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The relative importance of material and non-material incentives for community health workers: Evidence from a discrete choice experiment in Western Kenya
Journal Pre-proof The relative importance of material and non-material incentives for community health workers: Evidence from a discrete choice experiment in Western Kenya Indrani Saran, Laura Winn, Joseph Kipkoech Kirui, Diana Menya, Wendy Prudhomme O'Meara PII:
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Received Date: 18 October 2018 Revised Date:
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Accepted Date: 6 December 2019
Please cite this article as: Saran, I., Winn, L., Kipkoech Kirui, J., Menya, D., Prudhomme O'Meara, W., The relative importance of material and non-material incentives for community health workers: Evidence from a discrete choice experiment in Western Kenya, Social Science & Medicine (2020), doi: https:// doi.org/10.1016/j.socscimed.2019.112726. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Title: The Relative Importance of Material and Non-material incentives for Community Health Workers: Evidence from a Discrete Choice Experiment in Western Kenya Indrani Sarana,1*, Laura Winna, Joseph Kipkoech Kiruib, Diana Menyac, Wendy Prudhomme O’Mearaa,c a
Duke University, Durham, NC, USA Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya c Moi University, Eldoret. Kenya b
*Corresponding Author 1
Present Affiliation & Address Boston College School of Social Work 140 Commonwealth Avenue Chestnut Hill, MA 02467 Ph: (617) 552-0197 Email: [email protected]
Keywords: Kenya; Community Health Workers, Satisfaction, Discrete Choice Experiment
1
Abstract
2
Motivating community health workers (CHWs), many of whom are volunteers, is important for
3
the sustainability of integrated community case management programs. Given the limited
4
budgets of many of these programs, and the increasingly important role played by CHWs, it is
5
crucial to not only identify important motivators driving their engagement, but also which
6
incentives could have the greatest impact on CHW motivation in their role. In this study, we
7
aimed to assess CHWs’ relative preferences for material and non-material incentives.
8
We conducted a discrete choice experiment (DCE) with 199 randomly selected CHWs, working
9
in 32 communities in western Kenya, to measure the relative importance that CHWs place on
10
different incentives. Each CHW completed a series of 10 choice tasks (8 random, 2 fixed), where
11
they had to choose between two hypothetical positions that had varying levels of monthly mobile
12
phone airtime, training, monthly transport bonus, community appreciation and health facility
13
staff appreciation of their work. Data was analyzed using mixed logit models. CHWs’ most
14
preferred job characteristic was high levels of community appreciation for their work which was
15
valued approximately equivalently to receiving a 2000 Kenya Shillings (~US $20) monthly
16
transport allowance. These incentives were valued more than appreciation from health facility
17
staff or trainings six times per year. This study demonstrates that investing in efforts to improve
18
community members’ knowledge and recognition of CHWs’ contribution to community health
19
may have a significant impact on CHWs’ motivation and retention in their role.
20 21 22 23
1
24
Introduction
25 26
Community health workers (CHWs) are a cadre of lay health workers who provide health
27
promotion and disease prevention services to their community (Olaniran et al., 2017). As part of
28
integrated community case management (iCCM) programs, they have also been trained to offer
29
diagnosis and treatment of common childhood illnesses such as malaria, diarrhea, pneumonia
30
and malnutrition (George et al., 2012). In 2012, the World Health Organization and UNICEF
31
issued a joint statement promoting iCCM as a strategy to expand coverage of basic health
32
services (Wolfheim et al., 2012). There is some evidence that these programs have successfully
33
reduced child mortality (Amouzou et al., 2014; Freeman et al., 2017).
34
CHWs generally do not receive a salary, and are instead offered a mix of both financial
35
and non-financial incentives (Bosch–Capblanch and Marceau, 2014; George et al., 2012; Smith
36
Paintain et al., 2014). However, as their service delivery roles are scaled up, there is a need to
37
better understand what types of incentives motivate CHWs’ participation in these programs. A
38
broad review of the literature finds that, in general, both intrinsic motivation and extrinsic
39
incentives are strong predictors of performance (Cerasoli et al., 2014). For CHWs specifically,
40
greater motivation in their role is associated with better performance as well as improved
41
retention (Abbey et al., 2014; Kok et al., 2015; Naimoli et al., 2014; Vareilles et al., 2015). The
42
latter is a major concern given high reported attrition from some CHW programs (Nkonki et al.,
43
2011).
44
Previous research has identified a number of both material and non-material factors
45
important for CHW motivation in their role. These include a desire to serve and be recognized by
46
the community, training and learning new skills, supervision, formal linkages with the health
2
47
system, financial compensation, and material incentives (bicycles, T-shirts, badges, phones, job
48
aides, supplies) (Alhassan et al., 2016; Brunie et al., 2014; Busza et al., 2018; Geldsetzer et al.,
49
2017; Haile et al., 2014; Kok et al., 2017; Lohfeld et al., 2016; Momanyi et al., 2016; Mpembeni
50
et al., 2015; Oliver et al., 2015; Pallas et al., 2013; Sanou et al., 2016; Strachan et al., 2015; Topp
51
et al., 2015; Vareilles et al., 2015; Winn et al., 2018; Zulu et al., 2014). However, there is little
52
evidence on the relative importance of different types of incentives for CHWs’ motivation in
53
their role. Even as countries are adopting policies supporting delivery of care by CHWs,
54
inadequate funding threatens the sustainability of these programs (Bennett et al., 2014;
55
Rasanathan et al., 2014). With limited resources, it is crucial to not only identify important CHW
56
motivators, but also which incentives will have the greatest impact on CHW motivation, and
57
potentially retention, in their role.
58
This study uses a discrete choice experiment (DCE) to quantitatively assess CHWs’
59
preferences for community and health facility staff appreciation relative to monetary incentives
60
and training. DCEs are a powerful tool to understand people’s relative preferences for different
61
job characteristics under hypothetical, but realistic situations. In a DCE, respondents make a
62
series of choices between two or more hypothetical jobs that are described using a list of several
63
characteristics. The relative value that respondents place on each characteristic can be inferred
64
from the tradeoffs that they make in selecting their preferred choice. DCEs have been previously
65
used to understand job preferences for formal health workers in resource-constrained settings.
66
These studies have highlighted the importance of both pecuniary and non-pecuniary incentives
67
for these workers, particularly opportunities for training and upgrading of skills (Brown et al.,
68
2017; Lagarde and Blaauw, 2009; Mandeville et al., 2016, 2014; McAuliffe et al., 2016;
69
Nurelhuda et al., 2018). However, as a largely volunteer workforce, CHWs may have different
3
70
preferences and, to our knowledge, only two studies have applied a DCE to elicit CHWs’ relative
71
preferences for different types of incentives and both these studies focused on CHWs trained to
72
perform specific health services such as family planning or iCCM (Brunie et al., 2014; Kasteng
73
et al., 2016).
74 75 76
Methods
77 78
Study Context and Population
79 80
This study was conducted as part of a cluster randomized controlled trial (RCT) that
81
aimed to evaluate the public health impact of community-based malaria diagnostic testing
82
(Prudhomme O’Meara et al., 2018). The RCT was done between July 2015 and May 2017 in 32
83
community units (CUs) in western Kenya, half of which were assigned to the intervention. A
84
community unit consists of approximately 1000 households.
85
CHWs’ main roles in Kenya involve health education and referrals to health care
86
facilities (Kenya Ministry of Health, 2007). As part of the RCT, CHWs working in intervention
87
areas were trained to offer free malaria diagnostic testing to community members. In addition, if
88
an individual tested positive for malaria the CHW provided them with a voucher that enabled
89
them to purchase a discounted anti-malarial drug at a local drug shop. CHWs in the comparison
90
areas continued to deliver their usual services.
