Severe acute respiratory syndrome: temporal stability and geographic variation in case-fatality rates and doubling times.
Ontology highlight
ABSTRACT:
SUBMITTER: Galvani AP
PROVIDER: S-EPMC3020622 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectivesWe aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic.MethodsWe develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.ResultsThe estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14.ConclusionsThe proposed estimation framework can be used to identify geographic variation in IFRs across settings.
| S-EPMC10990719 | biostudies-literature
Project description:ImportanceTimely access to medical care is an important determinant of health and well-being. The US Congress passed the Veterans Access, Choice, and Accountability Act in 2014 and the VA MISSION (Maintaining Systems and Strengthening Integrated Outside Networks) Act in 2018, both of which allow veterans to access care from community-based clinicians, but geographic variation in appointment wait times after the passage of these acts have not been studied.ObjectiveTo describe geographic variation in wait times experienced by veterans for primary care, mental health, and other specialties.Design, setting, and participantsThis is a cross-sectional study using data from the Veterans Health Administration (VHA) Corporate Data Warehouse. Participants include veterans who sought medical care from January 1, 2018, to June 30, 2021. Data analysis was performed from February to June 2022.ExposuresReferral to either VHA or community-based clinicians.Main outcomes and measuresTotal appointment wait times (in days) for 3 care categories: primary care, mental health, and all other specialties. VHA medical centers are organized into regions called Veterans Integrated Services Networks (VISNs); wait times were aggregated to the VISN level.ResultsThe final sample included 22 632 918 million appointments for 4 846 892 unique veterans (77.3% male; mean [SD] age, 61.6 [15.5] years). Among non-VHA appointments, mean (SD) VISN-level appointment wait times were 38.9 (8.2) days for primary care, 43.9 (9.0) days for mental health, and 41.9 (5.9) days for all other specialties. Among VHA appointments, mean (SD) VISN-level appointment wait times were 29.0 (5.5) days for primary care, 33.6 (4.6) days for mental health, and 35.4 (2.7) days for all other specialties. There was substantial geographic variation in appointment wait times. Among non-VHA appointments, VISN-level appointment wait times ranged from 25.4 to 52.4 days for primary care, from 29.3 to 65.7 days for mental health, and from 34.7 to 54.8 days for all other specialties. Among VHA appointments, wait times ranged from 22.4 to 43.4 days for primary care, from 24.7 to 42.0 days for mental health, and from 30.3 to 41.9 days for all other specialties. There was a correlation between wait times across care categories and setting (VHA vs community care).Conclusions and relevanceThis cross-sectional study found substantial variation in wait times across care type and geography, and VHA wait times in a majority of VISNs were lower than those for community-based clinicians, even after controlling for differences in specialty mix. These findings suggest that liberalized access to community care under the Veterans Access, Choice, and Accountability Act and the VA MISSION Act may not result in lower wait times within these regions.
| S-EPMC9412224 | biostudies-literature
Project description:Exacerbations are important disease events for patients with chronic obstructive pulmonary disease (COPD) as they are relatively frequent, result in significant resource use and can indicate worsening disease. Little is known about variation in COPD exacerbation rates across a health system in various geographic regions.To compare COPD exacerbation rates by regional service networks called Veterans Integrated Service Network (VISN) in the Veterans Health Administration (VA) system.Retrospective, observational study.Patients with a COPD diagnosis from October 1999 to September 2000 with follow-up to September 2002.Acute exacerbations of COPD during the baseline and follow-up periods.A total of 198,981 patients were identified. Average exacerbation rate at baseline was 0.503 events per person per year. In the follow-up period, there were 187,686 exacerbations experienced by 87,494 persons (44.0% of cohort). During follow-up, the average adjusted exacerbation rate was 0.589 per person per year and varied from 0.335 (95% CI, 0.328-0.342) in VISN 1 to 0.749 (95% CI, 0.735-0.0.763) in VISN 9. Using the median rate of exacerbation during the baseline period as the referent, 9 VISNs had lower adjusted rate ratios and 12 VISNs had higher adjusted rate ratios in the follow-up period.Geographic variation in the VA VISN system supports evidence that the medical care system including provider factors, and less so patient factors, affect COPD exacerbations. Understanding the reasons underlying this variation in COPD exacerbation rates may lead to improvements in future care and outcomes.
