Using administrative health data for palliative and end-of-life care research in Ireland

Administrative health data is under-used as a research tool for palliative care research in Ireland. The overarching aim of this thesis was to explore the potential of population based cancer registry data linked to hospital episode data and death certificate data to examine palliative and end-of-life care (PEoLC) for cancer patients, in Ireland. Research objectives include using the available linked data to identify vulnerable subgroups in need of palliative care, to identify and compare characteristics of those who receive palliative with those who do not and to explore the relationship between receiving palliative care and place of death. The knowledge gained was extrapolated to other non-cancer health and social care data collections to evaluate the potential and challenges of these data for PEoLC research and so develop guidelines to maximise its potential as a research tool while identifying the limitations of its use. A population based study of lung cancer decedents found those patients who die within 30 days of diagnosis (short-term survivors) were older, (aged 80 years and over), had more comorbid disease and were more likely to present through the emergency department than those who survived longer. These characteristics are available at diagnosis and could be used to guide decisions on early assessment for palliative care for lung cancer patients at admission. (Short-term survivors) are more likely to die in hospital so reporting place of death by survival time may be useful to evaluate interventions to reduce deaths in acute hospitals. A second study compared the characteristics and place of death of cancer patients receiving specialist palliative care in acute hospitals with those who do not. Almost two thirds of cancer patients who attended a cancer centre in 2016 and died in 2016 had an inpatient palliative care encounter. They were younger, less likely to be married, and more likely to be from a deprived area. Having accounted for sociodemographic factors, there was evidence of regional variation in receiving palliative care. Place of death differed by palliative care encounter, 45% of those who were seen by the palliative care team died in hospital, 33% died in a hospice and 18% died at home. Of those who had no record of a palliative care encounter 50% died in hospital, 16% died in hospice and 28% died at home. These studies demonstrate that Irish administrative health data can be used for PEoLC research however a thorough advance knowledge of the datasets to be used is critical. For each data set, information on how the data are organised (data models) and what data are collected (data dictionaries) are required. Knowledge of what is missing within each dataset is as important as knowing what is available. These considerations inform most aspects of study planning, design and implementation and were used to evaluate the potential of other national and social care data collections for PEoLC research. Efforts to identify and control for bias, both known and unknown are a particular concern when using administrative health data for research. This thesis contributes to population based PEoLC research in Ireland and to the international body of work that uses administrative health care and social care data for PEoLC research. It can inform future use of Irish health data for population based research and has identified several avenues for further research. It is hoped the thesis findings will give impetus to ongoing initiatives to improve the research potential of Ireland’s health and social data collections

Specialist palliative cancer care in acute hospitals and place of death: a population study

Objective This study compares the characteristics and place of death of patients with cancer receiving specialist palliative care in acute hospitals with those who do not. Methods All patients with incident invasive cancer in Ireland (1994–2016 inclusive), excluding non-melanoma skin cancer, who attended a cancer centre and died in 2016 were identified from cancer registry data. Patients were categorised based on a diagnosis code ‘Encounter for palliative care’ from linked hospital episode data. Place of death was categorised from death certificate data. Data were analysed using descriptive statistics, χ2 tests and logistic regression. Results Of n=4103 decedents identified, 62% had a hospital-based palliative care encounter in the year preceding death. Age (p<0.001), marital status (p=0.017), deprivation index (p<0.001) and health board region (p=0.008) were independent predictors of having a palliative care encounter. Place of death differed by palliative care encounter group: 45% of those with an encounter died in hospital versus 50% without an encounter, 33% vs 16% died in a hospice and 18% vs 28% died at home (p<0.001). Conclusion Almost two-thirds of patients with cancer who attended a cancer centre and died in 2016 had a palliative care encounter. They were younger, less likely to be married and more likely to be from deprived areas. Having accounted for sociodemographic factors, there was evidence of regional variation in receiving care. Demographic and clinical factors and the provision of health services in a region need to be considered together when assessing end-of-life care

Using administrative health data for palliative and end of life care research in Ireland: potential and challenges

Background: This study aims to examine the potential of currently available administrative health and social care data for palliative and end-of-life care (PEoLC) research in Ireland. Objectives include to i) identify data sources for PEoLC research ii) describe the challenges and opportunities of using these and iii) evaluate the impact of recent health system reforms and changes to data protection laws. Methods: The 2017 Health Information and Quality Authority catalogue of health and social care datasets was cross-referenced with a recognised list of diseases with associated palliative care needs. Criteria to assess the datasets included population coverage, data collected, data dictionary and data model availability, and mechanisms for data access. Results: Nine datasets with potential for PEoLC research were identified, including death certificate data, hospital episode data, pharmacy claims data, one national survey, four disease registries (cancer, cystic fibrosis, motor neurone and interstitial lung disease) and a national renal transplant registry. The ad hoc development of the health system in Ireland has resulted in i) a fragmented information infrastructure resulting in gaps in data collections particularly in the primary and community care sector where much palliative care is delivered, ii) ill-defined data governance arrangements across service providers, many of whom are not part of the publically funded health service and iii) systemic and temporal issues that affect data quality. Initiatives to improve data collections include introduction of i) patient unique identifiers, ii) health entity identifiers and iii) integration of the Eircode postcodes. Recently enacted general data protection and health research regulations will clarify legal and ethical requirements for data use. Conclusions: Ongoing reform initiatives and recent changes to data privacy laws combined with detailed knowledge of the datasets, appropriate permissions, and good study design will facilitate future use of administrative health and social care data for PEoLC research in Ireland

