A Multi-Stage Process to Develop Quality Indicators for Community-Based Palliative Care Using interRAI Data

Background: Individuals receiving palliative care (PC) are generally thought to prefer to receive care and die in their homes, yet little research has assessed the quality of home- and community-based PC. This project developed a set of valid and reliable quality indicators (QIs) that can be generated using data that are already gathered with interRAI assessments-an internationally validated set of tools commonly used in North America for home care clients. The QIs can serve as decision-support measures to assist providers and decision makers in delivering optimal care to individuals and their families. Methods: The development efforts took part in multiple stages, between 2017-2021, including a workshop with clinicians and decision-makers working in PC, qualitative interviews with individuals receiving PC, families and decision makers and a modified Delphi panel, based on the RAND/ULCA appropriateness method. Results: Based on the workshop results, and qualitative interviews, a set of 27 candidate QIs were defined. They capture issues such as caregiver burden, pain, breathlessness, falls, constipation, nausea/vomiting and loneliness. These QIs were further evaluated by clinicians/decision makers working in PC, through the modified Delphi panel, and five were removed from further consideration, resulting in 22 QIs. Conclusions: Through in-depth and multiple-stakeholder consultations we developed a set of QIs generated with data already collected with interRAI assessments. These indicators provide a feasible basis for quality benchmarking and improvement systems for care providers aiming to optimize PC to individuals and their families

Additional file 1 of Evaluation of children with severe neurological impairment admitted to hospital with pain and irritability

Additional file 1: Table S1. ICD-10 codes of diagnoses compatible with severe neurological impairment. Table S2. Physical examinations completed and abnormalities documented at time of hospital admission. Table S3. Investigations completed and abnormalities documented during hospital admission. Table S4. Specialist consultations during hospital admission

Intensity of end-of-life care among children with life-threatening conditions : a national population-based observational study

Background: Children with life-threatening conditions frequently experience high intensity care at the end of life, though most of this research only focused on children with cancer. Some research suggests inequities in care provided based on age, disease type, socioeconomic status, and distance that the child lives from a tertiary hospital. We examined: 1) the prevalence of indicators of high intensity end-of-life care (e.g., hospital stays, intensive care unit [ICU] stays, death in ICU, use of cardiopulmonary resuscitation [CPR], use of mechanical ventilation) and 2) the association between demographic and diagnostic factors and each indicator for children with any life-threatening condition in Canada. Methods We conducted a population-based retrospective cohort study using linked health administrative data to examine care provided in the last 14, 30, and 90 days of life to children who died between 3 months and 19 years of age from January 1, 2008 to December 31, 2014 from any underlying life-threatening medical condition. Logistic regression was used to model the association between demographic and diagnostic variables and each indicator of high intensity end-of-life care except number of hospital days where negative binomial regression was used. Results Across 2435 child decedents, the most common diagnoses included neurology (51.1%), oncology (38.0%), and congenital illness (35.9%), with 50.9% of children having diagnoses in three or more categories. In the last 30 days of life, 42.5% (n = 1035) of the children had an ICU stay and 36.1% (n = 880) died in ICU. Children with cancer had lower odds of an ICU stay (OR = 0.47; 95% CI = 0.36–0.62) and ICU death (OR = 0.37; 95%CI = 0.28–0.50) than children with any other diagnoses. Children with 3 or more diagnoses (vs. 1 diagnosis) had higher odds of > 1 hospital stay in the last 30 days of life (OR = 2.08; 95%CI = 1.29–3.35). Living > 400 km (vs < 50 km) from a tertiary pediatric hospital was associated with higher odds of multiple hospitalizations (OR = 2.09; 95%CI = 1.33–3.33). Conclusion High intensity end of life care is prevalent in children who die from life threatening conditions, particularly those with a non-cancer diagnosis. Further research is needed to understand and identify opportunities to enhance care across disease groups.Medicine, Faculty ofNon UBCPediatrics, Department ofReviewedFacultyResearcherOthe

The validity of using health administrative data to identify the involvement of specialized pediatric palliative care teams in children with cancer in Ontario, Canada

