GAP ESTIMATING MODEL: Future supply vs demand in Indonesia s healthcare system

(1)

GAP ESTIMATING MODEL:

Future supply vs demand in Indonesia’s healthcare system

National Team for Accelerating Poverty Reduction

Ufara Zuwasti Curran, Prastuti Soewondo, Halimah, James P. Thompson

(2)

(3)

Hypertension: Diagnosed vs

Unmet Needs

Source: Riskesdas 2010 K al s e l Ja ti m S ul ba r S ul ba r S ul te n g B ab e l D I Y o g y ak a rta Ri a u NTB S um s e l M al u k u Su lt ra J am b i K al te n g Ba li S um b ar M al ut S ul s e l NTT K ep ri G o ro nt al o Ka lt im K al ba r S um u t A c eh J ab a r S ul ut B an ten DK I Ja ka rta P ap u a L am p un g L am p un g P ab a r 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 Sick Treated | 3

(4)

• Research question:

–

What is the gap between the medical needs of Indonesians and the health care

system capacity to fulfil those needs?

• Aims:

–

To build structure that permits deeper analysis and understanding about gaps in

supply and demand

–

Examine future demand of healthcare services as healthcare insurance expands and

healthcare supply to serve it

–

Provide recommendations to improve supply adequacy

• GEM study is a

system approach

. It accounts for dynamics of and relationship between

supply and demand from three perspectives:

–

Accessibility

–

Affordability

–

Availability

(5)

| 5

(6)

(7)

Methodology

• Who is our

population

?

–

Population is dynamic

–

32 cohorts

= 2 gender groups *

4 age groups * 4 health

insurance status

• Askes rates (by age and

gender) were extrapolated to

all venues and adjusted,

accounting for insured status

groups and accessibility.

• Healthcare

capacities

(supply)

–

Doctors, nurses, midwives,

hospital facilities

• How to estimate

medical

needs (demands)

of

population?

–

What is the

health status

of

population?

–

What is the agreed

standards

of care

received by

population?

• Assumption used as

standards of care: Askes

insured population prior to

2014

• Why not other utilization

(8)

• “

Small models

” for

–

Population and insured status

–

For doctors, nurses, midwives, and

hospital beds

• Parameters & initial values*

: birth,

mortality, migration rates, insured

status, current capacity for supply,

enrolment, graduation, attrition

rates, practice patters for HCW,

hospital capacity growth rates,

admission rates, ALOS, etc

• Small models to larger model

that

simulates the whole country

(

national model

)  200+

parameters and initial values

• Demand

is affected by

affordability, accessibility, and

availability – and is measured as

unconstrained demand,

desired/expected/real demand,

and

constrained demand

• Gap

was calculated between

capacity and assumed standard of

care

• The concept was carried to 34

provincial models

, with 200+

parameters and initial models for

each province

Methodology

(2)

*Data Sources : BPS 2010 population data; BPS 2010-2035 population projection; UNDESA 2010 – 2015; BPS birth, mortality, migration rates; MOH insured

status; Registered physicians – MOH; Practicing nurses and midwives – MOH; Hospital capacity and ALOS – MOH; Askes utilization rates – MOH; Susenas 2013; PODES 2011; etc.

(9)

Methodology

(3)

Gender Age Outpatient (primary) Inpatient (primary) Outpatient (hospital) Inpatient (hospital)

Askes Adjusted Askes Adjusted Askes Adjusted Askes Adjusted

Male 0-14 261.68 201.16 1.91 1.45 24.54 18.87 6.23 4.72 Female 0-14 378.50 290.96 2.42 1.84 28.00 21.52 6.97 5.28 Male 15-44 209.95 161.39 1.16 0.88 28.82 22.15 5.02 3.80 Female 15-44 243.10 186.88 2.50 1.89 32.90 25.29 5.62 4.26 Male 45-64 428.84 329.66 1.35 1.03 72.61 55.82 6.97 5.28 Female 45-64 558.69 429.48 1.90 1.44 82.34 63.30 7.74 5.87 Male 65+ 437.97 336.68 1.64 1.24 77.96 59.93 8.41 6.37 Female 65+ 635.84 488.79 2.53 1.92 87.90 67.57 9.30 7.04 Average 394.32 303.13 1.93 1.46 54.38 41.81 7.03 5.33

Adjustment factor

(relative to Askes)

Outpatient Inpatient

Midwife

Uninsured

0.5

0.2

0.5 JKN

0.9

0.8

0.9 Jamkesda

0.7

0.8

0.7 Private

1.1

1.2

1.1 Illustration for JKN insured group rates relative to Askes after adjusted using adjustment* and

accessibility** factors

(10)

Population Projection and Change in Insurance Status

Age 00 to 14 Indonesia 40 M 36 M 32 M 28 M 24 M 20 M 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 peopl e Age 15 to 44 Indonesia 60 M 58 M 56 M 54 M 52 M 50 M 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 peopl e Age 45 to 64 Indonesia 30 M 28 M 26 M 24 M 22 M 20 M 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 peopl e

