Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.
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ABSTRACT:
SUBMITTER: Li Y
PROVIDER: S-EPMC8656247 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:For patients on dialysis, hospitalizations remain a major risk factor for mortality and morbidity. We use data from a large national database, United States Renal Data System, to model time-varying effects of hospitalization risk factors as functions of time since initiation of dialysis. To account for the three-level hierarchical structure in the data where hospitalizations are nested in patients and patients are nested in dialysis facilities, we propose a multilevel mixed effects varying coefficient model (MME-VCM) where multilevel (patient- and facility-level) random effects are used to model the dependence structure of the data. The proposed MME-VCM also includes multilevel covariates, where baseline demographics and comorbidities are among the patient-level factors, and staffing composition and facility size are among the facility-level risk factors. To address the challenge of high-dimensional integrals due to the hierarchical structure of the random effects, we propose a novel two-step approximate EM algorithm based on the fully exponential Laplace approximation. Inference for the varying coefficient functions and variance components is achieved via derivation of the standard errors using score contributions. The finite sample performance of the proposed estimation procedure is studied through simulations.
| S-EPMC7303000 | biostudies-literature
Project description:ObjectiveStudy the impact of local policies on near-future hospitalization and mortality rates.Materials and methodsWe introduce a novel risk-stratified SIR-HCD model that introduces new variables to model the dynamics of low-contact (e.g., work from home) and high-contact (e.g., work on-site) subpopulations while sharing parameters to control their respective R0(t) over time. We test our model on data of daily reported hospitalizations and cumulative mortality of COVID-19 in Harris County, Texas, from May 1, 2020, until October 4, 2020, collected from multiple sources (USA FACTS, U.S. Bureau of Labor Statistics, Southeast Texas Regional Advisory Council COVID-19 report, TMC daily news, and Johns Hopkins University county-level mortality reporting).ResultsWe evaluated our model's forecasting accuracy in Harris County, TX (the most populated county in the Greater Houston area) during Phase-I and Phase-II reopening. Not only does our model outperform other competing models, but it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches.DiscussionMortality and hospitalization rates are significantly impacted by local quarantine and reopening policies. Existing models do not directly account for the effect of these policies on infection, hospitalization, and death rates in an explicit and explainable manner. Our work is an attempt to improve prediction of these trends by incorporating this information into the model, thus supporting decision-making.ConclusionOur work is a timely effort to attempt to model the dynamics of pandemics under the influence of local policies.
| S-EPMC8555375 | biostudies-literature
Project description:Background and objectivesThe vast majority of US dialysis facilities are for-profit and profit status has been associated with processes of care and outcomes in patients on dialysis. This study examined whether dialysis facility profit status was associated with the rate of hospitalization in patients starting dialysis.Design, setting, participants, & methodsThis was a retrospective cohort study of Medicare beneficiaries starting dialysis between 2005 and 2008 using data from the US Renal Data System. All-cause hospitalization was examined and compared between for-profit and nonprofit dialysis facilities through 2009 using Poisson regression. Companion analyses of cause-specific hospitalization that are likely to be influenced by dialysis facility practices including hospitalizations for heart failure and volume overload, access complications, or hyperkalemia were conducted.ResultsThe cohort included 150,642 patients. Of these, 12,985 (9%) were receiving care in nonprofit dialysis facilities. In adjusted models, patients receiving hemodialysis in for-profit facilities had a 15% (95% confidence interval [95% CI], 13% to 18%) higher relative rate of hospitalization compared with those in nonprofit facilities. Among patients receiving peritoneal dialysis, the rate of hospitalization in for-profit versus nonprofit facilities was not significantly different (relative rate, 1.07; 95% CI, 0.97 to 1.17). Patients on hemodialysis receiving care in for-profit dialysis facilities had a 37% (95% CI, 31% to 44%) higher rate of hospitalization for heart failure or volume overload and a 15% (95% CI, 11% to 20%) higher rate of hospitalization for vascular access complications.ConclusionsHospitalization rates were significantly higher for patients receiving hemodialysis in for-profit compared with nonprofit dialysis facilities.
| S-EPMC3878699 | biostudies-literature
Project description:Background and objectivesAssisted peritoneal dialysis is a treatment option for individuals with barriers to self-care who wish to receive home dialysis, but previous research suggests that this treatment modality is associated with a higher rate of hospitalization. The objective of our study was to determine whether assisted peritoneal dialysis has a different rate of hospital days compared to in-center hemodialysis.Design, setting, participants, & measurementsWe conducted a multicenter, retrospective cohort study by linking a quality assurance dataset to administrative health data in Ontario, Canada. Subjects were accrued between January 1, 2004 and July 9, 2013. Individuals were grouped into assisted peritoneal dialysis (family or home care assisted) or in-center hemodialysis on the basis of their first outpatient dialysis modality. Inverse probability of treatment weighting using a propensity score was used to create a sample in which the baseline covariates were well balanced.ResultsThe study included 872 patients in the in-center hemodialysis group and 203 patients in the assisted peritoneal dialysis group. Using an intention to treat approach, patients on assisted peritoneal dialysis had a similar hospitalization rate of 11.1 d/yr (95% confidence interval, 9.4 to 13.0) compared with 12.9 d/yr (95% confidence interval, 10.3 to 16.1) in the hemodialysis group (P=0.19). Patients on assisted peritoneal dialysis were more likely to be hospitalized for dialysis-related reasons (admitted for 2.4 d/yr [95% confidence interval, 1.8 to 3.2] compared with 1.6 d/yr [95% confidence interval, 1.1 to 2.3] in the hemodialysis group; P=0.04). This difference was partly explained by more hospital days because of peritonitis. Modality switching was associated with high rates of hospital days per year.ConclusionsAssisted peritoneal dialysis was associated with similar rates of all-cause hospitalization compared with in-center hemodialysis. Patients on assisted peritoneal dialysis who experienced peritonitis and technique failure had high rates of hospitalization.
