Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment.

BACKGROUND: Inadequate and unequal distribution of health workers are significant barriers to provision of health services in Malawi, and challenges retaining health workers in rural areas have limited scale-up initiatives. This study therefore aims to estimate cost-effectiveness of monetary and non-monetary strategies in attracting and retaining nurse midwife technicians (NMTs) to rural areas of Malawi.
METHODS: The study uses a discrete choice experiment (DCE) methodology to investigate importance of job characteristics, probability of uptake, and intervention costs. Interviews and focus groups were conducted with NMTs and students to identify recruitment and retention motivating factors. Through policymaker consultations, qualitative findings were used to identify job attributes for the DCE questionnaire, administered to 472 respondents. A conditional logit regression model was developed to produce probability of choosing a job with different attributes and an uptake rate was calculated to estimate the percentage of health workers that would prefer jobs with specific intervention packages. Attributes were costed per health worker year.
RESULTS: Qualitative results highlighted housing, facility quality, management, and workload as important factors in job selection. Respondents were 2.04 times as likely to choose a rural job if superior housing was provided compared to no housing (CI 1.71-2.44, p<0.01), and 1.70 times as likely to choose a rural job with advanced facility quality (CI 1.47-1.96, p<0.01). At base level 43.9% of respondents would choose a rural job. This increased to 61.5% if superior housing was provided, and 72.5% if all facility-level improvements were provided, compared to an urban job without these improvements. Facility-level interventions had the lowest cost per health worker year.
CONCLUSIONS: Our results indicate housing and facility-level improvements have the greatest impact on rural job choice, while also creating longer-term improvements to health workers' living and working environments. These results provide practical evidence for policymakers to support development of workforce recruitment and retention strategies.

Introduction

Inadequate and unequal distribution of health workers are significant barriers to the provision of essential health services in Malawi. Malawi faces health workforce shortages of 48% against its national targets, with only 1.48 health workers per 1,000 population [1], far below the WHO recommended minimum density of 4.45 doctors, nurses and midwives per 1,000 population for countries to meet the Sustainable Development Goals [2]. Workforce shortages are particularly acute in rural areas where 84% of Malawi’s population resides, contributing to disparities in access to health services and health outcomes between urban and rural areas [3,4]. For example, in rural areas in 2014 there were 0.7 clinicians per 1,000 persons as compared to 1.8 per 1,000 persons in urban areas [5].

In 2004, to respond to severe health workforce shortages, the Government of Malawi began implementing a 6-year Emergency Human Resources Programme (EHRP). The EHRP increased the number of health workers in 11 priority cadres from 5,453 to 8,369 by 2009, achieving a provider-to-population ratio of 1.44 per 1,000 population [6]. Among prioritized cadres, the EHRP supported the rapid scale up of Nurse Midwife Technicians (NMTs), a 3-year diploma nursing cadre drawn predominantly from rural areas and meant to serve in rural health facilities. NMTs are core to Malawi’s primary care architecture, with the largest number of established posts among facility-based cadres [1]. Despite large investments during the EHRP period, which led to a 39% increase in the number of nurses to 4,812 by 2009, there have been minimal gains over the past decade [6]. In 2017, there were 5,441 nurses and a vacancy rate of 62% against established posts [1]. Retention of NMTs and other critical cadres has remained a major challenge and has limited the effectiveness of workforce scale-up initiatives. National attrition estimates range from 3% to 15%, but likely underestimate actual attrition as these data are not routinely captured [1,7].

Malawi’s Human Resources for Health (HRH) Strategic Plan 2017–2022 emphasizes the importance of improving retention and motivation of health workers as critical to effective, efficient and equitable health service delivery. Qualitative literature has pointed to key strategies for rural recruitment and retention in low- and middle-income countries (LMICs), including educational and development opportunities, financial incentives, improved living and working conditions, staff recognition and management improvements, and regulatory policy changes such as task-shifting, creation of new cadres, and compulsory service agreements [8,9].

Job choices are highly context specific and the effectiveness of different strategies in facilitating a decision to take a job can vary greatly across countries and cadres, with the most effective strategies responding to clear challenges in the national workforce landscape [10,11]. Health workforce research in Malawi has found health workers were unsatisfied with their salaries, benefits, living conditions, workload, lack of supplies and management relationships [12-14]. A discrete choice experiment (DCE) conducted in Malawi in 2008 highlighted that nursing officers were willing to trade between various monetary and non-monetary benefits and that multiple attributes could have a significant impact on recruitment; however, this study did not investigate the differential effects of these incentives in rural and urban areas, the ability of the incentives to attract health workers to rural areas, or the costs of incentives relative to their expected impact [15]. While previous evidence aimed to understand drivers of health worker retention in general, an investigation of factors that can influence rural job uptake and retention was needed, with a focus on mid-level providers who are the frontline workers in rural facilities in Malawi. We conducted a DCE to estimate the cost-effectiveness of policy-relevant monetary and non-monetary strategies in attracting and retaining public sector NMTs to job posts in rural areas of Malawi.

Methods

This study used a DCE methodology to capture information on the relative importance of different job characteristics, the probability of uptake of jobs defined by those characteristics and associated costs of the interventions. The study was conducted in two phases, including a) interviews, focus group discussions, and government consultations to identify incentives for inclusion in the DCE questionnaire and b) administration of the DCE questionnaire to practicing NMTs and NMT students. This study was approved by the Malawi National Health Sciences Research Committee (protocol approval number NHSRC #15/3/1394), and the U.S.-based Chesapeake Ethics Review Board (Pro00013609). Written informed consent was obtained from all study participants.

DCEs are a useful tool to provide quantitative information on the relative importance of various job characteristics that influence the job choices of health workers, as well as the trade-offs between these factors and thus the probability of uptake of jobs [16]. This method goes beyond more frequent qualitative assessments, and through asking participants to choose between different job scenarios, DCEs can be used to provide quantifiable data to guide the selection of the most appropriate strategies for recruitment and retention in underserved areas [16-19].

Qualitative data collection and analysis

Interviews and focus group discussions were used to identify factors that would motivate NMTs and NMT students to select and remain in a rural job using a purposive sampling approach to reach a diverse group of urban and rural NMTs and students, with the aim of achieving data saturation. Three focus group discussions were conducted in three different schools, with each focus group including 12 NMT students in their final year of study who were randomly selected from the class register. At the time of the study, there were 12 schools that trained NMTs in Malawi, and one school was purposively selected from each of the three regions of the country (northern, central and southern). Twelve in-depth interviews were conducted with practicing NMTs serving in both rural and urban facilities operated by the Ministry of Health (MoH) and the Christian Health Association of Malawi (CHAM). CHAM is a large, faith-based non-governmental healthcare provider in Malawi, providing approximately 30% of Malawi’s healthcare through service level agreements with the MoH. One district from each region was purposively selected and within each of the three selected districts four facilities were chosen, including one with each of the following classifications: MoH rural, MoH urban, CHAM rural and CHAM urban. Within those facilities, one NMT was selected who was available at the time of interview.