91 92
In Kenya, CHWs generally work on a volunteer basis, but sometimes receive incentives if they are participating in special health programs such as immunization campaigns or health
4
93
screening programs. CHWs in the intervention arm of the RCT received reimbursement for
94
transport to monthly supervision meetings with study staff, a small amount of mobile phone
95
airtime, and a group performance bonus given every six months. The bonus was intended to
96
support group income-generating programs such as fish farming, small business loans, or other
97
investments.
98 99 100
The CHWs surveyed for this study were randomly selected, stratified by CU. In case any of the CHWs were not able to be reached, we also designated two “alternate” CHWs that could be contacted from each CU.
101 102 103
Data A discrete choice experiment (DCE) is a method used to quantitatively determine relative
104
preferences, in this case for different characteristics of the CHW role. CHWs were asked to
105
imagine that they worked in a hypothetical CU serving approximately 50 households. The CHWs
106
were then asked to choose between two positions with different characteristics, or to say if they
107
would choose not to be a CHW if those were the only options (an opt-out option). To decide
108
between the positions in each pair, CHWs had to trade-off the advantages and disadvantages of
109
each position. We included an opt-out option because as a volunteer workforce, CHWs can
110
choose to do something else (for example paid work). Indeed, some studies have suggested that
111
attrition from CHW programs is a major problem (Nkonki et al., 2011). Moreover, we chose to
112
define the opt-out as not being a CHW rather than as the status quo since the latter varies across
113
respondents and therefore is not well-defined.
114
Each position had varying levels of five attributes: monthly mobile phone airtime,
115
frequency of training, monthly transport bonus, and levels of community appreciation and health
5
116
facility staff appreciation of their work (Table 1). The attributes and levels for the DCE were
117
selected based on a review of the literature, feedback from study staff who frequently interact
118
with CHWs, and the results of a previous survey with 70 CHWs in the intervention area. (Winn
119
et al., 2018). This previous study consisted of semi-structured interviews with CHWs and found
120
that community appreciation, supervision from program staff, and upgrading of knowledge and
121
skills were important for CHW motivation in their role. CHWs also reported challenges with
122
transportation, compensation and support from health facility staff.
123
The attributes and levels were further refined after a pilot experiment with seven CHWs
124
from both control and intervention areas. For example, we found that community appreciation
125
was so valuable that hypothetical CHW positions that offered no community appreciation were
126
unacceptable. Similarly, options with no training opportunities would not be chosen. Thus, the
127
minimum level of training we chose to offer was one time per year, and the lowest level of
128
community appreciation included some amount of appreciation. We also included two attributes
129
in the pilot study that were dropped for the main experiment: the level of supervision and a group
130
bonus. We dropped these from the main study for two reasons: (1) The pilot suggested that seven
131
attributes was cognitively challenging and CHWs tended to simply pick the option with higher
132
community appreciation rather than considering all the attributes and (2) the intervention group
133
had more experience with a group bonus and with intensive supervision as part of the study and
134
we were concerned that, as a result, CHWs in the intervention group might perceive these
135
attributes differently than those in the control group.
136
We used Lighthouse Studio Version 9.3.0 (Sawtooth Software Inc, Provo, UT)(Sawtooth
137
Software, 2017) to generate the set of choices using a fractionally factorial design that is both
138
orthogonal (attributes are statistically independent of each other) and balanced (each attribute
6
139
level appears an equal number of times). We used the “balanced overlap” random task
140
generation method and created 10 blocks of 10 choice tasks, which included 8 random tasks and
141
2 fixed tasks. We chose to use a blocked design because it allows us to include more choice tasks
142
while limiting the cognitive burden for an individual respondent. The “balanced overlap” random
143
task method allows for some attribute levels to be the same across the two positions in a choice
144
task. This ensures that if a respondent has a certain “must-have” level for an attribute, then when
145
the level is the same for that attribute across the two choices the respondent is forced to make a
146
choice based on the other attributes (Reed Johnson et al., 2013). Each individual was randomly
147
assigned to one of the 10 blocks. The order of the attributes in each job profile was randomized
148
across blocks so the order varied across individuals, but not across choice tasks for a given
149
individual.
150 151
Sample Size Calculations
152
We calculated our sample size for the DCE using an empirical power-test formula
153
developed by Yang et al (Yang et al., 2015). To achieve a utility-difference standard deviation of
154
0.8, and assuming an experimental design efficiency of 22 (the mean levels of these design
155
characteristics in the studies examined by Yang et al), a DCE with 5 attributes, 3 maximum
156
levels per attribute, 8 random choice tasks, no probabilistic attributes, and an opt-out option
157
required a sample size of 193.
158 159
Survey Administration
160
7
161
Paper surveys were administered between June and July 2017 in English or Kiswahili,
162
according to the CHW’s preference. The interviewers described in detail the definition of each
163
attribute as well as the different levels. Interviewers also went through an example choice to help
164
the CHWs understand the DCE exercise. The DCE included visual aids (downloaded from the
165
website https://thenounproject.com/) to improve understanding of the levels and attributes and to
166
make it easier for the respondent to compare across profiles (see Figure 1, the full set of images
167
can be seen in Appendix Figure A1). After explaining the DCE exercise, the interviewer allowed
168
the respondent to complete the choice tasks on their own, though they were available in case the
169
respondent had any questions. The survey took approximately 20-30 minutes to complete.
170 171 172
Analytical Approach We use means and standard deviations to describe the demographic characteristics of the
173
CHWs who were included in the study. To estimate CHWs’ relative preferences for different
174
job-related attributes, we used mixed logit models with 5000 Halton draws (for the models in the
175
appendix we use 500 Halton draws), and only included the 8 random choice tasks. We chose the
176
mixed logit model because it allows for unobserved heterogeneity in preferences across
177
individuals and estimates both the mean preference weight as well as the standard deviation.
178
Moreover, the mixed logit model accounts for multiple observations per respondent and allows
179
for violation of the independence of irrelevant alternatives assumption (the assumption that
180
adding or removing an alternative has no effect on the choice between the other alternatives
181
(Hauber et al., 2016)). We assumed that all attribute variables, except for the constant, had a
182
random component and that the preference weights were normally distributed. We also ran a
183
model with the monetary parameters—airtime and transport reimbursement— with lognormal
8
184
distributions to ensure only positive coefficients and found similar results (Appendix Table A1).
185
All attribute variables were coded as dummy variables. We included an alternative-specific
186
constant for the two positions in order to estimate the value of choosing to be a CHW versus
187
opting out. We present the results from models that estimated only the main effects for each
188
attribute level. We also estimated a set of models that included interactions of each attribute with
189
gender, years of experience, location, and intervention status (Appendix Tables A2-A5).
190
We tested the validity of our study using the two fixed tasks that were the same for all
191
respondents. The first fixed task assessed respondents’ comprehension of the exercise by
192
presenting a choice where one alternative dominated the other. For this fixed task, we set levels
193
of all attributes the same for both alternatives, except that one alternative had more airtime and a
194
larger transport reimbursement. We chose this weak dominance test because we were concerned
195
that people might simply pick the option with the higher levels of community appreciation
196
without considering the other attributes and, therefore, this would not be an accurate assessment
197
of the extent to which respondents were thoughtfully evaluating the two choices. For the other
198
non-monetary attributes—health facility staff appreciation and training— the direction of
199
people’s preferences ex-ante was not clear since some people could prefer fewer trainings or less
200
appreciation from the health facility staff. The second fixed task was used to test the internal
201
validity of our model by comparing the predicted choices from the model with the actual choices.
