scholarly journals Patterns of emergency department attendance among older people in the last three months of life and factors associated with frequent attendance: a mortality follow-back survey

2019 ◽  
Vol 48 (5) ◽  
pp. 680-687
Author(s):  
Anna E Bone ◽  
Catherine J Evans ◽  
Lesley A Henson ◽  
Wei Gao ◽  
Irene J Higginson ◽  
...  

Abstract Background frequent emergency department (ED) attendance at the end of life disrupts care continuity and contradicts most patients’ preference for home-based care. Objective to examine factors associated with frequent (≥3) end of life ED attendances among older people to identify opportunities to improve care. Methods pooled data from two mortality follow-back surveys in England. Respondents were family members of people aged ≥65 who died four to ten months previously. We used multivariable modified Poisson regression to examine illness, service and sociodemographic factors associated with ≥3 ED attendances, and directed content analysis to explore free-text responses. Results 688 respondents (responses from 42.0%); most were sons/daughters (60.5%). Mean age at death was 85 years. 36.5% had a primary diagnosis of cancer and 16.3% respiratory disease. 80/661 (12.1%) attended ED ≥3 times, accounting for 43% of all end of life attendances. From the multivariable model, respiratory disease (reference cancer) and ≥2 comorbidities (reference 0) were associated with frequent ED attendance (adjusted prevalence ratio 2.12, 95% CI 1.21–3.71 and 1.81, 1.07–3.06). Those with ≥7 community nursing contacts (reference 0 contacts) were more likely to frequently attend ED (2.65, 1.49–4.72), whereas those identifying a key health professional were less likely (0.58, 0.37–0.88). Analysis of free-text found inadequate community support, lack of coordinated care and untimely hospital discharge were key issues. Conclusions assigning a key health professional to older people at increased risk of frequent end of life ED attendance, e.g. those with respiratory disease and/or multiple comorbidities, may reduce ED attendances by improving care coordination.

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S186-S186
Author(s):  
Peter Mazonson ◽  
Theoren Loo ◽  
Jeff Berko ◽  
Sarah-Marie Chan ◽  
Ryan Westergaard ◽  
...  

Abstract Background Frailty is a concern among older people living with HIV (PLHIV). There is a paucity of research characterizing PLHIV who are at risk of becoming frail (pre-frailty). To investigate how HIV impacts older PLHIV in the United States, a new study called Aging with Dignity, Health, Optimism and Community (ADHOC) was launched at ten sites to collect self-reported data. This analysis uses data from ADHOC to identify factors associated with pre-frailty. Methods Pre-frailty was assessed using the Frailty Index for Elders (FIFE), where a score of zero indicated no frailty, 1–3 indicated pre-frailty, and 4–10 indicated frailty. A cross-sectional analysis was performed on 262 PLHIV (age 50+) to determine the association between pre-frailty and self-reported sociodemographic, health, and clinical indicators using bivariate analyses. Factors associated with pre-frailty were then included in a logistic regression analysis using backward selection. Results The average age of ADHOC participants was 59 years. Eighty-two percent were male, 66% were gay or lesbian, and 56% were white. Forty-seven percent were classified with pre-frailty, 26% with frailty, and 27% with no frailty. In bivariate analyses, pre-frailty was associated with depression, low cognitive function, depression, multiple comorbidities, low income, low social support and unemployment (Table 1). In the multiple logistic regression analysis, pre-frailty was associated with having low cognitive function (Odds Ratio [OR] 8.56, 95% Confidence Interval [CI]: 3.24–22.63), 4 or more comorbid conditions (OR 4.00, 95% CI: 2.23–7.06), and an income less than $50,000 (OR 2.70, 95% CI: 1.56–4.68) (Table 2). Conclusion This study shows that commonly collected clinical and sociodemographic metrics can help identify PLWH who are more likely to have pre-frailty. Early recognition of factors associated with pre-frailty among PLHIV may help to prevent progression to frailty. Understanding markers of increased risk for pre-frailty may help clinicians and health systems better target multi-modal interventions to prevent negative health outcomes associated with frailty. Disclosures All authors: No reported disclosures.