Indicators for early assessment of palliative care in lung cancer patients: a population study using linked health data

Background: Analysing linked, routinely collected data may be useful to identify characteristics of patients with suspected lung cancer who could benefit from early assessment for palliative care. The aim of this study was to compare characteristics of newly diagnosed lung cancer patients dying within 30 days of diagnosis (short term survivors) with those surviving more than 30 days. To identify indicators for early palliative care assessment we distinguished between characteristics available at diagnosis (age, gender, smoking status, marital status, comorbid disease, admission type, tumour stage and histology) from those available post diagnosis. A second aim was to examine the association between receiving any tumour-directed treatment, place of death and survival time. Methods: A retrospective observational population based study comparing lung cancer patients who died within 30 days of diagnosis (short term survivors) with those who survived longer using Chi-squared tests and logistic regression. Incident lung cancer (ICD-03:C34) patients diagnosed 2005–2012 inclusive who died before 01–01-2014 (n = 14,228) were identified from the National Cancer Registry of Ireland linked to death certificate data and acute hospital episode data. Results: One in five newly diagnosed lung cancer patients died within 30 days of diagnosis. After adjusting for stage and histology, death within 30 days was higher in patients who were aged 80 years or older (adjusted OR 2.46; 95%CI 2.05–3.96; p < 0.001), patients with emergency admissions at diagnosis (adjusted OR 2.96; 95%CI 2.61–3.37; p < 0.001) and patients with any comorbidities at diagnosis (adjusted OR 1.32 95%CI 1.15–1.52; p < 0.001). Overall, 75% of those who died within 30 days died in hospital compared to 43% of longer term survivors. Conclusions: We have shown a high proportion of lung cancer patients who die within 30 days of diagnosis are older, have comorbidities and are admitted through the emergency department. These characteristics, available at diagnosis, may be useful prognostic factors to guide decisions on early assessment for palliative care for lung cancer patients. Patients who die shortly after diagnosis are more likely to die in hospital so reporting place of death by survival time may be useful to evaluate interventions to reduce deaths in acute hospitals

Five-year cause-specific survival (age-standardized) and age-adjusted mortality hazard ratios for female breast cancer patients, Ireland, 1994–2008: by deprivation stratum and diagnosis cohort.

<p>Five-year cause-specific survival (age-standardized) and age-adjusted mortality hazard ratios for female breast cancer patients, Ireland, 1994–2008: by deprivation stratum and diagnosis cohort.</p

Five-year (a) and ten-year (b) cause-specific survival of female breast cancer patients, Ireland, 1999–2008: by deprivation stratum, all ages (age-standardized) and by diagnosis age.

<p>Five-year (a) and ten-year (b) cause-specific survival of female breast cancer patients, Ireland, 1999–2008: by deprivation stratum, all ages (age-standardized) and by diagnosis age.</p

Hazard ratios for cause-specific mortality by deprivation stratum for female breast cancer patients, Ireland, 1999–2008: comparison of models from A (age-stratified) to D (fully adjusted).

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111729#pone-0111729-t002" target="_blank">Table 2</a> for further details of the models.</p

Relative risks for breast cancer treatment by deprivation stratum, Ireland, 1999–2008: age-adjusted (black symbols) and fully adjusted (grey).

<p>Adjusted for diagnosis cohort, age-group, HSE area of residence, T, N and M categories of stage, tumour grade, method of presentation, smoking status, marital status (except hormone therapy), tumour morphology, hormone receptor status and HER2 status (except radiotherapy), also surgery type (for radiotherapy only).</p

Hazard ratios for breast cancer mortality by deprivation stratum, Ireland, 1999–2008: by (a) age of diagnosis and (age-adjusted) by (b) region of residence, (c) Charlson Index of comorbidity, (d) TNM 5^th-edition stage, (e) method of presentation, and (f) surgical and hormonal treatment.

<p>Trends by deprivation were less heterogeneous for other subgroups.</p

Hazard ratios for cause-specific mortality by deprivation stratum for female breast cancer patients, Ireland, 1999–2008.

<p>(See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111729#pone-0111729-t002" target="_blank">Table 2</a> for fuller details of Model C.).</p>a<p><i>Model A:</i> stratified by age (to allow for non-proportional hazards).</p>b<p><i>Model B:</i> as model A but also stratified for T, N and M categories of stage.</p>c<p><i>Model C:</i> as model B but also stratified for tumour grade, and adjusted for method of presentation, smoking status, region of residence, diagnosis year, tumour morphology and hormone receptor status; standard errors adjusted for clustering by region of residence; marital status and HER2 status excluded as they did not significantly contribute to model-fit); Charlson Index of co-morbidity (available for 82% of cases 2002–2008) excluded as it did not contribute significantly to model-fit.</p>d<p><i>Model D:</i> as model C but also adjusted for treatments within 12 months after diagnosis: breast surgery (mastectomy, BCS, or none); radiotherapy (month of treatment, or none); chemotherapy & other non-hormonal medical oncology (month of treatment, or none); hormone therapy (< = median month to treatment, yes>median, none); any tumour-directed treatment (yes, no); regional lymph-node excision/biopsy (yes/no). Time to surgical treatment or to first tumour-directed treatment did not improve model fit further.</p><p>* P<0.05, ** P<0.01, *** P<0.001.</p><p>Hazard ratios for cause-specific mortality by deprivation stratum for female breast cancer patients, Ireland, 1999–2008.</p