202
In order to present the DCE results visually, we used the mixlpred command following
203
the main model estimation in STATA to calculate the predicted probability of CHWs’ taking up
204
a position with some community appreciation and no other incentives, compared to a position
205
with high levels of community appreciation and no other incentives. We then show how these
206
predicted probabilities change as additional incentives are added to the position with some
9
207
community appreciation. The predicted probabilities were determined for a sample of 199
208
observations and the distribution was used to calculate the 95% confidence intervals. Statistical
209
analyses were conducted using STATA 15 (College Station, TX) (StataCorp, 2017).
210 211
Results
212 213
CHW Sample and Characteristics
214 215
We conducted the experiment with a sample of 199 CHWs, of the nearly 600 who are
216
working in the 32 CUs that were part of the study (33%). We were unable to include 21 of the
217
200 CHWs (10.5%) who were originally assigned to participate either because we were unable to
218
reach them (N=17) or because they did not show up (N=4). Of these, 20 were replaced by other
219
CHWs, 16 of whom had been previously randomized as alternates. Thus, of our sample of 199
220
CHWs, only 4 had not been randomly assigned ex-ante to participate. In our sample, 100 CHWs
221
worked in intervention areas, 99 in control areas.
222
We present characteristics of the CHWs interviewed in Table 2. Approximately 72% of
223
CHWs were women, the mean age was 43 years (SD=10) and 88% were married. CHWs had, on
224
average, 7.2 years of experience. The majority of CHWs had at least a primary education (94%).
225
Outside of being a CHW, the most common occupations were farming (76%) and business
226
(34%).
227 228
Discrete Choice Experiment
10
229
Of the expected total of 1592 random choice tasks (199 X 8 choices) in the DCE, we
230
have complete data for 1580 choices (99%). Of these 1580 choices, the “neither” option was
231
chosen 28 times (1.8%) which suggests that the combinations of attributes and levels offered
232
were generally acceptable. We tested the internal validity of the model by comparing the
233
predicted choice probabilities for one of the fixed tasks to the actual choice probabilities. The
234
model’s predicted probabilities of uptake for alternatives 1, 2 and 3 (opt-out) were 54%, 45%
235
and 1.8%, respectively, while the actual uptake was 51%, 47% and 1.5%, respectively,
236
suggesting that this a reasonable model for CHWs’ preferences.
237
The results of the DCE are presented in Table 3. The coefficient estimates represent the
238
change in utility (or satisfaction) received from that level of the attribute, relative to the reference
239
level, holding all other factors constant. Moreover, the magnitude of the coefficient indicates the
240
strength of preference for that level of the attribute. The ratio of two coefficients indicates the
241
marginal rate of substitution between two attributes i.e. the relative value that respondents place
242
on the two attributes. Since the coefficients are all positive, the results suggest that, holding all
243
else constant, CHWs generally prefer more of each attribute. Moreover, a positive constant
244
indicates that the respondents’ generally preferred to be a CHW, rather than opting out.
245
The highest utility gain comes from increasing community appreciation from a level
246
where CHWs only receive some support, to a level where all community members appreciate
247
their work (estimate=1.68, SE=0.18). This is approximately equivalent to the utility gain from
248
increasing the transport reimbursement from Kenya shillings (Ksh) 0/month to Ksh 2000/month
249
(estimate=1.63, SE=0.18) and nearly twice the utility gain from increasing the number of health
250
facility workers who support and understand CHWs’ work from a “few” to “many”
251
(estimate=0.86, SE=0.13) (1 United States dollar (USD)=100 Kenya Shillings).
11
252
The results also show that although CHWs prefer four trainings per year to only one
253
training per year (estimate=0.84, SE=0.13), the utility gain of six trainings per year (relative to
254
one training per year) is approximately equivalent that of four trainings per year (estimate=0.95,
255
SE=0.17). Lastly, although we find a positive estimate on the airtime level of Ksh 200/month
256
(relative to Ksh 0/month), only the estimate on the airtime level of Ksh 500/month (relative to
257
Ksh 0/month) is statistically significant (estimate=0.63, SE=0.14).
258
The standard deviation estimates are an indication of the degree of preference
259
heterogeneity in the sample of CHWs for that attribute-level. We find large, and statistically
260
significant, standard deviation estimates for several attribute-levels, suggesting that while there
261
are generally strong preferences for high levels of all these attributes, there is also considerable
262
variation among CHWs in their degree of preference.
263
The strength of CHWs’ preference for high levels of community appreciation is
264
highlighted in Figure 2, which shows the relative probabilities that CHWs would accept
265
opportunities with different characteristics. If CHWs were offered the choice between a position
266
with no airtime or transport reimbursements, only one training per year, and low levels of health
267
facility and community support, and a position with the same characteristics but that instead had
268
high levels of community appreciation, 72% would take the latter position (blue bars), while 9%
269
would opt out. A position with low levels of community appreciation would need to include a
270
significant monetary investment— KSH 500/month for airtime, and a transport reimbursement of
271
KSH 2000/month – in addition to trainings at least 4 times per year, for the probability of take-up
272
to be much higher than that of a position that offered high levels of community appreciation but
273
no other incentives (green bars).
12
274 275
We did not find statistically significant interactions of each attribute with CHW gender, years of experience, location, and intervention status (Appendix Tables A2-A5).
276 277 278
Discussion Our study examined the preferences of CHWs working in 32 communities in Western
279
Kenya. The results demonstrate that CHWs’ most preferred job characteristic was high levels of
280
community appreciation for their work, which was valued approximately equivalently to
281
receiving a 2000 Kenya Shillings (~USD 20) monthly transport stipend.
282
In terms of policy implications, our results suggest that since CHWs in Western Kenya
283
highly value community appreciation, interventions to increase awareness and recognition by the
284
community could increase their motivation. These interventions could include, for example,
285
regular village meetings where CHWs are publicly recognized and rewarded for their work,
286
providing certificates for good performance, designing transparent CHW selection mechanisms
287
and offering uniforms and badges to add legitimacy to their role. These interventions, however,
288
require investment in strong supervision and management systems to continuously monitor
289
CHW performance and ensure that they are appropriately recognized for their work. Training
290
CHWs to perform services that the community finds valuable, such as malaria diagnostic testing,
291
could also increase awareness, use, and appreciation of CHWs. Lastly, even small monetary
292
incentives, such as a monthly airtime reimbursement, could demonstrate to CHWs that their
293
work is valuable and substantially increase their motivation in their role. Our results also suggest
294
that larger monetary incentives, such as in the form of a transport reimbursement, are another
295
intervention that can increase CHW motivation in their role. The relative costs and feasibility of
13
296
intervening to increase community appreciation versus offering monetary incentives will likely
297
depend on the program and context.
298
To our knowledge, only two DCEs have been previously conducted with CHWs, both in
299
Uganda. In one, CHWs valued specific types of material incentives (ie a badge and a bicycle)
300
more than others (mobile phone and transport refunds) but they did not examine the relative
301
importance of community or health facility appreciation and support (Brunie et al., 2014). In the
302
other, similar to the results of our DCE, a monthly stipend and community appreciation were the
303
most preferred CHW work benefits (when compared to flexible hours, regular trainings and a
304
mobile phone), but the sample size surveyed was relatively small (Kasteng et al., 2016).
305
Our DCE shows that one of the most valued job characteristics for CHWs in Western
306
Kenya is high levels of community appreciation of their work. This is consistent with much of
307
the previous literature which underscores CHWs’ desire to serve the community and appreciation
308
from the community as important motivators (Kasteng et al., 2016; Mpembeni et al., 2015;
309
Sanou et al., 2016; Strachan et al., 2015; Winn et al., 2018; Zulu et al., 2014). However, while
310
integration with the health system has been emphasized as crucial to the successful scale-up of
311
CHW programs (Pallas et al., 2013; Schneider and Lehmann, 2016; The Lancet Global Health,
312
2017; Tulenko et al., 2013) our results suggest that recognition from the health facility staff is
313
less important to CHWs in Western Kenya than relationships with the community.