| S-EPMC2219808 | biostudies-other
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant of concern has spread rapidly across Brazil since late 2020, causing substantial infection and death waves. Here we used individual-level patient records after hospitalization with suspected or confirmed coronavirus disease 2019 (COVID-19) between 20 January 2020 and 26 July 2021 to document temporary, sweeping shocks in hospital fatality rates that followed the spread of Gamma across 14 state capitals, during which typically more than half of hospitalized patients aged 70 years and older died. We show that such extensive shocks in COVID-19 in-hospital fatality rates also existed before the detection of Gamma. Using a Bayesian fatality rate model, we found that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates were primarily associated with geographic inequities and shortages in healthcare capacity. We estimate that approximately half of the COVID-19 deaths in hospitals in the 14 cities could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization and pandemic preparedness are critical to minimize population-wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries.
| S-EPMC9307484 | biostudies-literature
Project description:Hierarchical Bayes models have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of mortality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level mortality rates based on fewer than 20 deaths are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatio-temporal variation in less common causes of mortality outcomes such as suicide rates (SRs) at the county level using direct estimates. Existing Bayesian spatio-temporal modeling strategies can be applied via Integrated Nested Laplace Approximation (INLA) in R to a large number of rare causes of mortality outcomes to enable examination of spatio-temporal variations on smaller geographic scales such as counties. This method allows examination of spatiotemporal variation across the entire U.S., even where the data are sparse. We used mortality data from 2005-2015 to explore spatiotemporal variation in SRs, as one particular application of the Bayesian spatio-temporal modeling strategy in R-INLA to predict year and county-specific SRs. Specifically, hierarchical Bayesian spatio-temporal models were implemented with spatially structured and unstructured random effects, correlated time effects, time varying confounders and space-time interaction terms in the software R-INLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation.
| S-EPMC5839164 | biostudies-literature
Project description:Generation time varies widely across organisms and is an important factor in the life cycle, life history and evolution of organisms. Although the doubling time (DT) has been estimated for many bacteria in the laboratory, it is nearly impossible to directly measure it in the natural environment. However, an estimate can be obtained by measuring the rate at which bacteria accumulate mutations per year in the wild and the rate at which they mutate per generation in the laboratory. If we assume the mutation rate per generation is the same in the wild and in the laboratory, and that all mutations in the wild are neutral, an assumption that we show is not very important, then an estimate of the DT can be obtained by dividing the latter by the former. We estimate the DT for five species of bacteria for which we have both an accumulation and a mutation rate estimate. We also infer the distribution of DTs across all bacteria from the distribution of the accumulation and mutation rates. Both analyses suggest that DTs for bacteria in the wild are substantially greater than those in the laboratory, that they vary by orders of magnitude between different species of bacteria and that a substantial fraction of bacteria double very slowly in the wild.
| S-EPMC6015860 | biostudies-literature
Project description:The current pandemic of COVID-19 is caused by the SARS-CoV-2 virus for which many variants at the Single Nucleotide Polymorphism (SNP) level have now been identified. We show here that different allelic variants among 692 SARS-CoV-2 genome sequences display a statistically significant association with geographic origin (p < 0.000001) and COVID-19 case severity (p = 0.016). Geographic variation in itself is associated with both case severity and allelic variation especially in strains from Indian origin (p < 0.000001). Using an new alternative bioinformatics approach we were able to confirm that the presence of the D614G mutation correlates with increased case severity in a sample of 127 sequences from a shared geographic origin in the US (p = 0.018). While leaving open the question on the pathogenesis mechanism involved, this suggests that in specific geographic locales certain genotypes of the virus are more pathogenic than others. We here show that viral genome polymorphisms may have an effect on case severity when other factors are controlled for, but that this effect is swamped out by these other factors when comparing cases across different geographic regions.
