Biomedical subjects

Peter A Rogerson

Publications and source records attributed to Peter A Rogerson.

11 recordsLinked to original sources

PubMed2006

Statistical methods for the detection of spatial clustering in case-control data.

In this paper, I develop new approaches for the detection of spatial clustering in case-control data. One method is based upon drawing Thiessen polygons around each control. It is unnecessary to actually draw or compute the boundaries of the polygons; it is sufficient to count, for each control, the number of cases that are closer to that control than to any other control. A second method is similar to the Cuzick-Edwards method, which is based on counts of cases that are among the k-nearest neighbours of cases, but is instead based upon the number of cases within a specified distance of cases. These first two methods are global methods in the sense that they provide a single statistic that measures the degree of spatial clustering. The third method suggests a local statistic, for tests of the null hypothesis of no spatial clustering around a prespecified focus. The method is based upon the cumulative chi2 test, which is typically used to test whether cases are more prevalent than expected around a prespecified location. This is also extended to the case where all observational locations are considered as potential cluster locations and multiple testing is carried out. Each of the new methods is illustrated using data on childhood leukaemia and lymphoma cases in North Humberside.

Bayes Theorem↗

PubMed2006

Recent changes in the spatial pattern of prostate cancer in the U.S.

INTRODUCTION: Spatial-temporal trends in prostate cancer mortality are of interest because of the introduction and increasing use of the prostate-specific antigen (PSA) screening test after 1986. This article describes spatial-temporal changes in U.S. prostate cancer mortality from 1968 to 1998. METHODS: Prostate cancer mortality data were obtained from Compressed Mortality Files available from the National Center for Health Statistics. To minimize potential problems such as small numbers or missing data, the analysis was limited to white males aged 25 and over, and located in 2970 counties with complete data. Statistical analyses included the global distance between observed and expected multinomial probabilities, Hoover's Index of Concentration, and a retrospective test for change in spatial patterns. RESULTS: Fairly steady declines were observed in prostate cancer mortality from 1968 until 1993, with an increasing tendency toward spatial uniformity. Spatial concentration increased from 1994 to 1998, and by 1998 the level of spatial concentration had returned to levels that prevailed during the early to mid-1980s. Comparing 1991-1998 to 1968-1990, the observed number of prostate deaths increased the most rapidly with respect to the expected number in western Appalachia and the south central U.S. Recent relative declines in mortality were observed in southern California and parts of Florida. CONCLUSIONS: The observed results are generally consistent with prior evaluations of prostate cancer spatial-temporal patterns. However, the current study identified a heretofore unnoticed recent pattern of change in western Appalachia and the south central U.S. Recent declines in Florida and southern California may have contributed to recent increases in spatial concentration of prostate cancer mortality, and may possibly be associated with realized benefits from screening programs.

Aged↗

PubMed2005

Geographical variation of cerebrovascular disease in New York State: the correlation with income.

BACKGROUND: Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. RESULTS: We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075-1.22 x 10(-4)(income)+ 8.068x10(-10)(income2), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each 10,000 dollars increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. CONCLUSION: Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors.

Journal Article↗

PubMed2005

Population distribution and redistribution of the baby-boom cohort in the United States: recent trends and implications.

Over 70 million people were born into the baby-boom cohort between 1946 and 1964. Over 65 million of these individuals are presently alive, and thus the cohort continues to exert a powerful influence on regional population change in the United States. In this article, we examine the recent and current geographic distribution of the baby-boom cohort. In 1990, the members of the cohort comprised a particularly high proportion of the population in a small number of dynamic metropolitan areas. We also highlight the recent migration trends exhibited by this cohort; these trends are potentially important early indicators of the retirement-related migration patterns that the cohort might follow. The spatial redistribution of the cohort has many implications, including potentially significant consequences for intergenerational relationships and caregiving. Also highlighted in the article are the temporal and geographical implications for intergenerational caregiving. There has been much attention given to the "sandwich" generation, with its members having dual caregiving responsibilities to both parents and children. A more appropriate designation may be the "stretched" generation, because caregiving seems to extend over a long period. In particular, many members of the baby-boom cohort are beginning to care for their aging parents just as they finish child rearing.

Demography↗

PubMed2005

Assessing spatio-temporal variability of risk surfaces using residential history data in a case control study of breast cancer.