The discussion guide asked questions about participants’ preferences on job characteristics and is available in S1 File. Interviews and focus group discussions were conducted by three data clerks fluent in both Chichewa and English, who received training from the study team on the protocol and data collection methods. The interviews and focus group discussions were audio recorded and transcribed verbatim in English.

Transcripts were independently coded by two members of the research team in Dedoose, a web-based qualitative analysis software. Using an inductive approach, the team identified and coded attributes mentioned in the interviews and focus group discussions that participants identified as informing their decision to choose and remain in rural and hard to reach facilities. The qualitative analysis generated a list of 31 coded attributes.

DCE questionnaire development

The 31 attributes that emerged from the qualitative analysis were reviewed in a consultation workshop with government staff from the Departments of Nursing and Midwifery Services, Planning and Policy Development, and Human Resources Management and Development, as well as civil society organizations and development partners. Through a facilitated exercise, the workshop participants reviewed each attribute and associated qualitative quotations, and then prioritized attributes for inclusion in the DCE questionnaire using several inclusion criteria, such as the strength of qualitative preferences and the feasibility of implementing those incentives in Malawi. The workshop goal was to ensure that all interventions included in the DCE questionnaire were policy relevant and could be practically implemented in Malawi. The selected attributes included: housing, facility quality, access to long-term career progression opportunities (upgrading), workload, supportive management, and choice of location. Salary considerations emerged during focus group discussions and salary was specifically included as an attribute to allow for more detailed cost comparisons in the analysis. Within each attribute category, a base level and one or more higher-level options were defined to represent incentives that the government may offer to attract and retain health workers. The attributes and levels are shown in Table 1.

Table 1

Job characteristics included in the discrete choice experiment questionnaire.

Attribute	Job attribute levels	Offered in rural jobs	Offered in urban jobs
Housing	No housing or housing allowance provided	✓	✓
	Free basic housing provided (eg. semi-detached house with two bedrooms)	✓
	Free superior housing provided (eg. detached house with reliable electricity and three bedrooms)	✓
Facility quality	Basic (e.g. unreliable electricity; equipment, drugs and supplies not always available)	✓	✓
	Advanced (e.g. reliable electricity; equipment, drugs and supplies always available)	✓	✓
Access to long-term upgrading opportunities	Eligible to apply for upgrading opportunities after 4 years of service	✓	✓
	Eligible to apply for upgrading opportunities after 3 years of service	✓	✓
Workload	Heavy workload (you work longer hours because the facility does not have enough staff)	✓	✓
	Manageable workload (you work within scheduled hours because the facility has sufficient staff)	✓	✓
Supportive management	The management at the facility is not supportive and makes work more difficult	✓	✓
	The management at the facility is supportive and makes work easier	✓	✓
Salary	125,069 MWK per month	✓	✓
	156,365 MWK per month (25% top-up)	✓	✓
	187,604 MWK per month (50% top-up)	✓	✓
Choice of location	You are randomly assigned to a health facility	✓	✓
	You are given a choice of district in which you will work	✓	✓

A labeled design was used for this DCE, so that each job choice set included one rural and one urban job. The DCE attributes and levels were combined to create job choice sets using R 3.5.2 software. A fractional factorial design was used to select a fraction of the total job choice sets in a way that allows for estimation of preferences for all job profiles, not just those presented in the questionnaire [16]. R software was used to select choice sets that optimize D-efficiency, maximize level balance and orthogonality, and minimize overlap among attribute levels. The purpose of having an efficient design is to maximize the precision of estimated model parameters [20]. Twenty-four choice sets were created and randomly assigned to one of two questionnaire blocks (blocks A and B), and participants randomly received one of the two blocks. The aim of blocking was to reduce the burden on any individual respondent while still achieving optimal experimental design across all choice set options [20].

In the final DCE questionnaire, each participant was presented with a series of 12 choice sets that each described two potential employment scenarios. For each of the two scenarios, a description of each job attribute was provided based on the selection of one of the levels for that attribute and the job location. The 12 choice sets in block A of the DCE questionnaire are shown in S2 File. Study staff read consent to all respondents, explaining the attributes in detail, and elaborating that choices should be based on respondents’ preferences of factors that would both motivate them to choose and remain in a particular job. In addition to the DCE choice sets, demographic and background questions were included in the questionnaire.

DCE sampling and data collection

The DCE instrument was pre-tested with five deployed NMTs and 40 second year NMT students. The full DCE questionnaire was anonymously self-administered by 472 participants in September and October 2016. The sample size was determined through review of literature which indicates that obtaining reliable estimates of preferences requires at least 30 to 50 respondents per sub-group to be analyzed [16]. The appropriate sample size is impacted by the number of attributes and levels, and the number of choice sets provided to each participant. The overall precision of DCE parameters is impacted by a balance between statistical efficiency and response efficiency [19]. Using multiple DCEs with simulated sample sizes to estimate the sample size where precision would improve, a DCE methodological review revealed that precision steadily increases when the sample size is below 150 and flattens at around 300 observations and above [20]. We therefore aimed to sample a minimum of 300 participants.

To reach the minimum target sample size, we used a census approach, and collected data from a total of 472 respondents. All graduating NMT students were invited to participate from five randomly selected training institutions of the 12 training institutions in-country, representing geographic diversity across the country’s five administrative zones (north, central west, central east, southwest, southeast). Although there was overlap in schools selected for focus groups and for the DCE, individual students included in the focus groups were not eligible for the DCE. For practicing NMTs, ten districts were randomly selected, including two districts from each of the five zones. Districts selected in the first qualitative phase were excluded from the second phase. From each district, six rural facilities and one urban facility were randomly selected. Private facilities, health posts and village clinics were excluded from the sample. At each health facility, all NMTs present on the day of the survey were invited to participate. Data were entered electronically using EpiData with 100% double-entry to ensure accuracy. The full DCE dataset is available in S3 and S4 Files.

DCE data analysis

Demographic, education, and work experience characteristics were analyzed using univariate, descriptive statistics. Bivariate logistic regression was then used to explore associations between participants’ self-reported likelihood of working in a rural area in the future and various demographic or background characteristics. For the data from the DCE choice sets, a conditional logit model was developed to investigate the preferences for job attributes among respondents. The conditional logit model is based on three assumptions: (1) independence of irrelevant alternatives; (2) error terms are independent and identically distributed across observations; and (3) no preference heterogeneity across respondents. Goodness-of-fit criteria, including Akaike and Bayesian information criteria and pseudo R2, were used to assess model fit. Dummy variables were established for each attribute level in a rural or an urban setting and the probability of choosing a job with a higher-level attribute compared to the base level was produced. To investigate potential impact of demographic characteristics on job attributes, we also ran separate conditional logit models for the various demographic sub-groups of the population.

