The Government of Kenya introduced the free maternity services (FMS) policy to enable mothers deliver at a health facility and thus improve maternal health indicators.
The aim of this study was to determine if there was a differential effect of the policy by region (sub-county) and by facility type (hospitals vs. primary healthcare facilities [PHCFs]).
The study was conducted in Nyamira County in western Kenya.
This was an interrupted time series study where 42 data sets (24 pre- and 18 post-intervention) were collected for each observation. Monthly data were abstracted from the District Health Information System-2, verified, keyed into and analysed by using IBM-Statistical Package for the Social Sciences (SPSS-17).
The relative effect of the policy on facility deliveries in the county was an increase of 22.5%, significant up to the 12th month (
The effect of the FMS policy was varied by region (sub-county) and by facility type.
Kenya has not made sufficient progress towards improving maternal health because there is a high maternal mortality rate (MMR) and low utilisation of maternal health services.
Because of the insufficient progress towards improving maternal health, the Government of Kenya (GoK) introduced the Free Maternity Services (FMS) policy to enable mothers to deliver at a health facility nearest to them at no cost at all on 01 June 2013. The policy is financed by the National Government. Under the programme, the health centres and dispensaries, that is, primary healthcare facilities (PHCFs), are reimbursed Ksh2500.00 ($27.70) for every delivery, whereas the hospitals are reimbursed KSh5000.00 ($55.60) for every delivery, normal or caesarean. These funds are paid directly to the facilities.
Generally, removal of user fees often results in increases in the use of health services, as has been the case in South Africa and Mali.
A baseline report by the Ministry of Health (MoH) demonstrated that only 41% of all deliveries in public health facilities occur in PHCF despite the geographical and financial accessibility.
Therefore, this interrupted time series (ITS) study was undertaken to determine if there was a differential effect of the FMS policy by region (sub-counties) and by facility type (hospitals vs. PHCFs, that is, dispensaries and health centres) in the sub-counties in Nyamira County.
The ITS study design was used to conduct the study. This is a quasi-experimental longitudinal study design that involves statistical comparison of time trends before and after an intervention. It was used because there was a definite point in time when the implementation of the policy began and data could be obtained from the District Health Information System (DHIS-2) for the time period before and after policy implementation. There was no parallel event that would have affected utilisation of maternal health services for deliveries supervised by skilled birth attendants in the county. Devolution of health services was delayed and mainly focused on the provision of services. However, the study design and the method of analysis would remove the effect of any confounding factors or biases that may affect the results of the study. The manner in which deliveries from health facilities were captured and reported to the DHIS-2 did not change in any way to affect study findings (i.e. there was no change in mode of measurement).
The study was conducted in Nyamira County, one of the 47 counties in Kenya. Nyamira County is in the former Nyanza province and borders Homabay County to the north, Kisii County to the west, Bomet County to the south-east and Kericho County to the east. Nyamira County was selected for the study because of its maternal health indicators, particularly uptake of maternal health services and maternal mortality. The county covers an area of 899.4 km2 and is home to five sub-counties: Nyamira (South), Nyamira North, Borabu, Manga and Masaba North.
There are 130 health facilities in the county and eight of these are hospitals. The rest are dispensaries and health centres (i.e. PHCF). The average distance to a health facility in Nyamira County is 7 km and agriculture is the main economic activity, with tea and coffee being the main cash crops grown.
All the deliveries from all the health facilities were incorporated so as to achieve population-level outputs and outcomes and representativeness. Data were therefore collected for all the health facilities in the county.
Data were abstracted from the DHIS-2 for each sub-county, and for all the hospitals and the PHCFs in the county using a data abstraction form. These were then verified using facility-level data. Data were collected for the period between 01 June 2011 and 30 November 2014. A records officer was trained and employed to collect the data from the DHIS-2 and to enter the findings in an Excel spreadsheet. Data from hospitals and the PHCFs were summated to find the total from hospitals and PHCFs, respectively. Data were also retrieved by sub-county. The retrieved data were verified by a second officer who counter-checked if the entries were similar with what was in the DHIS-2 and with facility-level data.
After data verification and cleaning, the data were transferred to the International Business Machines Corporation-Statistical Package for the Social Sciences (IBM-SPSS) version 17 for an ITS analysis using the auto-regressive integrated moving-average (ARIMA) model that takes into account any time or cyclical trends and autocorrelation among observations so that there are no overestimations or underestimations of the intervention effects. Analysis was performed for each sub-county, and by facility type (hospital and PHCF) in the sub-county.
The regression model used for the study given the coefficient
Births attended by skilled attendant [skilled care deliver (SCD)] =
Where,
Estimates for regression coefficients corresponding to two standardised effect sizes are obtained: a change in level (step change, i.e.
