Transcript An Investigation of the Job Preferences of Mid-Level

UNIVERSITY
EDUARDO
MONDLANE
Faculty of Medicine
An Investigation of the Job Preferences of
Mid-Level Healthcare Providers in SubSaharan Africa:
Results from Large Sample Discrete Choice Experiments in
Malawi, Mozambique and Tanzania
Dr Eilish McAuliffe, Centre for Global Health, Trinity College,
University of Dublin
& HSSE team
Supported by:
Irish Aid & Ministry of Foreign Affairs, Denmark
[email protected]
Partners to the Project
Centre for Global Health, University of Dublin, Trinity College, Dublin (Eilish McAuliffe, Susan
Bradley)
Averting Maternal Death and Disability Program (AMDD), Heilbrunn Department of Population
and Family Health, Mailman School of Public Health, Columbia University, USA (Lynn
Freedman
(Helen de Pinho, Samantha Lobis, Rachel Waxman and Sang Hee Won)
Realizing Rights: the Ethical Globalization Initiative, USA ( Mary Robinson, Peggy Clark, Ibadat
Dhillon, Naoko Otani)
Regional Prevention of Maternal Mortality network, Accra, Ghana (Angela Sawyer, Dora
Shehu)
Ifakara Health Institute, Mikocheni, Dar Es Salaam, Tanzania (Godfrey Mbaruku, Honorati
Masanja, Tumaini Mikindo, Neema Wilson, Debby Wason, Abdallah Mkopi, Aloisia Shemdoe)
University of Malawi, College of Medicine, Centre for Reproductive Health, Malawi (Francis
Kamwendo, Mwizapanyuma Simkonda, Wanangwa Chimwaza, Andrew Ngwira, Effie Chipeta,
Linda Kalilani)
Department of Community Health, Faculty of Medicine, Eduardo Mondlane University,
Mozambique (Mohsin Sidat, Maria de Fatima Cuembelo, Sozinho Daniel Ndima)
Project objectives
•
Expand the evidence base in support of effective use of mid-level health
workers within an enabling environment through the generation of new
evidence and a critical analysis of existing evidence;
•
Increase recognition and effective use of mid-level health workers among
national, regional, and global policymakers to address the human resources
crisis in district health systems based on project evidence;
•
Advocate for an enabling environment that optimises performance of midlevel providers in order to strengthen health systems; and
•
In partnership with African institutions, deepen local capacity to research
and analyse human resource and health systems problems, develop
innovative solutions, influence policymakers at local and global levels, and
sustainably implement new strategies; and build the capacity of northern
institutions to successfully engage in and support partnerships of this kind.
Research Advocacy
Review of previous DCE work
• Previous studies mostly with students
• Prior experience influences choice – important to focus
on established health workers as their choices may be
very different
• All except one previous study conducted with doctors
and nurses – yet health systems staffed by mid-level
providers
• Most studies - single country
• Previous DCE work tells us little about the factors that
are important in motivating and retaining this majority
component of human resources for health.
Distinctive features of this study
• Large sample (2,072)
• Across three countries (Malawi, Tanzania,
Mozambique)
• Health workers in the health system
• Includes mid-level cadres
• Variables – human resource management and
continuing professional development
Table 1:Facilities and Providers of EmOC
Malawi
Mozambique
Tanzania
729
622
922
679
607
859
Provider questionnaires
returned
631
587
854
Participation rate (among
eligible providers)
87%
97%
93%
84
138
90
Eligible providers
approached
Providers consented
No. of Facilities sampled
Inclusion Criteria
Involvement in obstetric care
defined as having completed at least one emergency obstetric care signal
function in the past three months.
The 9 signal functions assessed were:
(i) administered parenteral antibiotics,
(ii) administered uterotonic drugs (e.g. parenteral oxytocin, parenteral ergometrine),
(iii) administered parenteral anticonvulsants for pre-eclampsia and eclampsia (e.g.
magnesium sulphate),
(iv) performed manual removal of placenta,
(v) performed removal of retained products (e.g. manual vacuum aspiration, dilation and
curettage),
(vi) performed assisted vaginal delivery (e.g. vacuum extraction, forceps delivery),
(vii) performed neonatal resuscitation (e.g. with bag and mask),
(viii) performed surgery (e.g. caesarean section),
(ix) performed blood transfusion.
