Dataset Information

Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013.

ABSTRACT: In the United States, influenza season typically begins in October or November, peaks in February, and tapers off in April. During the winter holiday break, from the end of December to the beginning of January, changes in social mixing patterns, healthcare-seeking behaviors, and surveillance reporting could affect influenza-like illness (ILI) rates. We compared predicted with observed weekly ILI to examine trends around the winter break period. We examined weekly rates of ILI by region in the United States from influenza season 2003-2004 to 2012-2013. We compared observed and predicted ILI rates from week 44 to week 8 of each influenza season using the auto-regressive integrated moving average (ARIMA) method. Of 1,530 region, week, and year combinations, 64 observed ILI rates were significantly higher than predicted by the model. Of these, 21 occurred during the typical winter holiday break period (weeks 51-52); 12 occurred during influenza season 2012-2013. There were 46 observed ILI rates that were significantly lower than predicted. Of these, 16 occurred after the typical holiday break during week 1, eight of which occurred during season 2012-2013. Of 90 (10 HHS regions x 9 seasons) predictions during the peak week, 78 predicted ILI rates were lower than observed. Out of 73 predictions for the post-peak week, 62 ILI rates were higher than observed. There were 53 out of 73 models that had lower peak and higher post-peak predicted ILI rates than were actually observed. While most regions had ILI rates higher than predicted during winter holiday break and lower than predicted after the break during the 2012-2013 season, overall there was not a consistent relationship between observed and predicted ILI around the winter holiday break during the other influenza seasons.

SUBMITTER: Gao H

PROVIDER: S-EPMC4674102 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013.

Gao Hongjiang H Wong Karen K KK Zheteyeva Yenlik Y Shi Jianrong J Uzicanin Amra A Rainey Jeanette J JJ

PloS one 20151209 12

In the United States, influenza season typically begins in October or November, peaks in February, and tapers off in April. During the winter holiday break, from the end of December to the beginning of January, changes in social mixing patterns, healthcare-seeking behaviors, and surveillance reporting could affect influenza-like illness (ILI) rates. We compared predicted with observed weekly ILI to examine trends around the winter break period. We examined weekly rates of ILI by region in the Un ...[more]

PMID: 26649568

Similar Datasets

Project description:ObjectiveAvailable reports on critically ill adults with cystic fibrosis (CF) suggest improving short-term outcomes. However, there is marked heterogeneity in reported findings, with studies mostly based on single-centered data, limiting generalizability. We sought to examine population-level patterns of demand for critical care resources, and the characteristics, resource utilization, and outcomes of ICU-managed adults with CF.MethodsWe used the Texas Inpatient Public Use Data File to identify ICU admissions with CF aged ≥18 years in Texas between 2004-2013. We examined ICU utilization at population level (using CF Foundation annual reports) and, among ICU admissions, socio-demographic characteristics, burden of comorbidities, organ failure, life-support utilization and hospital disposition. Linear regression and multilevel logistic regression were used to examine temporal trends and predictors of short-term mortality (hospital death and discharge to hospice), respectively.ResultsOf 9,579 hospitalizations of adults with CF, 1,249 (13%) were admitted to ICU. The incidence of ICU admission among adults with CF in Texas increased between 2004-2005 and 2012-2013 from 16.7 to 19.2 per 100 person-years (p = 0.0181), with ICU admissions aged ≥30 years accounting for 80.3% of the change. Among ICU admissions the following changes were noted between 2004-2005 and 2012-2013: any organ failure 30.2% vs. 56.3% (p = 0.0004), mechanical ventilation 11.5% vs. 19.2% (p = 0.0216), and hemodialysis 1.0% vs. 8.1% (p = 0.0007). Short-term mortality for the whole cohort and for those with mechanical ventilation was 11.4% and 41.8%, respectively, with corresponding home discharge among survivors 84% and 62.1%, respectively. Key predictors (adjusted odds ratios [aOR (95% CI)]) of short-term mortality included age ≥45 years (2.051 [1.231-3.415]), female gender (1.907 [1.237-2.941]), and mechanical ventilation (7.982 [5.001-12.739]).ConclusionsAdults with CF had high and rising population-level burden of critical illness. Although ICU admissions were increasingly older and sicker, the majority survived hospitalization, with most discharged home, supporting short-term benefits of critical care in the present cohort.

