Admission and readmission rate incidences from deprived areas-impact of a classical or multi-dimensional model.

INTRODUCTION: Classical deprivation instruments use a factor analytical approach relying on a smaller number of dimensions, factors or components. Multi-dimensional deprivation models attempt classification in fine detail-even down to street level. METHODS: Single-centre retrospective cohort study using routinely collected aggregated and anonymised data on emergency medical admissions (96,526 episodes in 50,731 patients; 2002-2016). We calculated admission/readmission rate incidences for the 74 small areas within the hospital catchment area. We compared a classical Small Area Health Research Unit (SAHRU) to the multi-dimensional POBAL Haase and Pratschke Deprivation Index for Small Areas (POBAL) deprivation instrument and their deprivation ranks for two Irish censuses (2006/ 2011). RESULTS: There was poor agreement between the instruments of the Deprivation Ranks by Quintile-with agreement in 46 and 42% of small areas for the respective 2006 and 2011 censuses. The classical model (SAHRU) suggested more areas with severe deprivation (Q5 66 and 55%) compared with POBAL (Q5 32 and 24%) from the respective censuses. SAHRU classical instrument had a higher prediction level incidence rate ratio (IRR) 1.48 (95% CI 1.47, 1.49)) compared with POBAL IRR 1.28 (95% CI 1.27, 1.28) and systematically lower estimates of hospital admission and readmission rate incidences. Earlier Census data modelled more powerfully, suggesting a long latency between social circumstances and the ultimate expression of the emergency medical admission. CONCLUSION: Deprivation influences hospital incidence rates for emergency medical admissions and readmissions; instruments focusing at the very small area (individual or street level) have a utility but appear inferior in terms of representing the population risk of environmental/socio-economic factors which seem best approximated at a larger scale.

Deprivation status and the hospital costs of an emergency medical admission.

BACKGROUND: Deprivation has been shown to adversely affect health outcomes. However, whether deprivation increases hospitalisation costs is uncertain. We have examined the relationship between deprivation and the costs of emergency medical admissions in a single centre between 2008-2014. METHODS: We calculated the total hospital costs of emergency admissions related to their deprivation status, based on area of residence (Electoral Division - small census area). We used truncated Poisson and quantile regression methods to examine relationships between predictor variables and total hospital episode costs. RESULTS: Over the study period, 29,508 episodes were recorded in 15,932 patients. Compared with the least deprived (Q1), the incidence rate ratios (IRR) for annual costs were increased to Q3 1.15 (95% CI: 1.12, 1.19), Q4 2.39 (95% CI: 2.30, 2.49) and Q5 2.76 (95% CI: 2.68, 2.85). The margin statistic cost estimate per thousand population increased from 183.8 K€ in Q1 to 507.9 K€ in Q5. The total bed days/1000 population increased as follows (compared with Q1): Q3 IRR 1.41 (95% CI: 1.37, 1.45), Q4 1.96 (95% CI: 1.89, 2.03) and Q5 3.04 (95% CI: 2.96, 3.12). The margin statistic bed day estimate (/1000 population) increased from 218.7 in Q1 to 664.0 in Q5. CONCLUSION: Deprivation status had a profound impact on total hospital costs for emergency medical admissions. This was primarily mediated through a tripling of total bed days in the most deprived groups.

Social Factors Determine the Emergency Medical Admission Workload.

We related social factors with the annual rate of emergency medical admissions using census small area statistics. All emergency medical admissions (70,543 episodes in 33,343 patients) within the catchment area of St. James's Hospital, Dublin, were examined between 2002 and 2016. Deprivation Index, Single-Parent status, Educational level and Unemployment rates were regressed against admission rates. High deprivation areas had an approximately fourfold (Incidence Rate Ratio (IRR) 4.0 (3.96, 4.12)) increase in annual admission rate incidence/1000 population from Quintile 1(Q1), from 9.2/1000 (95% Confidence Interval (CI): 9.0, 9.4) to Q5 37.3 (37.0, 37.5)). Single-Parent families comprised 40.6% of households (95% CI: 32.4, 49.7); small areas with more Single Parents had a higher admission rate-IRR (Q1 vs. for Q5) of 2.92 (95% CI: 2.83, 3.01). The admission incidence rate was higher for Single-Parent status (IRR 1.50 (95% CI: 1.46, 1.52)) where the educational completion level was limited to primary level (Incidence Rate Ratio 1.45 (95% CI: 1.43, 1.47)). Small areas with higher educational quintiles predicted lower Admission Rates (IRR 0.85 (95% CI: 0.84, 0.86)). Social factors strongly predict the annual incidence rate of emergency medical admissions.