Age 65 and over Indonesia

10 M 9 M 8 M 7 M 6 M 5 M 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 peopl e

TOTAL NATIONAL POPULATION

300 M 280 M 260 M 240 M 220 M 200 M 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Date peopl e

total natl population : Indonesia

-50,000,000 100,000,000 150,000,000 200,000,000 250,000,000 Laki-laki Perempuan Jamkesda JKN Private Uninsured | | 1010

(11)

<(20,000) (10,001)-(20,000) (5,001)-(10,000) (2,001)-(5,000) (1)-(2,000) 0 1-2,000 2,001-5,000 5,001-10,000 10,001-20,000 >20,000 100,000 200,000 300,000 2014 2015 2016 2017 2018 2019 2020 Primary Care

Province Total Primary care Hospital

Jatim Jateng Jabar Jakarta Bali Banten Lampung NTB Sulsel Sumsel Sumbar Kalsel Sumut Jambi Aceh Kalbar Bengkulu NTT Sultra Sulteng Babel Gorontalo Kalteng Sulbar Maluku Riau Kaltara Kaltim Pabar Malut Sulut Kepri Jogja Papua Gap DOCTORS

Practicing doctors Desired demand Constrained demand

Supply and Demand for Doctors

Desired demand Practicing doctors 50,000 100,000 2014 2015 2016 2017 2018 2019 2020 Hospital | 11

(12)

50,000 100,000 150,000 200,000 250,000 2014 2015 2016 2017 2018 2019 2020 Hospital

Perawat di RS Permintaan perawat di RS 50,000 100,000 150,000 200,000 250,000 2014 2015 2016 2017 2018 2019 2020 Primary care

Perawat di Yankes Primer

Permintaan perawat di Yankes Primer

<(20,000) (10,001)-(20,000) (5,001)-(10,000) (2,001)-(5,000) (1)-(2,000) 0 1-2,000 2,001-5,000 5,001-10,000 10,001-20,000 >20,000 Gap

Province Total Primary care Hospital

Jatim Jabar Jateng Banten Bali Sulsel Lampung Sumsel Jakarta Sumut Sumbar NTB Jogja Kalsel Jambi Babel Gorontalo Sulbar Kepri Sulut Kaltara Bengkulu Sulteng Kalbar Malut Kaltim Pabar Sultra Riau NTT Kalteng Maluku Aceh Papua NURSES

Supply and Demand for Nurses

Practicing nurses Desired demand Constrained demand

(13)

<(20,000) (10,001)-(20,000) (5,001)-(10,000) (2,001)-(5,000) (1)-(2,000) 0 1-2,000 2,001-5,000 5,001-10,000 10,001-20,000 >20,000 Province Midwife Kaltara Sulbar Babel Gorontalo Jogja Pabar Kaltim Malut Kepri Maluku Jakarta Banten Kalteng Sulut Sultra Sulsel Papua Kalbar Sulteng Jambi Bali Kalsel Bengkulu Lampung Riau NTB NTT Sumsel Sumbar Jabar Aceh Sumut Jatim Jateng Surplus MIDWIVES

Supply and Demand for Midwives

Practicing doctors Desired demand

Gap

Supply and Demand for Hospital Beds

Capacity Desired demand Constrained demand

HOSPITAL BEDS

Province Hospital beds Jakarta Jateng Jabar Jatim Sulsel Sumsel Bali Jambi Lampung Banten Sumbar Sumut Riau Kalsel Sulteng NTB NTT Sulut Babel Aceh Kalteng Kalbar Sultra Bengkulu Malut Sulbar Gorontalo Maluku Kaltara Kepri Pabar Kaltim Papua Jogja | 13

(14)

Provincial Gap

Summary

Doctors

33

1

Nurses Deficiency

17

Worst:

Jatim, Jateng, Jabar, Jakarta, Bali, Banten

Worst:

Sulsel, Lampung, DKI Jakarta, Jawa Tengah

Hospital beds

Deficiency

29

5

Worst:

Jakarta, Jateng, Jabar, Jatim

Deficiency

Surplus:

Papua Surplus:

Papua, Aceh, Maluku, Kalteng, NTT, Riau, Sultra, Pabar, Kaltim, Malut, Kalbar, Sulteng Bengkulu, Kaltara, Sulut, Kepri, Sulbar

Surplus:

Jogja, Papua, Kaltim, Pabar, Kepri

Midwives

No deficiency

34

(15)

• Availability of healthcare services is the greatest constraint on utilization

• Shortfalls in physicians, nurses, and hospital beds, and no deficiency in midwives

• In particular, the likely demand for physician and nurse services exceeds available capacities at all care levels

• For nurses, the gap will widen when numbers of physicians and hospital beds reach ideal figures • For midwives, however, local customs and need for

more midwives where the population is spread out indicate that the surplus is smaller than estimated • Insufficient capacity at the primary care level

increases the burden at the hospital level

• The generous governmental funding of healthcare costs makes shortfall in capacities even more

evident

• While remote areas will remain difficult to serve in future, it is possible and even likely that more

physicians and allied healthcare workers will be drawn to metropolitan areas, exacerbating access issues for Indonesians living in rural areas

• Quality and distribution of healthcare workers are still main problems, in addition to quantity