CHAPTER 3 DATA AND METHODS
3.4 Detailed methods to estimate baseline utilisation
This section describes in detail the methods applied to derive our findings for baseline utilisation which are presented in Chapters 5 to 11. The data sources are further described in Appendix 2.
Data and methods | 83
3.4.1 Acute public hospital services (Chapter 5)
Name Data
HSE Emergency Dept.
Attendance Data • Total number of Emergency Department (ED) attendances at 30 public hospital designated EDs in 2015. Disaggregated by age group (0-15, 15-64, 65+).
HSE Patient Experience
Time (PET) Data • Total number of ED attendances at 28 public hospital designated EDs in 2015. Disaggregated by SYOA and sex.
HSE Public Outpatient Dept.
(OPD) Attendance Data • Total number of outpatient attendances at 50 public outpatient clinics in 2015. Disaggregated by age cohort (0-15, 16-64, 65+).
Hospital Inpatient Enquiry
(HIPE) Data • Total number of inpatient and day-patient discharges in 53
21 acute public
hospitals 2015.22 Disaggregated by SYOA and sex.
Methods
Emergency Department Attendances
•To estimate ED attendance rates by SYOA and Sex, HSE Attendance data and PET data are used. As data were
missing from both data sources, a number of adjustments are made.
To account for missing age and sex in the PET database, age and sex are imputed based on the distribution of
these variables in the PET database from 2016.23
To account for the differences in the numbers of attendances reported in the PET and the HSE Attendance
database24 adjustments have been made for the differences in the number of attendances at the aggregate
level with attendances added as per the age and sex distribution in the available 2016 PET data.
People attending who ‘did not wait’25 are not counted as attendances because they did not receive any
treatment and are excluded from the analysis. Outpatient Department Attendances
•To estimate OPD utilisation rates, baseline utilisation rates are calculated by taking age cohort (0-15, 16-64,
65+) volumes of public outpatient attendances captured in the HSE Public OPD Attendance data for 2015 and dividing by corresponding age-specific population volumes for 2015.
Inpatient and Day-patient Admissions and Bed Days
•To estimate inpatient and day-patient (day case) (IPDC) utilisation rates, SYOA-specific and sex-specific
volumes of utilisation (e.g. day patient, emergency inpatient, elective inpatient) in HIPE 2015 are divided by corresponding age- and sex-specific population volumes for 2015.
Utilisation in HIPE can be measured both in terms of the number of discharges or the number of bed days. Bed days are calculated as the product of the number of discharges and their average length of stay (LOS) for
each age and sex cohort.26 In this analysis the predominant focus is on discharges as a measure of utilisation.
The reason for this focus is that discharges will be used as the basis for costing in the second phase of this project where demand is converted to expenditure. However, bed days provide a useful measure of utilisation that better account for variation in resource use and can also be readily converted into estimated annual bed numbers to examine capacity requirements.
Maternity utilisation, that is those who were admitted in relation to their obstetrical experience (from conception to six weeks post-delivery), are considered separately. This approach is justified in that maternity discharges represent a unique subset of hospital activity. All maternity discharges are female and are within a narrow age range. Additionally, maternity discharges report a very narrow range of diagnoses and procedures and tend to have a shorter inpatient average length of stay compared to non-maternity discharges (42).
21 We exclude 62 hospice discharges reported to HIPE in 2015. Additionally, two long-stay hospitals are excluded which are included in the analysis in Chapter 9.
22 This chapter also examines trends in total hospital discharges between 2006 and 2015, however these trends are not used as a basis for projections.
23 BIU provided data from January to June 2016 and contained a lower proportion of missing values than the 2015 data. 24 For three hospitals only new attendances were reported to the PET while all attendances (new and return) were
returned to the attendance database. A further three hospitals were returning ED clinic activity to PET which should not have been included and a further three did not report PET data for some or all of 2015.
25 There is a variable in the PET Database which reports the discharge destination. One category is ‘did not wait’; these are patients who registered their attendance but left the ED before they were treated. In 2015, an estimated 60,100 attendances ‘did not wait’ for treatment.
26 In this analysis ‘same-day’ inpatients (i.e. inpatients admitted and discharged on the same day) are given a LOS of 1. We do not exclude them from the analysis nor do we attempt to attach a part day LOS (e.g. 0.5) to their period of care.
3.4.2 Private hospital services (Chapter 6)
Name Data
Healthy Ireland Survey Wave 2 Data
• Private hospital inpatient utilisation for those aged 15 years and over. Data are
disaggregated by age cohort27 and sex.