The Relationship Between Social Deprivation and a Weekend Emergency Medical Admission.

BACKGROUND: Deprivation increases admission rates; the specific effect of deprivation with regard to weekend admissions is unknown. METHODS: We calculated annual weekend admission rates for each small area population unit and related these to quintiles of Deprivation Index from 2002-2014. Univariate and multivariable risk estimates were calculated using truncated Poisson regression. RESULTS: There were 30,794 weekend admissions in 16,665 patients. The admission rate was substantially higher for more deprived areas, 12.7 per 1000 (95%CI 9.4, 14.7) vs 4.6 per 1000 (95%CI 3.3, 5.8). More deprived patients admitted at the weekend had a significantly lower 30-day in-hospital mortality (10.3% vs 14.5%, p<0.001). CONCLUSION: Deprivation is a powerful determinant of weekend admissions, however these comprise a group of patients with better outcomes.

The Relationship between Resource Utilization, Clinical Risk and Hospital Costs for Emergency Medical Admissions.

There has been little study of the relationship between resource utilisation, clinical risks and hospital costs in acute medicine with the question remaining as to whether current funding models reflect patient acuity. We examined the relationship between resource use for investigations/allied professional and patient episode costs in all emergency medical admissions admitted to our institution during 2008-2013. Univariate estimates were compared with a multivariate model adjusted for major cost predictors. Interestingly, the model adjusted cost estimates changed considerably when compared with univariate analysis. We used both linear and non-linear (quantile regression) methods due to the highly skewed nature of hospital costs. The data suggested that hospital episode costs were predictable and driven by objective measures of clinical complexity. The use of expensive investigations and healthcare professional time was secondary to the clinical acuity. Thus, cost was heavily weighted towards higher complexity, and lower resource utilisation associated with lower risk patient groups. However, the non-linear nature of the costings would caution against simple predictor models and non-linear techniques such as quantile regression may, as we have demonstrated, prove superior in defining the underlying relationships.

Social deprivation, population dependency ratio and an extended hospital episode - Insights from acute medicine.

BACKGROUND: Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; this study aimed to investigate the extent to which Deprivation status and the population Dependency Ratio influenced extended hospital episodes. METHODS: All Emergency Medical admissions (75,018 episodes of 41,728 patients) over 12 years (2002-2013) categorized by quintile of Deprivation Index and Population Dependency Rates (proportion of non-working/working) were evaluated against length of stay (LOS). Patients with an Extended LOS (ELOS), >30 days, were investigated, by Deprivation status, Illness Severity and Co-morbidity status. Univariate and multi-variable risk estimates (Odds Rates or Incidence Rate Ratios) were calculated, using truncated Poisson regression. RESULTS: Hospital episodes with ELOS had a frequency of 11.5%; their median LOS (IQR) was 55.0 (38.8, 97.6) days utilizing 57.6% of all bed days by all 75,018 emergency medical admissions. The Deprivation Index independently predicted the rate of such ELOS admissions; these increased approximately five-fold (rate/1000 population) over the Deprivation Quintiles with model adjusted predicted admission rates of for Q1 0.93 (95% CI: 0.86, 0.99), Q22.63 (95% CI: 2.55, 2.71), Q3 3.84 (95% CI: 3.77, 3.91), Q4 3.42 (95% CI: 3.37, 3.48) and Q5 4.38 (95% CI: 4.22, 4.54). Similarly the Population Dependency Ratio Quintiles (dependent to working structure of the population by small area units) independently predicted extended LOS admissions. CONCLUSION: The admission of patients with an ELOS is strongly influenced by the Deprivation status and the population Dependency Ratio of the catchment area. These factors interact, with both high deprivation and Dependency cohorts having a major influence on the numbers of emergency medical admission patients with an extended hospital episode.

Deprivation index and dependency ratio are key determinants of emergency medical admission rates.