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243373
Author(s):  
Pei-Fang Huang ◽  
Pei-Tseng Kung ◽  
Wen-Yu Chou ◽  
Wen-Chen Tsai

Objectives Taiwan has implemented the Diagnosis Related Groups (DRGs) since 2010, and the quality of care under the DRG-Based Payment System is concerned. This study aimed to examine the characteristics, related factors, and time distribution of emergency department (ED) visits, readmission, and hospital transfers of inpatients under the DRG-Based Payment System for each Major Diagnostic Category (MDC). Methods We conducted a retrospective cohort study using data from the National Health Insurance Research Database (NHIRD) from 2012 to 2013 in Taiwan. Multilevel logistic regression analysis was used to examine the factors related to ED visits, readmissions, and hospital transfers of patients under the DRG-Based Payment System. Results In this study, 103,779 inpatients were under the DRG-Based Payment System. Among these inpatients, 4.66% visited the ED within 14 days after their discharge. The factors associated with the increased risk of ED visits within 14 days included age, lower monthly salary, urbanization of residence area, comorbidity index, MDCs, and hospital ownership (p < 0.05). In terms of MDCs, Diseases and Disorders of the Kidney and Urinary Tract (MDC11) conferred the highest risk of ED visits within 14 days (OR = 4.95, 95% CI: 2.69–9.10). Of the inpatients, 6.97% were readmitted within 30 days. The factors associated with the increased risk of readmission included gender, age, lower monthly salary, comorbidity index, MDCs, and hospital ownership (p < 0.05). In terms of MDCs, the inpatients with Pregnancy, Childbirth and the Puerperium (MDC14) had the highest risk of readmission within 30 days (OR = 20.43, 95% CI: 13.32–31.34). Among the inpatients readmitted within 30 days, 75.05% of them were readmitted within 14 days. Only 0.16% of the inpatients were transferred to other hospitals. Conclusion The study shows a significant correlation between Major Diagnostic Categories in surgery and ED visits, readmission, and hospital transfers. The results suggested that the main reasons for the high risk may need further investigation for MDCs in ED visits, readmissions, and hospital transfers.


Author(s):  
Francesco Barbabella ◽  
Francesco Balducci ◽  
Carlos Chiatti ◽  
Antonio Cherubini ◽  
Fabio Salvi

2018 ◽  
Vol 29 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Anna E Bone ◽  
Catherine J Evans ◽  
Simon N Etkind ◽  
Katherine E Sleeman ◽  
Barbara Gomes ◽  
...  

2015 ◽  
Vol 33 (4) ◽  
pp. 370-376 ◽  
Author(s):  
Lesley A. Henson ◽  
Wei Gao ◽  
Irene J. Higginson ◽  
Melinda Smith ◽  
Joanna M. Davies ◽  
...  

Purpose To explore factors associated with emergency department (ED) attendance by patients with cancer in their last month of life. Methods Five electronic databases (MEDLINE, EMBASE, CINAHL, PsycINFO, and the Cochrane Library) were searched through February 2014 for studies investigating ED attendance toward the end of life by adult patients (age 18 years or older) with cancer. No time or language limitations were applied. We performed meta-analysis of factors using a random-effects model, with results expressed as odds ratios (OR) for ED attendance. Sensitivity analyses explored heterogeneity. Results Thirty studies were identified, reporting three demographic, five clinical, and 13 environmental factors, combining data from five countries and 1,181,842 patients. An increased likelihood of ED attendance was found for men (OR, 1.24; 95% CI, 1.19 to 1.29; I2, 58.2%), black race (OR, 1.45; 95% CI, 1.40 to 1.50; I2, 0.0%; reference, white race), patients with lung cancer (OR, 1.17; 95% CI, 1.10 to 1.23; I2, 59.5%; reference, other cancers), and those patients of the lowest socioeconomic status (SES; OR, 1.15; 95% CI, 1.10 to 1.19; I2, 0.0%; reference, highest SES). Patients receiving palliative care were less likely to attend the ED in their last month of life (OR, 0.43; 95% CI, 0.36 to 0.51; I2, 59.4%). Conclusion We identified demographic (men; black race), clinical (lung cancer), and environmental (low SES; no palliative care) factors associated with an increased risk of ED attendance by patients with cancer in their last month of life. Our findings may be used to develop screening interventions and assist policy-makers to direct resources. Future studies should also investigate previously neglected areas of research, including psychosocial factors, and patients' and caregivers' emergency care preferences.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefanie P. Albert ◽  
Rosa Ergas ◽  
Sita Smith ◽  
Gillian Haney ◽  
Monina Klevens