Population-based research to identify underserviced populations and the impact of palliative care (PC) is limited as the validity of such data to identify PC services is largely unknown.To determine the validity of using such data to identify the involvement of specialized pediatric PC teams among children with cancer.Retrospective cohort.Ontario children with cancer who died between 2000 and 2012, received care through a pediatric institution with a specialized PC team and a clinical PC database.All patients in the clinical databases were linked to population-based health services administrative databases. Six algorithms were created to indicate the use of formal pediatric PC teams based on the record type (physician billings vs. inpatient records vs. both) and number of eligible codes required (≥1 vs. ≥2). Each was validated against the pediatric PC clinical databases.The cohort comprised 572 children; 243 were in the clinical databases. Algorithms using only inpatient records had high specificity (80%-95%) but poor sensitivity (21%-56%). Including physician billings increased sensitivity but lowered specificity. The algorithm with overall best performance required ≥2 physician billing or inpatient diagnosis codes indicating PC [sensitivity 0.79 (95% CI 0.73-0.84), specificity 0.58 (95% CI 0.53-0.64)].Health administrative data identifies involvement of specialized pediatric PC teams with good sensitivity but low specificity. Studies using such data alone to compare patients receiving and not receiving specialized pediatric PC are at significant risk of misclassification and potential bias. Population-based PC databases should be established to conduct rigorous population-based PC research

Health-related quality of life and tuberculosis: a longitudinal cohort study

Active tuberculosis (TB) disease can impose substantial morbidity, while treatment for latent TB infection (LTBI) has frequent side effects. We compared health-related quality of life (HRQOL) between persons diagnosed and treated for TB disease, persons treated for LTBI, and persons screened but not treated for TB disease or LTBI, over one year following diagnosis/initial assessment

Predictors of specialized pediatric palliative care involvement and impact on patterns of end-of-life care in children with cancer

PurposeThe impact of specialized pediatric palliative care (SPPC) teams on patterns of end-of-life care is unknown. We sought to determine (1) which children with cancer access SPPC and (2) the impact of accessing SPPC on the risk of experiencing high-intensity end-of-life care (intensive care unit admission, mechanical ventilation, or in-hospital death).MethodsUsing a provincial childhood cancer registry, we assembled a retrospective cohort of Ontario children with cancer who died between 2000 and 2012 and received care through pediatric institutions with an SPPC team. Patients were linked to population-based administrative data capturing inpatient, outpatient, and emergency visits. Children were classified as having SPPC, general palliative care, or no palliative care on the basis of SPPC clinical databases, physician billing codes, or inpatient diagnosis codes.ResultsOf the 572 children, 166 (29%) received care from an SPPC team for at least 30 days before death, and 100 (17.5%) received general palliative care. SPPC involvement was significantly less likely for children with hematologic cancers (OR, 0.3; 95% CI, 0.3 to 0.4), living in the lowest income areas (OR, 0.4; 95% CI, 0.2 to 0.8), and living further from the treatment center (OR, 0.5; 95% CI, 0.4 to 0.5). SPPC was associated with a five-fold decrease in odds of intensive care unit admission (OR, 0.2; 95% CI, 0.1 to 0.4), whereas general palliative care had no impact. Similar associations were seen with all secondary indicators.ConclusionWhen available, SPPC, but not general palliative care, is associated with lower intensity care at the end of life for children with cancer. However, access remains uneven. These results provide the strongest evidence to date supporting the creation of SPPC teams. (C) 2018 by American Society of Clinical Oncolog

A multi-stage process to develop quality indicators for community-based palliative care using interRAI data

Background Individuals receiving palliative care (PC) are generally thought to prefer to receive care and die in their homes, yet little research has assessed the quality of home- and community-based PC. This project developed a set of valid and reliable quality indicators (QIs) that can be generated using data that are already gathered with interRAI assessments—an internationally validated set of tools commonly used in North America for home care clients. The QIs can serve as decision-support measures to assist providers and decision makers in delivering optimal care to individuals and their families. Methods The development efforts took part in multiple stages, between 2017–2021, including a workshop with clinicians and decision-makers working in PC, qualitative interviews with individuals receiving PC, families and decision makers and a modified Delphi panel, based on the RAND/ULCA appropriateness method. Results Based on the workshop results, and qualitative interviews, a set of 27 candidate QIs were defined. They capture issues such as caregiver burden, pain, breathlessness, falls, constipation, nausea/vomiting and loneliness. These QIs were further evaluated by clinicians/decision makers working in PC, through the modified Delphi panel, and five were removed from further consideration, resulting in 22 QIs. Conclusions Through in-depth and multiple-stakeholder consultations we developed a set of QIs generated with data already collected with interRAI assessments. These indicators provide a feasible basis for quality benchmarking and improvement systems for care providers aiming to optimize PC to individuals and their families