BACKGROUND: Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; there has been debate as to the extent to which deprivation and population structure influences hospital admission rate. METHODS: All emergency medical admissions to an Irish hospital over a 12-year period (2002-2013) categorized by quintile of Deprivation Index and Dependency Ratio (proportion of population <15 or ≥ 65 years) from small area population statistics (SAPS), were evaluated against hospital admission rates. Univariate and multivariable risk estimates (Odds Ratios (OR) or Incidence Rate Ratios (IRR)) were calculated, using logistic or zero truncated Poisson regression as appropriate. RESULTS: 66,861 admissions in 36,214 patients occured during the study period. The Deprivation Index quintile independently predicted the admission rate/1000 population, Q1 9.4 (95%CI 9.2 to 9.7), Q2 16.8 (95%CI 16.6 to 17.0), Q3 33.8 (95%CI 33.5 to 34.1), Q4 29.6 (95%CI 29.3 to 29.8) and Q5 45.4 (95%CI 44.5 to 46.2). Similarly the population Dependency Ratio was an independent predictor of the admission rate with adjusted predicted rates of Q1 20.8 (95%CI 20.5 to 21.1), Q2 19.2 (95%CI 19.0 to 19.4), Q3 27.6 (95%CI 27.3 to 27.9), Q4 43.9 (95%CI 43.5 to 44.4) and Q5 34.4 (95%CI 34.1 to 34.7). A high concurrent Deprivation Index and Dependency Ratio were associated with very high admission rates. CONCLUSION: Deprivation Index and population Dependency Ratio are key determinants of the rate of emergency medical admissions.

Factors associated with length of stay following an emergency medical admission.

BACKGROUND: Hospitals are under pressure to use resources in the most efficient manner. We have examined the factors predicting Length of Stay (LOS) in one institution, using a database of all episodes of emergency medical admissions prospectively collected over 12 years. AIM: To examine the ability to predict hospital LOS following an emergency medical hospital admission. METHODS: All emergency admissions (66,933 episodes; 36,271 patients) to St. James's Hospital, Dublin, Ireland over a 12-year period (2002-2013) were evaluated in relation to LOS. Predictor variables (identified univariately) were entered into a multiple logistic regression model to predict a longer or shorter LOS (bivariate at the median). The data was also modelled as count data (absolute LOS), using zero truncated Poisson regression methodology. Appropriate post-estimation techniques for model fit were then applied to assess the resulting model. RESULTS: The major predictors of LOS included Acute Illness Severity (biochemical laboratory score at admission), Charlson co-morbidity, Manchester Triage Category at admission, Diagnosis Related Group, sepsis status (based on blood culture result), and Chronic Disease Score Indicator. The full model to predict a LOS above or below the median had an Area Under Receiver Operating Characteristic (AUROC) of 0.71 (95% CI: 0.70, 0.71). The truncated Poisson model appeared to achieve a good model fit (R(2) statistic=0.76). CONCLUSION: Predictor variables strongly correlated with LOS; there were linear increases within categories and summation between variables. More predictor variables may improve model reliability but predicting LOS ranges or quantiles may be more realistic, based on these results.

Consultant duration of clinical practice as a cost determinant of an emergency medical admission.

BACKGROUND: Little data exists relating years of hospital consultant work experience, from time of consultant certification, and costs incurred for emergency medical patients under their care. We examined the total cost of emergency medical episodes in relation to certified consultant years experience using a database of emergency admissions. METHODS: All emergency admissions (19,295 patients) from January 2008 to December 2012 were studied. Consultants were categorized by total years of certified experience according to four experience categories (< 15, 15-20, > 20 to ≤ 25, and > 25 years). Costs per case calculations included all pay, non-pay, and diagnostic/support infra-structural costs. We used quantile regression analysis to examine the impact of predictor variables on total costs over the predictor distribution and logistic regression on outcomes and costs, adjusting for other major predictors of cost. RESULTS: Major predictors of costs were identified. Quantile regression cost parameter estimates of hospital episode costs decreased with experience; the unit change at the Q25 point of the years experience distribution was - 62 (95 % CI - 87, - 37), - 162 (95 % CI - 203, - 120) at the median, but decreased at the Q75 point to - 340 (95 % CI - 416, - 264). The odds ratio of a hospital episode cost being below the median for each category of consultant experience >15 years qualified were 0.75 (95 % CI 0.68, 0.83), 0.77 (95 % CI 0.70, 0.86), and 0.70 (95 % CI 0.64, 0.78): p < 0.001 for each experience category vs. <15 years qualified. CONCLUSIONS: There appear to be cost advantages to care delivered by certified consultants of >20 years in clinical practice.

Factors predicting the hospital episode costs of emergency medical admissions.