ObjectiveWe sought to measure the burden of emergency department (ED) visits associated with injection drug use (IDU), HIV infection, and homelessness; and the intersection of homelessness with IDU and HIV infection in Massachusetts via syndromic surveillance data.IntroductionIn Massachusetts, syndromic surveillance (SyS) data have been used to monitor injection drug use and acute opioid overdoses within EDs. Currently, Massachusetts Department of Public Health (MDPH) SyS captures over 90% of ED visits statewide. These real-time data contain rich free-text and coded clinical and demographic information used to categorize visits for population level public health surveillance.Other surveillance data have shown elevated rates of opioid overdose related ED visits, Emergency Medical Service incidents, and fatalities in Massachusetts from 2014-20171,2,3. Injection of illicitly consumed opioids is associated with an increased risk of infectious diseases, including HIV infection. An investigation of an HIV outbreak among persons reporting IDU identified homelessness as a social determinant for increased risk for HIV infection.MethodsTo accomplish our objectives staff used an existing MDPH SyS IDU syndrome definition4, developed a novel syndrome definition for HIV-related visits, and adapted Maricopa County's homelessness syndrome definition. Syndromes were applied to Massachusetts ED data through the CDC’s BioSense Platform. Visits meeting the HIV and homelessness syndromes were randomly selected and reviewed to assess accuracy; inclusion and exclusion criteria were then revised to increase specificity. The final versions of all three syndrome definitions incorporate free-text elements from the chief complaint and triage notes, as well as International Statistical Classification of Diseases and Related Health Problems, 9th (ICD-9) and 10th Revision (ICD-10) diagnostic codes. Syndrome categories were not mutually exclusive, and all reported visits occurring at Massachusetts EDs were included in the analysis.Syndromes CreatedFor the HIV infection syndrome definition, we incorporated the free-text term “HIV” in both the chief complaint and triage notes. Visit level review demonstrated that the following exclusions were needed to reduce misspellings, inclusion of partial words, and documentation of HIV testing results: “negative for HIV”, “HIV neg”, “negative test for HIV”, “hive”, “hivies”, and “vehivcle”. Additionally, the following diagnostic codes were incorporated: V65.44 (Human immunodeficiency virus [HIV] counseling), V08 (asymptomatic HIV infection status), V01.79 (contact with or exposure to other viral diseases), 795.71 (nonspecific serologic evidence of HIV), V73.89 (special screening examination for other specified viral diseases), 079.53 (HIV, type 2 [HIV-2]), Z20.6 (contact with and (suspected) exposure to HIV), Z71.7 (HIV counseling), B20 (HIV disease), Z21 (asymptomatic HIV infection status), R75 (inconclusive laboratory evidence of HIV), Z11.4 (encounter for screening for HIV), and B97.35 (HIV-2 as the cause of diseases classified elsewhere).Building on the Maricopa County homeless syndrome definition, we incorporated a variety of free-text inclusion and exclusion terms. To meet this definition visits had to mention: “homeless”, or “no housing”, or, “lack of housing”, or “without housing”, or “shelter” but not animal and domestic violence shelters. We also selected the following ICD-10 codes for homelessness and inadequate housing respectively, Z59.0 and Z59.1.We analyzed MDPH SyS data for visits occurring from January 1, 2016 through June 30, 2018. Rates per 10,000 ED visits categorized as IDU, HIV, or homeless were calculated. Subsequently, visits categorized as IDU, HIV, and meeting both IDU and HIV syndrome definitions (IDU+HIV) were stratified by homelessness.ResultsSyndrome Burden on EDThe MDPH SyS dataset contains 6,767,137 ED visits occurring during the study period. Of these, 82,819 (1.2%) were IDU-related, 13,017 (0.2%) were HIV-related, 580 (<0.01%) were related to IDU + HIV, and 42,255 visits (0.6%) were associated with homelessness.The annual rate of IDU-related visits increased 15% from 2016 through June of 2018 (from 113.63 to 130.57 per 10,000 visits); while rates of HIV-related and IDU + HIV-related visits remained relatively stable. The overall rate of visits associated with homelessness increased 47% (from 49.99 to 73.26 per 10,000 visits).Rates of IDU, HIV, and IDU + HIV were significantly higher among visits associated with homelessness. Among visits that met the homeless syndrome definition compared to those that did not: the rate of IDU-related visits was 816.0 versus 118.03 per 10,000 ED visits (X2= 547.12, p<0. 0001); the rate of visits matching the HIV syndrome definition was 145.54 versus 18.44 per 10,000 ED visits (X2= 99.33, p<0.0001); and the rate of visits meeting the IDU+HIV syndrome definition was 15.86 versus 0.76 per 10,000 visits (X2= 13.72, p= 0.0002).ConclusionsMassachusetts is experiencing an increasing burden of ED visits associated with both IDU and homelessness that parallels increases in opioid overdoses. Higher rates of both IDU and HIV-related visits were associated with homelessness. An understanding of the intersection between opioid overdoses, IDU, HIV, and homelessness can inform expanded prevention efforts, introduction of alternatives to ED care, and increase consideration of housing status during ED care.Continued surveillance for these syndromes, including collection and analysis of demographic and clinical characteristics, and geographic variations, is warranted. These data can be useful to providers and public health authorities for planning healthcare services.References1. Vivolo-Kantor AM, Seth P, Gladden RM, et al. Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses — United States, July 2016–September 2017. MMWR Morbidity and Mortality Weekly Report 2018; 67(9);279–285 DOI: http://dx.doi.org/10.15585/mmwr.mm6709e12. Massachusetts Department of Public Health. Chapter 55 Data Brief: An assessment of opioid-related deaths in Massachusetts, 2011-15. 2017 August. Available from: https://www.mass.gov/files/documents/2017/08/31/data-brief-chapter-55-aug-2017.pdf3. Massachusetts Department of Public Health. MA Opioid-Related EMS Incidents 2013-September 2017. 2018 Feb. Available from: https://www.mass.gov/files/documents/2018/02/14/emergency-medical-services-data-february-2018.pdf4. Bova, M. Using emergency department (ED) syndromic surveillance to measure injection-drug use as an indicator for hepatitis C risk. Powerpoint presented at: 2017 Northeast Epidemiology Conference. 2017 Oct 18 – 20; Northampton, Massachusetts, USA.


