Study Setting and Dataset

This study data came from Korogocho and Viwandani, two informal settlements where the African Population and Health Research Center (APHRC) has been running the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) since 2003. Each settlement covers about one kilometer square and is located within five to 10 kilometers from the city center. The NUHDSS records demographic events (births, deaths and migration) every four months and detailed household expenditures data once a year. The Viwandani informal settlement neighbors the Industrial Area of Nairobi and was a magnet for young males and relatively educated migrants in search of work while Korogocho was home for more settled families[12], some of whom had been living there for multiple generations. As a corollary of the different demographics in both settlements, households sizes were bigger in Korogocho than Viwandani while household income per capita was higher in Viwandani [13]. As of end of 2011, the latest data available showed that 32,746 households with 83,484 individuals lived in the area covered by the NUHDSS. This surveillance system provided vital statistics and other information on a population for whom these data would otherwise be unavailable. Nested within NUHDSS was the Maternal and Child Health (MCH) project, which recruited cohorts of mother-child pairs and followed them up every four months. A mother-child pair was recruited if the mother resided in the slum when pregnant and the child was 6 months old or younger at the time of recruitment. The MCH study covered the years 2006 to 2010 with a couple of recruitment suspensions between June and September 2009 and February to June 2010. During recruitment suspensions, follow-up interviews of existing cohorts were conducted. The INDEPTH (International Network for the Demographic Evaluation of Populations and Their Health) vaccination project (IVP) succeeded the MCH project from 2011 and took over existing cohorts of children while recruiting all children born from 2010 when recruitment into the MCH project ended. That strategy allowed for continuity in the recruitment and follow-up of children born in the slums since 2006 in the ongoing cohort studies. Both MCH and IVP projects were run by the same institution and team using the similar procedures and questionnaires so that the data quality was similar across all the rounds of data collections.

The MCH/IVP projects collected information on the child’s place of delivery during the recruitment interview but the information on RH-OBA knowledge and use was collected at the second interview. Hence, information on the RH-OBA voucher was unavailable for mothers who dropped out of the MCH/IVP projects after the first recruitment interview, 22% in the MCH project and 27% in the INDEPTH Vaccination Project.

This study included mother-children pairs from the MCH and the IVP datasets from 2006 to 2012. We selected mothers of at least two children, and for whom no important information—like the date of birth of the child or the mother—was missing or implausible. For twin births, we kept only one record knowing the mother’s information was the same and assuming both children were born in the same place. Since this paper focused on longitudinal effect of the voucher, we excluded women who did not buy the voucher for the index child but for the subsequent child.

The APHRC owns the datasets used in this analysis. APHRC has a data sharing policy that enables other researchers to access this dataset and others. APHRC data sharing policy is available at http://www.aphrc.org/insidepage/page.php?app=data

Empirical Analyses

The analytical dataset included 352 Index-OBA mothers, and 514 Non-OBA mothers. We first conducted descriptive analyses of all dependent and independent variables to gain an understanding of the main features of the dataset. We calculated unadjusted odds of FBD of the subsequent child if the index child was delivered in a facility. We then calculated adjusted odds-ratio with two models. Model 1 controlled for the specific settlements, maternal age, parity, year of birth of the subsequent child, whether the mother was in union, her education, and Model 2 included additionally the monthly household per-capita expenditures. We used Stata12 SE (State College) to merge the datasets and run the analyses.

Descriptive Statistics

Table 1 indicated that there were no systematic differences in the individual characteristics of mothers’ education, parity and age across the subgroups. Women in both groups were similar in their low education levels: 34% to 41% of mothers had no formal education or had not finished primary school. The women were also similar in the upper level as 18% of them in both groups had some secondary level of education or beyond. Parity at the birth of the index child was also similar across the subgroups ranging from 2.28 to 2.42. Although, mothers who did not buy the voucher appeared slightly older than those who did, the difference was not statistically different.