Health Insurance Authority (HIA) Risk Equalisation Returns
• Captures the number of day-patient admissions28 and inpatient hospital days
claimed on private health insurance (for the four open market insurers). Data are disaggregated by SYOA and sex.
Methods
• The (weighted) total number of private hospital admissions by age and sex is divided by the (weighted)
number of total sample respondents in their corresponding age and sex cohort to calculate an inpatient private hospital admission rate.
• HIA data did not allow for the direct calculation of private hospital activity rates as activity data that were
collected were not disaggregated in terms of whether the insured activity took place in public or private
hospitals. As such, the following procedure is employed to estimate private hospital insured day-patient
admission and inpatient bed day activity. To estimate private hospital insured day-patient activity first we calculated the total number of privately-insured day-patient discharges in HIPE by single year of age and
sex.29 Then we subtracted these values from total privately-insured day-patient activity submitted to the
HIA to leave estimates of private hospital insured day-patient activity by single year of age and sex.30 The
same method is employed to estimate private hospital insured inpatient bed days by single year of age and sex. Finally, we divided these single year of age and sex utilisation estimates by their corresponding population volumes to calculate private hospital insured admission (day patient) and bed days (inpatient) rates by single year of age and sex.
3.4.3 General practice services (Chapter 7)
Name Data
Health Ireland Survey
Wave 1 • GP visit rate in 2015 for those aged 15 years and older. Disaggregation by age cohort31 and sex.
• Practice nurse visit rate in 2015 for those aged 15 years and older. Disaggregation
by age cohort and sex. Growing Up in Ireland
Data •• Infant Cohort Wave 1: GP visit rate in 2007-2008 for nine-month-olds. Infant Cohort Wave 2: GP visit rate in 2011-2012 for three-year-olds.
• Infant Cohort Wave 2: Practice nurse visit rate in 2011-2012 for three-year-olds.
• Child Cohort Wave 1: GP visit rate in 2008-2009 for nine-year-olds.
• Child Cohort Wave 2: GP visit rate in 2011 for 13-year-olds.
• Child Cohort Wave 2: Practice nurse visit rate in 2011 for 13-year-olds.
Methods
•To estimate GP visit rates in those aged younger than 15 years, the GP visit rate from Growing Up in Ireland
(GUI) Infant Cohort Wave 1 are multiplied by 1.33 (as the rates apply to nine-month-olds) applied to those children aged less than 1; the GP visit rate from GUI Infant Cohort Wave 2 is applied to those children aged 1- 5 years old; the GP visit rate from GUI Child Cohort Wave 1 is applied to those children aged 6-11 years old; the GP visit rate from GUI Child Cohort Wave 2 is applied to those children aged 12-14 years old.
27 Age cohorts: 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, and 75+.
28 We follow HIA terminology and refer to day-patient activity as an admission (43). In HIPE the same activity is referred to as a discharge.
29 In HIPE, all those with a private discharge status are asked their private health insurance status. Responses available are Named Health Insurer/Not Stated/Other/No Insurance (44). In 2015, 96.3 per cent of private inpatient and 91.4 per cent of private day-patient discharges reported having private health insurance. However, this may represent an underestimation of the number of private discharges with insurance if some patients fail to disclose their insurer. 30 An assumption underlying the approach is that the definition of a day patient in HIPE is comparable to the HIA day-
patient definition. For instance, if HIA data also capture some private side-room or more outpatient-orientated care in their day-patient activity this would impact on our estimates somewhat.
Data and methods | 85
•To estimate GP visit rates in those aged 15 years and older, the GP visit rates from Healthy Ireland Wave 1 is
applied to the respective age and sex cohorts.
•To estimate practice nurse visit rates in those aged younger than 15 years, the practice nurse visit rate from
GUI Infant Cohort Wave 2 is applied to those children aged 1-5 years old; the practice nurse visit rate from GUI Child Cohort Wave 2 is applied to those children aged 6-14 years old.
•To estimate practice nurse visit rates in those aged 15 years and older, the practice nurse visit rates from
Healthy Ireland Wave 1 is applied to the respective age and sex cohorts.
•General practice visit rates are estimated by adding the respective GP visit and practice nurse visit rates.
3.4.4 Pharmaceuticals and pharmacy services in the community
(Chapter 8)
Name Data
PCRS Drug
Reimbursement Data
• Number of prescription items in the General Medical Services Scheme (GMS),
Drugs Payment Scheme (DPS), Long-Term Illness (LTI) Scheme, and High Tech
Drugs (HTD) Scheme. Data are disaggregated by age cohorts32 and sex.
QNHS 2010 Quarter 3
Health Module • Number of pharmaceutical consultations recorded by respondents in the last 12 months prior to survey. These data are disaggregated by age cohorts33 and sex.
Methods
•To estimate PCRS prescription items rates the number of prescription items for GMS, DPS, LTI and HTD
schemes are divided by their corresponding age- and sex-specific population volumes for 2014. At the time of analysis 2015 data were not available to the research team. Consequently 2014 activity rates are assumed for 2015.
•To estimate the pharmaceutical consultation rate the (weighted) number of pharmaceutical consultations
by age and sex is divided by the (weighted) number of total sample respondents in their corresponding age and sex cohort. 2010 activity rates are assumed for 2015.
3.4.5 Long-term and intermediate care services (Chapter 9)
Name Data
HSE Social Care Division
Data • Total number of residents in NHSS-funded long-stay beds at 31 December 2015. Data disaggregated by SYOA and sex.
• Total number of residents in publicly-financed long-stay beds under legacy
schemes at 31 December 2015. Data disaggregated by SYOA/age cohort and sex.
• Short-stay public and voluntary beds not included in the HIQA register at end
2015.
HIQA Bed Register Data • Total number of beds and beds by category of ownership in HIQA-registered
long-term care centres at 31 December 2015. DoH Long-Stay Activity
Statistics (LSAS) • Age cohort and sex distribution of patients in limited-stay beds in 2014.
Nursing Homes Ireland
2014/2015 Survey Data • Proportion of privately-financed residents in 2014. Proportion of short-stay residents in 2014.
Methods
•Undertaking this baseline utilisation analysis required overcoming data inadequacies by combining evidence from administrative data sources and from surveys. While numbers of beds registered with the Health
32 Age cohorts: <5, 5-11, 12-15, 16-24, 25-34, 35-44, 45-54, 55-64, 65-69, 70-74, and 75+. 33 Age cohorts: 18-19, 20-24,... , 80-84, 85+.
Information and Quality Authority (HIQA) and numbers of long-term residents financed by the NHSS (and, in the case of 2015 data, by some but not all legacy funding schemes)34 are published by the Department of Health (45), the HIQA series does not include all the beds in this sector and the published numbers of residents exclude short-stay and privately-financed residents. Therefore, to estimate long-term care utilisation by SYOA and sex, a number of steps are undertaken:
HIQA-registered beds and unregistered short-stay public and voluntary bed numbers supplied by the HSE Social Care Division are summed to give total beds. HIQA data included the numbers of public, voluntary, and private nursing home beds. The apportionment of private beds between long- and short-stay is assumed to be in proportion to residents in these categories (46). The apportionment of public and voluntary beds between long- and short-stay was supplied by the HSE Social Care Division.35
•Numbers of residents in each category of facility are estimated as follows: NHSS-funded long-
stay residents • Data available by SYOA and sex Publicly-financed
long-stay residents under legacy schemes
• Data available by SYOA and sex for some schemes and by age cohort and sex for others. Age-cohort data further disaggregated assuming the distribution by SYOA within the cohort of the other legacy-funded schemes.
Privately-financed long and short-stay residents
• Privately-financed proportion of private nursing home residents is estimated at 12 per cent based on 2014 Nursing Homes Ireland survey (46), assuming bed occupancy at 94 per cent (47). Estimated total residents disaggregated assuming NHSS-funded SYOA and sex distribution, on the assumption that all long-stay residents have a similar age distribution
Short-stay residents in private nursing homes excluding private payers
• Short-stay proportion of private nursing home residents estimated at 8 per cent based on 2014 Nursing Homes Ireland survey (46), assuming bed occupancy at 94 per cent (47). Estimated total residents disaggregated assuming age cohort and sex distribution of residents in limited-stay beds in 2014 (47). Age cohort data further disaggregated assuming the distribution by SYOA within the cohort of the NHSS-funded residents.
Short-stay residents in public and voluntary units
• Estimated by assuming occupancy of public and voluntary short-stay beds at 91.5 per cent (47). Estimated total residents disaggregated assuming age cohort and sex distribution of residents in limited-stay beds in 2014 (47). Age cohort data further disaggregated assuming the distribution by SYOA within the cohort of the NHSS-funded residents.
•Total residents are calculated as the sum of the resident categories above. Due to data limitations, these numbers are estimates. The aggregate estimate of residents has been validated by calculating an overall occupancy rate as total residents divided by total beds at 94 per cent, which is marginally higher than the 93.4 per cent occupancy rate in 2014 but is consistent with the reduction in NHSS waiting times during 2015 and providers’ subjective experience at end-2015.36 Baseline residential long-term care utilisation rates are estimated as numbers of residents at end-2015 by single year of age (SYOA) and sex dividing by corresponding age- and sex-specific population volumes for 2015.
34 Personal communication from Department of Health, 27 April 2017.
35 Despite detailed interrogation of the HIQA and HSE Social Care bed registers, there remained some discrepancies in bed counts in different categories. Any resulting possible over-statement of numbers of residents is estimated at one per cent.
Data and methods | 87
3.4.6 Home care services (Chapter 10)
Name Data
HSE Social Care Division
Administrative Data • Total number of publicly-financed (public) home help recipients, Home Care Package (HCP) recipients, and public home help hours in 2015. No disaggregation by age or sex.
TILDA Wave 3 Data • Recipient rate of public home help, privately-purchased (private) home help, and HCPs. Disaggregation by age cohort37 and sex.
• Rate of public home help hours. Disaggregation by age cohort and sex. Methods
Recipients
•To estimate the recipient rate across age cohorts for public home help and HCPs, the distribution of recipient rates from TILDA data is apportioned to the HSE administrative data using the following steps. Firstly, public home help recipient rates across age cohorts are estimated using TILDA data. Within each TILDA age cohort, the SYOA age distribution from the long-term care analysis is applied. Secondly, these rates are multiplied by the number of people in each cohort using 2015 population estimates. Thirdly, the SYOA age distribution of public home help from step 2 is apportioned to the overall number of public home help and HCP recipients recorded by the HSE administrative data.
•No administrative data were available for private home help recipients; the recipient rates for each age cohort are estimated using TILDA data.
Hours
•To estimate the public home help hour rate across age cohorts the distribution of hour rates from TILDA data is apportioned to the HSE administrative data using the following steps. Firstly, public home help hours across age cohorts are estimated using TILDA data by multiplying the average number of days they received home help in the previous month by 12, and multiplying this by the average number of hours they received on a given day. Within each TILDA age cohort, the SYOA age distribution from the long-term care analysis is applied. Secondly, these rates are multiplied by the number of people in each cohort using 2015 population estimates. These numbers provided an age distribution to be estimated. Thirdly, the SYOA age distribution of public home help from step 2 is apportioned to the overall number of public home help hours recorded by the HSE administrative data.
•No data were available for private home help hours. The average public home help hours from TILDA data for each age cohort are applied to private home help recipients.
3.4.7 Public health nursing and community therapy services (Chapter
11)
Name Data
HSE BIU Data • Total number of attended referrals in non-acute settings, in each month, for public health nurses, physiotherapists, occupational therapists, and speech and language therapists, in 2015. Data disaggregated to the level of four age cohorts (0-4, 5-17, 18-64, and 65+) for public health nurses, occupational therapists, and speech and language therapists, and three age cohorts (0-17, 18-64, and 65+) for physiotherapists. No disaggregation by sex.
TILDA Wave 3 Data • Recipient (accepted referral) and visit rate of public allied healthcare professionals’ services. Disaggregation by age cohort38 and sex.
• Rate of public allied healthcare professionals’ service visits. Disaggregation by age cohort and sex.
Methods
Referrals
•To estimate accepted referral rates, the number of accepted referrals in each 0-4, 5-17, and 18-64 age cohort from the HSE BIU data are included directly. The distribution of recipients (referrals) from TILDA data is apportioned to the official HSE BIU data for those aged 65 and older using the following steps. Firstly, referral rates across age cohorts are estimated using TILDA data. Secondly, these rates are multiplied by the number of people in each cohort using 2015 population estimates. These numbers provided an age and sex distribution to be estimated. Thirdly, the age and sex distribution from step 2 is apportioned to the number of accepted referrals in the HSE BIU data for those aged 65 and older.
Visits
•To estimate the visiting rate for public health nursing, the number of visits in the 0-4, 5-17, and 18-64 age cohorts from the HSE BIU data is included with an adjustment factor of 1.45 used for the 5-17 and 18-64 age cohorts to account for data gaps in the HSE data.39 To account for extra visits among young children, five extra visits are included (at the appropriate SYOA) for the 0-4 age cohort. The visiting rate for those aged 65 and older disaggregated by five-year age cohort is included from TILDA data due to the data gaps which exist for public health nursing visits in the BIU data.
•To estimate the visiting rate for physiotherapy the total number of physiotherapy visits from HSE administrative data is divided by the total number of referrals from the HSE administrative data to provide