An uptake rate or preference impact measure was calculated to estimate the percentage of health workers that would prefer a job posting that offers a specific package of strategies as compared to other job postings [21]. Several validity tests were conducted to determine the appropriateness of model specifications. Specifically, we investigated dominance and internal or predictive validity. Dominance indicates that a participant always selected job options on the basis of one attribute (such as always choosing the higher salary). Such behavior is in violation of the basic assumptions of random utility theory, which informs the DCE model design, and assumes that individuals make trade-offs between various characteristics when making choices [22,23]. Therefore, we examined the number of participants that always chose jobs that offered the highest level of any characteristic and excluded from our analysis the 79 respondents who expressed a dominant preference. To assess internal or predictive validity, we compared the percentage of participants that chose a job option to the uptake predicted by the model [23,24]. All analyses were performed using Stata 13.

Results

Qualitative findings on factors motivating rural uptake and retention

In the qualitative interviews and focus groups, topics related to accessibility, opportunities for career development, housing, availability of utilities, road accessibility and distance, access to long-term upgrading, sufficient staffing, and equipment and supplies arose most frequently. The nuances provided by NMTs on factors they consider in their employment choices, as well as the interrelationships between these factors and feasibility in Malawi, were used to cluster, prioritize and define job attributes and levels for the DCE questionnaire. Several examples of the key topics discussed are described below.

Participants reflected on the lack of housing options in rural areas and the impact of housing shortages on their decisions to both choose and remain in a rural job, in particular as their personal lives evolved alongside their careers. As one NMT student noted in a focus group discussion: “Before they consider increasing the staff, increase the houses that they will live in. You cannot have a nurse living with the village head because there is no accommodation for her. In some areas there are not even houses that you can rent. A nurse cannot stay in a place like that […] But if the health center has more good housing, electricity and passable roads then that will help when considering increasing the numbers [of health workers].”

While salary was an important consideration, salary alone was not a sufficient motivating factor for respondents, in particular where housing, electricity and water were unavailable. As one NMT shared: “When people are aware that there are good houses, with potable water and electricity, they get motivated and rush to that place. But when there is a good salary, and the living conditions are pathetic, you will still think of your life first and decline to go to such a place. But good houses, potable water and electricity are a priority, and these are good motivating factors.” (Interviewee, male, rural MoH facility).

In addition to housing, issues of facility quality, supportive management and mentorship, and a sufficient size team to manage workload also emerged repeatedly throughout the interviews and FGDs. Respondents discussed the importance of these factors, not only for their own motivation to stay in a rural health facility, but also for the quality of care they were able to provide to patients. For example, one NMT shared: "The other thing is that teamwork boosts the quality of care provided to the patients because where you don’t know, a colleague will show you what to do. But in the rural [area] who will you ask? This is very important to consider when working.” (Interviewee, female, rural MoH facility). Another NMT highlighted: "It makes me sad that we only wish that we had some of the equipment within the rural health facility to meaningfully save lives. So many times, I have seen cases where the referral system has failed and patients have died when such deaths could have been avoided if nurses in the rural area were equipped to a minimum. This to me, as a nurse and a human being, has an effect, that I would have saved a life but couldn’t because of limitations. You cannot live everyday with such kinds of regrets. It is very sad and therefore you sometimes decide to move and go where you are able to manage to give the best you can." (Interviewee, female, urban CHAM facility).

DCE sample characteristics

For the DCE questionnaire, data was collected from 472 respondents, including 179 (37.9%) practicing NMTs and 293 (62.1%) NMT students. The participants were 65% female, 67.2% were 29 years or younger, and 86.8% had lived in a rural area. These proportions are broadly reflective of characteristics of Malawi’s NMT workforce. Table 2 presents the demographic characteristics.

Table 2

Participant demographic characteristics.

Characteristic	n (%)1
Gender
Female	307 (65.0)
Male	165 (35.0)
Age
29 years or younger	317 (67.2)
30 to 39 years	124 (26.3)
40 years or older	30 (6.4)
Marital status
Not married	235 (49.8)
Married	171 (36.2)
Dependents
Has dependents	189 (40.1)
No dependents	282 (59.9)
Health worker status
Practicing NMT	179 (37.9)
NMT student	292 (61.9)
Under bonding agreement
No	211 (45.6)
Yes	252 (54.4)
Ever lived in rural area
No	62 (13.2)
Yes	409 (86.8)
Experience working in rural area for more than 3 months
No	270 (57.6)
Yes	199 (42.4)
Stated likelihood of working in a rural area in the future
Very likely	79 (23.8)
Likely	154 (46.4)
Unlikely	64 (19.3)
Very unlikely	35 (10.5)

1. Percentages may not sum to 100% due to missing data and rounding.

Likelihood of working in a rural area

As shown in Table 2, 70.2% of participants reported they were either “very likely” or “likely” to work in a rural area in the future. Through bivariate logistic regression analysis we explored associations between stated likelihood of working in a rural area, and demographic characteristics and rural work experiences (see Table 3). There were significant associations between self-reported likelihood of working in a rural area and rural living experience, bonding agreements, and positive experiences working in rural areas previously. Students had two times higher odds of reporting they were “likely” or “very likely” to work in a rural area compared with currently practicing NMTs (odds ratio [OR] 2.20, confidence interval [CI] 1.35–3.59, p<0.01). NMTs and NMT students who had lived in a rural area previously (OR 3.40, CI 1.71–6.79, p<0.01), and those who had a bonding agreement with the government which states terms for compulsory service after graduation (OR 3.66, CI 2.22–6.04, p<0.01) were significantly more likely to report they were “likely” or “very likely” to work in a rural area in the future than their comparators. Those with “very good” experience working in a rural area previously were 6.14 times as likely (CI 1.89–19.93, p<0.01), and those with “good” experience were 7.50 times as likely (CI 2.33–24.09, p<0.01) to report they were “likely” or “very likely” to work in a rural area in the future as compared to those with a poor prior experience working in a rural area.

Table 3

Association between likelihood of working in a rural area and participant characteristics.

Characteristic	Likely or very likely to work in rural area n (%)1	Unlikely or very unlikely to work in rural area n (%)1	Odds Ratio (Confidence Interval)	p value
Gender
Male	97 (76.4)	30 (23.6)	1.64 (0.99–2.71)	0.05
Female	136 (66.3)	69 (33.7)	ref.
Age
30 years or older	92 (73.6)	33 (26.4)	1.30 (0.80–2.14)	0.29
29 years or younger	141 (68.1)	66 (31.9)	ref.
Marital status
Not married	112 (72.3)	43 (27.7)	1.65 (0.81–2.19)	0.02
Married	92 (66.2)	47 (33.8)	ref.
Dependents
Has children	109 (70.3)	46 (29.7)	1.02 (0.64–1.64)	0.93
No children	123 (69.9)	53 (30.1)	ref.
Health worker status
NMT student	122 (78.7)	33 (21.3)	2.20 (1.35–3.59)	<0.01
Practicing NMT	111 (62.7)	66 (37.3)	ref.
Ever lived in rural area
Yes	215 (73.4)	78 (26.6)	3.40 (1.71–6.79)	<0.01
No	17 (44.7)	21 (55.3)	ref.
Worked in rural area for more than 3 months
No	119 (72.6)	45 (27.4)	1.26 (0.79–2.03)	0.33
Yes	113 (67.7)	54 (32.3)	ref.
Bonding agreement or other rural obligation
Has bonding agreement or other obligation	147 (82.1)	32 (17.9)	3.66 (2.22–6.04)	<0.01
No bonding agreement or other obligation	84 (55.6)	67 (44.4)	ref.
Rating of experience working in rural area
Excellent	15 (65.2)	8 (34.8)	2.56 (0.80–8.14)	0.11
Very good	27 (81.8)	6 (18.2)	6.14 (1.89–19.93)	<0.01
Good	33 (84.6)	6 (15.4)	7.50 (2.33–24.09)	<0.01
Fair	26 (57.8)	19 (42.2)	1.87 (0.70–4.96)	0.21
Poor	11 (42.3)	15 (57.7)	ref.