314
The second most valuable job characteristic was a monthly transport reimbursement.
315
Other studies from sub-Saharan Africa also highlight the importance of material benefits for
316
CHW motivation in their role (Brunie et al., 2014; Geldsetzer et al., 2017; Lohfeld et al., 2016;
317
Pallas et al., 2013; Schuster et al., 2016). Trainings are often cited in the literature as an
318
important factor in CHW motivation (Busza et al., 2018; Haile et al., 2014; Lohfeld et al., 2016;
14
319
Momanyi et al., 2016; Sanou et al., 2016). However, this study shows that six trainings per year
320
was valued about the same as four trainings per year, suggesting that there may be diminishing
321
returns for CHW motivation from additional training sessions.
322
There are some limitations of this study. We included most of the job attributes and levels
323
that our previous study with CHWs identified as most relevant; for practical reasons, however,
324
we were not able to include supervision, which both our own prior work and other studies with
325
CHWs have found to be important. Other studies have also identified certain types of in-kind
326
incentives (t-shirts, bicycles, phones, supplies) as potentially important for CHW preferences
327
though our own previous work in this context did not find these as major motivators. Second,
328
while preferences for different incentives may be associated with performance, there could also
329
be structural factors that influence good performance (for example workload or availability of
330
supplies). Third, some CHWs may have had difficulty understanding the DCE exercise
331
(approximately 14% “failed” the comprehension check, but our results were robust to excluding
332
these CHWs). Fourth, although respondents were told that the opt-out option in the DCE was
333
equivalent to choosing not to be a CHW, some may have interpreted the opt-out option as
334
maintaining their status quo situation, which varies across CHWs. Lastly, since the DCE is a
335
hypothetical exercise, CHWs’ actual behavior may be different depending on the context.
336
Our study also has several strengths in terms of geographic scope and the type of CHW
337
program. The CHWs in this study were representative of nearly 600 CHWs in Western Kenya
338
who are responsible for approximately 32,000 households. Moreover, the participants are part of
339
a long-existing CHW program, and had varying levels of responsibility, with some performing
340
malaria diagnostic tests in addition to their usual services. The broad representation of CHWs, in
341
terms of both geography and role, enhances the likelihood that our results are generalizable
15
342
beyond Western Kenya, though further research is needed to confirm this. Moreover, our
343
findings about the importance of both monetary and non-monetary incentives for CHW
344
motivation are consistent with what other studies have reported across sub-Saharan Africa. This
345
suggests that these are widespread preferences though the relative values placed on these
346
incentives may vary across context.
347
Another strength of our study is that most CHWs seemed to understand the DCE design
348
and providing concrete choices with respect to job characteristics may give a more accurate view
349
of preferences, especially for less educated cadres of workers. The DCE included job
350
characteristics – both material and non-material – that are relevant to a volunteer program. In
351
particular, while previous research has identified community appreciation as an important CHW
352
motivator, this DCE allowed us to quantify its value in this context relative to other incentives.
353
CHWs are increasingly being trained to offer a broad array of health services to
354
underserved populations. As volunteers, their preferences may be different from those of other
355
cadres of health workers. Our results demonstrate that CHWs in Western Kenya value both
356
material and non-material incentives and that improving community members’ recognition of
357
CHWs’ contributions may have a significant impact on CHWs’ motivation and retention in their
358
role.
359 360
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539 540 541 20
542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569
Figures and Tables
570 571
Table 1: Attributes and Levels included in Discrete Choice Experiment Attribute
Level 21
Airtime
1. KSH 0/ month 2. KSH 200/ month 3. KSH 500/ month
Training
1. Once per year 2. Four times per year 3. Six times per year
Transport
1. KSH 0/month 2. KSH 1000/month 3. KSH 2000/month
Health Facility Appreciation
1. Few health facility staff recognize and appreciate your work 2. Many health facility staff recognize and appreciate your work
1. Some community members recognize and appreciate your work 2. All community members recognize and appreciate your work Notes: 1 United States (US) dollar~100 Kenya Shillings (Ksh)
Community Appreciation 572 573 574 575 576 577 578 579 580 581 582 583 584 585
Table 2: Demographic Characteristics of CHWs Interviewed (N=199)
22
N (count) Age Years of Experience as CHW Female Highest Education Level Less than Primary Primary Completed Less than Secondary Secondary Completed Some College Marital Status Married Widowed Divorced/Separated Single/Never Married Occupation (outside of CHW) Farmer Business Pastor Other Volunteer work Other Occupation
586 587 588 589 590 591 592 593
% or Mean ±SD
143
43 ± 10 7.2 ± 4.5 72%
11 25 58 61 44
6% 13% 29% 31% 22%
176 9 9 5
88% 5% 5% 3%
152 68 21 26 77
76% 34% 11% 13% 39%
Notes: CHWs could report multiple occupations so the sum of the percentages for occupations do not add up to 100. The "Other" Category includes occupations such as shopkeeper, teacher, government work and administrative roles at church.
594 595 596 597 Table 3: Results of mixed logit models of discrete choice experiment data on CHW preferences in Western Kenya Mixed Logit Model (N=199 CHWs, 1580 choices) Estimated Mean Preference Attribute
Level
23
(SE)
Estimated SD
(SE)
Airtime
None (Ref) KSH 200/month KSH 500/month
Ref. 0.23 0.63**
Ref. (0.12) (0.14)
Ref. 0.22 -0.00
Ref. (0.46) (0.64)
Training
1X/year (Ref) 4X/ year 6X/year
Ref. 0.84** 0.95**
Ref. (0.13) (0.17)
Ref. 0.12 1.28**
Ref. (0.55) (0.20)
None (Ref) KSH 1000/month KSH 2000/month
Ref. 1.04** 1.63**
Ref. (0.14) (0.18)
Ref. -0.11 1.07**
Ref. (0.71) (0.21)
Community appreciation
Some support (Ref) All support
Ref. 1.68**
Ref. (0.18)
Ref. 1.31**
Ref. (0.19)
Health Facility appreciation
Few support (Ref) Many support
Ref. 0.86**
Ref. (0.13)
Ref. 0.96**
Ref. (0.17)
Transport
Constant
0.69**
(0.26)
598 599 600 601 602 603
Notes: Each CHW made 8 choices that entered into the analysis. The ordering of the attributes was randomized across all participants. *p<0.05, **p<0.01 All estimates are relative to the reference level for that attribute. Model was run using 5000 Halton draws.
24
604 605
Figure 1: Example of DCE choice exercise give to CHWs (1 US dollar~100 Kenya Shillings)
606 607 608
25
609 610 611 612 613 614 615 616 617 618 619 620
Figure 2: Comparisons of the probability that a CHW would accept a position with different levels of incentives. The characteristics of the first job are the same in all three pairs, those of the second job are different. For all positions, there is no health facility staff appreciation. The comparisons are relative to each other, so probabilities of take-up always add up to one. Error bars indicate 95% confidence intervals. Probabilities are calculated using the STATA command “mixlpred” following the model estimated in Table 3.
26
Research Highlights • • • •
Community health workers’ strongly value community appreciation for their work. Community appreciation is almost twice as valuable as health facility staff appreciation Community health workers also have strong preferences for monetary incentives Six trainings per year was not preferred over four trainings per year.