Table 2. Descriptive statistics for the demographic characteristics
and cadre breakdown of participants in Malawi, Tanzania, and
Mozambique
Variable
Average age
Female
Cadre
Enrolled nurses
Registered nurses
Medical attendants /
Medical assistants*/
Clinical officers
Doctors
Midwives
Other
Cadre Missing
Malawi
(N = 631)
34
(SD = 10.73)
65.6% (413)
8.6% (54)
62.3% (393)
26.1% (165)
Medical
assistants* &
1.7% (11)
1.3% (8)
Tanzania
(N = 825)
Mozambique
(N = 587)
39.69
(SD = 9.51)
75.3% (614)
32.49
(SD = 8.04)
81.79% (476)
20.8% (172)
36.5% (301)
40% (330)
Medical
assistants* &
26.)1% (8)
60.8% (357)
16.9% (99)
Medical
assistants* &&
1.7 % (14)
18.6% (109)
2.6% (15)
1.2% (7)
DCE - Basic Approach
• Present different composite jobs
• Respondents evaluate jobs relative to each other
– Rate, rank, discrete choices
• Analyse choices
– Infer underlying value system from the choices made about jobs
• Can provide estimation of:
–
–
–
–
Relative importance of different attributes
Willingness of respondents to trade-off between attributes
Relative benefit/utility scores of different combinations
Values of different subgroups
Selection of Attributes
• Key attributes which define job
• Limited by experimental design consideration
• Attributes and levels should be actionable
• Based on:
–
–
–
–
–
–
–
Literature review
Expert opinion
Key informant interviews
Focus group discussions
Surveys
Policy relevance
Findings from previous studies (MaxHR)
Job Attributes
Geographic Location
• This attribute specifies whether your place of work is in an urban or rural
area.
Net Monthly Pay (including regular allowances)
• Base represents the base salary for a health worker at an “average” grade
in the civil service pay scale, while higher levels are multiples (1.5 times and
2 times) of this average base level. Note that the base salary does not
necessarily reflect your current actual salary.
Government-provided Housing
• None means there is no housing provided by the government as part of the
conditions of employment.
• Basic housing means the government provides housing for the health
worker, but that it is rudimentary, having no electricity or running water, and
with at best an outside toilet.
• Superior housing means the government provides housing of higher quality,
including the presence of electricity and running water, including an inside
flush toilet.
Job Attributes (cont.)
Availability of Equipment and Drugs
• Inadequate is the standard of equipment and availability of drugs that you
might expect in a poorly equipped public facility in the given location.
• Improved is the level of supplies that would result from a doubling of the
budget currently spent on equipment and drugs.
Access to Continuing Professional Development
• This attribute measures the availability of continuing professional
development, in terms of access to further education and upgrading.
Limited access means there are very few opportunities, with no clear
guidelines on who can avail of them.
• Improved access means there are sufficient opportunities available, with
clear policies on the criteria needed to qualify for places.
Job Attributes (cont.)
Human Resources Management Systems
• Poor describes a management system with either no mechanisms or poorly
administered mechanisms for staff support, supervision and appraisal.
• Functioning describes a system where there are transparent, accountable
and consistent systems for staff support, supervision and appraisal.
Design
• Fractional factorial design
• 15 choice sets
• 6 attributes
– 4 with two levels
– 2 with three levels
• Job 1 held constant
Table 3: Coding format for the attribute levels (design)
Attribute
Location
Net monthly pay
Housing
Equipment and Drugs
Professional Development
Human Resources Management
Levels
Variable code
Rural
Code format
0
Urban
location
1
Base
pay1
0
1.5 x base
pay2
1
2 x base
pay3
2
None
houseno
0
Basic
houseba
1
Superior
housese
2
Inadequate
Improved
0
equi
Limited
Improved
0
pdev
Poor
Functioning
1
1
0
hrm
1
Section L: Discrete Choice Experiment
If your circumstances permitted it, which of the two
jobs described would you choose?
