A structured additive modeling of diabetes and hypertension in Northeast India

Background Multiple factors are associated with the risk of diabetes and hypertension. In India, they vary widely even from one district to another. Therefore, strategies for controlling diabetes and hypertension should appropriately address local risk factors and take into account the specific causes of the prevalence of diabetes and hypertension at sub-population levels and in specific settings. This paper examines the demographic and socioeconomic risk factors as well as the spatial disparity of diabetes and hypertension among adults aged 15–49 years in Northeast India. Methods The study used data from the Indian Demographic Health Survey, which was conducted across the country between 2015 and 2016. All men and women between the ages of 15 and 49 years were tested for diabetes and hypertension as part of the survey. A Bayesian geo-additive model was used to determine the risk factors of diabetes and hypertension. Results The prevalence rates of diabetes and hypertension in Northeast India were, respectively, 6.38% and 16.21%. The prevalence was higher among males, urban residents, and those who were widowed/divorced/separated. The functional relationship between household wealth index and diabetes and hypertension was found to be an inverted U-shape. As the household wealth status increased, its effect on diabetes also increased. However, interestingly, the inverse was observed in the case of hypertension, that is, as the household wealth status increased, its effect on hypertension decreased. The unstructured spatial variation in diabetes was mainly due to the unobserved risk factors present within a district that were not related to the nearby districts, while for hypertension, the structured spatial variation was due to the unobserved factors that were related to the nearby districts. Conclusion Diabetes and hypertension control measures should consider both local and non-local factors that contribute to the spatial heterogeneity. More importance should be given to efforts aimed at evaluating district-specific factors in the prevalence of diabetes within a region.

A latent-class heteroskedastic hurdle trajectory model: patterns of adherence in obstructive sleep apnea patients on CPAP therapy

Background Sleep apnea patients on CPAP therapy exhibit differences in how they adhere to the therapy. Previous studies have demonstrated the benefit of describing adherence in terms of discernible longitudinal patterns. However, these analyses have been done on a limited number of patients, and did not properly represent the temporal characteristics and heterogeneity of adherence. Methods We illustrate the potential of identifying patterns of adherence with a latent-class heteroskedastic hurdle trajectory approach using generalized additive modeling. The model represents the adherence trajectories on three aspects over time: the daily hurdle of using the therapy, the daily time spent on therapy, and the day-to-day variability. The combination of these three characteristics has not been studied before. Results Applying the proposed model to a dataset of 10,000 patients in their first three months of therapy resulted in nine adherence groups, among which 49% of patients exhibited a change in adherence over time. The identified group trajectories revealed a non-linear association between the change in the daily hurdle of using the therapy, and the average time on therapy. The largest difference between groups was observed in the patient motivation score. The adherence patterns were also associated with different levels of high residual AHI, and day-to-day variability in leakage. Conclusion The inclusion of the hurdle model and the heteroskedastic model into the mixture model enabled the discovery of additional adherence patterns, and a more descriptive representation of patient behavior over time. Therapy adherence was mostly affected by a lack of attempts over time, suggesting that encouraging these patients to attempt therapy on a daily basis, irrespective of the number of hours used, could drive adherence. We believe the methodology is applicable to other domains of therapy or medication adherence.

Combining probability scores to optimize clinical use of the NICHD Neonatal BPD Outcome Estimator

Objective: Bronchopulmonary dysplasia (BPD) continues to prevail among very preterm infants. While NICHD BPD Outcome Estimator is easy to use, the clinical interpretation remains challenging. This study aims to optimize its use. Study Design: A retrospective study was conducted with 469 infants born between 2015 and 2020. Data were entered into the Estimator to obtain probability scores. Trajectories of the probability scores were modeled using generalized additive modeling. The optimal cutoff number for predicting severe BPD or death was identified by a grid search from a range established by the original population distribution and the ROC curve. Result: Combining probability scores from the severe and death categories and the no-BPD and mild categories may improve BPD outcome prediction. A cutoff of 21% combining outcome probabilities from severe and death categories is predictive of severe BPD or death. Conclusion: Combining probability scores of different categories improves BPD outcome prediction.

1475Effects of climate factors on the dengue fever in Paraguay: generalized additive model in 2014-2020

Background Dengue fever is disease transmitted by mostly Aedes spp. This study aims investigate the association between climate factors and dengue incidence in Paraguay, considered as an endemic disease since 2009. Methods We extracted incidence of dengue by week from 2014-2020 national health surveillance, Paraguay. The climate factors, including rainfall, sunshine, minimum temperate, air pressure, relative humidity and wind were extracted from Directorate of Meteorology and Hydrology and aggregated as a weekly data. Generalized additive modeling was performed, adjusted by seasonality and population. Lags between 0-10 weeks were chosen for according to rho statistics of Spearman’s test. Results A total of dengue fever was 40,593 in study period. The mean cumulative incidence per 10,000 populations was 22.37 (standard deviation: 93.27). All six climate factors and seasonality were significant in the final model with the adjusted R-square 18.6%. Rainfall (relative risk [RR]: 0.51), relative humidity (RR: 0.25) and wind (RR: 0.19) showed negative trends with the increase of dengue while atmospheric pressure (RR: 9.32) and sunshine (RR: 0.12) showed positive associations. Minimum temperature showed increasing trend until 15ºC (1ºC increase in 4-fold incidence). The lag of each factor was selected between 2 to 10 weeks. Conclusion Climate factors showed associations with dengue fever in Paraguay. Such climate factors should be considered along with the dengue surveillance in endemic areas for effective dengue control. Key messages Climate factors showed significant dynamic associations with dengue incidence in Paraguay.