BACKGROUND: Important outcome predictor variables for emergency medical admissions are the Manchester Triage Category, Acute Illness Severity, Chronic Disabling Disease and Sepsis Status. We have examined whether these are also predictors of hospital episode costs. METHODS: All patients admitted as medical emergencies between January 2008 and December 2012 were studied. Costs per case were adjusted by reference to the relative cost weight of each diagnosis related group (DRG) but included all pay costs, non-pay costs and infra-structural costs. We used a multi-variate logistic regression with generalized estimating equations (GEE), adjusted for correlated observations, to model the prediction of outcome (30-day in-hospital mortality) and hospital costs above or below the median. We used quantile regression to model total episode cost prediction over the predictor distribution (quantiles 0.25, 0.5 and 0.75). RESULTS: The multivariate model, using the above predictor variables, was highly predictive of an in-hospital death-AUROC of 0.91 (95% CI: 0.90, 0.92). Variables predicting outcome similarly predicted hospital episode cost; however predicting costs above or below the median yielded a lower AUROC of 0.73 (95% CI: 0.73, 0.74). Quantile regression analysis showed that hospital episode costs increased disproportionately over the predictor distribution; ordinary regression estimates of hospital episode costs over estimated the costs for low risk and under estimated those for high-risk patients. CONCLUSION: Predictors of outcome also predict costs for emergency medical admissions; however, due to costing data heteroskedasticity and the non-linear relationship between dependant and predictor variables, the hospital episode costs are not as easy to predict based on presentation status.

Hyponatraemia in Emergency Medical Admissions-Outcomes and Costs.

Healthcare systems in the developed world are struggling with the demand of emergency room presentations; the study of the factors driving such demand is of fundamental importance. From a database of all emergency medical admissions (66,933 episodes in 36,271 patients) to St James' Hospital, Dublin, Ireland, over 12 years (2002 to 2013) we have explored the impact of hyponatraemia on outcomes (30 days in-hospital mortality, length of stay (LOS) and costs). Identified variables, including Acute Illness Severity, Charlson Co-Morbidity and Chronic Disabling Disease that proved predictive univariately were entered into a multivariable logistic regression model to predict the bivariate of 30 days in-hospital survival. A zero truncated Poisson regression model assessed LOS and episode costs and the incidence rate ratios were calculated. Hyponatraemia was present in 22.7% of episodes and 20.3% of patients. The 30 days in-hospital mortality rate for hyponatraemic patients was higher (15.9% vs. 6.9% p < 0.001) and the LOS longer (6.3 (95% CI 2.9, 12.2) vs. 4.0 (95% CI 1.5, 8.2) p < 0.001). Both parameters worsened with the severity of the initial sodium level. Hospital costs increased non-linearly with the severity of initial hyponatraemia. Hyponatraemia remained an independent predictor of 30 days in-hospital mortality, length of stay and costs in the multi-variable model.

Increased winter mortality: the effect of season, temperature and deprivation in the acutely ill medical patient.

BACKGROUND: Studies examining seasonal mortality have found excess winter mortality, particularly in the elderly. We examined the seasonal mortality variations for all emergency medical admissions to St James' Hospital, Dublin, over 10 years (2002-2011). We explored the effects of ambient temperature, deprivation markers, case-mix, co-morbidity and illness severity on seasonal mortality. METHODS: All emergency admissions to an acute hospital were categorised by season. We examined season as a predictor of 30-day hospital mortality. RESULTS: 30-day in-hospital mortality was lowest in autumn (7.5%) and highest in winter (9.6%). Winter admission had 17% (p=0.009) increased unadjusted risk of a death by day 30 (OR 1.17: 95% CI 1.07, 1.28). A clinical classification system identified that chronic obstructive disease, pneumonia, epilepsy/seizures and congestive heart failure had more presentations in the winter. Multivariate analysis found that winter was not an independent predictor (OR 1.08: 95% CI 0.97, 1.19). Predictors including illness severity and the Charlson Index accounted for the increased risk of winter admission. The minimum daily temperature independently predicted outcome; there was a 20% increased in-hospital death rate when it was colder (OR 1.20: 95% CI 1.09, 1.33; p<0.001). Deprivation was a univariate and multivariate (OR 1.22 95%CI 1.07, 1.39; p=0.002) predictor of mortality, but did not show marked seasonal variation. CONCLUSION: Patients admitted in the winter have an approximate 17% increased risk of an in-hospital death by 30 days; this is related to cold along with increased illness severity and co-morbidity burden. The disease profile is different with winter admissions.

Implementing a pharmacist-led sequential antimicrobial therapy strategy: a controlled before-and-after study.