Author Contributions (Pending Approval of Co-authors) Indrani Saran- Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Data Curation, Writing- Original Draft, Writing- Review & Editing, Visualization Laura Winn-Conceptualization, Methodology, Writing- Review & Editing Joseph Kipkoech Kirui- Methodology, Investigation, Project administration, Writing- Review & Editing Diana Menya- Methodology, Project administration, Supervision, Writing- Review & Editing Wendy Prudhomme O’Meara- Conceptualization, Methodology, Validation, Investigation, WritingReview & Editing, Supervision, Project administration, Funding acquisition
S0277-9536(19)30721-X
DOI:
https://doi.org/10.1016/j.socscimed.2019.112726
Reference:
SSM 112726
To appear in:
Social Science & Medicine
Received Date: 18 October 2018 Revised Date:
2 December 2019
Accepted Date: 6 December 2019
Please cite this article as: Saran, I., Winn, L., Kipkoech Kirui, J., Menya, D., Prudhomme O'Meara, W., The relative importance of material and non-material incentives for community health workers: Evidence from a discrete choice experiment in Western Kenya, Social Science & Medicine (2020), doi: https:// doi.org/10.1016/j.socscimed.2019.112726. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Title: The Relative Importance of Material and Non-material incentives for Community Health Workers: Evidence from a Discrete Choice Experiment in Western Kenya Indrani Sarana,1*, Laura Winna, Joseph Kipkoech Kiruib, Diana Menyac, Wendy Prudhomme O’Mearaa,c a
Duke University, Durham, NC, USA Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya c Moi University, Eldoret. Kenya b
*Corresponding Author 1
Present Affiliation & Address Boston College School of Social Work 140 Commonwealth Avenue Chestnut Hill, MA 02467 Ph: (617) 552-0197 Email: [email protected]
Keywords: Kenya; Community Health Workers, Satisfaction, Discrete Choice Experiment
1
Abstract
2
Motivating community health workers (CHWs), many of whom are volunteers, is important for
3
the sustainability of integrated community case management programs. Given the limited
4
budgets of many of these programs, and the increasingly important role played by CHWs, it is
5
crucial to not only identify important motivators driving their engagement, but also which
6
incentives could have the greatest impact on CHW motivation in their role. In this study, we
7
aimed to assess CHWs’ relative preferences for material and non-material incentives.
8
We conducted a discrete choice experiment (DCE) with 199 randomly selected CHWs, working
9
in 32 communities in western Kenya, to measure the relative importance that CHWs place on
10
different incentives. Each CHW completed a series of 10 choice tasks (8 random, 2 fixed), where
11
they had to choose between two hypothetical positions that had varying levels of monthly mobile
12
phone airtime, training, monthly transport bonus, community appreciation and health facility
13
staff appreciation of their work. Data was analyzed using mixed logit models. CHWs’ most
14
preferred job characteristic was high levels of community appreciation for their work which was
15
valued approximately equivalently to receiving a 2000 Kenya Shillings (~US $20) monthly
16
transport allowance. These incentives were valued more than appreciation from health facility
17
staff or trainings six times per year. This study demonstrates that investing in efforts to improve
18
community members’ knowledge and recognition of CHWs’ contribution to community health
19
may have a significant impact on CHWs’ motivation and retention in their role.
20 21 22 23
1
24
Introduction
25 26
Community health workers (CHWs) are a cadre of lay health workers who provide health
27
promotion and disease prevention services to their community (Olaniran et al., 2017). As part of
28
integrated community case management (iCCM) programs, they have also been trained to offer
29
diagnosis and treatment of common childhood illnesses such as malaria, diarrhea, pneumonia
30
and malnutrition (George et al., 2012). In 2012, the World Health Organization and UNICEF
31
issued a joint statement promoting iCCM as a strategy to expand coverage of basic health
32
services (Wolfheim et al., 2012). There is some evidence that these programs have successfully
33
reduced child mortality (Amouzou et al., 2014; Freeman et al., 2017).
34
CHWs generally do not receive a salary, and are instead offered a mix of both financial
35
and non-financial incentives (Bosch–Capblanch and Marceau, 2014; George et al., 2012; Smith
36
Paintain et al., 2014). However, as their service delivery roles are scaled up, there is a need to
37
better understand what types of incentives motivate CHWs’ participation in these programs. A
38
broad review of the literature finds that, in general, both intrinsic motivation and extrinsic
39
incentives are strong predictors of performance (Cerasoli et al., 2014). For CHWs specifically,
40
greater motivation in their role is associated with better performance as well as improved
41
retention (Abbey et al., 2014; Kok et al., 2015; Naimoli et al., 2014; Vareilles et al., 2015). The
42
latter is a major concern given high reported attrition from some CHW programs (Nkonki et al.,
43
2011).
44
Previous research has identified a number of both material and non-material factors
45
important for CHW motivation in their role. These include a desire to serve and be recognized by
46
the community, training and learning new skills, supervision, formal linkages with the health
2
47
system, financial compensation, and material incentives (bicycles, T-shirts, badges, phones, job
48
aides, supplies) (Alhassan et al., 2016; Brunie et al., 2014; Busza et al., 2018; Geldsetzer et al.,
49
2017; Haile et al., 2014; Kok et al., 2017; Lohfeld et al., 2016; Momanyi et al., 2016; Mpembeni
50
et al., 2015; Oliver et al., 2015; Pallas et al., 2013; Sanou et al., 2016; Strachan et al., 2015; Topp
51
et al., 2015; Vareilles et al., 2015; Winn et al., 2018; Zulu et al., 2014). However, there is little
52
evidence on the relative importance of different types of incentives for CHWs’ motivation in
53
their role. Even as countries are adopting policies supporting delivery of care by CHWs,
54
inadequate funding threatens the sustainability of these programs (Bennett et al., 2014;
55
Rasanathan et al., 2014). With limited resources, it is crucial to not only identify important CHW
56
motivators, but also which incentives will have the greatest impact on CHW motivation, and
57
potentially retention, in their role.
58
This study uses a discrete choice experiment (DCE) to quantitatively assess CHWs’
59
preferences for community and health facility staff appreciation relative to monetary incentives
60
and training. DCEs are a powerful tool to understand people’s relative preferences for different
61
job characteristics under hypothetical, but realistic situations. In a DCE, respondents make a
62
series of choices between two or more hypothetical jobs that are described using a list of several
63
characteristics. The relative value that respondents place on each characteristic can be inferred
64
from the tradeoffs that they make in selecting their preferred choice. DCEs have been previously
65
used to understand job preferences for formal health workers in resource-constrained settings.
66
These studies have highlighted the importance of both pecuniary and non-pecuniary incentives
67
for these workers, particularly opportunities for training and upgrading of skills (Brown et al.,
68
2017; Lagarde and Blaauw, 2009; Mandeville et al., 2016, 2014; McAuliffe et al., 2016;
69
Nurelhuda et al., 2018). However, as a largely volunteer workforce, CHWs may have different
3
70
preferences and, to our knowledge, only two studies have applied a DCE to elicit CHWs’ relative
71
preferences for different types of incentives and both these studies focused on CHWs trained to
72
perform specific health services such as family planning or iCCM (Brunie et al., 2014; Kasteng
73
et al., 2016).
74 75 76
Methods
77 78
Study Context and Population
79 80
This study was conducted as part of a cluster randomized controlled trial (RCT) that
81
aimed to evaluate the public health impact of community-based malaria diagnostic testing
82
(Prudhomme O’Meara et al., 2018). The RCT was done between July 2015 and May 2017 in 32
83
community units (CUs) in western Kenya, half of which were assigned to the intervention. A
84
community unit consists of approximately 1000 households.
85
CHWs’ main roles in Kenya involve health education and referrals to health care
86
facilities (Kenya Ministry of Health, 2007). As part of the RCT, CHWs working in intervention
87
areas were trained to offer free malaria diagnostic testing to community members. In addition, if
88
an individual tested positive for malaria the CHW provided them with a voucher that enabled
89
them to purchase a discounted anti-malarial drug at a local drug shop. CHWs in the comparison
90
areas continued to deliver their usual services.