1. Percentages may not sum to 100% due to missing data and rounding.

Impact of attributes on rural and urban job choice

The results of the conditional logit model showed that salary, housing and facility quality had the greatest impact on likelihood of choosing a rural job. As shown in Table 4, respondents were 6.67 times as likely to choose a rural job with a 50% salary increase compared to a job with the base salary (CI 5.66–8.08, p<0.01). A 25% salary increase had a lesser impact on rural job choice (OR 1.78, CI 1.51–2.10, p<0.01). Following a 50% salary increase, the second most impactful job attribute was superior housing, with respondents 2.04 times as likely to choose a rural job if superior housing was provided compared to no housing (CI 1.71–2.44, p<0.01). Respondents were 1.70 times as likely to choose a rural job where there was advanced facility quality (CI 1.47–1.96, p<0.01).

Table 4

Determinants of job preferences.

Incentive category and level	Odds Ratio	95% CI	p value
Location (reference = rural)
Urban	1.30	0.99–1.65	0.06
Rural Job Characteristics
Salary (reference = base salary only)
Base salary + 25% top-up	1.78	1.51–2.10	<0.01
Base salary + 50% top-up	6.76	5.66–8.08	<0.01
Housing (reference = no housing)
Basic housing provided	1.54	1.29–1.83	<0.01
Superior housing provided	2.04	1.71–2.44	<0.01
Facility quality (reference = basic)
Advanced facility quality	1.70	1.47–1.96	<0.01
Access to education (reference = after 4 years)
After 3 years	1.29	1.12–1.49	<0.01
Workload (reference = heavy)
Manageable workload	1.32	1.13–1.54	<0.01
Management (reference = not supportive)
Supportive	1.51	1.32–1.73	<0.01
Choice of location (reference = random)
Choice of location	1.19	1.03–1.37	0.02
Urban Job Characteristics
Salary (reference = base salary only)
Base salary + 25% top-up	1.55	1.29–1.85	<0.01
Base salary + 50% top-up	3.84	3.22–4.59	<0.01
Facility quality (reference = basic)
Advanced facility quality	1.98	1.69–2.33	<0.01
Access to education (reference = after 4 years)
After 3 years	1.20	1.04–1.40	0.01
Workload (reference = heavy)
Manageable workload	1.40	1.21–1.62	<0.01
Management (reference = not supportive)
Supportive	1.52	1.32–1.74	<0.01
Choice of location (reference = random)
Choice of location	1.20	1.04–1.40	0.01
Model diagnostics
Number of participants	472
Number of observations	9402
Log likelihood	-2702.40
Pseudo R2	0.1691
AIC	5448.80
BIC	5570.33
Prob > chi2	<0.001

AIC, Akaike information criterion; BIC, Bayesian information criterion.

A 50% salary increase and improved facility quality were also the attributes with the greatest influence on urban job choice, albeit in different magnitudes than in the rural job scenarios. Respondents were 3.84 times as likely to choose an urban job with a 50% salary increase (CI 3.22–4.59, p<0.01), and 1.98 times as likely with advanced facility quality (CI 1.69–2.33, p<0.01). A 25% salary increase had similar impact on job preference in urban settings (OR 1.55, CI 1.29–1.85, p<0.01). Following salary, housing, and facility quality, supportive management had a similar effect on job choice in both rural (OR 1.51, CI 1.32–1.73, p<0.01) and urban (OR 1.52, CI 1.32–1.74, p<0.01) scenarios.

The conditional logit model was also run separately for NMT students and practicing NMTs, as well as for the various demographic sub-groups of the population (results not shown). However, there were no significant differences in the impact of attributes on job preferences in these different analyses.

Predicted job uptake

We used the coefficients from the conditional logit model to transform data into percentages of health workers estimated to take a rural job compared to an urban job with various incentives provided, presented in Fig 1. With all interventions set to the base level, 43.9% of respondents would prefer the rural job and 56.1% the urban job. This increased to 61.5% of respondents who would prefer a rural job with free superior housing compared to an urban job with no housing. We created a composite attribute that included all facility-level improvements (advanced facility quality, manageable workload, and supportive management). With all facility level improvements set to the highest level, 72.5% of respondents would be expected to select a rural job compared to an urban job without these improvements. With only improved facility quality and supportive management, and excluding hiring additional health workers to improve workload, 66.7% of respondents would be expected to select the rural job.

Fig 1

Expected rural and urban job uptake with job characteristics.

Cost of incentives

We calculated the total cost of implementing each intervention per working year of an individual health worker using details of the attribute descriptions to generate assumptions on cost items, and validating assumptions and unit costs with the MoH. By combining the DCE results on increased odds of selecting a particular job with incentive cost per year, we estimated the additional or marginal cost of implementing each incentive compared to the cost of the base level and applied these marginal costs to the rural job uptake percentages to calculate a marginal cost per percentage point increase in rural job uptake.

The annual salary for an NMT was used, based on the most recent salary bands from 2014 with top-ups calculated from the gross total. We used average construction costs based on MoH and CHAI experience constructing staff housing for basic and superior public sector housing, assuming occupancy for 25 years and maintenance costs at 30% of total construction costs spread over 25 years. Upgrading costs assume payment of salary while the health worker is on study leave for two years, divided by the number of years the individual worked prior to school leave. Supportive management is assumed to be achieved through increased funding for district-level supervision and mentorship, costed as procurement and maintenance of one vehicle per district and costs associated with monthly supervision visits from district-level managers. Improved workload would be achieved by increasing the number of NMTs per site from the current average of two for a health center to three, and is costed as the annual cost of adding an additional NMT to a site. Facility quality and choice of job location were not costed due to insufficient standard assumptions to cost these interventions.

