delayed-onset model, the coefficient of variation (CV) was generally large (CV > 1) and increased with the delay between the start of the epidemic and intervention, and with the average outbreak size. These results suggest that early warning systems for infectious diseases should not focus exclusively on predicting outbreak size but should consider other characteristics of outbreaks such as the timing of disease emergence

Evaluation of school absenteeism data for early outbreak detection, New York City

BACKGROUND: School absenteeism data may have utility as an early indicator of disease outbreaks, however their value should be critically examined. This paper describes an evaluation of the utility of school absenteeism data for early outbreak detection in New York City (NYC). METHODS: To assess citywide temporal trends in absenteeism, we downloaded three years (2001–02, 2002–03, 2003–04) of daily school attendance data from the NYC Department of Education (DOE) website. We applied the CuSum method to identify aberrations in the adjusted daily percent absent. A spatial scan statistic was used to assess geographic clustering in absenteeism for the 2001–02 academic year. RESULTS: Moderate increases in absenteeism were observed among children during peak influenza season. Spatial analysis detected 790 significant clusters of absenteeism among elementary school children (p < 0.01), two of which occurred during a previously reported outbreak. CONCLUSION: Monitoring school absenteeism may be moderately useful for detecting large citywide epidemics, however, school-level data were noisy and we were unable to demonstrate any practical value in using cluster analysis to detect localized outbreaks. Based on these results, we will not implement prospective monitoring of school absenteeism data, but are evaluating the utility of more specific school-based data for outbreak detection

Lumping or splitting: seeking the preferred areal unit for health geography studies

BACKGROUND: Findings are compared on geographic variation of incident and late-stage cancers across Connecticut using different areal units for analysis. RESULTS: Few differences in results were found for analyses across areal units. Global clustering of incident prostate and breast cancer cases was apparent regardless of the level of geography used. The test for local clustering found approximately the same locales, populations at risk and estimated effects. However, some discrepancies were uncovered. CONCLUSION: In the absence of conditions calling for surveillance of small area cancer clusters ('hot spots'), the rationale for accepting the burdens of preparing data at levels of geography finer than the census tract may not be compelling

Analysis of the geographic distribution of HFRS in Liaoning Province between 2000 and 2005

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic fever with renal syndrome (HFRS) is endemic in Liaoning Province, China, and this province was the most serious area affected by HFRS during 2004 to 2005. In this study, we conducted a spatial analysis of HFRS cases with the objective to determine the distribution of HFRS cases and to identify key areas for future public health planning and resource allocation in Liaoning Province.</p> <p>Methods</p> <p>The annual average incidence at the county level was calculated using HFRS cases reported between 2000 and 2005 in Liaoning Province. GIS-based spatial analyses were conducted to detect spatial distribution and clustering of HFRS incidence at the county level, and the difference of relative humidity and forestation between the cluster areas and non-cluster areas was analyzed.</p> <p>Results</p> <p>Spatial distribution of HFRS cases in Liaoning Province from 2000 to 2005 was mapped at the county level to show crude incidence, excess hazard, and spatial smoothed incidence. Spatial cluster analysis suggested 16 and 41 counties were at increased risk for HFRS (p < 0.01) with the maximum spatial cluster sizes at ≤ 50% and ≤ 30% of the total population, respectively, and the analysis showed relative humidity and forestation in the cluster areas were significantly higher than in other areas.</p> <p>Conclusion</p> <p>Some clustering of HFRS cases in Liaoning Province may be etiologically linked. There was strong evidence some HFRS cases in Liaoning Province formed clusters, but the mechanism underlying it remains unknown. In this study we found the clustering was consistent with the relative humidity and amount of forestation, and showed data indicating there may be some significant relationships.</p

Simulated Anthrax Attacks and Syndromic Surveillance

Bioterrorism surveillance systems can be assessed using modeling to simulate real-world attacks