Dataset Information

Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing.

ABSTRACT: Many measures of chronic diseases, including respiratory disease, exhibit seasonal variation together with residual correlation between consecutive time periods and neighboring areas. We demonstrate a strategy for modeling data that exhibit both seasonal trend and spatiotemporal correlation, using an application to respiratory prescribing. We analyzed 55 months (2002-2006) of prescribing data from the northeast of England, in the United Kingdom. We estimated the seasonal pattern of prescribing by fitting a dynamic harmonic regression (DHR) model to salbutamol prescribing in relation to temperature. We compared the output of DHR models to static sinusoidal regression models. We used the DHR-fitted values as an offset in mixed-effects models that aimed to account for the remaining spatiotemporal variation in prescribing rates. As diagnostic checks, we assessed spatial and temporal correlation separately and jointly. Our application of a DHR model resulted in a better fit to the seasonal variation of prescribing than was obtained with a static model. After adjusting for the fitted values from the DHR model, we did not detect any remaining spatiotemporal correlation in the model's residuals. Using a DHR model and temperature data to account for the periodicity of prescribing proved to be an efficient way to capture its seasonal variation. The diagnostic procedures indicated that there was no need to model any remaining correlation explicitly.

SUBMITTER: Sofianopoulou E

PROVIDER: S-EPMC5860516 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing.

Sofianopoulou Eleni E Pless-Mulloli Tanja T Rushton Stephen S Diggle Peter J PJ

American journal of epidemiology 20170701 1

Many measures of chronic diseases, including respiratory disease, exhibit seasonal variation together with residual correlation between consecutive time periods and neighboring areas. We demonstrate a strategy for modeling data that exhibit both seasonal trend and spatiotemporal correlation, using an application to respiratory prescribing. We analyzed 55 months (2002-2006) of prescribing data from the northeast of England, in the United Kingdom. We estimated the seasonal pattern of prescribing b ...[more]

PMID: 28453604

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Project description:ObjectivesInappropriate antibiotic use for respiratory tract infection (RTI) is an ongoing problem linked to the emergence of drug resistance and other adverse effects. Less is known about the prescribing practices of individual physicians or the impact of physician prescribing habits on patient outcomes. We studied the prescribing practices of providers for acute RTIs in an integrated health system, identified patient factors associated with receipt of an antibiotic and assessed the relation between providers' adjusted prescribing rates and a number of patient outcomes.MethodsThis was a retrospective analysis of adults with an RTI visit to any primary care providers across the Cleveland Clinic Health System in 2011-2012. Patients with a history of chronic obstructive pulmonary disease or immunocompromised status were excluded. Logistic regression was used to examine patient factors associated with receipt of an antibiotic.ResultsOf 31,416 patients with an RTI, 54.8% received an antibiotic. Patient factors associated with antibiotic prescribing included white race (odds ratio [OR] 1.35, P < 0.001), presence of fever (OR 1.66, P < 0.001), and a diagnosis of bronchitis (OR 10.98, P < 0.001) or sinusitis (OR 33.85, P < 0.001). Among 290 providers with ≥10 RTI visits, adjusted antibiotic prescribing rates ranged from 0% to 100% (mean 49%). Antibiotics were prescribed more often for sinusitis (OR 33.85, P < 0.001), bronchitis (OR 10.98, P < 0.001), or pharyngitis (OR 1.76, P < 0.001) compared with upper respiratory tract infection. Patients who were prescribed antibiotics at the index visit were more likely to return for RTI within 1 year (adjusted OR 1.26, P < 0.001). Emergency department visits for respiratory complications were rare and not associated with antibiotic receipt.ConclusionsAntibiotic prescribing for RTI varies widely among physicians and cannot be explained by patient factors. Patients prescribed antibiotics for RTI were more likely to return for RTI.

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Project description:ObjectScarlet fever is an acute respiratory infectious disease that endangers public health and imposes a huge economic burden. In this paper, we systematically studied its spatial and temporal evolution and explore its potential ecological drivers. The goal of this research is to provide a reference for analysis based on surveillance data of scarlet fever and other acute respiratory infectious illnesses, and offer suggestions for prevention and control.MethodThis research is based on a spatiotemporal multivariate model (Endemic-Epidemic model). Firstly, we described the epidemiology status of the scarlet fever epidemic in Sichuan Province from 2016 to 2019. Secondly, we used spatial autocorrelation analysis to understand the spatial pattern. Thirdly, we applied the endemic-epidemic model to analyze the spatiotemporal dynamics by quantitatively decomposing cases into endemic, autoregressive, and spatiotemporal components. Finally, we explored potential ecological drivers that could influence the spread of scarlet fever.ResultsFrom 2016 to 2019, the incidence of scarlet fever in Sichuan Province varied much among cities. In terms of temporal distribution, there were 1-2 epidemic peaks per year, and they were mainly concentrated from April to June and October to December. In terms of transmission, the endemic and temporal spread were predominant. Our findings imply that the school holiday could help to reduce the spread of scarlet fever, and a standard increase in Gross Domestic Product (GDP) was associated with 2.6 folds contributions to the epidemic among cities.ConclusionScarlet fever outbreaks are more susceptible to previous cases, as temporal spread accounted for major transmission in many areas in Sichuan Province. The school holidays and GDP can influence the spread of infectious diseases. Given that covariates could not fully explain heterogeneity, adding random effects was essential to improve accuracy. Paying attention to critical populations and hotspots, as well as understanding potential drivers, is recommended for acute respiratory infections such as scarlet fever. For example, our study reveals GDP is positively associated with spatial spread, indicating we should consider GDP as an important factor when analyzing the potential drivers of acute infectious disease.

Dataset Information

Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing.

Publications

Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing.

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