As shown in Table 5, the lowest total cost interventions per health worker per year were those related to facility-level improvements, including supportive management ($332) and manageable workload ($451). Whereas the highest costs per health worker per year were related to individual-level benefits of salary increase ($2,624 for 25% increase, $3,138 for 50% increase), free superior housing ($1,282), and educational upgrading opportunities after three years ($902). However, the marginal costs of a 25% salary increase compared to the base ($514) and eligibility for upgrading after three years ($352) are among the lowest marginal costs. Salary top-ups and supportive management have the lowest cost per percentage point increase in job uptake. A salary increase of 25% is estimated to lead to a 14.3 percentage point increase in rural job uptake and a marginal cost per percentage point increase of $36, while improved supportive management is estimated to lead to a 10.2 percentage point increase in rural job uptake with a marginal cost per percentage point of $33. Our analysis predicts a 17.6 percentage point increase in rural job uptake with provision of superior housing at a cost of $73 per percentage point increase.

Table 5

Cost per percentage point increase in rural job uptake.

Job attribute	Total cost per health worker per year	Marginal cost (compared to base)	% that would take rural job	Percentage point increase	Marginal cost per percentage point increase
Salary
Base salary	$2,110	-	-	-	-
Base salary + 25% top-up	$2,624	$514	58.2%	14.3	$36
Base salary + 50% top-up	$3,138	$1,028	84.1%	40.2	$26
Housing
No housing provided	-	-	-	-	-
Free basic housing provided	$855	$855	54.6%	10.7	$80
Free superior housing provided	$1,282	$1,282	61.5%	17.6	$73
Facility quality
Basic	Not costed		-	-	-
Advanced	Not costed		57.0%	13.1	-
Access to long-term upgrading opportunities
Eligible to apply after 4 years	$676	-	-	-	-
Eligible to apply after 3 years	$902	$352	50.2%	6.3	$56
Supportive management
Unsupportive management	-	-	-	-	-
Supportive management	$332	$332	54.1%	10.2	$33
Choice of location
Randomly assigned to health facility	Not costed		-	-	-
Choice of district	Not costed		48.1%	4.2	-
Workload
Heavy workload	-	-	-	-	-
Manageable workload	$451	$703	50.8%	6.9	$102

Discussion

The results of this study indicate that salary, housing, and facility quality interventions had the greatest impact on rural job choice and retention. Salary and improved facility quality were also the attributes with greatest influence on urban job choice and retention, though in different magnitudes than the rural scenario. These findings are consistent with past qualitative assessments in Malawi, but offer more nuanced information on probability of choice, expected uptake, and cost of interventions. As the Government of Malawi considers development of a national retention strategy in line with recommendations in its HRH Strategic Plan 2017–2022, these findings provide evidence to inform national policy design shaped by health worker preferences and feasibility of interventions.

While a 50% salary increase exerted the greatest influence on job choice, it is unlikely to be implemented in Malawi’s context, where a large portion of the public sector health budget is spent on salaries and the allocation is unlikely to increase significantly [25]. Wages bills generally absorb a large proportion of total spending in LMICs, and therefore increases in compensation can have adverse consequences on the fiscal balance [26]. Malawi, as with many LMICs, pays health workers on the civil service scale, and salaries must be carefully managed across sectors to contain overall government spending [27]. Recognizing these fiscal constraints, the 50% salary increase attribute was included to allow for more detailed cost comparisons in our analysis, though was not prioritized by government. A 25% salary increase, more feasible in Malawi, was less impactful than other non-monetary interventions in the DCE. In addition to limited cost-effectiveness [28], research has suggested that increased salaries alone may not be sufficient to address health worker motivation and retention, and that nonfinancial incentives can significantly influence health worker motivation [29].

This study found that health workers were 2.04 times as likely to select a rural job if superior housing was provided. As highlighted in the qualitative findings, in rural areas in Malawi there are limited options for health workers to find rental housing, making it impractical for health workers to remain in rural areas long-term unless housing is provided. The 2016 MoH infrastructure assessment revealed a critical shortage of staff housing at nearly all health facilities. Recognizing this challenge, the Health Sector Capital Investment Plan 2017–2022 prioritizes construction of staff housing following the government’s Umoyo Housing model [30]. The superior housing intervention included in our study is designed and costed following the Umoyo Housing specifications, a detached house with electricity and three bedrooms. While housing is a high priority and impactful long-term investment, it will require significant investment by government and development partners. To reduce overall costs and encourage community ownership, government and development partners can consider a community engagement approach in housing construction, such as including local communities as part of the labor force and in planning and governance of housing projects, and mobilizing local building materials. This approach has been successfully utilized in the education sector to build teacher housing in rural areas.

Facility quality, defined in our study as reliable infrastructure and available essential supplies, remains a significant barrier to service delivery in Malawi. In 2015/16, an average of only 24% of facilities were able to maintain enough stocks to cover 1 to 3 months for 23 tracer medicines and supplies, and only 63% had regular electricity [31]. Our findings highlight that improvements to facility quality are impactful in rural job choice, mirroring qualitative findings where NMTs emphasized that poor facility quality limits their ability to effectively treat patients, which significantly impacts their willingness to choose and remain in rural jobs. Combined interventions that mix several incentives can be highly motivating to health workers in their job choice, retention, and performance [32]. We designed a composite facility-level intervention, including supportive management, facility quality, and manageable workload attributes, which had a high impact on job uptake with 72.5% of participants expected to take a rural job with these conditions compared to an urban job without these improvements. Facility quality interventions can have far reaching impact on health worker motivation and retention, while also improving health worker performance and patient health outcomes [33]. This aligns with MoH priorities, where the government has recently established a Quality Management Directorate to provide leadership and coordinate quality management and improvement initiatives across the health sector, including a focus on facility-level quality improvement.

Though opportunities for career upgrading were discussed in the qualitative interviews and FGDs, and are frequently highlighted in retention literature, upgrading was a less impactful intervention according to the results of our DCE. This may be due to a number of factors. The time to upgrade was reduced by only one year in the incentive (3 compared to 4 years of service prior to upgrading), which many not have been seen as a significant reduction. Further, at the time of the study, an upgrading NMT would receive their Registered Nurse (RN) Diploma and reenter the workforce at the same level, and thus NMTs may not have seen this as a significant pathway for career growth. NMTs are now able to upgrade to Degree-level nurses, which offers significant career advancement, and it would therefore be interesting to re-examine the impact of upgrading on NMTs’ career choices with this new pathway available.

Limitations

There are several limitations to this study. While the attributes we included in the DCE are reflective of health worker preferences, as articulated during FGDs and interviews and aligned with national and regional literature, there may be other retention interventions that would be meaningful to health workers in Malawi which were not included in our study. We sought to select interventions that were highly ranked by health workers, while also deemed feasible and aligned with government priorities, to ensure findings were applicable to policy discussions. While a DCE aims to present plausible scenarios, as with any model, a DCE cannot capture all complexities of real-world choices. Further, motivation to choose and remain in a job are jointly considered in this study as they are influenced by interrelated factors [7], however, the decision to remain in a job is complex and may change overtime as a health worker gains more experience, and these nuances cannot be fully captured by a DCE.