UNLABELLED: Sequential antimicrobial therapy is an important part of antimicrobial stewardship and intends to improve the timeliness of switch to oral antimicrobials. The aim of this study was to assess the impact of the introduction of guidelines and criteria for switching to oral antimicrobials. SETTING: The study was conducted in a 753-bed academic hospital in Ireland. METHODS: The study was prospective and of controlled before and after design. Patients admitted under the care of a medical consultant were screened for inclusion. The study was divided into pre-intervention and post-intervention phases. Patients admitted and prescribed IV antimicrobials were enrolled into either a study group or control group. Post-intervention, the intervention to the study group consisted of application of stickers and criteria for switch to oral antimicrobial therapy to the drug chart. Pre-intervention in the study group and in both phases in the control group, conventional practice of clinical pharmacists reviewing drug charts and contacting prescribers to discuss a switch to an oral antimicrobial continued. The duration of intravenous treatment, the timeliness of switch to oral therapy, length of stay and cost savings were measured. MAIN OUTCOME MEASURE: The duration of intravenous antimicrobial therapy in the pre-intervention and post-intervention phases in both study and control groups. RESULTS: Pre-intervention, 85 courses of IV antimicrobials were prescribed to study group patients, compared to 60 in the control group. Post-intervention, there were 92 courses in the study group and 53 in the control group. The duration of IV antimicrobial treatment reduced significantly in the study group post-intervention, compared to the control group (P = 0.02). The timeliness of the switch also improved significantly in the study group post-intervention (P = 0.017). No improvement occurred in the control group. The median length of stay was not reduced post-intervention. Antimicrobial costs reduced by a mean of €6.41 in the study group post-intervention. CONCLUSION: This controlled before and after study demonstrates successful implementation of a pharmacist-led antimicrobial stewardship strategy. Duration of IV antimicrobial treatment reduced significantly and the timeliness of switch significantly improved. This study may be used as a template for the introduction of further pharmacist-led antimicrobial stewardship initiatives.

Factors influencing the costs of emergency medical admissions to an Irish teaching hospital.

This study examined whether there is a relationship between coded diseases at the time of hospital discharge and costs of hospital re-admission. We carried out a systematic review of data relating to emergency medical patients admitted to St. James' Hospital in Dublin between 1 January 2002 and 31 October 2004. Data on discharges from hospital were analyzed as recorded in the hospital in-patient enquiry (HIPE) system. Of 15,876 episodes recorded among 11,201 patients admitted the number of re-admissions numbered up to 43. Age, year of admission, and frequency of admission were factors associated with increased hospital costs. HIPE coding at first discharge predicted increased costs: codes related to heart failure, pneumonia, stroke, diabetes, malignancy, psychiatric, and anaemia-related codes. Clinical coding using the HIPE database thus strongly predicted hospital costs.

Assessment of cardiac autonomic modulation during adolescent obesity.

OBJECTIVE: To investigate the cardiovascular autonomic function in pediatric obesity of different duration by using standard time domain, spectral heart rate variability (HRV), and nonlinear methods. RESEARCH METHODS AND PROCEDURES: Fifty obese children (13.9 +/- 1.7 years) were compared with 12 lean subjects (12.9 +/- 1.6 years). Obese children were classified as recent obese (ROB) (<4 years), intermediate obese (IOB) (4 to 7 years), and long-term obese (OB) (>7 years). In all participants, we performed blood pressure (BP) measurements, laboratory tests, and 24-hour electrocardiogram/ambulatory BP monitoring. The spectral power was quantified in total power, very low-frequency (LF) power, high-frequency (HF) power, and LF to HF ratio. Total, long-term, and short-term time domain HRV were calculated. Poincaré plot and quadrant methods were used as nonlinear techniques. RESULTS: All obese groups had higher casual and ambulatory BP and higher glucose, homeostasis model assessment, and triglyceride levels. All parameters reflecting parasympathetic tone (HF band, root mean square successive difference, proportion of successive normal-to-normal intervals, and scatterplot width) were significantly and persistently reduced in all obese groups in comparison with lean controls. LF normalized units, LF/HF, and cardiac acceleration (reflecting sympathetic activation) were significantly increased in the ROB group. In IOB and OB groups, LF, but not nonlinear, measures were similar to lean controls, suggesting biphasic behavior of sympathetic tone, whereas nonlinear analysis showed a decreasing trend with the duration of obesity. Long-term HRV measures were significantly reduced in ROB and IOB. DISCUSSION: Autonomic nervous system changes in adolescent obesity seem to be related to its duration. Nonlinear methods of scatterplot and quadrant analysis permit assessment of autonomic balance, despite measuring different aspects of HRV.