Browse
Submit Data
Databases
API
Help

Dataset Information

16 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A time-varying vulnerability index for COVID-19 in New Mexico, USA using generalized propensity scores.

ABSTRACT: The coronavirus disease (COVID-19) pandemic has highlighted systemic inequities in the United States and resulted in a larger burden of negative social outcomes for marginalized communities. New Mexico, a state in the southwestern US, has a unique population with a large racial minority population and a high rate of poverty that may make communities more vulnerable to negative social outcomes from COVID-19. To identify which communities may be at the highest relative risk, we created a county-level vulnerability index. After the first COVID-19 case was reported in New Mexico on March 11, 2020, we fit a generalized propensity score model that incorporates sociodemographic factors to predict county-level viral exposure and thus, the generic risk to negative social outcomes such as unemployment or mental health impacts. We used four static sociodemographic covariates important for the state of New Mexico-population, poverty, household size, and minority population-and weekly cumulative case counts to iteratively run our model each week and normalize the exposure score to create a time-varying vulnerability index. We found the relative vulnerability between counties varied in the first eight weeks from the initial COVID-19 case before stabilizing. This framework for creating a location-specific vulnerability index in response to an ongoing disaster may be used as a quick, deployable metric to inform health policy decisions such as allocating state resources to the county level.

SUBMITTER: Gorris ME

PROVIDER: S-EPMC8416291 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Cardiovascular event risk dynamics over time in older patients on dialysis: a generalized multiple-index varying coefficient model approach.

Project description:Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Although recent studies have found that the risk of cardiovascular events is higher after an infection-related hospitalization, studies have not fully elucidated how the risk of cardiovascular events changes over time for patients on dialysis. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization, and (3) the patient's age at the start of dialysis. This is achieved by using a new class of generalized multiple-index varying coefficient (GM-IVC) models. The proposed GM-IVC models utilize a multiplicative structure and one-dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection-related hospitalization among the dynamic cohort of survivors. We develop a two-step estimation procedure for the GM-IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk using the United States Renal Data System database, which collects data on nearly all patients with end-stage renal disease in the United States. Finally, simulation studies assess the performance of the proposed estimation procedures.

| S-EPMC4209204 | biostudies-literature

Generalized Michaelis-Menten rate law with time-varying molecular concentrations.

Project description:The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

| S-EPMC10735182 | biostudies-literature

Mapping Heat Vulnerability Index Based on Different Urbanization Levels in Nebraska, USA.

Project description:Heatwaves cause excess mortality and physiological impacts on humans throughout the world, and climate change will intensify and increase the frequency of heat events. Many adaptation and mitigation studies use spatial distribution of highly vulnerable local populations to inform heat reduction and response plans. However, most available heat vulnerability studies focus on urban areas with high heat intensification by Urban Heat Islands (UHIs). Rural areas encompass different environmental and socioeconomic issues that require alternate analyses of vulnerability. We categorized Nebraska census tracts into four urbanization levels, then conducted factor analyses on each group and captured different patterns of socioeconomic vulnerabilities among resultant Heat Vulnerability Indices (HVIs). While disability is the major component of HVI in two urbanized classes, lower education, and races other than white have higher contributions in HVI for the two rural classes. To account for environmental vulnerability of HVI, we considered different land type combinations for each urban class based on their percentage areas and their differences in heat intensifications. Our results demonstrate different combinations of initial variables in heat vulnerability among urban classes of Nebraska and clustering of high and low heat vulnerable areas within the highest urbanized sections. Less urbanized areas show no spatial clustering of HVI. More studies with separation on urbanization level of residence can give insights into different socioeconomic vulnerability patterns in rural and urban areas, while also identifying changes in environmental variables that better capture heat intensification in rural settings.

| S-EPMC8533801 | biostudies-literature

The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores.

Project description:Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.

| S-EPMC5617809 | biostudies-literature

A varying-coefficient generalized odds rate model with time-varying exposure: An application to fitness and cardiovascular disease mortality.

Project description:Varying-coefficient models have become a common tool to determine whether and how the association between an exposure and an outcome changes over a continuous measure. These models are complicated when the exposure itself is time-varying and subjected to measurement error. For example, it is well known that longitudinal physical fitness has an impact on cardiovascular disease (CVD) mortality. It is not known, however, how the effect of longitudinal physical fitness on CVD mortality varies with age. In this paper, we propose a varying-coefficient generalized odds rate model that allows flexible estimation of age-modified effects of longitudinal physical fitness on CVD mortality. In our model, the longitudinal physical fitness is measured with error and modeled using a mixed-effects model, and its associated age-varying coefficient function is represented by cubic B-splines. An expectation-maximization algorithm is developed to estimate the parameters in the joint models of longitudinal physical fitness and CVD mortality. A modified pseudoadaptive Gaussian-Hermite quadrature method is adopted to compute the integrals with respect to random effects involved in the E-step. The performance of the proposed method is evaluated through extensive simulation studies and is further illustrated with an application to cohort data from the Aerobic Center Longitudinal Study.