This DCE specifically focused on the NMT cadre, based on the high priority given to this cadre within Malawi’s overall workforce strategy. While the findings have broad relevance in policy development, in particular for mid-level, rural providers, health workers in different cadres may have different preferences. In addition, while costing adds a unique dimension to the study, facility quality and choice of job location could not be costed due to insufficient standard assumptions and therefore are excluded from cost comparisons.

Finally, 79 people (16.7%) expressed a dominant preference for a certain job characteristic, 62 for rural jobs, and 17 for urban jobs. A dominant preference occurs when a respondent always selects a job based on one attribute irrespective of other attributes, and thus is unlikely to be influenced by any attribute. We excluded these individuals from our analysis as their responses violate the model assumptions and therefore cannot be used in the modeling approaches used for DCE analysis [22].

Conclusion

The MoH has tested retention interventions over the past two decades and is committed to developing a national health worker retention policy to address Malawi’s critical workforce shortages and inequitable distribution of health workers. Our study builds on previous national and regional literature which highlights factors that are important to mid-level providers in job choice and retention, and adds quantitative data on probability of choice, predicted uptake, and cost. Our study considers a range of monetary and non-monetary incentives that are feasible from a policy perspective and have the potential to influence health worker job choice, retention, motivation and performance. Our results indicate that housing and interrelated facility-level improvements would have the greatest impact on rural job choice, while also creating longer-term improvements to health workers’ working and living environment. These results provide practical evidence for policymakers in Malawi to use in the design of national retention strategies, and can be used beyond Malawi to support policy discussions on workforce recruitment and retention.

Qualitative interview and focus group discussion guides.

(DOCX)

Click here for additional data file.

Twelve choice sets in Block A of the DCE questionnaire.

(DOCX)

Click here for additional data file.

DCE participant information and choice selections.

This file contains one record for each participant in the study including demographic and survey data. This dataset shows whether the participant received DCE Block A or B, and whether they chose job option 1 or 2 in the 12 job choice sets provided. This must be combined with S4 File for full DCE analysis.

(DTA)

Click here for additional data file.

Reference data file for DCE job sets.

This file contains reference information about the choice sets provided to participants in the DCE questionnaire. There were 2 blocks of 12 job choice sets, with each job choice set containing 2 alternatives. This file therefore contains 48 records with the details for each job alternative offered. For each of the incentive categories (housing, facility quality, upgrading, workload and management) each job alternative included one incentive level, which is denoted with the value 1.

(DTA)

Click here for additional data file.

18 Jan 2021

PONE-D-20-33356

Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment

PLOS ONE

Dear Author,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Ramesh Kumar, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. When reporting the results of qualitative research, we suggest consulting the COREQ guidelines: http://intqhc.oxfordjournals.org/content/19/6/349. In this case, please consider including more information on the number of interviewers, their training and characteristics; and please provide the interview guide used.

Furthermore, in your Methods section, please provide a justification for the sample size used in your study, including any relevant power calculations (if applicable)

3.Thank you for stating the following in the Financial Disclosure section:

"This study was made possible with financial support from the Norwegian Ministry of Foreign Affairs through a grant from the Royal Norwegian Embassy of Malawi (https://www.norway.no/en/malawi/) to the Clinton Health Access Initiative Malawi. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

We note that one or more of the authors are employed by a commercial company: Clinton Health Access Initiative, Inc. and Analytics and Implementation Research Team, Clinton Health Access Initiative, Inc.

a) Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

b) Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

Additional Editor Comments:

Please see comments and revise your paper accordingly.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Corresponding Author, Please include comments highlighted

Plos one: Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment

Manuscript Number: PONE-D-20-33356

Corresponding Author: Leslie Renee Berman Clinton Health Access Initiative Lilongwe 3, MALAWI

Comments:

1. Line 23 Background not clearly indicated problem of study in abstract section

2. Line 34: In methodology section conditional logit regression model assumption not well described. Include comments accordingly

3. Line 45 – Include qualitative findings in the result section of the abstract

4. Line 53- Indicate gaps in the introduction section

5. Line 101 Study area not described in the method section. In addition level of retention of midwives from reports in the study area not stated. Elaborate this issues accordingly.

6. Line 118: How FGD and key informant participants selected in the study?

7. Line 177: How you calculated 472 samples in this study? What are assumptions used.

8. Line194: What is likelihood of working in rural areas? How you measured it? It needs clarification

9. Line 240: ‘House is motivating factors’. Do you think this is right? Explain it

10. General Questions? How you engaged stakeholders during policy analysis? What type of policy analysis methodology used? Forward your answer accordingly

11. Line 279: Why research team members not included confidence interval while reporting significant variables

12. Line 320: Give Operational definitions for quality …

Reviewer #2: Comments on PONE-D-20-33356 “Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment”

This paper uses responses from 472 nurse midwife technicians and students to assess the relative importance of various factors influencing the decision to accept employment in rural areas. Data were collected in Fall 2016. Results suggest that housing provision is the most important factor influencing the decision to locate to a rural area. The analysis is based on respondent selection between two hypothetical jobs, one urban and one rural, with each job having a random set of job attributes. Each respondent chose between 12 pairs of jobs. The analysis then examined how probability of rural job selection was influenced by the job attributes.

Comments

1) The paper argues (page 11) that it is a violation of random utility theory for a respondent to always choose the higher paying option as there must be tradeoffs between pay and job attributes. That is not true. If the respondent does not care about the other job attributes, they will always select the higher paying option. The analysis assumes that the nonpecuniary job factors raise worker utility, but that is not necessarily true.

2) The paper uses acronyms to excess. OR for odds ratios, FGD for focs group discussion, and so on. This gets annoying. You should use Odds Ratio in the tables and not the acronym

3) Table 3 should be a multinomial logit model, I think, but no goodness of fit statistics are presented. If it is just a series of separate bivariate relationships, it is not very useful.

4) It is not clear that the demographic variables included in Table 3 are also included in Table 4. If the model is set up as a multinomial logit, the demographic variables can be included. As a conditional logit, it may be that only the job attributes are allowed to affect the choice unless the demographic variables are interacted with the job attributes. It seems that the importance of housing or salary to the choice of rural area would differ by gender, marital status, and presence of children

5) If the authors want to stick with the conditional logit, they could replicate the tradeoffs shown in table 5 by marital status, gender, and rural upbringing. I suspect the results would be quite interesting. Urban origin would require greater payoffs to accept a rural posting, for example.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Adem Abdulkadir Abdi

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

14 Feb 2021

Below please find a response to each point raised by the academic editor and peer reviewers. The line numbers referenced in our responses below correspond to edits we have made in the “Revised Manuscript with Track Changes” in response to the reviewer comments.

Points Raised by Academic Editor

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: We have reviewed the PLOS ONE style requirements and adjusted the manuscript accordingly, including renaming files as per the requirements for file naming.

2. When reporting the results of qualitative research, we suggest consulting the COREQ guidelines: http://intqhc.oxfordjournals.org/content/19/6/349. In this case, please consider including more information on the number of interviewers, their training and characteristics; and please provide the interview guide used. Furthermore, in your Methods section, please provide a justification for the sample size used in your study, including any relevant power calculations (if applicable).