This study was approved by Jaramogi Oginga Odinga Teaching and Referral Hospital’s Ethics Review Committee (accreditation number 01713). As this study involved data abstraction, no written consent was obtained from the study participants for using their records; however, participant reports or information was anonymised and de-identified prior to analysis.
There were 130 facilities and five sub-counties reporting to the DHIS-2 in Nyamira County.
Population and service data for Nyamira County before and after the free maternity services policy and deliveries attended by skilled care providers by sub-county.
Variable | Pre-intervention | Post-intervention |
---|---|---|
Population data | June 2011–May 2013 | June 2013–November 2014 |
County Pop | 637 295 | 661 106 |
Expected deliveries | 21 668 | 22 478 |
Average expected deliveries per month | 902.8 | 1248.8 |
Number of hospitals | 8 | 8 |
Number of PHCFs | 114 | 122 |
Total number of health facilities | 122 | 130 |
Number of sub-county | 5 | 5 |
Service data | - | - |
Hospital deliveries | 7972 | 8667 |
PHCF deliveries | 12 868 | 14 903 |
Total facility-based deliveries and (ratio) |
20 840 (96.2%) | 23 570 (104.9%) |
Average facility deliveries per month | 868.3 | 1309.4 |
Fourth ANC visits and (ratio) |
14 947 (69.0) | 14 051 (62.5%) |
Average fourth ANC per month | 622.8 | 780.6 |
Caesarean sections performed and (ratio) |
753(3.6%) | 809(3.4%) |
Average CS per month | 31.4 | 44.9 |
Maternal deaths | 21 | 18 |
Complications | 84 | 22 |
Neonatal deaths | 141 | 142 |
SCD by sub-counties | - | - |
Borabu | 3250 | 3224 |
Manga | 1986 | 2171 |
Masaba North | 3775 | 4813 |
Nyamira South | 9148 | 9326 |
Nyamira North | 3762 | 4886 |
PHCF, primary healthcare facility; ANC, antenatal care; SCD, skilled care deliveries; CS, caesarean section.
, Total facility deliveries × 100 / expected deliveries.
, Fourth ANC × 100 / expected deliveries.
, Caesarean sections performed × 100 / total facility-based deliveries.
There was an increase in facility deliveries for all sub-counties. Borabu, Manga, Masaba North, Nyamira South, and Nyamira North had 32.6%, 45.8%, 70.0%, 40.0% and 72.6% increases in facility deliveries per month, respectively.
There had been an increase of ten facility deliveries per month (95% CI: 3.4–16.8;
Parameter estimates for Nyamira South and Nyamira North and by facility type for free maternity services policy from June 2011 to November 2014.
Variable | Nyamira County |
Nyamira South |
Nyamira North |
||||||
---|---|---|---|---|---|---|---|---|---|
LE | RE (%) | LE | RE (%) | LE | RE (%) | ||||
Pre-slope | 10.086 | 0.004 | - | 5.836 | 0.003 | - | 0.510 | 0.385 | - |
Interact | −1.895 | 0.753 | - | −4.563 | 0.198 | - | 1.027 | 0.338 | - |
Post-slope | 8.191 | - | - | 1.273 | - | - | 1.537 | - | - |
1 | 257 | < 0.001 | 24 | 53 | 0.136 | 12 | 95 | 0.000 | 57 |
3 | 254 | < 0.001 | 25 | 48 | 0.157 | 10 | 97 | 0.000 | 59 |
6 | 249 | 0.001 | 23 | 30 | 0.415 | 6 | 100 | 0.000 | 60 |
9 | 244 | 0.003 | 23 | 16 | 0.694 | 4 | 103 | 0.000 | 62 |
12 | 239 | 0.009 | 21 | 3 | 0.956 | 1 | 106 | 0.000 | 62 |
18 | 228 | 0.055 | 19 | −25 | 0.702 | −4 | 112 | 0.000 | 65 |
Pre-slope | −0.390 | 0.833 | - | −0.466 | 0.563 | - | −0.130 | 0.689 | - |
