Sexual Health and Sexually Transmitted Infections in the North American Arctic

STI rates reported for the Arctic are much higher than those reported for their southern counterparts

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

Abstract Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset

Who’s cooking tonight? A time-use study of coupled adults in Toronto, Canada

Understanding how coupled adults arrange food-related labor in relation to their daily time allocation is of great importance because different arrangements may have implications for diet-related health and gender equity. Studies from the time-use perspective argue that daily activities such as work, caregiving, and non-food-related housework can potentially compete for time with foodwork. However, studies in this regard are mostly centered on individual-level analyses. They fail to consider cohabiting partners’ time spent on foodwork and non-food-related activities, a factor that could be helpful in explaining how coupled partners decide to allocate time to food activities. Using 108 daily time-use logs from seventeen opposite-gender couples living in Toronto, Canada, this paper examines how male and female partners’ time spent on non-food-related activities impact the total amount of time spent on foodwork by coupled adults and the difference in time spent on foodwork between coupled women and men. Results show that both male and female partners took a higher portion of foodwork when their partner worked longer. When men worked for additional time, the couple-level duration of foodwork decreased. Without a significant impact on the gender difference in foodwork duration, women’s increased caregiving duration was associated with a reduction of total time spent on foodwork by couples. An increase in caregiving and non-food-related chores by men was associated with an increased difference in duration of foodwork between women and men, which helped secure a constant total amount of foodwork at the couple level. These behavioral variations between men and women demonstrate the gender differences in one’s responsiveness to the change of partners’ non-food-related tasks. The associations found among non-food-related activities and foodwork are suggestive of a need to account for partners’ time allocation when studying the time-use dynamics of foodwork and other daily activities

Influence of Detection Method and Study Area Scale on Syphilis Cluster Identification in North Carolina

Identifying geographical clusters of sexually transmitted infections can aid in targeting prevention and control efforts. However, detectable clusters can vary between detection methods because of different underlying assumptions. Furthermore, because disease burden is not geographically homogenous, the reference population is sensitive to the study area scale, affecting cluster outcomes. We investigated the influence of cluster detection method and geographical scale on syphilis cluster detection in Mecklenburg County, North Carolina

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

Abstract Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset

Does Core Area Theory Apply to STIs in Rural Environments?

Our objective was to determine the extent to which geographical core areas for gonorrhea and syphilis are located in rural areas, as compared to urban areas

Sexual Networks, Surveillance, and Geographical Space During Syphilis Outbreaks in Rural North Carolina

Sexually transmitted infections (STIs) spread along sexual networks whose structural characteristics promote transmission that routine surveillance may not capture. Cases who have partners from multiple localities may operate as spatial network bridges, thereby facilitating geographical dissemination. We investigated the relationships between surveillance, sexual networks, and spatial bridges for syphilis outbreaks in rural counties of North Carolina

Are Neighborhood Sociocultural Factors Influencing the Spatial Pattern of Gonorrhea in North Carolina?

PURPOSE: To determine if the spatial pattern of gonorrhea observed for North Carolina was influenced by neighborhood-level sociocultural determinants of health, including race/ethnicity. METHODS: A generalized linear mixed model with spatially correlated random effects was fit to measure the influence of socio-cultural factors on the spatial pattern of gonorrhea reported to the North Carolina State Health Department (January 1, 2005 to March 31, 2008). RESULTS: Neighborhood gonorrhea rates increased as the percent single mothers increased (25th to 75th neighborhood percentile Relative Rate 1.18, 95% CI 1.12, 1.25), and decreased as socioeconomic status increased (Relative Rate 0.89, 95% CI 0.84, 0.95). Increasing numbers of men in neighborhoods with more women than men did not change the gonorrhea rate, but was associated with decreased rates in neighborhoods with more men than women. Living in the mountains was protective for all race/ethnicities. Rurality was associated with decreased rates for Blacks and increased rates for Native Americans outside the mountains. PURPOSE: Neighborhood-level sociocultural factors, primarily those indicative of neighborhood deprivation, explained a significant proportion of the spatial pattern of gonorrhea in both urban and rural communities. Race/ethnicity was an important proxy for social and cultural factors not captured by measures of socioeconomic status

Geomasking sensitive health data and privacy protection: an evaluation using an E911 database

Geomasking is used to provide privacy protection for individual address information while maintaining spatial resolution for mapping purposes. Donut geomasking and other random perturbation geomasking algorithms rely on the assumption of a homogeneously distributed population to calculate displacement distances, leading to possible under-protection of individuals when this condition is not met. Using household data from 2007, we evaluated the performance of donut geomasking in Orange County, North Carolina. We calculated the estimated k-anonymity for every household based on the assumption of uniform household distribution. We then determined the actual k-anonymity by revealing household locations contained in the county E911 database. Census block groups in mixed-use areas with high population distribution heterogeneity were the most likely to have privacy protection below selected criteria. For heterogeneous populations, we suggest tripling the minimum displacement area in the donut to protect privacy with a less than 1% error rate