Response: We have reviewed the COREQ guidelines as suggested, and for the qualitative research we have added information on the number and background of interviewers in the Methods section (line 145), and have uploaded the interview and focus group discussion guides as Supporting Information File 1 (S1 File). We have also added additional information on the rationale for the qualitative sample size (line 128), and the sample size calculation for the DCE questionnaire (line 195). More detail on the sample size calculation for the DCE questionnaire is provided below in this document in response to Reviewer #1, Question 7.

3. We note that one or more of the authors are employed by a commercial company: Clinton Health Access Initiative, Inc. and Analytics and Implementation Research Team, Clinton Health Access Initiative, Inc.

a. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study.

b. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc. Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials.

Response: We have included an updated Funding Statement and Competing Interests Statement in our cover letter. In the Funding Statement we have indicated that six authors have an affiliation as employees of the Clinton Health Access Initiative, and added a statement on the role of these authors in the design and implementation of the study and publication of this manuscript. We have also updated our Competing Interests Statement to declare this professional affiliation. In the Competing Interests Statement, we have also confirmed that this affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials.

4. While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/.

Response: We have uploaded the figure (Fig 1) to the PACE digital diagnostic tool, and have attached the PACE corrected image along with our submission.

Points Raised by Reviewer #1

1. Line 23: Background not clearly indicated problem of study in abstract section.

Response: We have added a sentence in the background section of the abstract which further elaborates the problem statement (line 25).

2. Line 34: In methodology section conditional logit regression model assumption not well described. Include comments accordingly.

Response: We have added a sentence in the methods section of the abstract which provides more detail on the conditional logit regression model (line 38).

3. Line 45: Include qualitative findings in the result section of the abstract.

Response: We have added a sentence in the results section of the abstract which summarizes the qualitative findings (line 45).

4. Line 53: Indicate gaps in the introduction section.

Response: In the introduction section of the manuscript we have aimed to highlight gaps in the health workforce environment in Malawi, including high vacancy rates, unequal distribution of health workers between urban and rural areas, and high rates of attrition. We have also noted gaps in the existing literature which this study aims to fill in order to provide evidence for policymakers to develop context-specific rural retention strategies. In particular, we explain that while previous evidence aimed to understand drivers of health worker retention in general, a mixed-methods investigation of interventions that can influence rural job uptake and retention and costs of interventions is needed, with a focus on mid-level providers who are the frontline workers in rural facilities in Malawi.

5. Line 101: Study area not described in the method section. In addition, level of retention of midwives from reports in the study area not stated. Elaborate these issues accordingly.

Response: For the focus groups, 12 randomly selected NMT students were drawn from three purposively selected NMT training institutions, one in each of the country’s three regions (north, central, south). For interviews, one district from each region was purposively selected and within each of the three selected districts four facilities were chosen, including one with each of the following classifications: Ministry of Health (MoH) rural, MoH urban, Christian Health Association of Malawi (CHAM) rural and CHAM urban. Within those facilities, one NMT was selected who was available at the time of interview. Please see the paragraph which begins on line 127.

For the DCE questionnaire, all graduating NMT students were invited to participate from five randomly selected training institutions of the 12 training institutions in-country, representing geographic diversity across the country’s five administrative zones (north, central west, central east, southwest, southeast). For practicing NMTs, ten districts were randomly selected, including two districts from each of the five zones. From each district, six rural facilities and one urban facility were randomly selected. At each health facility, all NMTs present on the day of the survey were invited to participate. Please see the paragraph which begins on line 205.

Regarding attrition rates, it is difficult to calculate “level of retention” of nurse midwives as the Government of Malawi does not routinely collect these data, and it is not possible to disaggregate national retention estimates by cadre. However, estimates in Malawi’s national Human Resources for Health Strategic Plan indicate an attrition rate of between 3-15%, noting that this is likely an underestimate. We have included this information on attrition rates in the background section on line 82.

6. Line 118: How FGD and key informant participants selected in the study?

Response: The sampling methods for interview and focus group participants are explained in the methods sub-section titled “Qualitative methods and questionnaire development”. Regarding focus group participants, at the time of the study there were 12 schools that trained NMTs in Malawi, and one school was purposively sampled from each of the country’s three regions (north, central, south), and 12 graduating students from each school were invited to the focus groups. The 12 students were randomly selected from the class register. For interviews, one district from each region was selected, and four facilities were chosen per district that met the classifications MoH Rural, MoH Urban, CHAM rural and CHAM Urban. One NMT was selected per facility, depending on their availability for the scheduled day of the interview.

7. Line 177: How you calculated 472 samples in this study? What are assumptions used.

Response: We have added an explanation for how the sample size was calculated for the DCE questionnaire beginning on line 195. Literature indicates that obtaining reliable estimates of preferences requires at least 30 to 50 respondents per sub-group to be analyzed. The appropriate sample size is impacted by the number of attributes to be analyzed, the number of options within each attribute, and the number of question sets provided to each participant. Other DCE sample size analyses have concluded that the overall precision of DCE parameters is impacted by a balance between statistical efficiency, which minimizes the confidence interval around a parameter estimate for a given sample size, and response efficiency, which minimizes measurement errors resulting from respondents’ inattention to choice questions. While large sample sizes yield smaller confidence intervals around parameters, caution needs to be exercised in resource constrained settings where such samples sizes may not be feasible. Using multiple DCEs with simulated sample sizes to estimate the sample size where precision would improve, a DCE methodological review revealed that precision steadily increases when the sample size is below 150 and flattens at around 300 observations and above. We therefore aimed to sample a minimum of 300 participants, by recruiting all graduating NMTs from five randomly selected training institutions, and all available NMTs at seven randomly selected health facilities (six rural, one urban) in each of ten randomly selected districts.

8. Line 194: What is likelihood of working in rural areas? How you measured it? It needs clarification.

Response: Likelihood of working in a rural area is a self-reported measure by respondents to the DCE questionnaire. Respondents were asked, “Please rate how likely you are to continue working in a rural facility or to start working at a rural facility at some point in the future” with Likert Scale response options from “Very Likely” to “Very Unlikely”. We have clarified this in the text on line 221.

9. Line 240: ‘House is motivating factors’. Do you think this is right? Explain it.

Response: This statement is a direct quote from a respondent where they indicate, “But good houses, portable water and electricity are a priority, these are good motivating factors.” A central finding of the paper, both from the qualitative and quantitative data, is that provision of free superior housing, which includes water and electricity, is a motivating factor to attract and retain health workers in rural areas. We have discussed the implications of this finding throughout the discussion section.

10. General Questions? How you engaged stakeholders during policy analysis? What type of policy analysis methodology used? Forward your answer accordingly.

Response: We created a listing of all the coded job attributes that emerged from the qualitative data collection and grouped them into common thematic areas. We then asked government stakeholders to rank these attributes using several inclusion criteria such as feasibility of implementing the intervention in Malawi’s context and importance/strength of the preferences based on qualitative interviews. We have added more detail on this in the methods section where we discuss stakeholder consultations beginning on line 156.

