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

Numbers of screening invitations received by women in various birth-cohorts in regions 1 and 2, together with mortality rates and their ratios.

<p>Insets show the extent of each region, and (in purple) the fractions of those aged 50–85 in each quintile of the deprivation index, with ‘-‘ denoting the least and ‘+’ the most deprived. For each birth cohort, the numbers of screening invitations received by the end of the indicated years are indicated by squares ranging in colour from white (0) to black (7), and the numbers received by the end of 2013 are shown to the right of their last follow-up year. The <i>Region 1 vs</i>. <i>Region 2 comparison</i> limited to the years lived by women who were <i>65 or older in the year 2000</i> (cells inside the red boundary) measures the difference in the background mortality rates in the two Regions. The corresponding comparison limited to the years lived by women who were <i>64 or younger in the year 2000</i> (cells inside the blue boundary) measures the amalgam of this same (background) difference and the difference due to the greater duration of screening in Region 1.</p

Year-specific differences between region 1 and region 2 in incidence rates (per 100,000WY) of stage 2–4 breast cancer.

<p>Yearly differences, expressed as percentages, are derived from age-matched Mantel-Haenszel summary rate ratios restricted to the cells within the blue polygon in the Lexis diagram (women who could have benefitted from screening). The smoothed differences are derived from a conditional Poisson regression (i.e. matched on age and year) that used all 36 x 14 = 504 Lexis cells, but that included a term to account for ‘background’ regional differences in the incidence (estimated from the cells enclosed in red in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188947#pone.0188947.g002" target="_blank">Fig 2</a>), as well as linear and quadratic-in-time terms to allow the ‘net’ benefit of earlier access to screening in Region 1 to depend on year.</p

The ages when they were diagnosed with, and died of, breast cancer: 66 women in one selected cohort in region 2.

<p>Some 9,274 women, aged 54 in the year 2000, followed to the end of 2013. This cohort received just two screening invitations, at ages 62 and 64, too late to alter the course of these 66 fatal cancers. The lengths of the lighter portions of the lines are the maximal amounts by which screening might have advanced their diagnosis and treatment. Lines are drawn diagonally to orient readers to the full Lexis diagrams used in Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188947#pone.0188947.g002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188947#pone.0188947.g003" target="_blank">3</a>.</p

Numbers of deaths from breast cancer at each age and in each year <i>in region 1 and region 2</i>, plotted on the same grids as in Fig 1.

<p>The numbers in each cell have been smoothed, by averaging the actual numbers of deaths in the 9 cells in 3 x 3 square centered on the cell in question. To highlight the reach of screening that ends at age 64, also shown within the 4 x 5 rectangles, are the numbers of cancers that proved fatal at ages 72–76. For many of those in Region 1, the last screening invitation (shown by the thick grey horizontal line) was 8–12 years earlier.</p

Additional file 1 of Words and numbers: a comparative study of medical and journalism students’ descriptors of risk, numeracy and preferences for health risk communication

Supplementary Material

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

Additional file 1: of Barriers and solutions to online learning in medical education – an integrative review

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How do multi-morbidity and polypharmacy affect general practice attendance and referral rates? A retrospective analysis of consultations

Background As prevalence of multi morbidity and polypharmacy rise, health care systems must respond to these challenges. Data is needed from general practice regarding the impact of age, number of chronic illnesses and medications on specific metrics of healthcare Methods This was a retrospective study of general practices in a university-affiliated education and research network, consisting of 72 practices. Records from a random sample of 100 patients aged 50 years and over who attended each participating practice in the previous two years were analysed. Through manual record searching, data were collected on patient demo graphics, number of chronic illnesses and medications, numbers of attendances to the general practitioner (GP), practice nurse, home visits and referrals to a hospital doctor. Attendance and referral rates were expressed per person-years for each demographic variable and the ratio of attendance to referral rate was also calculat

Prescription of psychotropic medication in patients with type two diabetes mellitus: A multi-practice study from Ireland

Background: Comorbid anxiety and depression and type two diabetes mellitus (T2DM) are commonly managed by General Practitioners (GPs). Objectives: To investigate the proportion of people with T2DM who are prescribed either antidepressant or benzodiazepine medications in general practice; to compare people with T2DM that have a prescription with those that do not in terms of patient characteristics, glycaemic control and healthcare utilization. Methods: Anonymized data was collected by GPs and senior medical students from electronic medical records of patients with T2DM in 34 Irish general practices affiliated with the University of Limerick Graduate Entry Medical School during the 2013/14 academic year. Data included demographics, healthcare utilization, prescriptions and most recent glycosylated haemoglobin (HbA1c) measurement. Results: The sample included 2696 patients with T2DM, of which 733 (36.7%) were female, and with a median age of 66 years. The percentage with a current prescription for an antidepressant or benzodiazepine was 22% (95%CI: 18.9–24.9). Those with a current prescription for either drug were more likely to have attended the emergency department (28.3% vs 15.7%, P<0.001), to have been admitted to hospital (35.4% vs 21.3%, P<0.001) in the past year and attend their GP more frequently (median of 9 vs 7, P<0.001) than those without a prescription. Rates of poor glycaemic control were similar in those with and without a current prescription. Conclusion: Over one-fifth of people with T2DM in Irish general practice are prescribed an antidepressant or benzodiazepine medication. Prescription of these is associated with increased healthcare utilization but not poorer glycaemic control