91 92
In Kenya, CHWs generally work on a volunteer basis, but sometimes receive incentives if they are participating in special health programs such as immunization campaigns or health
4
93
screening programs. CHWs in the intervention arm of the RCT received reimbursement for
94
transport to monthly supervision meetings with study staff, a small amount of mobile phone
95
airtime, and a group performance bonus given every six months. The bonus was intended to
96
support group income-generating programs such as fish farming, small business loans, or other
97
investments.
98 99 100
The CHWs surveyed for this study were randomly selected, stratified by CU. In case any of the CHWs were not able to be reached, we also designated two “alternate” CHWs that could be contacted from each CU.
101 102 103
Data A discrete choice experiment (DCE) is a method used to quantitatively determine relative
104
preferences, in this case for different characteristics of the CHW role. CHWs were asked to
105
imagine that they worked in a hypothetical CU serving approximately 50 households. The CHWs
106
were then asked to choose between two positions with different characteristics, or to say if they
107
would choose not to be a CHW if those were the only options (an opt-out option). To decide
108
between the positions in each pair, CHWs had to trade-off the advantages and disadvantages of
109
each position. We included an opt-out option because as a volunteer workforce, CHWs can
110
choose to do something else (for example paid work). Indeed, some studies have suggested that
111
attrition from CHW programs is a major problem (Nkonki et al., 2011). Moreover, we chose to
112
define the opt-out as not being a CHW rather than as the status quo since the latter varies across
113
respondents and therefore is not well-defined.
114
Each position had varying levels of five attributes: monthly mobile phone airtime,
115
frequency of training, monthly transport bonus, and levels of community appreciation and health
5
116
facility staff appreciation of their work (Table 1). The attributes and levels for the DCE were
117
selected based on a review of the literature, feedback from study staff who frequently interact
118
with CHWs, and the results of a previous survey with 70 CHWs in the intervention area. (Winn
119
et al., 2018). This previous study consisted of semi-structured interviews with CHWs and found
120
that community appreciation, supervision from program staff, and upgrading of knowledge and
121
skills were important for CHW motivation in their role. CHWs also reported challenges with
122
transportation, compensation and support from health facility staff.
123
The attributes and levels were further refined after a pilot experiment with seven CHWs
124
from both control and intervention areas. For example, we found that community appreciation
125
was so valuable that hypothetical CHW positions that offered no community appreciation were
126
unacceptable. Similarly, options with no training opportunities would not be chosen. Thus, the
127
minimum level of training we chose to offer was one time per year, and the lowest level of
128
community appreciation included some amount of appreciation. We also included two attributes
129
in the pilot study that were dropped for the main experiment: the level of supervision and a group
130
bonus. We dropped these from the main study for two reasons: (1) The pilot suggested that seven
131
attributes was cognitively challenging and CHWs tended to simply pick the option with higher
132
community appreciation rather than considering all the attributes and (2) the intervention group
133
had more experience with a group bonus and with intensive supervision as part of the study and
134
we were concerned that, as a result, CHWs in the intervention group might perceive these
135
attributes differently than those in the control group.
136
We used Lighthouse Studio Version 9.3.0 (Sawtooth Software Inc, Provo, UT)(Sawtooth
137
Software, 2017) to generate the set of choices using a fractionally factorial design that is both
138
orthogonal (attributes are statistically independent of each other) and balanced (each attribute
6
139
level appears an equal number of times). We used the “balanced overlap” random task
140
generation method and created 10 blocks of 10 choice tasks, which included 8 random tasks and
141
2 fixed tasks. We chose to use a blocked design because it allows us to include more choice tasks
142
while limiting the cognitive burden for an individual respondent. The “balanced overlap” random
143
task method allows for some attribute levels to be the same across the two positions in a choice
144
task. This ensures that if a respondent has a certain “must-have” level for an attribute, then when
145
the level is the same for that attribute across the two choices the respondent is forced to make a
146
choice based on the other attributes (Reed Johnson et al., 2013). Each individual was randomly
147
assigned to one of the 10 blocks. The order of the attributes in each job profile was randomized
148
across blocks so the order varied across individuals, but not across choice tasks for a given
149
individual.
150 151
Sample Size Calculations
152
We calculated our sample size for the DCE using an empirical power-test formula
153
developed by Yang et al (Yang et al., 2015). To achieve a utility-difference standard deviation of
154
0.8, and assuming an experimental design efficiency of 22 (the mean levels of these design
155
characteristics in the studies examined by Yang et al), a DCE with 5 attributes, 3 maximum
156
levels per attribute, 8 random choice tasks, no probabilistic attributes, and an opt-out option
157
required a sample size of 193.
158 159
Survey Administration
160
7
161
Paper surveys were administered between June and July 2017 in English or Kiswahili,
162
according to the CHW’s preference. The interviewers described in detail the definition of each
163
attribute as well as the different levels. Interviewers also went through an example choice to help
164
the CHWs understand the DCE exercise. The DCE included visual aids (downloaded from the
165
website https://thenounproject.com/) to improve understanding of the levels and attributes and to
166
make it easier for the respondent to compare across profiles (see Figure 1, the full set of images
167
can be seen in Appendix Figure A1). After explaining the DCE exercise, the interviewer allowed
168
the respondent to complete the choice tasks on their own, though they were available in case the
169
respondent had any questions. The survey took approximately 20-30 minutes to complete.
170 171 172
Analytical Approach We use means and standard deviations to describe the demographic characteristics of the
173
CHWs who were included in the study. To estimate CHWs’ relative preferences for different
174
job-related attributes, we used mixed logit models with 5000 Halton draws (for the models in the
175
appendix we use 500 Halton draws), and only included the 8 random choice tasks. We chose the
176
mixed logit model because it allows for unobserved heterogeneity in preferences across
177
individuals and estimates both the mean preference weight as well as the standard deviation.
178
Moreover, the mixed logit model accounts for multiple observations per respondent and allows
179
for violation of the independence of irrelevant alternatives assumption (the assumption that
180
adding or removing an alternative has no effect on the choice between the other alternatives
181
(Hauber et al., 2016)). We assumed that all attribute variables, except for the constant, had a
182
random component and that the preference weights were normally distributed. We also ran a
183
model with the monetary parameters—airtime and transport reimbursement— with lognormal
8
184
distributions to ensure only positive coefficients and found similar results (Appendix Table A1).
185
All attribute variables were coded as dummy variables. We included an alternative-specific
186
constant for the two positions in order to estimate the value of choosing to be a CHW versus
187
opting out. We present the results from models that estimated only the main effects for each
188
attribute level. We also estimated a set of models that included interactions of each attribute with
189
gender, years of experience, location, and intervention status (Appendix Tables A2-A5).
190
We tested the validity of our study using the two fixed tasks that were the same for all
191
respondents. The first fixed task assessed respondents’ comprehension of the exercise by
192
presenting a choice where one alternative dominated the other. For this fixed task, we set levels
193
of all attributes the same for both alternatives, except that one alternative had more airtime and a
194
larger transport reimbursement. We chose this weak dominance test because we were concerned
195
that people might simply pick the option with the higher levels of community appreciation
196
without considering the other attributes and, therefore, this would not be an accurate assessment
197
of the extent to which respondents were thoughtfully evaluating the two choices. For the other
198
non-monetary attributes—health facility staff appreciation and training— the direction of
199
people’s preferences ex-ante was not clear since some people could prefer fewer trainings or less
200
appreciation from the health facility staff. The second fixed task was used to test the internal
201
validity of our model by comparing the predicted choices from the model with the actual choices.
202
In order to present the DCE results visually, we used the mixlpred command following
203
the main model estimation in STATA to calculate the predicted probability of CHWs’ taking up
204
a position with some community appreciation and no other incentives, compared to a position
205
with high levels of community appreciation and no other incentives. We then show how these
206
predicted probabilities change as additional incentives are added to the position with some
9
207
community appreciation. The predicted probabilities were determined for a sample of 199
208
observations and the distribution was used to calculate the 95% confidence intervals. Statistical
209
analyses were conducted using STATA 15 (College Station, TX) (StataCorp, 2017).