11. Line 279: Why research team members not included confidence interval while reporting significant variables?

Response: We have included confidence intervals for all variables throughout the results section, in both the narrative and tables.

12. Line 320: Give operational definitions for quality.

Response: The operational definition for “facility quality” is included in the methods section in Table 1 (line 168) where we provide definitions for all job attributes and attribute levels. Basic facility quality includes unreliable electricity, and equipment, drugs and supplies that are not always available. Advanced facility quality includes reliable electricity and equipment, drugs and supplies that are always available.

Points Raised by Reviewer #2

1. The paper argues (page 11) that it is a violation of random utility theory for a respondent to always choose the higher paying option as there must be tradeoffs between pay and job attributes. That is not true. If the respondent does not care about the other job attributes, they will always select the higher paying option. The analysis assumes that the nonpecuniary job factors raise worker utility, but that is not necessarily true.

Response: We have clarified this statement from line 235. We agree that a respondent may have a preference for only one attribute, and therefore would always make choices based on this attribute. However, respondents who express a dominant preference and make selections based on only one attribute violate the model assumptions. As noted in the literature, a DCE model is not equipped to handle such responses. Therefore, we excluded the 79 respondents who expressed a dominant preference for a certain job characteristic from our analysis, as their responses violate the model assumptions.

2. The paper uses acronyms to excess. OR for odds ratios, FGD for focus group discussion, and so on. This gets annoying. You should use Odds Ratio in the tables and not the acronym.

Response: We have removed certain acronyms throughout the paper, including the use of FGD for focus group discussion. We have changed “OR” to “Odds Ratio” in the table headings.

3. Table 3 should be a multinomial logit model, I think, but no goodness of fit statistics are presented. If it is just a series of separate bivariate relationships, it is not very useful.

Response: Table 3 shows the values for bivariate logistic regressions comparing each sub-group to show how self-stated likelihood of working in a rural area in the future varies by level of demographic characteristics. We have clarified this in the manuscript on line 300. As the focus of our research was on the responses to the DCE survey itself, we did not conduct extensive analysis on participants’ responses to introductory and demographic questions on the questionnaire, such as stated likelihood of working in rural areas. However, we do believe these bivariate relationships add value, and have therefore included them in the paper, as they indicate significant associations between certain demographic characteristics and stated likelihood of working in a rural area in the future, which may be relevant to policymakers and implementers aiming to design, and more appropriately target, retention interventions.

4. It is not clear that the demographic variables included in Table 3 are also included in Table 4. If the model is set up as a multinomial logit, the demographic variables can be included. As a conditional logit, it may be that only the job attributes are allowed to affect the choice unless the demographic variables are interacted with the job attributes. It seems that the importance of housing or salary to the choice of rural area would differ by gender, marital status, and presence of children.

Response: As points 4 and 5 are interrelated, we have responded jointly to these two points. Please see our response underneath question 5 below.

5. If the authors want to stick with the conditional logit, they could replicate the tradeoffs shown in table 5 by marital status, gender, and rural upbringing. I suspect the results would be quite interesting. Urban origin would require greater payoffs to accept a rural posting, for example.

Response: Through literature review and expert consultation, we decided to use a conditional logit model, as one of the most widely used analytical approaches for DCEs. To investigate the potential impact of demographic variables on job attributes, we ran separate conditional logit models for each of the various demographic sub-groups of the population. As we did not find meaningful differences in the results of these separate analyses, we have not presented these results in the manuscript. We have added this information to the methods section on line 235 and in the results section on line 340.

9 Apr 2021

PONE-D-20-33356R1

Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment

PLOS ONE

Dear Author,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 24 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Ramesh Kumar, PhD

Academic Editor

PLOS ONE

Journal Requirements:

Additional Editor Comments (if provided):

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comments are addressed in manuscript line by line. But I advise you to improve following comments:

1. Indicate qualitative data analysis in the method section. In the result section please indicate sex and age of key informant interview result while reporting.

2. Please indicate assumptions of conditional logit regression and model fittness test undergone. In the sampling section if you included all study participants please amend sample size calculation area and improve to census type of sampling strategy.

3. Indicate Policy analysis steps used and how you engaged stakeholders in the policy analysis.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Adem Abdulkadir Abdi

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

19 May 2021

Thank you for your careful review of our manuscript titled “Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment.” Below please find a response to each point raised by the peer reviewer. The line numbers referenced in our responses below correspond to edits we have made in the “Revised Manuscript with Track Changes” in response to the reviewer comments.

Points Raised by Reviewer 1

1. Indicate qualitative data analysis in the method section. In the result section please indicate sex and age of key informant interview result while reporting.

Response: We have expanded the qualitative data analysis described in the Methods section starting from line 148. We have also added in sub-headings to clearly demarcate the qualitative and quantitative data analysis sections presented in the Methods. We have added the sex of key informant interviewees in the Results section; however, we did not retain age information for key informant interviews. Focus group discussions were anonymized during transcription, and therefore it is not possible to disaggregate sex and age for individual speakers in the focus groups. Of the four quotations included in the Results, one is from a focus group, and we have clarified this in the manuscript.

2. Please indicate assumptions of conditional logit regression and model fittness test undergone. In the sampling section if you included all study participants please amend sample size calculation area and improve to census type of sampling strategy.

Response: We have added the three key assumptions of the model to the DCE data analysis section on line 230 as follows: “The conditional logit model is based on three assumptions: (1) independence of irrelevant alternatives; (2) error terms are independent and identically distributed across observations; and (3) no preference heterogeneity across respondents.” Goodness-of-fit criteria, including Akaike and Bayesian information criteria and pseudo R2, were used to assess model fit. These measures are included in the Results section in Table 4. Regarding the sampling strategy, on line 210 we have indicated that to reach our minimum target sample size, we used a census approach to sampling all NMT students and practicing NMTs in a subset of randomly selected NMT training colleges and health facilities.

3. Indicate policy analysis steps used and how you engaged stakeholders in the policy analysis.

Response: As elaborated from line 156 onwards, government and other stakeholders were engaged in a workshop to review the qualitative data outputs and prioritize which coded attributes should be included in the DCE questionnaire from a policy perspective, using specific selection criteria. The goal was to ensure that all final attributes included in the DCE questionnaire were interventions that could be practically implemented by the Government of Malawi in the future. As noted in the Discussion section, the final results of this study were then used to inform recommendations in Malawi’s HRH Strategic Plan.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

8 Jun 2021

Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment

PONE-D-20-33356R2

Dear Author,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ramesh Kumar, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Majority of Comments raised in the first comments were addressed. But still not clearly indicated type of type of policy analysis performed. Try to shorten the manuscript

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

10 Jun 2021

PONE-D-20-33356R2

Analysis of policy interventions to attract and retain nurse midwives in rural areas of Malawi: A discrete choice experiment

Dear Dr. Berman:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Ramesh Kumar

Academic Editor

PLOS ONE