Personal Characteristics and Experience of Primary Care Predicting Frequent Use of Emergency Department: A Prospective Cohort Study

<div><p>Objective</p><p>A small number of patients frequently using the emergency department (ED) account for a disproportionate amount of the total ED workload and are considered using this service inappropriately. The aim of this study was to identify prospectively personal characteristics and experience of organizational and relational dimensions of primary care that predict frequent use of ED.</p><p>Methods</p><p>This study was conducted among parallel cohorts of the general population and primary care patients (N = 1,769). The measures were at baseline (T₁), 12 (T₂) and 24 months (T₃): self-administered questionnaire on current health, health behaviours and primary care experience in the previous year. Use of medical services was confirmed using administrative databases. Mixed effect logistic regression modeling identified characteristics predicting frequent ED utilization.</p><p>Results</p><p>A higher likelihood of frequent ED utilization was predicted by lower socioeconomic status, higher disease burden, lower perceived organizational accessibility, higher number of reported healthcare coordination problems and not having a complete annual check-up, above and beyond adjustment for all independent variables.</p><p>Conclusions</p><p>Personal characteristics such as low socioeconomic status and high disease burden as well as experience of organizational dimensions of primary care such as low accessibility, high healthcare coordination problems and low comprehensiveness of care are prospectively associated with frequent ED utilization. Interventions developed to prevent inappropriate ED visits, such as case management for example, should tailor low socioeconomic status and patients with high disease burden and should aim to improve experience of primary care regarding accessibility, coordination and comprehensiveness.</p></div

Sample flow chart: the sample size at sampling steps and the percentage kept from the previous step.

<p>Sample flow chart: the sample size at sampling steps and the percentage kept from the previous step.</p

Mixed effects logistic regression modeling for the study.

<p>Mixed effects logistic regression modeling for the study.</p

Mixed model regression results for all studied univariate and multivariate models: odds ratio of frequent ED use with 95% confidence intervals.

<p>Mixed model regression results for all studied univariate and multivariate models: odds ratio of frequent ED use with 95% confidence intervals.</p

Sociodemographic data of both cohorts.

<p>Sociodemographic data of both cohorts.</p

Personal characteristics about health behaviors and status.

<p>Personal characteristics about health behaviors and status.</p

Organizational and relational characteristics.

<p>Organizational and relational characteristics.</p

Hypotheses related to the decay of virion-producing cells after treatment initiation.

<p>A) Virion-producing cells are short-lived infected CD4 cells and all new cell infections are prevented due to high drug exposure. This translates into only one phase of viral decay; B) There are two types of virion-producing cells having half-lives of around 1 (short-lived) and 14 days (long-lived), respectively, and all new cell infections are prevented due to high drug exposure.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198090#pone.0198090.ref001" target="_blank">1</a>] This translated into two phases of viral decay C) Virion-producing cells are mostly short-lived infected CD4 cells located in two compartments, one with high (pink) and one with low (blue) drug exposure. The compartment with low drug exposure partially allows new cell infections, effectively leading to two phases of viral decay. The compartment is not associated to a specific tissue at this point, as its existence is hypothesized.</p

Regression line (black) for the typical patient undertaking a treatment combining efavirenz, tenofovir DF and emtricitabine, model fit (red), and associated <i>f</i>_{<i>u</i>,<i>2</i>} and <i>φ</i>₂ values.

<p><i>f</i>_{<i>u</i>,<i>2</i>} is the average fraction of total infection events not affected by the drugs and <i>φ</i>₂ is the fraction of maximum plasma viral load. Both parameters refer to the drug-limited compartment. Regression curve based on data from Karris et al.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198090#pone.0198090.ref026" target="_blank">26</a>].</p

Boxplot of the risk of virologic failure as predicted by the model and obtained from 200 simulations, one per sampling point of drug penetration values (<i>k</i>_<i>l</i>^<i>EFV</i>, <i>k</i>_<i>l</i>^<i>TFV</i>, <i>k</i>_<i>l</i>^<i>FTC</i>).

<p>For comparison purposes, the area shaded in darker blue is the confidence interval for the equivalent but observed in a real patient sample.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198090#pone.0198090.ref027" target="_blank">27</a>].</p