210 211
Results
212 213
CHW Sample and Characteristics
214 215
We conducted the experiment with a sample of 199 CHWs, of the nearly 600 who are
216
working in the 32 CUs that were part of the study (33%). We were unable to include 21 of the
217
200 CHWs (10.5%) who were originally assigned to participate either because we were unable to
218
reach them (N=17) or because they did not show up (N=4). Of these, 20 were replaced by other
219
CHWs, 16 of whom had been previously randomized as alternates. Thus, of our sample of 199
220
CHWs, only 4 had not been randomly assigned ex-ante to participate. In our sample, 100 CHWs
221
worked in intervention areas, 99 in control areas.
222
We present characteristics of the CHWs interviewed in Table 2. Approximately 72% of
223
CHWs were women, the mean age was 43 years (SD=10) and 88% were married. CHWs had, on
224
average, 7.2 years of experience. The majority of CHWs had at least a primary education (94%).
225
Outside of being a CHW, the most common occupations were farming (76%) and business
226
(34%).
227 228
Discrete Choice Experiment
10
229
Of the expected total of 1592 random choice tasks (199 X 8 choices) in the DCE, we
230
have complete data for 1580 choices (99%). Of these 1580 choices, the “neither” option was
231
chosen 28 times (1.8%) which suggests that the combinations of attributes and levels offered
232
were generally acceptable. We tested the internal validity of the model by comparing the
233
predicted choice probabilities for one of the fixed tasks to the actual choice probabilities. The
234
model’s predicted probabilities of uptake for alternatives 1, 2 and 3 (opt-out) were 54%, 45%
235
and 1.8%, respectively, while the actual uptake was 51%, 47% and 1.5%, respectively,
236
suggesting that this a reasonable model for CHWs’ preferences.
237
The results of the DCE are presented in Table 3. The coefficient estimates represent the
238
change in utility (or satisfaction) received from that level of the attribute, relative to the reference
239
level, holding all other factors constant. Moreover, the magnitude of the coefficient indicates the
240
strength of preference for that level of the attribute. The ratio of two coefficients indicates the
241
marginal rate of substitution between two attributes i.e. the relative value that respondents place
242
on the two attributes. Since the coefficients are all positive, the results suggest that, holding all
243
else constant, CHWs generally prefer more of each attribute. Moreover, a positive constant
244
indicates that the respondents’ generally preferred to be a CHW, rather than opting out.
245
The highest utility gain comes from increasing community appreciation from a level
246
where CHWs only receive some support, to a level where all community members appreciate
247
their work (estimate=1.68, SE=0.18). This is approximately equivalent to the utility gain from
248
increasing the transport reimbursement from Kenya shillings (Ksh) 0/month to Ksh 2000/month
249
(estimate=1.63, SE=0.18) and nearly twice the utility gain from increasing the number of health
250
facility workers who support and understand CHWs’ work from a “few” to “many”
251
(estimate=0.86, SE=0.13) (1 United States dollar (USD)=100 Kenya Shillings).
11
252
The results also show that although CHWs prefer four trainings per year to only one
253
training per year (estimate=0.84, SE=0.13), the utility gain of six trainings per year (relative to
254
one training per year) is approximately equivalent that of four trainings per year (estimate=0.95,
255
SE=0.17). Lastly, although we find a positive estimate on the airtime level of Ksh 200/month
256
(relative to Ksh 0/month), only the estimate on the airtime level of Ksh 500/month (relative to
257
Ksh 0/month) is statistically significant (estimate=0.63, SE=0.14).
258
The standard deviation estimates are an indication of the degree of preference
259
heterogeneity in the sample of CHWs for that attribute-level. We find large, and statistically
260
significant, standard deviation estimates for several attribute-levels, suggesting that while there
261
are generally strong preferences for high levels of all these attributes, there is also considerable
262
variation among CHWs in their degree of preference.
263
The strength of CHWs’ preference for high levels of community appreciation is
264
highlighted in Figure 2, which shows the relative probabilities that CHWs would accept
265
opportunities with different characteristics. If CHWs were offered the choice between a position
266
with no airtime or transport reimbursements, only one training per year, and low levels of health
267
facility and community support, and a position with the same characteristics but that instead had
268
high levels of community appreciation, 72% would take the latter position (blue bars), while 9%
269
would opt out. A position with low levels of community appreciation would need to include a
270
significant monetary investment— KSH 500/month for airtime, and a transport reimbursement of
271
KSH 2000/month – in addition to trainings at least 4 times per year, for the probability of take-up
272
to be much higher than that of a position that offered high levels of community appreciation but
273
no other incentives (green bars).
12
274 275
We did not find statistically significant interactions of each attribute with CHW gender, years of experience, location, and intervention status (Appendix Tables A2-A5).
276 277 278
Discussion Our study examined the preferences of CHWs working in 32 communities in Western
279
Kenya. The results demonstrate that CHWs’ most preferred job characteristic was high levels of
280
community appreciation for their work, which was valued approximately equivalently to
281
receiving a 2000 Kenya Shillings (~USD 20) monthly transport stipend.
282
In terms of policy implications, our results suggest that since CHWs in Western Kenya
283
highly value community appreciation, interventions to increase awareness and recognition by the
284
community could increase their motivation. These interventions could include, for example,
285
regular village meetings where CHWs are publicly recognized and rewarded for their work,
286
providing certificates for good performance, designing transparent CHW selection mechanisms
287
and offering uniforms and badges to add legitimacy to their role. These interventions, however,
288
require investment in strong supervision and management systems to continuously monitor
289
CHW performance and ensure that they are appropriately recognized for their work. Training
290
CHWs to perform services that the community finds valuable, such as malaria diagnostic testing,
291
could also increase awareness, use, and appreciation of CHWs. Lastly, even small monetary
292
incentives, such as a monthly airtime reimbursement, could demonstrate to CHWs that their
293
work is valuable and substantially increase their motivation in their role. Our results also suggest
294
that larger monetary incentives, such as in the form of a transport reimbursement, are another
295
intervention that can increase CHW motivation in their role. The relative costs and feasibility of
13
296
intervening to increase community appreciation versus offering monetary incentives will likely
297
depend on the program and context.
298
To our knowledge, only two DCEs have been previously conducted with CHWs, both in
299
Uganda. In one, CHWs valued specific types of material incentives (ie a badge and a bicycle)
300
more than others (mobile phone and transport refunds) but they did not examine the relative
301
importance of community or health facility appreciation and support (Brunie et al., 2014). In the
302
other, similar to the results of our DCE, a monthly stipend and community appreciation were the
303
most preferred CHW work benefits (when compared to flexible hours, regular trainings and a
304
mobile phone), but the sample size surveyed was relatively small (Kasteng et al., 2016).
305
Our DCE shows that one of the most valued job characteristics for CHWs in Western
306
Kenya is high levels of community appreciation of their work. This is consistent with much of
307
the previous literature which underscores CHWs’ desire to serve the community and appreciation
308
from the community as important motivators (Kasteng et al., 2016; Mpembeni et al., 2015;
309
Sanou et al., 2016; Strachan et al., 2015; Winn et al., 2018; Zulu et al., 2014). However, while
310
integration with the health system has been emphasized as crucial to the successful scale-up of
311
CHW programs (Pallas et al., 2013; Schneider and Lehmann, 2016; The Lancet Global Health,
312
2017; Tulenko et al., 2013) our results suggest that recognition from the health facility staff is
313
less important to CHWs in Western Kenya than relationships with the community.
314
The second most valuable job characteristic was a monthly transport reimbursement.