| S-EPMC6736699 | biostudies-literature

Ecosystem impacts by the Ancestral Puebloans of Chaco Canyon, New Mexico, USA

Project description:The Ancestral Puebloans occupied Chaco Canyon, in what is now the southwestern USA, for more than a millennium and harvested useful timber and fuel from the trees of distant forests as well as local woodlands, especially juniper and pinyon pine. These pinyon juniper woodland products were an essential part of the resource base from Late Archaic times (3000–100 BC) to the Bonito phase (AD 800–1140) during the great florescence of Chacoan culture. During this vast expanse of time, the availability of portions of the woodland declined. We posit, based on pollen and macrobotanical remains, that the Chaco Canyon woodlands were substantially impacted during Late Archaic to Basketmaker II times (100 BC–AD 500) when agriculture became a major means of food production and the manufacture of pottery was introduced into the canyon. By the time of the Bonito phase, the local woodlands, especially the juniper component, had been decimated by centuries of continuous extraction of a slow-growing resource. The destabilizing impact resulting from recurrent woodland harvesting likely contributed to the environmental unpredictability and difficulty in procuring essential resources suffered by the Ancestral Puebloans prior to their ultimate departure from Chaco Canyon.

| S-EPMC8550600 | biostudies-literature

Using generalized linear models to implement g-estimation for survival data with time-varying confounding.

Project description:Using data from observational studies to estimate the causal effect of a time-varying exposure, repeatedly measured over time, on an outcome of interest requires careful adjustment for confounding. Standard regression adjustment for observed time-varying confounders is unsuitable, as it can eliminate part of the causal effect and induce bias. Inverse probability weighting, g-computation, and g-estimation have been proposed as being more suitable methods. G-estimation has some advantages over the other two methods, but until recently there has been a lack of flexible g-estimation methods for a survival time outcome. The recently proposed Structural Nested Cumulative Survival Time Model (SNCSTM) is such a method. Efficient estimation of the parameters of this model required bespoke software. In this article we show how the SNCSTM can be fitted efficiently via g-estimation using standard software for fitting generalised linear models. The ability to implement g-estimation for a survival outcome using standard statistical software greatly increases the potential uptake of this method. We illustrate the use of this method of fitting the SNCSTM by reanalyzing data from the UK Cystic Fibrosis Registry, and provide example R code to facilitate the use of this approach by other researchers.

| S-EPMC7612171 | biostudies-literature

Estimating the palliative effect of percutaneous endoscopic gastrostomy in an observational registry using principal stratification and generalized propensity scores.

Project description:Clinical disease registries offer a rich collection of valuable patient information but also pose challenges that require special care and attention in statistical analyses. The goal of this paper is to propose a statistical framework that allows for estimating the effect of surgical insertion of a percutaneous endogastrostomy (PEG) tube for patients living with amyotrophic lateral sclerosis (ALS) using data from a clinical registry. Although all ALS patients are informed about PEG, only some patients agree to the procedure which, leads to the potential for selection bias. Assessing the effect of PEG is further complicated by the aggressively fatal disease, such that time to death competes directly with both the opportunity to receive PEG and clinical outcome measurements. Our proposed methodology handles the "censoring by death" phenomenon through principal stratification and selection bias for PEG treatment through generalized propensity scores. We develop a fully Bayesian modeling approach to estimate the survivor average causal effect (SACE) of PEG on BMI, a surrogate outcome measure of nutrition and quality of life. The use of propensity score methods within the principal stratification framework demonstrates a significant and positive effect of PEG treatment, particularly when time of treatment is included in the treatment definition.

| S-EPMC5027570 | biostudies-literature

Development of a vulnerability index for diagnosis with the novel coronavirus, COVID-19, in Washington State, USA

Project description:Highlights ? In the United States, morbidity and mortality from COVID-19 infection varies substantially among populations and geographic regions? Variation has been attributed to chronic disease burden, and certain sociodemographic factors? We developed an index of COVID-19 vulnerability using a regression modeling approach with observed diagnosis at the Zipcode level in Washington State? The resulting index can be used by policy makers for the targeting of public health and health care resources.

| S-EPMC7318982 | biostudies-literature

Calendar time-specific propensity scores and comparative effectiveness research for stage III colon cancer chemotherapy.

Project description:Nonexperimental studies of treatment effectiveness provide an important complement to randomized trials by including heterogeneous populations. Propensity scores (PSs) are common in these studies but may not adequately capture changes in channeling experienced by innovative treatments. We use calendar time-specific (CTS) PSs to examine the effect of oxaliplatin during dissemination from off-label to widespread use.Stage III colon cancer patients aged 65+ years initiating chemotherapy between 2003 and 2006 were examined using cancer registry data linked with Medicare claims. Two PS approaches for receipt of oxaliplatin versus 5-flourouricil were constructed using logistic models with key components of age, sex, substage, grade, census-level income, and comorbidities: (i) a conventional, year-adjusted PS and (ii) a CTS PS constructed and matched separately within 1-year intervals, then combined. We compared PS-matched hazard ratios (HRs) for mortality using Cox models.Oxaliplatin use increased significantly; 8% (n?=?86) of patients received it in the first time period versus 52% (n?=?386) in the last. Channeling by comorbidities, income, and age appeared to change over time. The CTS PS improved covariate balance within calendar time strata and yielded an attenuated estimated benefit of oxaliplatin (HR?=?0.75) compared with the conventional PS (HR?=?0.69).In settings where prescribing patterns have changed and calendar time acts as a confounder, a CTS PS can characterize changes in treatment choices and estimating separate PSs within specific calendar time periods may result in enhanced confounding control. To increase validity of comparative effectiveness research, researchers should carefully consider drug lifecycles and effects of innovative treatment dissemination over time.

| S-EPMC3659185 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data