Transcript The health workforce: a recent priority for investment • 2006 – 2015: Health workforce decade • World Health Report 2006: Working together for health.

The health workforce:
a recent priority for investment
• 2006 – 2015: Health
workforce decade
• World Health Report
2006: Working
together for health
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Demand for information –
What do we want to know?
• Active workforce -- stocks of various types of
health professionals in the public or private sectors
(disaggregated by age, sex and geographic location)
• Entry -- annual numbers graduating from training
institutions
• Exit – annual numbers departing from service due
to movement to private sector, emigration, change
of occupation, retirement or death
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Demand for information –
What do we want to know?
• Performance -- the outputs of health workers
(e.g. outpatient attendance per capita;
hospitalizations per capita)
• Costs -- expenditures on remuneration (including
benefits) as well as on pre-service training
3
Key health workforce metrics
for a health system dashboard
Health worker densities
• by type of health worker
• by sector
South African health worker densities per 100,000
4
Total active
workforce
Public
sector
Private
sector
Physicians
(2002)
51.2
19.3
31.9
Nurses
(2001)
392
245
146
Sources: HST, HSRC, NDoH, HPCSA, SANC, StatsSA
Key health workforce metrics
if there is room on the dashboard
Health worker densities
• by type of health worker
• by sector
• by geographic location for the public sector
South African health worker densities per 100,000
Total
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Public
Private
Public in
most
advantaged
province
Public in
least
advantaged
province
Physicians
(2002)
51.2 19.3 31.9
33.1
9.1
Nurses
(2001)
392
267
181
245
146
Sources: HST, HSRC, NDoH, HPCSA, SANC, StatsSA
Supply –
What data sources exist?
Sources of data
for monitoring the health workforce
(see handout) :
– National population census
– Labour force survey
– Health facility census
– Professional registration database
– Civil service payroll database
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– Staffing reports from each health facility
Population census
• Strengths
• ·
counts all workers:
– - private and public
– - health professionals workers working in non-health sectors
– - management and support staff working in the health industry
• ·
geographical disaggregation to lowest level
• ·
rigorous collection and management of data
• Limitations
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• ·
once each ten years
• ·
occupational data often not coded with sufficient precision
• ·
census authorities may be reluctant to release the micro-data
• ·
provide little information on entry into and exit from the workforce
Labour force survey
• Strengths
•
·
Counts all occupations (including management & support staff)
• ·
Counts both public and private
• ·
Counts unemployed and part-time workers
• ·
Can provide information salaries & wages
• ·
Rigorous data collection and data management.
• Limitations
•
·
• ·
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Often only once each 5 years;
Small sample size [
– Very wide confidence intervals for countries with few health
workers
– Geographic disaggregation often not advisable
• ·
Occupation is often not coded with sufficient precision
• ·
Cross-sectional: can't track entry and exit
Health facility census
• Strengths
•
·
counts all health facility staff including management & support staff
• · allows geographical disaggregation
• · can be used to track in-service training/skills and productivity
• · (often) rigorous data collection and data management.