Interact | 5.458 | 0.114 | - | 2.265 | 0.131 | - | 1.979 | 0.002 | - |
Post-slope | 5.068 | - | - | 1.799 | - | - | 1.849 | - | - |
1 | 116 | 0.004 | 34 | 34 | 0.050 | 24 | 42 | 0.000 | 79 |
3 | 127 | 0.001 | 38 | 37 | 0.025 | 27 | 46 | 0.000 | 78 |
6 | 143 | 0.001 | 42 | 45 | 0.011 | 32 | 52 | 0.000 | 63 |
9 | 160 | 0.001 | 49 | 52 | 0.008 | 38 | 57 | 0.000 | 91 |
12 | 176 | 0.001 | 53 | 59 | 0.008 | 43 | 63 | 0.000 | 121 |
18 | 209 | 0.002 | 63 | 72 | 0.012 | 56 | 15 | 0.000 | 153 |
Pre-slope | 8.967 | 0.002 | - | 6.154 | 0.000 | - | 0.546 | 0.191 | - |
Interact | −9.224 | 0.070 | - | −7.388 | 0.018 | - | −0.748 | 0.325 | - |
Post-slope | −0.257 | - | - | −1.234 | - | - | −0.202 | - | - |
1 | 189 | 0.001 | 29 | 32 | 0.249 | 10 | 55 | 0.000 | 54 |
3 | 185 | 0.001 | 28 | 25 | 0.357 | 8 | 53 | 0.000 | 52 |
6 | 143 | 0.012 | 20 | −5 | 0.867 | −1 | 51 | 0.000 | 49 |
9 | 116 | 0.062 | 17 | −27 | 0.431 | −8 | 49 | 0.000 | 46 |
12 | 88 | 0.210 | 12 | −59 | 0.228 | −13 | 46 | 0.000 | 43 |
18 | 33 | 0.723 | 4 | −94 | 0.096 | −22 | 42 | 0.005 | 38 |
FMS, free maternity services; LE, level effect; RE, relative effect; SCD, skilled care deliveries; PHCF, primary healthcare facility.
Parameter estimates for Borabu, Manga and Masaba North Sub-county by facility type for free maternity services policy (June 2011–November 2014).
Variable | Borabu |
Manga |
Masaba North |
||||||
---|---|---|---|---|---|---|---|---|---|
LE | RE (%) | LE | RE (%) | LE | RE (%) | ||||
Pre-slope | 1.468 | 0.184 | - | 0.754 | 0.278 | - | 1.142 | 0.051 | - |
Interact | −3.266 | 0.122 | - | 0.208 | 0.871 | - | 3.502 | 0.002 | - |
Post-slope | −1.798 | - | - | 0.962 | - | - | 4.644 | - | - |
1 | 41 | 0.047 | 24 | 19 | 0.172 | 20 | 56 | 0.000 | 33 |
3 | 38 | 0.056 | 24 | 20 | 0.147 | 20 | 64 | 0.000 | 37 |
6 | 25 | 0.251 | 16 | 20 | 0.161 | 20 | 74 | 0.000 | 42 |
9 | 15 | 0.539 | 10 | 21 | 0.190 | 21 | 84 | 0.000 | 46 |
12 | 5 | 0.854 | 3 | 22 | 0.235 | 22 | 95 | 0.000 | 52 |
18 | −14 | 0.708 | −8 | 23 | 0.338 | 20 | 116 | 0.000 | 60 |
Pre-slope | 0.295 | 0.102 | - | −0.015 | 0.969 | - | 0.588 | 0.361 | - |
Interact | −0.139 | 0.669 | - | 1.077 | 0.131 | - | 0.618 | 0.601 | - |
Post-slope | 0.156 | - | - | 1.062 | - | - | 1.206 | - | - |
1 | 11 | 0.006 | 64 | 12 | 0.116 | 43 | 1 | 0.956 | 1 |
3 | 11 | 0.005 | 65 | 14 | 0.063 | 48 | 3 | 0.837 | 3 |
6 | 10 | 0.011 | 56 | 17 | 0.030 | 58 | 4 | 0.777 | 4 |
9 | 9 | 0.025 | 51 | 21 | 0.021 | 80 | 6 | 0.701 | 6 |
12 | 9 | 0.055 | 46 | 24 | 0.020 | 103 | 8 | 0.652 | 8 |
18 | 8 | 0.176 | 39 | 30 | 0.025 | 93 | 11 | 0.607 | 11 |
Pre-slope | 1.610 | 0.064 | - | 0.740 | 0.096 | - | 0.521 | 0.500 | - |
Interact | −2.914 | 0.074 | - | −0.802 | 0.322 | - | 2.823 | 0.054 | - |
Post-slope | −1.304 | - | - | −0.062 | - | - | 3.344 | - | - |
1 | 17 | 0.319 | 11 | 7 | 0.422 | 11 | 57 | 0.001 | 75 |
3 | 15 | 0.372 | 10 | 6 | 0.491 | 9 | 62 | 0.000 | 77 |
6 | 2 | 0.906 | 1 | 3 | 0.720 | 5 | 71 | 0.000 | 93 |
9 | −7 | 0.727 | −4 | 1 | 0.929 | 1 | 79 | 0.000 | 91 |
12 | −15 | 0.486 | −9 | −2 | 0.895 | −2 | 88 | 0.000 | 109 |
18 | −33 | 0.286 | −19 | −6 | 0.672 | −8 | 105 | 0.000 | 120 |
FMS, free maternity services; LE, level effect; RE, relative effect; SCD, skilled care deliveries; PHCF, primary healthcare facility.