315
Other studies from sub-Saharan Africa also highlight the importance of material benefits for
316
CHW motivation in their role (Brunie et al., 2014; Geldsetzer et al., 2017; Lohfeld et al., 2016;
317
Pallas et al., 2013; Schuster et al., 2016). Trainings are often cited in the literature as an
318
important factor in CHW motivation (Busza et al., 2018; Haile et al., 2014; Lohfeld et al., 2016;
14
319
Momanyi et al., 2016; Sanou et al., 2016). However, this study shows that six trainings per year
320
was valued about the same as four trainings per year, suggesting that there may be diminishing
321
returns for CHW motivation from additional training sessions.
322
There are some limitations of this study. We included most of the job attributes and levels
323
that our previous study with CHWs identified as most relevant; for practical reasons, however,
324
we were not able to include supervision, which both our own prior work and other studies with
325
CHWs have found to be important. Other studies have also identified certain types of in-kind
326
incentives (t-shirts, bicycles, phones, supplies) as potentially important for CHW preferences
327
though our own previous work in this context did not find these as major motivators. Second,
328
while preferences for different incentives may be associated with performance, there could also
329
be structural factors that influence good performance (for example workload or availability of
330
supplies). Third, some CHWs may have had difficulty understanding the DCE exercise
331
(approximately 14% “failed” the comprehension check, but our results were robust to excluding
332
these CHWs). Fourth, although respondents were told that the opt-out option in the DCE was
333
equivalent to choosing not to be a CHW, some may have interpreted the opt-out option as
334
maintaining their status quo situation, which varies across CHWs. Lastly, since the DCE is a
335
hypothetical exercise, CHWs’ actual behavior may be different depending on the context.
336
Our study also has several strengths in terms of geographic scope and the type of CHW
337
program. The CHWs in this study were representative of nearly 600 CHWs in Western Kenya
338
who are responsible for approximately 32,000 households. Moreover, the participants are part of
339
a long-existing CHW program, and had varying levels of responsibility, with some performing
340
malaria diagnostic tests in addition to their usual services. The broad representation of CHWs, in
341
terms of both geography and role, enhances the likelihood that our results are generalizable
15
342
beyond Western Kenya, though further research is needed to confirm this. Moreover, our
343
findings about the importance of both monetary and non-monetary incentives for CHW
344
motivation are consistent with what other studies have reported across sub-Saharan Africa. This
345
suggests that these are widespread preferences though the relative values placed on these
346
incentives may vary across context.
347
Another strength of our study is that most CHWs seemed to understand the DCE design
348
and providing concrete choices with respect to job characteristics may give a more accurate view
349
of preferences, especially for less educated cadres of workers. The DCE included job
350
characteristics – both material and non-material – that are relevant to a volunteer program. In
351
particular, while previous research has identified community appreciation as an important CHW
352
motivator, this DCE allowed us to quantify its value in this context relative to other incentives.
353
CHWs are increasingly being trained to offer a broad array of health services to
354
underserved populations. As volunteers, their preferences may be different from those of other
355
cadres of health workers. Our results demonstrate that CHWs in Western Kenya value both
356
material and non-material incentives and that improving community members’ recognition of
357
CHWs’ contributions may have a significant impact on CHWs’ motivation and retention in their
358
role.
359 360
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539 540 541 20
542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569
Figures and Tables
570 571
Table 1: Attributes and Levels included in Discrete Choice Experiment Attribute
Level 21
Airtime
1. KSH 0/ month 2. KSH 200/ month 3. KSH 500/ month
Training
1. Once per year 2. Four times per year 3. Six times per year
Transport
1. KSH 0/month 2. KSH 1000/month 3. KSH 2000/month
Health Facility Appreciation
1. Few health facility staff recognize and appreciate your work 2. Many health facility staff recognize and appreciate your work
1. Some community members recognize and appreciate your work 2. All community members recognize and appreciate your work Notes: 1 United States (US) dollar~100 Kenya Shillings (Ksh)
Community Appreciation 572 573 574 575 576 577 578 579 580 581 582 583 584 585
Table 2: Demographic Characteristics of CHWs Interviewed (N=199)
22
N (count) Age Years of Experience as CHW Female Highest Education Level Less than Primary Primary Completed Less than Secondary Secondary Completed Some College Marital Status Married Widowed Divorced/Separated Single/Never Married Occupation (outside of CHW) Farmer Business Pastor Other Volunteer work Other Occupation
586 587 588 589 590 591 592 593
% or Mean ±SD
143
43 ± 10 7.2 ± 4.5 72%
11 25 58 61 44
6% 13% 29% 31% 22%
176 9 9 5
88% 5% 5% 3%
152 68 21 26 77
76% 34% 11% 13% 39%
Notes: CHWs could report multiple occupations so the sum of the percentages for occupations do not add up to 100. The "Other" Category includes occupations such as shopkeeper, teacher, government work and administrative roles at church.
594 595 596 597 Table 3: Results of mixed logit models of discrete choice experiment data on CHW preferences in Western Kenya Mixed Logit Model (N=199 CHWs, 1580 choices) Estimated Mean Preference Attribute
Level
23
(SE)
Estimated SD
(SE)
Airtime
None (Ref) KSH 200/month KSH 500/month
Ref. 0.23 0.63**
Ref. (0.12) (0.14)
Ref. 0.22 -0.00
Ref. (0.46) (0.64)
Training
1X/year (Ref) 4X/ year 6X/year
Ref. 0.84** 0.95**
Ref. (0.13) (0.17)
Ref. 0.12 1.28**
Ref. (0.55) (0.20)
None (Ref) KSH 1000/month KSH 2000/month
Ref. 1.04** 1.63**
Ref. (0.14) (0.18)
Ref. -0.11 1.07**
Ref. (0.71) (0.21)
Community appreciation
Some support (Ref) All support
Ref. 1.68**
Ref. (0.18)
Ref. 1.31**
Ref. (0.19)
Health Facility appreciation
Few support (Ref) Many support
Ref. 0.86**
Ref. (0.13)
Ref. 0.96**
Ref. (0.17)
Transport
Constant
0.69**
(0.26)
598 599 600 601 602 603
Notes: Each CHW made 8 choices that entered into the analysis. The ordering of the attributes was randomized across all participants. *p<0.05, **p<0.01 All estimates are relative to the reference level for that attribute. Model was run using 5000 Halton draws.
24
604 605
Figure 1: Example of DCE choice exercise give to CHWs (1 US dollar~100 Kenya Shillings)
606 607 608
25
609 610 611 612 613 614 615 616 617 618 619 620
Figure 2: Comparisons of the probability that a CHW would accept a position with different levels of incentives. The characteristics of the first job are the same in all three pairs, those of the second job are different. For all positions, there is no health facility staff appreciation. The comparisons are relative to each other, so probabilities of take-up always add up to one. Error bars indicate 95% confidence intervals. Probabilities are calculated using the STATA command “mixlpred” following the model estimated in Table 3.
26
Research Highlights • • • •
Community health workers’ strongly value community appreciation for their work. Community appreciation is almost twice as valuable as health facility staff appreciation Community health workers also have strong preferences for monetary incentives Six trainings per year was not preferred over four trainings per year.
Author Contributions (Pending Approval of Co-authors) Indrani Saran- Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Data Curation, Writing- Original Draft, Writing- Review & Editing, Visualization Laura Winn-Conceptualization, Methodology, Writing- Review & Editing Joseph Kipkoech Kirui- Methodology, Investigation, Project administration, Writing- Review & Editing Diana Menya- Methodology, Project administration, Supervision, Writing- Review & Editing Wendy Prudhomme O’Meara- Conceptualization, Methodology, Validation, Investigation, WritingReview & Editing, Supervision, Project administration, Funding acquisition