The variable In-Union indicated that mothers who bought the voucher for their index children were less likely to be in union than mothers who did not. The main difference across the groups related to poverty and place of residence.

The monthly household per capita expenditures were captured at two different points in time: during the years of the birth of the index and the subsequent children. Initially, the OBA mothers had a per-capita household expenditures of KES 2464 (USD 30.80) which were KES 276 (USD 3.45) lower than the monthly expenditures of KES 2740 (USD 34.25) of the non-OBA mothers (p<0.05). Conversely, the OBA mothers had slightly higher household expenditures (> KES 32) during the year of the birth of the subsequent child but the difference was not statistically significant. Results using imputed values for the missing household expenditures indicate a similar pattern: OBA mothers had significantly lower household expenditures during the index child year of birth but not when the subsequent child was born.

The majority of women in the dataset come from Korogocho. Interestingly, six children out of 10 were born in a facility in both groups. However, the trend was mixed: OBA mothers had FBD proportions of 92% for their index child, but only 84% of the subsequent ones. On the contrary, non-OBA mother had FBD proportions of 64% and 71% respectively, which indicated an increasing secular trend toward FBD.

The quality of facilities where women delivered followed a trend similar to the FBD: 90% of OBA mothers attended a good quality facility for the index child but only 82% did for the subsequent child. Conversely, those proportions were 53% and 63% among non-OBA mothers.

Only 78% of women who bought the RH-OBA voucher for the index child used it for his/her delivery, leaving an open question on the place of delivery of the 22% who did not. Furthermore, only 54% of women who bought the voucher for the index child did so for the subsequent child as well. Among them, 94% had a FBD. In contrast, among women who did not buy the voucher for the subsequent child, only 72% delivered in a facility (p<0.01). No statistical difference was found in the per-capita expenditures or education level of the mothers who bought again the voucher or those who did not.

Unadjusted odds ratio

In both groups, the odds that a subsequent child was born in a facility if the index child was also born in a facility were higher (Table 2). The numbers were 3.68 (p<0.01) for the index-OBA, and 4.43 (p<0.01) non-OBA groups respectively. When mothers had three children in the dataset, the odds that the youngest child was born in a facility were also higher than that of the index children. The odds of FBD of the third child was 8.5 (p<0.01) among index-OBA mothers but only 3.43 (p<0.1) among non-OBA mothers.

Adjusted odds ratio

Table 3 indicated that among the OBA mothers, the odds of FBD delivery of the subsequent child given that the index child was born in a facility was 2.98 (p<0.05) for Model 1 controlling only for maternal characteristics and 3.89 (p<0.05) for Model 2, which added household expenditures per capita. Among the non-OBA mothers, the adjusted odds-ratio were 4.28 (p<0.01) for Model 1 and 4.73 (p<0.01) for Model 2.

For both models and groups, the year of delivery was statistically significant suggesting a secular trend toward FBD other factors held constant. The odds of FBD were also higher if the mother lived in Korogocho. Other variables were not significant in terms of either magnitude or statistics. Interestingly, while the expenditures per capita variable itself was not significant, its inclusion in the regression improved the precision of the odds-ratio of the main variables. We ran the again the analyses using the imputed values for household expenditures when those were missing. The results were similar to those obtained without imputation in terms of magnitude of the coefficients and statistical significance (not shown).

Discussion and Limitations

This study found that women who had an index child in a facility were more likely to have a subsequent child also in a facility. However, the odds of FBD were smaller among women who bought the voucher for their index child, compared to those who did not. A higher proportion index-OBA mothers had a subsequent child in a facility compared to non-OBA mothers.

Surprisingly, only 54% of the women who bought the voucher for the index child also bought it for the subsequent one. Many reasons might explain this fact. First, the OBA mothers might not have been poor anymore. Second, they might have decided against further participation in the program because of cumbersome checks of eligibility criteria. Finally, the OBA mothers might have given birth again during the year where no new voucher was issued as the stakeholders were in court over management dispute. In any case, the fact that those who bought again the voucher were significantly more likely to deliver in a facility corroborated the positive correlation between access to the OBA voucher and FBD.