Tick one:
Job 1 Job 2
Analysis
• Initially data was analyzed using the conditional logit
model (CLM).
• The CLM allows observing how the characteristics of the
alternatives affect individuals’ likelihood of choosing
them; it has been extensively used in the discrete choice
model literature (Louviere & Lancsar, 2009; Lanscar &
Louviere, 2008; Guttman et al., 2009).
• The baseline model tested assumed linear effects across
all attribute parameters.
Analysis (2)
• Additionally, to test for non-linear relationship between
an attribute and utility, three dummy variables were
included to represent each level of the three-level
attributes (housing and net monthly pay).
• The design above was then merged with the dataset
containing the choices made by respondents, and the
other socio-economic and job related information.
• control variables representing socio-economic and
demographic characteristics are also included in the final
dataset that was analyzed: zone, gender, education, age
and edu_level.
Dataset (Malawi as example)
• The original dataset contained 631 respondents and the
DCE answers were identified by dce_1, dce_2,…,
dce_15, indicating the respondents choices for each of
the 15 choice sets presented to them.
• The final dataset has 9,465 choices made (15 X 631).
74.84% of the choices were for alternative one (job1,
constant alternative) and 20.1% for alternative 2 (job 2).
• Approximately 5% of choice sets were not answered and
these were dropped from the final dataset.
• The final dataset therefore contained 8,986 choices
made.
Results
• All coefficients are statistically significant
indicating all attributes have influence on the
choice between job1 or job 2.
• They have positive values, indicating that
increases in the level of the attributes increases
the utility of choice. These are in accordance
with the a priori expectations (external validity).
Table 4: Conditional logit model results (Malawi) – baseline model
attribute
Coef.
z
location
0.215
4.09
0.0000
pay
1.233
29.47
0.0000
housing
0.652
17.41
0.0000
equi
0.402
7.07
0.0000
pdev
2.039
36.81
0.0000
hrm
2.276
29.89
0.0000
Number of
obs
17972
Log
likelihood
-7814.56
P>z
The attribute human resources
management has the highest
absolute value (hrm =2.276)
while the attribute location had
the smallest absolute
value (location=0.215).
Table 5: Conditional logit model results (Tanzania) –
baseline model
Attribute
Coef.
z
location
0.457
11.4
pay
0.479
17.99
housing
0.102
3.8
equi
0.012
0.32
pdev
1.199
31.6
hrm
1.181
25.61
Number of
obs
23034
Log
likelihood
-11894.99
P>z
0.000 Attributes with highest part-worth
utilities were professional
0.000 development (pdev=1,199) and
0.000 human resources management
0.000 (hrm =1,181).
0.000 An improvement in any of these
two attributes impacts more on
0.000
the utility than any other attribute
in the design.
Table 6: Conditional logit model results (Mozambique)– baseline model
Attribute
Coef.
z
P>z
location
0.316
6.52
0.000
pay
0.601
17.96
0.000
housing
0.265
8.16
0.000
equi
0.307
6.33
0.000
pdev
1.534
32.72
0.000
hrm
1.332
22.71
0.000
Number of
obs
16918
Log likelihood
-8577.44
Attributes with greater utility
were professional development
(pdev=1,534) and human
resources management (hrm
=1,332).
Testing for non-linear effects
• Two of the six attributes had 3 levels, net monthly pay and housing,
• To test for non-linear effects
–
including in the model the dummy variables for housing and pay attributes (Test
for non-linear effects allows observing whether the effect on utility from an
increasing in the salary level (or housing) from the basic salary to 1,5 the basic
(or from no housing to basic housing) is different from an increase from 1,5 the
basic to 2 times the basic (or from basic housing to superior housing).)