2018 ◽  
Vol 36 (34_suppl) ◽  
pp. 68-68
Author(s):  
Diogo Alexandre Martins-Branco ◽  
Silvia Lopes ◽  
Rita Canario ◽  
João Freire ◽  
Madalena Feio ◽  
...  

68 Background: There is growing concern about the aggressiveness of cancer care at the end of life (ACCEoL). Recognizing the most affected patients is a cornerstone to improve this public health unmet need. Our aim is to identify factors associated with ACCEoL for cancer patients dying in hospitals. Methods: Cohort study of adults with ICD9CM diagnosis of cancer, who died in public hospitals in mainland Portugal (Jan'10 - Dec'15), identified from the hospital morbidity database (HMD). HMD provided individual clinical and demographic data. The primary outcome is a composite ACCEoL measure of 16 indicators, expanding an earlier framework, validated by an expert panel. We obtained hospital and area-level variables from a hospital survey and National Statistics. We used multilevel random effects logistic regression modelling (p < 0.05). Results: We included 92,155 patients: median age 73 yo; 61.9% male; 53.0% metastatic. ACCEoL prevalence was 71.1%. The most prevalent indicators were > 14 days in hospital (42.7%) and surgery in last 30 days (27.8%). Older age (p < 0.001), breast cancer (OR 0.83; 95%CI 0.76-0.91) and metastatic disease (0.54; 0.50-0.58) were associated with less ACCEoL. In contrast, higher Deyo-Charlson comorbidity index (p < 0.001), gastrointestinal (GI) and hematological malignancies (p < 0.001), and death at a cancer center (1.31; 1.01-1.72) or hospital with medical oncology (MO) dept. (1.29; 1.02-1.63) were associated with higher ACCEoL. There was no association between existence of hospital palliative care services (HPCS) at the hospital of death and ACCEoL. Conclusions: Our study confirmed that clinical factors related with better understanding of disease course are associated with ACCEoL reduction. Patients with more comorbidities and GI malignancies might represent groups with complex needs, and hematological patients may be at increased risk because of unpredictable prognosis. It is important to better understand how to reduce ACCEoL in cancer centers and hospitals with MO dept.; improvement of HPCS could be an answer as these services are usually under-resourced, thus reaching few.


2021 ◽  
Vol 15 ◽  
Author(s):  
Simone Garruth dos Santos Machado Sampaio ◽  
Livia Costa Oliveira ◽  
Karla Santos da Costa Rosa

OBJECTIVE: To compare factors associated with death in adults and older people with advanced cancer who were hospitalized in a palliative care unit (PCU). METHODS: Case-control study with patients (adults vs older people) admitted to a PCU of National Cancer Institute José Alencar Gomes da Silva (INCA), in Rio de Janeiro, Brazil. Logistic regressions (odds ratio [OR] and 95% confidence interval [95%CI]) were used to identify factors associated with death. RESULTS: The study included 205 patients, most of which were aged over 60 years old (60.5%). Among the adult patients, a Karnofsky Performance Status ≤ 40% (OR 2.54 [95%CI 1.11–3.45]) and neutrophil-to-lymphocyte ratio (NLR) (OR 1.09 [95%CI 1.02–1.24]) were risk factors for death, while albumin (OR 0.30 [95%CI 0.12–0.78]) was a protective factor. Among older patients, NLR (OR: 1.13 [95%CI 1.02–1.24]), C-reactive protein (CRP) (OR 1.09 [95%CI 1.02–1.17]), modified Glasgow Prognostic Score (mGPS) 1 and 2 (OR 4.66 [95%CI 1.35–16.06]), CRP-to-albumin ratio (CAR) (OR 1.27 [95%CI 1.03–1.58]), and nutritional risk (OR 1.11 [95%CI 1.03–1.19]) were risk factors, whereas albumin (OR 0.23 [95%CI 0.09–0.57]) was a protective factor against death. CONCLUSIONS: Prognostic factors differed between groups. The NLR was a risk factor, and albumin was a protective factor regarding death in both groups. Additionally, CRP, mGPS, CAR, and nutritional risk were associated with an increased risk of death only among older people.


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