| S-EPMC5012487 | biostudies-literature
Project description:For chronic dialysis patients, a unique population requiring continuous medical care, methodologies to monitor patient outcomes, such as hospitalizations, over time, after initiation of dialysis, are of particular interest. Contributing to patient hospitalizations is a number of multilevel covariates such as demographics and comorbidities at the patient level and staffing composition at the dialysis facility level. We propose a varying coefficient model for multilevel risk factors (VCM-MR) to study the time-varying effects of covariates on patient hospitalization risk as a function of time on dialysis. The proposed VCM-MR also includes subject-specific random effects to account for within-subject correlation and dialysis facility-specific fixed effect varying coefficient functions to allow for the modeling of flexible time-varying facility-specific risk trajectories. An approximate EM algorithm and an iterative Newton-Raphson approach are proposed to address the challenge of estimation of high-dimensional parameters (varying coefficient functions) for thousands of dialysis facilities in the United States. The proposed modeling allows for comparisons between time-varying effects of multilevel risk factors as well as testing of facility-specific fixed effects. The method is applied to model hospitalization risk using the rich hierarchical data available on dialysis patients initiating dialysis between January 1, 2006 and December 31, 2008 from the United States Renal Data System, a large national database, where 331?443 hospitalizations over time are nested within patients, and 89?889 patients are nested within 2201 dialysis facilities. Patients are followed-up until December 31, 2013, where the follow-up time is truncated five years after the initiation of dialysis. Finite sample properties are studied through extensive simulations.
| S-EPMC6296494 | biostudies-literature
Project description:Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles.
| S-EPMC5756893 | biostudies-literature
Project description:Rationale & objectiveThe coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on hospitalizations in general and on dialysis patients in particular. This study modeled the impact of COVID-19 on hospitalizations of dialysis patients in 2020.Study designRetrospective cohort study.Setting & participantsMedicare patients on dialysis in calendar year 2020.PredictorsCOVID-19 status was divided into 4 stages: COVID1 (first 10 days after initial diagnosis), COVID2 (extends until the Post-COVID stage), Post-COVID (after 21 days with no COVID-19 diagnosis), and Late-COVID (begins after a hospitalization with a COVID-19 diagnosis); demographic and clinical characteristics; and dialysis facilities.OutcomeThe sequence of hospitalization events.Analytical approachA proportional rate model with a nonparametric baseline rate function of calendar time on the study population.ResultsA total of 509,609 patients were included in the study, 63,521 were observed to have a SARS-CoV-2 infection, 34,375 became Post-COVID, and 1,900 became Late-COVID. Compared with No-COVID, all 4 stages had significantly greater adjusted risks of hospitalizations with relative rates of 18.50 (95% CI, 18.19-18.81) for COVID1, 2.03 (95% CI, 1.99-2.08) for COVID2, 1.37 (95% CI, 1.35-1.40) for Post-COVID, and 2.00 (95% CI, 1.89-2.11) for Late-COVID.LimitationsFor Medicare Advantage patients, we only had inpatient claim information. The analysis was based on data from the year 2020, and the effects may have changed due to vaccinations, new treatments, and new variants. The COVID-19 effects may be somewhat overestimated due to missing information on patients with few or no symptoms and possible delay in COVID-19 diagnosis.ConclusionsWe discovered a marked time dependence in the effect of COVID-19 on hospitalization of dialysis patients, beginning with an extremely high risk for a relatively short period, with more moderate but continuing elevated risks later, and never returning to the No-COVID level.
| S-EPMC9398821 | biostudies-literature
Project description:We propose a new methodological framework for the analysis of hierarchical functional data when the functions at the lowest level of the hierarchy are correlated. For small data sets, our methodology leads to a computational algorithm that is orders of magnitude more efficient than its closest competitor (seconds versus hours). For large data sets, our algorithm remains fast and has no current competitors. Thus, in contrast to published methods, we can now conduct routine simulations, leave-one-out analyses, and nonparametric bootstrap sampling. Our methods are inspired by and applied to data obtained from a state-of-the-art colon carcinogenesis scientific experiment. However, our models are general and will be relevant to many new data sets where the object of inference are functions or images that remain dependent even after conditioning on the subject on which they are measured. Supplementary materials are available at Biostatistics online.
| S-EPMC2830578 | biostudies-literature
Project description:Respiratory syncytial virus (RSV) is a major cause of worldwide morbidity and mortality in infants, primarily through the induction of bronchiolitis. RSV epidemics are highly seasonal, occurring in the winter months in the northern hemisphere. Within the United States, RSV epidemic dynamics vary both spatially and temporally. This analysis employs a retrospective space–time scan statistic to locate spatiotemporal clustering of infant bronchiolitis in a very large Tennessee (TN) Medicaid cohort. We studied infants less than 6 months of age (N = 52,468 infants) who had an outpatient visit, emergency department visit, or hospitalization for bronchiolitis between 1995 and 2008. The scan statistic revealed distinctive and consistent patterns of deviation in epidemic timing. Eastern TN (Knoxville area) showed clustering in January and February, and Central TN (Nashville area) in November and December. This is likely due to local variation in geography-associated factors which should be taken into consideration in future modeling of RSV epidemics.
| S-EPMC3742041 | biostudies-literature
Project description:According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from ~18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (~100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.
| S-EPMC4755125 | biostudies-literature