BACKGROUND: Most analyses of spatial clustering of disease have been based on either residence at the time of diagnosis or current residence. An underlying assumption in these analyses is that residence can be used as a proxy for environmental exposure. However, exposures earlier in life and not just those in the most recent period may be of significance. In breast cancer, there is accumulating evidence that early life exposures may contribute to risk. We explored spatio-temporal patterns of risk surfaces using data on lifetime residential history in a case control study of breast cancer, and identified elevated areas of risk and areas potentially having more exposure opportunities, defined as risk surfaces in this study. This approach may be more relevant in understanding the environmental etiology of breast cancer, since lifetime cumulative exposures or exposures at critical times may be more strongly associated with risk for breast cancer than exposures from the recent period. RESULTS: A GIS-based exploratory spatial analysis was applied, and spatio-temporal variability of those risk surfaces was evaluated using the standardized difference in density surfaces between cases and controls. The significance of the resulting risk surfaces was tested and reported as p-values. These surfaces were compared for premenopausal and postmenopausal women, and were obtained for each decade, from the 1940s to 1990s. We found strong evidence of clustering of lifetime residence for premenopausal women (for cases relative to controls), and a less strong suggestion of such clustering for postmenopausal women, and identified a substantial degree of temporal variability of the risk surfaces. CONCLUSION: We were able to pinpoint geographic areas with higher risk through exploratory spatial analyses, and to assess temporal variability of the risk surfaces, thus providing a working hypothesis on breast cancer and environmental exposures. Geographic areas with higher case densities need further epidemiologic investigation for potential relationships between lifetime environmental exposures and breast cancer risk. Examination of lifetime residential history provided additional information on geographic areas associated with higher risk; limiting exploration of chronic disease clustering to current residence may neglect important relationships between location and disease.

Journal Article↗

PubMed2005

Use of CUSUM and Shewhart charts to monitor regional trends of birth defect reports in New York State.

BACKGROUND: Cumulative sum (CUSUM) charts were originally developed for industrial quality control, but may be adapted for the surveillance of health outcome data, such as birth defects. The reported prevalence of birth defects can vary due to differences in case ascertainment, surveillance practices, or true changes in prevalence. We examined the utility of CUSUM and Shewhart charts for detect-ing changes in prevalence of two different birth defect groups. We chose obstructive renal defects because we expected an increase in reporting due to improved diagnosis. We chose oral clefts for comparison because we expected reporting to be unaffected by changes in diagnostic technologies. METHODS: Data from the New York State Congenital Malformations Registry from 1992-1999 were analyzed using self-starting binomial CUSUM and Shewhart charts for four regions of New York State. RESULTS: CUSUM charts show that reports of obstructive urinary defects have increased from 1992-1999 in all regions of New York State. Reports of oral clefts increased only on Long Island. CONCLUSIONS: The CUSUM method proved useful for identifying changes in birth defect reporting and was able to detect the expected increases in obstructive renal defects. The apparent increase is likely due to improvements in diagnostic imaging techniques. In contrast, we only detected an increase in oral clefts on Long Island, which may be related to under report-ing of cases in the earlier years. CUSUM charts are useful in detecting small, sustained increases in prevalences over time while Shewhart charts are easier to interpret and can detect large sharp increases.

Birth Certificates↗

PubMed2004

Approaches to syndromic surveillance when data consist of small regional counts.

INTRODUCTION: Statistical systems designed for syndromic surveillance often must be able to monitor data received simultaneously from multiple regions. Such data might be of limited size, which would eliminate the possibility of using more common surveillance methods that assume data from a normal distribution. OBJECTIVES: The objectives of this study were to design and illustrate a multiregional surveillance system based on data inputs consisting of small regional counts, where frequencies are typically on the order of </=5. METHODS: Cumulative sum (CUSUM) methods designed for cumulating the sum of the deviations between observed and expected Poisson-distributed data were modified to account for changing expectations over time, including weekly and monthly effects. Data on lower respiratory tract infections during 1996-1999 at multiple Boston clinics among residents from 287 census tracts were used to illustrate the approach. RESULTS: When each region was monitored, 19% of the census tracts signaled a departure during 1999 from the base period (1996-1998) rates. When local statistics were used to monitor tracts and neighborhoods consisting of surrounding tracts, 60% of tracts experienced departures during 1999 from the base period. These results imply that the increases in lower respiratory tract infection that occurred during 1999 were geographically pervasive. CONCLUSIONS: Poisson CUSUM methods are useful for monitoring small regional counts over time. The methods can be generalized to account for time-varying expectations in the counts.