Project description:New insights into the intra-annual dynamics of tree-ring formation can improve our understanding of tree-growth response to environmental conditions at high-resolution time scales. Obtaining this information requires, however, a weekly monitoring of wood formation, sampling that is extremely time-intensive and scarcely feasible over vast areas. Estimating the timing of cambial and xylem differentiation by modeling thus represents an interesting alternative for obtaining this important information by other means. Temporal dynamics of cambial divisions can be extracted from the daily tree-ring growth rate computed by the Vaganov-Shashkin (VS) simulation model, assuming that cell production is tightly linked to tree-ring growth. Nonetheless, these predictions have yet to be compared with direct observations of wood development, i.e., via microcoring, over a long time span. We tested the performance of the VS model by comparing the observed and predicted timing of wood formation in black spruce [Picea mariana (Mill.)]. We obtained microcores over 15 years at 5 sites along a latitudinal gradient in Quebec (Canada). The measured variables included cell size and the timing of cell production and differentiation. We calibrated the VS model using daily temperature and precipitation recorded by weather stations located on each site. The predicted and observed timing of cambial and enlarging cells were highly correlated (R 2 = 0.8); nonetheless, we detected a systematic overestimation in the predicted timing of cambial cells, with predictions delayed by 1-20 days compared with observations. The growth rate of cell diameter was correlated with the predicted growth rate assigned to each cambial cell, confirming that cell diameter developmental dynamics have the potential to be inferred by the tree-ring growth curve of the VS model. Model performances decrease substantially in estimating the end of wood formation. The systematic errors suggest that the actual relationships implemented in the model are unable to explain the phenological events in autumn. The mismatch between the observed and predicted timing of wood formation in black spruce within our study area can be reduced by better adapting the VS model to wet sites, a context for which this model has been rarely used.

Project description:BACKGROUND:Different studies have suggested that web search data are useful in forecasting several phenomena from the field of economics to epidemiology or health issues. OBJECTIVE:This study aimed to (1) evaluate the correlation between suicide rates released by the Spanish National Statistics Institute (INE) and internet search trends in Spain reported by Google Trends (GT) for 57 suicide-related terms representing major known risks of suicide and an analysis of these results using a linear regression model and (2) study the differential association between male and female suicide rates published by the INE and internet searches of these 57 terms. METHODS:The study period was from 2004 to 2013. In this study, suicide data were collected from (1) Spain's INE and (2) local internet search data from GT, both from January 2004 to December 2013. We investigated and validated 57 suicide-related terms already tested in scientific studies before 2015 that would be the best predictors of new suicide cases. We then evaluated the nowcasting effects of a GT search through a cross-correlation analysis and by linear regression of the suicide incidence data with the GT data. RESULTS:Suicide rates in Spain in the study period were positively associated (r<-0.2) for the general population with the search volume for 7 terms and negatively for 1 from the 57 terms used in previous studies. Suicide rates for men were found to be significantly different than those of women. The search term, "allergy," demonstrated a lead effect for new suicide cases (r=0.513; P=.001). The next significant correlating terms for those 57 studied were "antidepressant," "alcohol abstinence," "relationship breakup" (r=0.295, P=.001; r=0.295, P=.001; and r=0.268, P=.002, respectively). Significantly different results were obtained for men and women. Search terms that correlate with suicide rates of women are consistent with previous studies, showing that the incidence of depression is higher in women than in men, and showing different gender searching patterns. CONCLUSIONS:A better understanding of internet search behavior of both men and women in relation to suicide and related topics may help design effective suicide prevention programs based on information provided by search robots and other big data sources.

Project description:BackgroundArtemisinin-resistant Plasmodium falciparum malaria has recently been identified on the Thailand-Cambodia border and more recently in parts of Thailand, Myanmar and Vietnam. There is concern that if this resistance were to spread, it would severely hamper malaria control and elimination efforts worldwide. Efforts are currently underway to intensify malaria control activities and ultimately eliminate malaria from Cambodia. To support these efforts, it is crucial to have a detailed picture of disease burden and its major determinants over time.MethodsAn analysis of spatial and temporal data on clinical malaria in Cambodia collected by the National Centre for Parasitology, Entomology and Malaria Control (CNM) and the Department of Planning and Health Information, Ministry of Health Cambodia from 2004 to 2013 is presented.ResultsThere has been a marked decrease of 81% in annual cases due to P. falciparum since 2009 coinciding with a rapid scale-up in village malaria workers (VMWs) and insecticide-treated bed nets (ITNs). Concurrently, the number of cases with Plasmodium vivax has greatly increased. It is estimated that there were around 112,000 total cases in 2012, 2.8 times greater than the WHO estimate for that year, and 68,000 in 2013 (an annual parasite incidence (API) of 4.6/1000). With the scale-up of VMWs, numbers of patients presenting to government facilities did not fall and it appears likely that those who saw VMWs had previously accessed healthcare in the private sector. Malaria mortality has decreased, particularly in areas with VMWs. There has been a marked decrease in cases in parts of western Cambodia, especially in Pailin and Battambang Provinces. In the northeast, the fall in malaria burden has been more modest, this area having the highest API in 2013.ConclusionThe clinical burden of falciparum malaria in most areas of Cambodia has greatly decreased from 2009 to 2013, associated with roll-out of ITNs and VMWs. Numbers of cases with P. vivax have increased. Possible reasons for these trends are discussed and areas requiring further study are highlighted. Although malaria surveillance data are prone to collection bias and tend to underestimate disease burden, the finding of similar trends in two independent datasets in this study greatly increased the robustness of the findings.

Dataset Information

Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013.

Publications

Comparing Observed with Predicted Weekly Influenza-Like Illness Rates during the Winter Holiday Break, United States, 2004-2013.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets