Unmet need Some funding is from the total allocation to compensate for gaps in utilisation data; this is targeted towards populations (those living in NZDep quintiles 4/5, Māori and Pacific peoples). Ten percent of the total funding will now be based on deprivation levels to reflect unmet need, with the intent of tackling health inequalities. An adjustment is made in the area of circulatory disease, as this is the only area in which there is consistent evidence of a shortfall in use of these services due to deprivation levels. The ‘Index for climate and latitude’ is applied to Somatic care. immigrants’ is applied to psychiatric services and ‘Metropolitan area’ is applied to substance abuse services. There is no adjustment for unmet need. Indigenous and homeless populations are given additional weighting in the population count to reflect unmet need. Ethnicity Ethnicity is a variable within the cost weights (Māori, Pacific peoples and Other). Inadequate information to summarise at this time. No adjustment is made for ethnicity, as the deprivation index sufficiently captures the additional needs of ethnic minorities that are to their deprivation. The variable immigrants’ is included in the Psychiatric Care component. The proportion of immigrants is factored in to the postcode area clustering. Aboriginal and Torres Strait Islanders are given additional weighting in the population count. They also receive additional weighting in the Health Need Index, to reflect their higher need for acute care hospital admissions.
Table 4: Review of existing and new variables against review criteria Criteria Universally recorded, consistent and verifiable Free from perverse incentives Not vulnerable to manipulation Reflect plausible determinants of individual need Not a measure of supply Explains significant variation in the model Calibration to the model feasible Age Met Met Met Met Met Met Met Sex Met Met Met Met Met No Met Ethnicity (Māori, Pacific peoples, Other) Largely met, but data quality issues exist Met Largely met Met Met Met Met, but data quality issues exist Socioeconomic status Met Met Met Met Met Met Met BMI Not met – data is not consistently available and not readily available routinely Depends on construction – if paid on % with high BMI, then perverse incentive exists Largely met – BMI is an objective measurement but there is risk of measurement errors Largely met – though BMI is not a perfect proxy of expected health need or health service utilisation in the next year Met – individual based Possibly; unlikely to fully explain all the variation in the model Met – but would require significant work. Major concerns about data quality Burden of disease Met – this is available at an aggregate level across the country Depends on construction, but can be managed Largely met – objective measurement, though ‘capacity to benefit’ could be open to interpretation Met Met Possibly, but not clear unless further modelling work done Met – but would require significant work Asian ethnicity Population projections are available for Asian ethnicities, but not available by Asian ethnic subgroups by age, sex and DHB Met Met No – due to difficulties in disaggregating ‘Asian’ Met Possibly, but not clear unless further modelling work done Met – but would require significant work Prescription drugs as markers Met Not met Met Not met Not met Possibly, but not clear unless further modelling work done Met Other socioeconomic indicators Not met – difficult to get consistent and reliable data on other socioeconomic factors Income would be most reliable and accessible Largely not met (depends on the indicator) Depends on the indicator – some objective factors such as income are difficult to manipulate, while housing quality is difficult to standardise Largely met – depends on the indicator. Income, for example, has a strong correlation with health status Met Possibly for some indicators, but not clear unless further modelling work done Met – but would require significant work
Accident flags Met – but data quality issues exist Not met Not met Not sure Met Possibly, but not clear unless further modelling work done Met – but would require significant work Environmental indicators Met Met Data is objective, but effect of environment on health status is up for debate (thus manipulation) Not met – as data is regional rather than individual. Also, environmental impact on health is influenced by other factors (such as income) Met Possibly, but not clear unless further modelling work done Met – but would require significant work
Table 16: Description of components in rural adjuster models Component Offshore islands Rural GP/PHO payments Travel and accommodation Inter-hospital transfers Community services Facilities Size (millions) $3.5 $21.5 $21 $28.4 $60 $35.1 Description Extra costs faced by DHBs in providing health services to offshore islands comprising a rural bonus/remote rural practice area funding payment; a rural workforce retention premium; a reasonable roster amount; and a rural after-hours payment DHBs reimbursement of eligible patients for travel and accommodation The cost of transporting patients between facilities The extra costs of providing community services to small and/or sparse populations – either services in the home, integrated family health clinics or small rural hospitals The diseconomy cost of providing a full role delineation model level 3 service to a small population Data DHB supplied 2014 Ministry stocktake of rural payments plus funding devolved since that date Payment system data extracted for validated by DHBs Expenditure recorded against GL codes 4950 (Ambulance) and 4955 (Air Ambulance) (2011/12–2013/14 years), supplied by DHBs National Cost Data and four DHB case studies (Northland, Bay of Plenty, Waikato, Nelson Marlborough) National Cost Data Pool calculation DHB-supplied costs less amounts covered in other components Total payments Total payments less the national population times the Auckland DHB cost per person Total payments less the national population times the Auckland metro cost per person Comprising an estimated top-up per person for the highly rural population and the rural population with low urban influence Estimation using cost data and a diseconomy model of the diseconomy for inpatient, emergency department, assessment treatment and rehabilitation and mental health Distribution enhanced status quo DHBs’ contribution to pool calculation DHBs’ contribution to pool calculation DHBs’ contribution to pool calculation DHBs’ contribution to pool calculation DHBs’ contribution to pool calculation DHBs’ contribution to pool calculation Distribution weighted rural population index DHBs’ contribution to pool calculation Weighted rural population index Weighted rural population index Weighted rural population index Weighted rural population index DHBs’ contribution to pool calculation Distribution weighted density index DHBs’ contribution to pool calculation Weighted rural density index Weighted rural density index Weighted rural density index Weighted rural density index DHBs’ contribution to pool calculation Figure 31: Process flow chart for options for calculating rural adjuster
Where there is no 2013/14 price, look for a 2014/15 price; if found, deflate using relevant contribution to cost pressure (CCP) rate to 2013/14 value. If a 2014/15 price is not found, look for a 2012/13 price. If a 2012/13 price is found, inflate using the relevant CCP rate. If a 2012/13 price is not found, look for a 2015/16 price. If a 2015/16 price is found, deflate using the relevant CCP rate. If a 2015/16 price is not found, look for a 2011/12 price. If a 2011/12 price is found, inflate using the relevant CCP rate. If a 2011/12 price is not found, set the price to zero. Table A8 presents the final prices the Team used. Table A8: Price list used for development of PBFF cost weights Key Uses deflated 1516 price (note 1516 prices are in 1415 values) Uses deflated 1415 price Uses inflated 1213 price Transplant PUC price is an estimated price calculated by dividing the 2013/14 top slice by the number of discharges for all transplant PUCs. Heart and lung transplants based on price volumes at devolution, plus risk pool allowance and annual growth Top slice 21,875,563 Volume 69 Average estimated cost 317,037 PUC Price AH01001 118.66 AH01003 149.74 AH01004 163.23 AH01005 82.81 AH01006 143.98 AH01007 167.79 AH01008 159.09 AH01009 384.27 AH01010 248.56 C01010 – C01011 – C01013 – C01014 – C01016 – COCH0011 – COCH0013 – COCH0015 – COCH0027 – COOC0042 – COPA0002 –