Epidemiologic Measurements↗

PubMed2004

Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches.

Prospective disease surveillance has gained increasing attention, particularly in light of recent concern for quick detection of bioterrorist events. Monitoring of health events has the potential for the detection of such events, but the benefits of surveillance extend much more broadly to the quick detection of change in public health. In this paper, univariate and multivariate cumulative sum methods for disease surveillance are compared. Although the univariate method has been previously used in the context of health surveillance, the multivariate method has not. The univariate approach consists of simultaneously and independently monitoring the disease rate in each region; the multivariate approach accounts explicitly for any covariation between regions. The univariate approaches are limited by their lack of ability to account for the spatial autocorrelation of regional data; the multivariate methods are limited by the difficulty in accurately specifying the multiregional covariance structure. The methods are illustrated using both simulated data and county-level data on breast cancer in the northeastern United States. When the degree of spatial autocorrelation is low, the univariate method is generally better at detecting changes in rates that occur in a small number of regions; the multivariate is better when change occurs in a large number of regions.

Breast Neoplasms↗

PubMed2004

Geographic clustering of residence in early life and subsequent risk of breast cancer (United States).

OBJECTIVE: This study focused on geographic clustering of breast cancer based on residence in early life and identified spatio-temporal clustering of cases and controls. METHODS: Data were drawn from the WEB study (Western New York Exposures and Breast Cancer Study), a population-based case-control study of incident, pathologically confirmed breast cancer (1996-2001) in Erie and Niagara counties. Controls were frequency-matched to cases on age, race, and county of residence. All cases and controls used in the study provided lifetime residential histories. The k-function difference between cases and controls was used to identify spatial clustering patterns of residence in early life. RESULTS: We found that the evidence for clustered residences at birth and at menarche was stronger than that for first birth or other time periods in adult life. Residences for pre-menopausal cases were more clustered than for controls at the time of birth and menarche. We also identified the size and geographic location of birth and menarche clusters in the study area, and found increased breast cancer risk for pre-menopausal women whose residence was within the cluster compared to those living elsewhere at the time of birth. CONCLUSION: This study provides evidence that early environmental exposures may be related to breast cancer risk, especially for pre-menopausal women.

Adult↗

PubMed2003

Evaluating the reliability of automated collision notification systems.

The use of an automated collision notification (ACN) device in vehicles can greatly reduce the time between crash occurrence and notification of emergency medical services (EMSs). Most ACN devices rely on cellular technology to report important crash information to the proper authorities. The objective of this study was to examine the ability of the existing western New York cellular analog system to support ACN systems. The first task was to develop a model predicting the probability of successfully completing an emergency ACN call at attenuated levels of received signal strength indicator (RSSI), a measurement of the bond between cell phone and tower. Then, empirical estimates were made of the time necessary for call completion at given levels of the RSSI. The RSSI is sampled at locations throughout Erie County, New York, and this information is used to determine the probability of successful call completion for different locations within the county. This model was then applied to historic data for selected past crashes. Finally, the findings were compared with real-world crash data obtained from the ACN Field Operational Test program, where 750 ACN devices were installed in cars and their performance examined over time. An interpolated map of the sampled RSSI values suggests that cellular coverage in Erie County is adequate to support the automated collision network technology. The models and techniques described here are applicable to other areas and regions of the country.

Accidents, Traffic↗

PubMed2002

The effects of migration on the detection of geographic differences in disease risk.

Human migration can make it more difficult to detect geographic differences in disease risk because of the spatial diffusion of people originally exposed in a given geographic area. There are also situations where migration can facilitate the detection of disease attributable to environmental hazards. This paper assesses the effects that migration has on the ability to detect regional variability in disease risk. Several characteristics of migration are discussed, including some that are not widely known. Because of regional variations in mobility rates and other characteristics of the migration process, there is substantial regional variation in the ability to detect spatial variation in risk.

Employment↗