A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method.

Understanding the spatial characteristics of dengue fever (DF) incidences is crucial for governmental agencies to implement effective disease control strategies. We investigated the associations between environmental and socioeconomic factors and DF geographic distribution, are proposed a probabilistic risk assessment approach that uses threshold-based quantile regression to identify the significant risk factors for DF transmission and estimate the spatial distribution of DF risk regarding full probability distributions. To interpret risk, return period was also included to characterize the frequency pattern of DF geographic occurrences. The study area included old Kaohsiung City and Fongshan District, two areas in Taiwan that have been affected by severe DF infections in recent decades. Results indicated that water-related facilities, including canals and ditches, and various types of residential area, as well as the interactions between them, were significant factors that elevated DF risk. By contrast, the increase of per capita income and its associated interactions with residential areas mitigated the DF risk in the study area. Nonlinear associations between these factors and DF risk were present in various quantiles, implying that water-related factors characterized the underlying spatial patterns of DF, and high-density residential areas indicated the potential for high DF incidence (e.g., clustered infections). The spatial distributions of DF risks were assessed in terms of three distinct map presentations: expected incidence rates, incidence rates in various return periods, and return periods at distinct incidence rates. These probability-based spatial risk maps exhibited distinct DF risks associated with environmental factors, expressed as various DF magnitudes and occurrence probabilities across Kaohsiung, and can serve as a reference for local governmental agencies

When and how do prosthetic hips fail after total hip arthroplasties?—A retrospective study

Understanding failure modes, time to revision, and vulnerable components in revision hips could help reduce the risk of revision surgeries. Our aim was to investigate the association between the index diagnosis and the failure mode in patients undergoing revision surgeries. Methods: A total of 402 patients who underwent a first revision surgery in a single hospital between 2000 and 2012 were recruited in a retrospective study. Multiple logistic regression analysis was used to evaluate the association of the index diagnosis of the primary total hip arthroplasty and short-term failure, as well as specific failure mode that occurred early, while controlling for sex, age, and the type of prosthesis. Results: The mean time to revision due to all failure modes was 9.48 (standard deviation = 6.08) years. Defining short-term failure as a time to revision <5 years after total hip arthroplasty, the primary failure mode was infection (32.4%), followed by loosening (25.7%) and instability (17.1%). In multivariate analysis, as compared to osteonecrosis, patients with index diagnosis as infection was significantly associated with revision due to infection (odds ratio = 9.69, p = 0.013). In addition, osteoarthritis increased the odds of loosening (odds ratio = 4.18, p = 0.012). In contrast to studies in the United States and Europe, acetabular component revisions were the most common type found in our study. Conclusion: This study demonstrates that, compared with patients with osteonecrosis, patients with infection and osteoarthritis had higher odds of revision due to infection and loosening, respectively. Further studies are needed to examine the cause–effect relationship between index diagnosis and mode of failure

Variations of Pseudo R-square across quantile levels of (a) the two most significant risk factors, and (b) the four most significant interaction effects.

<p>Variations of Pseudo R-square across quantile levels of (a) the two most significant risk factors, and (b) the four most significant interaction effects.</p

Comparison of pseudo R-squares across quantile levels among 1) conventional quantile regression model (CQR), 2) threshold-based quantile regression model (TBQR), and 3) threshold-based quantile regression considering interaction effects (TBQRI).

<p>Comparison of pseudo R-squares across quantile levels among 1) conventional quantile regression model (CQR), 2) threshold-based quantile regression model (TBQR), and 3) threshold-based quantile regression considering interaction effects (TBQRI).</p

Spatial distribution of average DF incidence rate in 535 Li's during 2004–2011.

<p>Spatial distribution of average DF incidence rate in 535 Li's during 2004–2011.</p

Map of Kaohsiung city and Fongshan district.

<p>Map of Kaohsiung city and Fongshan district.</p