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  • The study population is derived from a representative randomized sampling (using sampling intervals) of adult patients (18+ years of age) presenting to a level 1 trauma urban hospital’s emergency department (volume 62,000/ppy). The study population consists of patients self-presenting or arriving via Emergency Medical Services (EMS) and triaged using the Emergency Severity Index (ESI) assigned either level 4 or level 5, the least urgent/non-emergent classifications [75,76]. Sampling took place 24 hours, seven days a week over a period of approximately 8 weeks with an 89 percent response rate. Since emergency department presentments are seasonal-cyclical in nature, using historical data we projected the expected volume of patient flow for each day, AM and PM. Using these historical data and estimated response rates, we established a sampling interval for each working shift within a 24 hour period. As the study progressed, we periodically checked the evolving representativeness by cross referencing both the financial and descriptive characteristics of the study population relative to the historical data. Written consent was obtained from all study participants. The study design was vetted and approved by the Institutional Review Board of EVMS (#07-08-EX-0198). Descriptive statistics, including frequencies and averages, are offered for the variables. Relationships with the outcome variable, utilization, are tested using logistic regression to establish odds-ratios. The initial Andersen behavioral health model proposed factors that are predictive of healthcare utilization within three dimensions: predisposing characteristics, enabling characteristics, and need characteristics [73]. According to the behavioral health model, there is a loose causal ordering to the three primary dimensions explaining utilization. The predisposing dimension, which is most distant from utilization, embodies many health behaviors that may be considered stressors, predisposing the individual towards medical attention at some point [77]. Predisposing factors are those that are liable to place the individual at risk and in a predisposition to utilize the emergency department. Predisposing characteristics include substance abuse, obesity, and health-belief variables such as knowledge and values. This is followed by enabling factors that facilitate information and access to treatment, such as employment, income, insurance, and access to non-ED primary care doctors. The most proximate dimension to utilization is that of need. Need characteristics include variables related to personal assessments of health or health status. The Andersen-Aday healthcare utilization model has been adapted to address health utilization among specific populations such as released prisoners with HIV [78], marginally housed [33,79], underserved populations [80], insured populations [68,81], and minority populations [82,83], among others [49,84,85]. Our research uses the Andersen-Aday behavioral health model as the guide to select and organize factors we expect to explain emergency department frequent utilization for non-emergent presentments. In a later visitation of the behavioral health model, Andersen expresses the need to incorporate the additional roles of social networks, mental dysfunction, and consumer satisfaction within the model [74]. First, social relationships and networks are viewed as capital that may moderate utilization. The decision to seek the services of a health professional and present at a treatment venue, such as the emergency department, may be made within the context of an individual’s social and familial network. These networks provide access to information and resources that may be used to better manage the condition [86]. Second, mental health needs have been associated with complexity in the treatment of other conditions such as substance abuse and chronic disease [87–89]. Third, Andersen and others encourage the study of the role of consumer satisfaction in promoting utilization [44,74,90,91]. For example, the perceptions of quality of care received have been associated with a willingness to return to the treatment venue to seek additional service [45]. In response to Andersen’s call, we have incorporated within this study a measure of the individual social and familial network, measure of mental health drugs and metal health days as indicators of the psychological disposition, and measure of patients’ perceptions of service quality. The outcome variable is the self-reported number of emergency department encounters. Patients were asked to estimate the number of additional times they have gone to an emergency department to receive treatment for themselves within the past year. Noting that a patient’s timeframe recall may affect accuracy [92], selected responses were checked with some difficulty against accessible medical records to confirm agreement. The responses were folded into the several possible outcomes of two or more visits, three or more visits, four or more visits, and five or more visits within the past 12 months. Identification of a particular number of emergency department visits that may constitute a threshold or cutoff at which point the presentment may be characterized as frequent utilization is not well established [81,93–96]. By way of comparing multiple levels of utilization ranging from two or more encounters through five or more encounters we are able to distinguish differences in likelihoods among different population characteristics organized within the Andersen-Aday framework [67,97]. Several demographic variables are examined including Age (<21, 21–30, 31–40, 41–50, 51–60, >60), Gender (male, female), and Race/Ethnicity (white, African American, Hispanic/Latino, Asian/Pacific Islander). Participants self-identified these characteristics. Predisposing factors are those that indicate a predisposition the need to use health services. Participants were queried on being prescribed drugs for mental health issues as well as whether, within the past 30 days, poor mental health kept them from doing usual activities. In addition, prescription drug abuse includes recently taking prescription drugs such as pain killers or stimulants that were either not prescribed or not as prescribed. Further, participants were asked if they had done any drugs such as meth, crack, heroine, or marijuana within the past 24 hours. Gathered also are indicators of whether, within the past 30 days, alcohol use kept the participant from doing usual activities as well as frequency with which six or more drinks are consumed on one occasion. Lastly, participants were assigned ‘at risk’ due to being in either the obese or the overweight BMI CDC categories. Enabling factors are those that, when in place, facilitate the propensity to use health services. These include availability and access to health services as well as knowledge about the service. Both employment and insurance status are assessed. Further, participants were queried if they consulted with a health professional about their presenting condition soon before their decision to visit the emergency department. In addition, participants were asked if they spoke with a family member or friend about their presenting condition soon before their decision to visit the emergency department. Patients were assessed whether or not there was a time lag of more than 10 hours between the time that they realized they needed the attention of a medical professional and arrival at the emergency department. Also, respondents were queried about their efforts to make an appointment to see a doctor or nurse prior to their arrival at the emergency department. Lastly, the primary reason for choosing to seek the services of the emergency department, rather than some other healthcare treatment venue, was assessed; measured were participants’ reasons related to the quality of reputation, facilities, personnel, and services. Need factors are those that relate to health and functional status of the patient as well as the patient’s perception that the condition warrants professional attention. Participants were prompted to report the seriousness of their presenting condition as not serious, somewhat serious, or very serious as well as to report the seriousness of their presenting condition on a scale from one to ten, with ten being serious and one being not serious at all. Further, participants were asked if their most recent visit to the emergency department was for the same presenting condition. Lastly, participants were queried on the number of times they were admitted to the hospital within the past 12 months. We analyze the associations of these factors with the outcome variable, emergency department utilization. We perform logistic regression to identify those factors that are statistically related with the four levels of dichotomous emergency department utilization (two or more visits, three or more visits, four or more visits, and five or more visits), reporting the confidence intervals and adjusted odds ratio (AOR) at each level of utilization. The treatment of the utilization variable as dichotomous, rather than continuous, within these several analyses is justified within the context of the research question which seeks to identify the threshold at which point a particular utilization factor may become significant. Relationships are adjusted for patient characteristics and inflation due to multiple covariates with the Bonferroni adjustment. Covariates are ranked and entered according to explanatory power. Residuals are checked for poor fit including Cook’s Distance residual and Leverage residual. R-square presented for each regression includes Cox & Snell and Nagelkerke. The Roa’s/Wald statistic is checked for significance and Model Chi goodness tests (p < .01) are reported. When reporting the log-odds in the single table it is acknowledge that cases included within the first level of utilization (2 or more visits) will necessarily also be included within the subsequent levels of utilization (e.g., 3 or more visits). Although odds ratios appear within a single table, figures represent separate analyses and each level of utilization is a free-standing analysis. When reporting, caution has been taken not to compare across analyses.
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