• They were included separately and the goodness of fit was
compared with the baseline model of linear effect of each three
levels attribute
• a Wald test was applied to check whether or not the dummy
variables included were different from zero. If so, it implies that there
are non-linear effects on the three levels attributes, i.e., the impact
on utility is different when moving from pay1 to pay2 compared to a
change from pay2 to pay3 (or houseno to houseba compared to
houseba to housesu).
Results
• Expanded model did not provide a better
fit for the data.
• Non-linearity detected for pay only in
Malawi
Table 7: Conditional logit model results (Malawi) –
Model 2
attribute
Coef.
z
P>z
location
-0.123
-2.17
0.0000
Pay 1.5 base
1.995
30.24
0.0000
Pay 2 base
2.086
22.54
0.000
housingba
1.562
22.21
0.0000
housingsu
1.361
18.04
0.000
equi
1.019
15.73
0.0000
pdev
1.389
23.52
0.0000
hrm
1.818
22.67
0.0000
Number of obs
17972
Log likelihood
-7814.56
Marginal
diminishing return
for housing i.e.
moving from level
2 to level 3 has
less influence on
choice of job than
moving from level
1 to level 2
Table 8: Conditional logit model results (Tanzania) –
Model 2
attribute
Coef.
z
P>z
location
0.267
6.26
0.000
Pay 1.5 base
0.937
21.70
0.000
Pay 2 base
0.622
10.49
0.000
housingba
0.906
17.31
0.000
housingsu
0.298
5.3
0.000
equi
0.494
10.87
0.000
pdev
0.741
17,46
0.000
hrm
0.890
17.75
0.000
Number of obs
23034
Log likelihood
-11894.99
Marginal
diminishing return
for pay and
housing i.e.
moving from level
2 to level 3 has
less influence on
choice of job than
moving from level
1 to level 2
Table 9: Conditional logit model results (Mozambique)
– Model 2
attribute
Coef.
z
P>z
location
0.124
2.40
0.0000
Pay 1.5 base
1.058
19.99
0.0000
Pay 2 base
0.849
11.37
0.000
housingba
1.011
15.85
0.0000
housingsu
0.653
9.76
0.000
equi
0.762
13.69
0.0000
pdev
1.116
21.71
0.0000
hrm
1.023
16.09
0.0000
Number of obs
16918
Log likelihood
-8577.44
Marginal
diminishing
return for pay
and housing
i.e. moving from
level 2 to level 3
has less
influence on
choice of job
than moving
from level 1 to
level 2
In Summary
• Consistent results across three countries
• Strongest predictors of job choice - access to CPD and HRM
• Strong preferences for functioning HRM and available professional
development that operates with clear policies
• Consistent with other studies – pay is important but perhaps not as
fundamental as suggested by previous studies
• Further analysis – differences between cadres, demographic profiles
of health worker.
Additional data
Demographics
Provider survey
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Job title
Employment status
Employer type
Employer location
Gender
Age
Education
Professional affiliations
Length of time with employer
Work pattern
Payment patterns
Job satisfaction
Burnout levels
Work environment
Commitment
Intention to leave
Organisational justice
Supervision
Career progression
opportunities
Limitations of DCE
• Stated vs actual preferences
– Artificial / hypothetical constructs may not predict real
choices
• Limited number of attributes and levels
– Significant design constraints
• Have the most influential attributes been
selected?
– Different results with different attributes
In this study qualitative and quantitative data
collected using a variety of instruments are
consistent with DCE findings.
With Thanks
HSSE Team:
• AMDD, Mailman School of Public Health, Columbia University, USA
• Centre for Global Health, Trinity College, University of Dublin
• Centre for Reproductive Health, College of Medicine, Malawi
• Dept. of Community Health, Eduardo Mondlane University, Mozambique
• Ifakara Health Institute, Tanzania
• Realizing Rights: Ethical Globalization Initiative, USA
• Regional Prevention of Maternal Mortality Network, Ghana
Funders:
• IrishAid & Ministry of Foreign Affairs, Denmark
[email protected]