| S-EPMC7837616 | biostudies-literature
Project description:Several systemic treatments have been shown to increase survival for patients with metastatic castration-resistant prostate cancer. This study sought to characterize variation in use of the six "focus drugs" (docetaxel, abiraterone, enzalutamide, sipuleucel-T, radium-223, and cabazitaxel) that have been approved by the Food and Drug Administration for the treatment of metastatic castration-resistant prostate cancer during the years 2010-2015. We hypothesized that the use of these treatments would vary over time and by region of the country.We used Clinformatics DataMart™ Database (OptumInsight, Eden Prairie, MN), a de-identified claims database from a national insurance provider. Our sample included patients with prostate cancer who received any of the six drugs. We describe changes in usage patterns over time and geographic region of the United States via detailed descriptive statistics. We explore both patterns of first line therapy and sequence of treatments in our database.Our final analysis included 4275 patients with a mean age of 74 years. Docetaxel was the most commonly used first-line therapy in 2010 (97%), 2011 (66%), and 2012 (49%). Abiraterone was the most commonly used first-line therapy in 2013 (56%), 2014 (46%), and 2015 (34%). Approximately 14% of our study cohort received ?3 of the 6 drugs throughout their disease course. There was marked geographic variation in use of each of the drugs.Variation in treatment patterns were found with respect to both time and geographic location. Prescription rates of abiraterone outpaced docetaxel as the most commonly prescribed drug after 2013 when it became widely available. However, some regions of the country still lagged behind and prescribed less than would be expected.
| S-EPMC5840834 | biostudies-other
Project description:BackgroundIt is of interest to explore the variability in how the COVID-19 pandemic evolved geographically during the first twelve months. To this end, we apply inequality indices over regions to incidences, infection related mortality, and infection fatality rates. If avoiding of inequality in health is an important political goal, a metric must be implemented to track geographical inequality over time.MethodsThe relative and absolute Gini index as well as the Theil index are used to quantify inequality. Data are taken from international data bases. Absolute counts are transformed to rates adjusted for population size.ResultsComparing continents, the absolute Gini index shows an unfavorable development in four continents since February 2020. In contrast, the relative Gini as well as the Theil index support the interpretation of less inequality between European countries compared to other continents. Infection fatality rates within the EU as well as within the U.S. express comparable improvement towards more equality (as measured by both Gini indices).ConclusionsThe use of inequality indices to monitor changes in geographic inequality over time for key health indicators is a valuable tool to inform public health policies. The absolute and relative Gini index behave complementary and should be reported simultaneously in order to gain a meta-perspective on very complex dynamics.
| S-EPMC8118350 | biostudies-literature
Project description:In Chronic Myeloid Leukemia (CML), successful treatment requires accurate molecular monitoring to evaluate disease response and provide timely interventions for patients failing to achieve the desired outcomes. We wanted to determine whether measuring BCR-ABL1 mRNA doubling-times (DTs) could distinguish inconsequential rises in the oncogene's expression from resistance to tyrosine kinase inhibitors (TKIs). Thus, we retrospectively examined BCR-ABL1 evolution in 305 chronic-phase CML patients receiving imatinib mesylate (IM) as a first line treatment. Patients were subdivided in two groups: those with a confirmed rise in BCR-ABL1 transcripts without MR3.0 loss and those failing IM. We found that the DTs of the former patients were significantly longer than those of patients developing IM resistance (57.80 vs. 41.45 days, p = 0.0114). Interestingly, the DT values of individuals failing second-generation (2G) TKIs after developing IM resistance were considerably shorter than those observed at the time of IM failure (27.20 vs. 41.45 days; p = 0.0035). We next wanted to establish if decreases in BCR-ABL1 transcripts would identify subjects likely to obtain deep molecular responses. We therefore analyzed the BCR-ABL1 halving-times (HTs) of a different cohort comprising 174 individuals receiving IM in first line and observed that, regardless of the time point selected for our analyses (6, 12, or 18 months), HTs were significantly shorter in subjects achieving superior molecular responses (p = 0.002 at 6 months; p < 0.001 at 12 months; p = 0.0099 at 18 months). Moreover, 50 patients receiving 2G TKIs as first line therapy and obtaining an MR3.0 (after 6 months; p = 0.003) or an MR4.0 (after 12 months; p = 0.019) displayed significantly shorter HTs than individuals lacking these molecular responses. Our findings suggest that BCR-ABL1 DTs and HTs are reliable tools to, respectively, identify subjects in MR3.0 that are failing their assigned TKI or to recognize patients likely to achieve deep molecular responses that should be considered for treatment discontinuation.
| S-EPMC6700306 | biostudies-literature