• · relatively less costly
• Limitations
•
·
no data on entry and exit
• · double counts dually employed workers
• · may omit some private facilities, community workers, unemployed
• · (historically) conducted ad hoc and infrequently
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• · usually don't provide data on remuneration
Professional registration database
• Strengths
•
·
• ·
Counts registered health professionals (private as well as public)
Using a unique identifier the database could track entry and exit
• Limitations
•
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·
Requires developing the capacity of regulatory authorities
• ·
Accurate updating depends upon incentives and/or enforcement
• ·
Difficult to track non-professional health workers and support staff
Multiple data sources [
validation and interpolation
Data on nurses in South Africa
1. Total
registered
with SANC
2. Active
workforce
from LFS
3. Public
sector nurses
4. Private
sector
nurses
(#2 – #3)
190,449
155,484
97,423
58,061
(2001)
(NDoH, 2001)
(2001)
Source: HSRC, SANC, StatsSA, NDoH
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The gap between
Supply & Demand
The Global Atlas of the Health Workforce
(http://www.who.int/globalatlas/default.asp )
is now more extensively populated:
• Data on more types of health workers –
2004 -- 5 occupations
(physician, nurse, midwife, pharmacist, dentist)
2006 – 13 or more occupations
(also clinical officer/medical
assistant, radiographer, lab scientist, lab technician, dental technician,
pharmaceutical technician, community health worker or TBA, health management
and support workers)
• Recent data for more countries –
2004 -- 48% of sub-Saharan African countries
2006 -- 96%
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More data …
but some data are now less comparable
Number of
physicians
reported to
the Global
Atlas for
select
countries of
subSaharan
Africa
13
5000
4000
3000
2000
1000
0
1994 - 1997 1999 - 2002 2003 - 2004
Ethiopia
1415
1971
1,936
Ghana
1112
1842
3,240
Madagascar
1519
1428
5,201
Mali
474
529
1,053
Zimbabwe
1631
736
2,086
More data …
but some data are now less comparable
Number of
physicians
reported to
the Global
Atlas for
Ghana
3500
3000
2500
Cumulative number of physicians
ever trained in Ghana?
Physicians registered with
Ghana Medical council?
2000
1500
1000
3,240
1842
MoH payroll
1112
500
14
0
1994 - 1997
1999 - 2002
2003 - 2004
Significant inconsistencies between
data sources are common
Stocks of health workers in Malawi in 2004 – 2005
according to various data sources
Staffing
Professional r e p o r t s
MoH payroll registration from health
r e c o r d s database facilities
Physicians
Nurses
15
111
283
179
2,837
7,955
4,966
Priorities for consensus on methods
• Metadata must be improved:
detailed information on
– Source of data
– Known limitations – e.g. double counting
– Classification – Is private sector included?; Are health
professionals working for other Ministries included?
• Classification of health occupations needs
further development
– Revision of the ILO's ISCO
– Harmonization of MoH systems of classification
• Standards for disaggregation by other key
variables
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– Sub-national location
Standards for disaggregation -to compare geographic inequalities
For many countries, health workforce statistics
are already disaggregated by province:
Physicians per 100,000 population
Nat'l
Most
advantaged
province
Least
advantaged
province
Most /
Least
Burkina Faso (2001)
4.8
38.4
1.7
23
Ghana (1996)
6.2
20.7
1.2
17
Ethiopia (2001)
2.7
22.2
1.5
15
Malawi (2002)
1.3
3.7
0.0
∞
Mozambique (2003)
3.5
41.0
1.5
27
South Africa (2002)
19.3
33.1
9.1
3.6
Country
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Priorities for investment
in data sources
1. Capacity
Improving health workforce statistics will require
investments to build sustainable capacity of
– Ministries of Health
– regulatory bodies/professional councils
– training institutions
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Priorities for investment
in data sources
2. Computerization and linkage of administrative data
(on enrolment, graduation, registration/licensing,
hiring, pay, deployment, transfer, promotion)
Linkage possible if each worker has a unique identifier
Can strengthen management as well as strategic
M&E
Success depends upon incentives for accurate and
timely collection and reporting
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Priorities for investment
in data sources
3. Population census
Can generate statistics on the private sector and
health professionals working in non-health industries
Data can be dissaggregated to the lowest level
Requires census authorities to code occupational data
with greater precision and grant access to the census
micro-data.
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Priorities for investment
in data sources
4. Health facility census
Can generate data on not only the human resources
but their skills, productivity (volume of services
provided), absenteeism, availability of other inputs
(drugs, supplies, infrastructure) and quality of services.
Enumeration of private facilities is a challenge
Funding and political commitment needed to repeat
these surveys each 2 to 3 years
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Priorities for investment
in data sources
Distribution of nurses working in the public sector
by district, Rwanda, SAM, 2004
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