Note that not all FBD were equal. Women who used the voucher for FBD had to attend accredited facilities, so they probably received appropriate quality care. The same cannot be ascertained about women in the non-OBA group who could deliver in non-accredited facilities. The importance of accredited place of delivery should be emphasized as a previous study showed that although two third of women in Korogocho and Viwandani had delivered in a facility, fewer than half of them were assisted by a skilled birth attendant [5] who could prevent, diagnose or treat complications. Moreover, only 2 out of 25 facilities in those informal settlements could provide emergency obstetric quality care [14].

Another difference between the index-OBA and non-OBA groups related to poverty. Mothers who bought the voucher had statistically lower household per capita expenditures than mothers who did (p<0.05) during the year of birth of their index child. However, there was no statistical difference between the two groups of mothers at the birth of the subsequent child. We speculate that the absence of difference may be an artifact of the small sample size.

This study observed a secular trend of increasing facility-based deliveries among residents of Korogocho and Viwandani, similar to the previous findings. Nevertheless, among the non-OBA mothers, a substantial proportion of mothers did not have a FBD. A couple of qualitative studies conducted in the same slums in 2005 and 2006 just before the start of the voucher project found that poverty was one of the key factors behind homebirths. Study respondents including women who had a previous life-threatening birthing experience, their partners and other opinion leaders indicated that costs were a major impediment to seeking emergency obstetric care[16,17]. Those costs ranged from twenty KES (USD 0.20) for registration in a government hospital to KES 5,500 (USD=68.7) in a mission hospital for normal delivery. For caesarian section, the costs ranged from KES 3000 to 30,000 (USD=37.5 to 375). Transportation cost varying from KES 3,000 to 4,000 (37.5 to 50) should be added to the direct medical costs. These estimated costs of emergency care from patients’ perspective should be compared with the average expenditures per capita that did not exceed KES 2,784 in any group in this study. Respondents submitted that FBD were associated with such high costs that it should be considered only as the last recourse. Thus, pregnant women attend antenatal care to prepare for homebirth and to avoid FBD. The voucher program addressed partly the monetary obstacle by subsidizing the cost of care and removing the need to pay at the point of delivery.

Respondents in both qualitative surveys also pointed out other non-monetary reasons to avoid FBD such as the unfriendly/disrespectful attitude of providers toward them, poor physical access to hospital etc. To address the non-monetary issues, prequalified providers received an output-based reimbursement, thus tying the quality of their service to their financial rewards. Providers who offer quality care in a friendly manner were more likely to attract patients and reap the benefit of the program.

This paper presented some limitations, which suggests caution in the interpretation of the results and the policy recommendations. The first one was the small sample sizes of the study. Larger sample size would probably reduce standard errors and improve the statistical significance of the other variables in the models lending strength to the results. A second limitation was the measurement of the socio-economic status of the mother. We approximated it with per-capita household expenditures. This variable was not collected in the IVP survey but in the NUHDSS and only once a year. The expenditures during the year of birth of the child used in this study might fail to capture the household’s standing during of pregnancy. Compounding the issue was the missing values of the expenditures for at least 15% of the sample. We used imputed values and obtained similar results. Other explorations using an asset index or income per capita collected at the same time as the household assets gave similar results suggesting their robustness.

In addition, recruitment in the study was suspended twice for about three months each time, but those suspensions were unlikely to bias substantially the result of this study since they were limited in time and no significant event occurred then.

Despite issues of sample size, the study has a number of strengths. First, it was population-based cohort data and as such presents reduced instances of sampling bias. In addition, the data include a cohort of mothers with multiple births lending consistency to the information collected. Finally, information related to each child was collected before another was born, reducing the risk of recall bias or confusion between specific place of birth or OBA use for each child.