Agro-Environmental Determinants of Leptospirosis: A Retrospective Spatiotemporal Analysis (2004−2014) in Mahasarakham Province (Thailand)

Leptospirosis has been recognized as a major public health concern in Thailand following dramatic outbreaks. We analyzed human leptospirosis incidence between 2004 and 2014 in Mahasarakham province, Northeastern Thailand, in order to identify the agronomical and environmental factors likely to explain incidence at the level of 133 sub-districts and 1,982 villages of the province. We performed general additive modeling (GAM) in order to take the spatial-temporal epidemiological dynamics into account. The results of GAM analyses showed that the average slope, population size, pig density, cow density and flood cover were significantly associated with leptospirosis occurrence in a district. Our results stress the importance of livestock favoring leptospirosis transmission to humans and suggest that prevention and control of leptospirosis need strong intersectoral collaboration between the public health, the livestock department and local communities. More specifically, such collaboration should integrate leptospirosis surveillance in both public and animal health for a better control of diseases in livestock while promoting public health prevention as encouraged by the One Health approach.

Seasonally variable relationships between surface water temperature and inflow in the upper San Francisco Estuary

Water temperature and inflow are key environmental drivers in aquatic systems that are linked through a causal web of factors including climate, weather, water management, and their downstream linkages. However, we do not yet fully understand the relationship between inflow and water temperature, especially in complex managed systems such as estuaries. The San Francisco Estuary is the center of a critical water supply infrastructure and home to a deteriorating ecosystem with several declining fish species at the warm edge of their thermal range. We used generalized additive modeling of long-term monitoring data to evaluate the relationship between inflow and water temperature along with its spatio-seasonal variability. Most commonly, we found a negative temperature-inflow relationship in which water temperatures increased as inflow decreased, up to 2 °C from high to low-inflow years. However, the opposite (positive) relationship was observed in the winter months, and in the western (downstream) regions from July-September, up to -1.2 °C from high to low-inflow years. These results were upheld by models that included the long-term temperature trend or used salinity as a proxy for location. Upstream factors likely played the biggest role in the summer when local precipitation is negligible, while local precipitation and the related weather conditions may drive much of the winter pattern. Although further mechanistic studies are needed to infer the direct effect of dam releases on water temperatures, these results provide a broader understanding of the impacts of flood and drought dynamics for those tasked with managing estuarine ecosystems.

Nonlinearities in bilingual visual word recognition: An introduction to generalized additive modeling

This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

The Impact of Vessels on Humpback Whale Behavior: The Benefit of Added Whale Watching Guidelines

The concurrent increase in marine tourism and vessel traffic around the world highlights the need for developing responsible whale watching guidelines. To determine the impact of vessel presence on humpback whale behaviors in Maui Nui, a land-based study was conducted from 2015 to 2018 in Maui, Hawai'i. Theodolite tracks were used to summarize humpback whale swim speed, respiration rate, dive time, and path directness to determine the potential impacts of various types of vessel presence on whale behavior. Vessel presence, proximity, and approach type in conjunction with biological parameters were used in a generalized additive modeling framework to explain changes in whale behaviors. The results presented here show increases in swim speed, respiration rate, and path directness in conjunction with decreasing dive times, which has been shown to be an energetically demanding avoidance strategy. These observations, in conjunction with increasing awareness on the implication of non-lethal effects of human disturbance and changing oceanic environments on humpback whales, highlights the need for a pre-cautionary approach to management. Stricter guidelines on whale watching will limit the level of disturbance to individual humpback whales in Hawai'i and ensure they maintain the fitness required to compensate for varying ecological and anthropogenic conditions.

Spatial patterns in summertime surface ozone in the Southern Front Range of the U.S. Rocky Mountains

Summertime ozone in the Western United States presents a unique public health challenge. Changes in population, background ozone, wildland fire, and local precursor emissions combined with terrain-induced meteorology can affect surface ozone levels and compliance with the National Ambient Air Quality Standards (NAAQS). While there is considerable research on ozone in the Northern Front Range Metropolitan Area of Colorado, United States, less is known about the Southern Front Range. In Colorado Springs, approximately 100 km south of Denver, summertime maximum daily 8-h average (MDA8) ozone shows no significant (p < .05) trend at the 5th, 50th, or 95th percentile over the past 20 years. However, the region is at risk of nonattainment with the NAAQS based on observations from 2018 to 2020. From June through September 2018, the Colorado Department of Public Health and Environment measured hourly ozone at eight sites to characterize the spatial distribution of ozone in Colorado Springs. Mean ozone (±1σ) ranged from 34 ± 19 to 60 ± 9 ppb. The 95th percentile of hourly ozone increased approximately 1.1 ppb per 100 m of elevation, while the amplitudes of mean diurnal profiles decreased with elevation and distance from the interstate. MDA8 ozone was also highly correlated across all sites, and there is little evidence of local photochemical production or ozone transport from Denver. Further, results from generalized additive modeling show that summertime MDA8 in this region is strongly influenced by regional background air and wildfire, with smoke contributing an average of 4–5 ppb to the MDA8. Enhanced MDA8 values due to wildfires were especially pronounced in 2018 and 2020. Lastly, we find that the permanent monitoring sites represent the lower end of observed ozone in the region, suggesting that additional long-term monitoring for public health may be warranted in populated, higher elevation areas.