The effect of the FMS policy was larger and lasted longer in the hospitals (18 months) than in the PHCFs (6 months) in the county (relative effect of 46.5% for hospitals vs. 18.3% for PHCFs). In the sub-counties, there were mixed results of the FMS policy on hospitals and PHCFs. Nyamira North had the most significant (
Among the PHCFs, the effect of the FMS policy on facility deliveries was not significant in Nyamira South, Borabu and Manga, whereas it was significant in Nyamira North and Masaba North (
Comparing the post- to the pre-intervention durations, there was a 51% and a 25% increase in the average facility deliveries and the average fourth ANC visits per month, respectively. There was an increase in facility deliveries in all the sub-counties. Borabu, Manga, Masaba North, Nyamira South and Nyamira North had 32.6%, 45.8%, 70.0%, 40.0% and 72.6% increases in facility deliveries per month, respectively. The effect of the FMS policy was larger and lasted longer in the hospitals (18 months) than in the PHCFs (6 months) in the county (relative effect of 46.5% for hospitals vs. 18.3% for PHCFs). In the sub-counties, the policy had mixed results in hospital deliveries. Among the PHCFs, the effect of the FMS policy on facility deliveries was not significant in Nyamira South, Borabu and Manga, whereas it was significant in Nyamira North and Masaba North (
Despite the abolition of user fees in many countries to improve the utilisation of maternal health services, studies comparing the effect of the policy by region remain scant. This study found that there was a differential effect of the FMS policy by region (sub-counties). The FMS policy had long-term significant effects in Nyamira North and Masaba North sub-counties, but it had no significant effect in the other three neighbouring sub-counties. The observed differential effect of the policy may be influenced by the level of income, education and whether the region is urban or rural.
There was also a differential effect of the FMS policy by health facility type. In the county, the effect of the policy was most marked in the hospitals than in the PHCFs. In the sub-counties, all hospitals had a significant improvement in deliveries except Masaba North. Nevertheless, only Nyamira North and Masaba North had a significant increase in deliveries at the PHCF.
In addition, there was increase in deliveries in the PHCF that was immediate but tapered off. The initial increase in utilisation of maternity services at the PHCF could be because of their proximity to the population and comparative ease of accessibility. There could have been a hesitation and reluctance to seek care at the hospitals with people fearing that the free services were only available at the PHCF. A shift by clients to seek delivery services from PHCFs to hospitals because of perceived quality of care and the availability of staff at the hospitals round the clock could have caused the sustained rise in number of deliveries attended by skilled health personnel in the hospitals. Of note is that the hospitals also receive relatively higher reimbursements per delivery than the PHCFs and may have offered better quality services, better staff rewards and better gift hampers to mothers who deliver at their facilities.
There is a dearth of studies that have compared the effect of a similar policy at the various levels of the healthcare provision system.
There were no comparison groups in this study and it was relatively short term. This was addressed by using a longer pre-intervention period and a rigorous analysis method. The multiple-level effects were instrumental in defining how long it took for the policy’s effect to taper off. By conducting the study as such, the results are valid and conclusions can be drawn from this study.
As there is paucity of studies that have compared the effect of a similar policy at the various levels of the healthcare provision system, more studies should be undertaken to assess the effect of the policy in different regions and at various levels of healthcare provision. These studies should also aim to elucidate the factors that influence a policy’s differential effect. From the findings of this study, future implementations of similar policies should be determined by the need and possible responses of the population to the policy so as to maximise effects.
The effect of the FMS policy was varied by region (sub-county) and by type of health facility. The increase in deliveries in the PHCF was immediate but tapered off. However, the effect of the policy was most marked in the hospitals than in the PHCFs.
The authors would like to express their sincere gratitude to the staff in the Department of Health, Nyamira County, led by the County Director of Health for their support. They are also thankful to the School of Health Sciences Jaramogi Oginga Odinga University of Science and Technology (JOOUST) for their support and guidance during the conduct of this study. They also acknowledge the Ethical Review Committee of Jaramogi Oginga Odinga Teaching and Referral Hospital-Kisumu for going through their proposal and giving them the ethical approval.
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
H.O.O. made substantial contributions to the conception, study design, acquisition of data, data analysis, and interpretation of data and drafting of the manuscript. S.A.A. and S.O.A. were vital in conception, design, acquisition and interpretation of data, drafting and reviewing of the manuscript for important intellectual content.
The views expressed in this article are the authors’ own and not an official position of their institutions.