A space-time analysis of reported shigellosis and salmonellosis cases in Texas from 2000 to 2004

Background. The purpose of this study was to describe the risk factors and demographics of persons with salmonellosis and shigellosis and to investigate both seasonal and spatial variations in the occurrence of these infections in Texas from 2000 to 2004, utilizing time series analyses and the geographic information system digital mapping methods. ^ Methods. Spatial Analysis: MapInfo software was used to map the distribution of age-adjusted rates of reported shigellosis and salmonellosis in Texas from 2000–2004 by zip codes. Census data on above or below poverty level, household income, highest level of educational attainment, race, ethnicity, and urban/rural community status was obtained from the 2000 Decennial Census for each zip code. The zip codes with the upper 10% and lower 10% were compared using t-tests and logistic regression to determine whether there were any potential risk factors. ^ Temporal analysis. Seasonal patterns in the prevalence of infections in Texas from 2000 to 2003 were determined by performing time-series analysis on the numbers of cases of salmonellosis and shigellosis. A linear regression was also performed to assess for trends in the incidence of each disease, along with auto-correlation and multi-component cosinor analysis. ^ Results. Spatial analysis: Analysis by general linear model showed a significant association between infection rates and age, with young children aged less than 5 and those aged 5–9 years having increased risk of infection for both disease conditions. The data demonstrated that those populations with high percentages of people who attained a higher than high school education were less likely to be represented in zip codes with high rates of shigellosis. However, for salmonellosis, logistic regression models indicated that when compared to populations with high percentages of non-high school graduates, having a high school diploma or equivalent increased the odds of having a high rate of infection. ^ Temporal analysis. For shigellosis, multi-component cosinor analyses were used to determine the approximated cosine curve which represented a statistically significant representation of the time series data for all age groups by sex. The shigellosis results show 2 peaks, with a major peak occurring in June and a secondary peak appearing around October. Salmonellosis results showed a single peak and trough in all age groups with the peak occurring in August and the trough occurring in February. ^ Conclusion. The results from this study can be used by public health agencies to determine the timing of public health awareness programs and interventions in order to prevent salmonellosis and shigellosis from occurring. Because young children depend on adults for their meals, it is important to increase the awareness of day-care workers and new parents about modes of transmission and hygienic methods of food preparation and storage. ^

An investigation into the relationship between screen time, consumption of advertised foods, and physical activity among Texas 4th grade elementary school children

Background. The high prevalence of obesity among children has spurred creation of a list of possible causative factors, including the advertising of foods of minimal nutritional value, a decrease in physical activity, and increased media use. Few studies show prevalence rates of these factors among large cohorts of children. ^ Methods. Using data from the 2004-2005 School Physical Activity and Nutrition project (SPAN), a secondary analysis of 7907 4th-grade children (mean age 9.74 years) was conducted. In addition, a comic-book–based intervention that addressed advertised food consumption, physical activity, and media use was developed and evaluated using a pre-post test design among 4th-grade children in an urban school district. ^ Results. Among a cohort of 4th-grade children across the state of Texas, children who had more than 2 hours of video game or computer time the previous day were more than twice as likely to drink soda and eat candy or pastries. In addition, children who watched more than 2 hours of TV the previous day were more than three times as likely to consume chips, punch, soda, candy, frozen desserts, or pastries (AOR 3.41, 95% CI: 1.58, 7.37). A comic-book based intervention held great promise and acceptance among 4th-grade children. Outcome evaluation showed that while results moved in a positive direction, they were not statistically significant. ^ Conclusion. Statistically significant associations were found between screen time and eating various types of advertised food. The comic book intervention was widely accepted by the children exposed to it, and pre-post surveys indicated they moved constructs in a positive direction. Further research is needed to look at more specific ways in which children are exposed to TV, and the relationship of the TV viewing time with their consumption of advertised foods. In addition, researchers should look at comic book interventions more closely and attempt to utilize them in more in studies with a longer follow-up time. ^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^