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Influence of Distance to Health Facilities on the Use of Skilled Attendants at Birth in Kenya Remare R. Ettarh & James Kimani To cite this article: Remare R. Ettarh & James Kimani (2014): Influence of Distance to Health Facilities on the Use of Skilled Attendants at Birth in Kenya, Health Care for Women International, DOI: 10.1080/07399332.2014.908194 To link to this article: http://dx.doi.org/10.1080/07399332.2014.908194

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Date: 06 November 2015, At: 16:12

Health Care for Women International, 0:1–13, 2014 Copyright © Taylor & Francis Group, LLC ISSN: 0739-9332 print / 1096-4665 online DOI: 10.1080/07399332.2014.908194

Influence of Distance to Health Facilities on the Use of Skilled Attendants at Birth in Kenya

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REMARE R. ETTARH Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada

JAMES KIMANI Population Council, Nairobi, Kenya

We sought to determine the spatial variation in the use of skilled providers during deliveries across Kenya and the relationship between distance to health facilities and the use of skilled delivery. We found that women who resided 5 km or less from the nearest health facility were more likely to use skilled care at delivery than women residing at greater distances, although the pattern of choice of health facility level for delivery differed at this distance. Outreach maternity services are urgently required in counties with remote communities in order to improve access to skilled attendants during deliveries in these areas.

BACKGROUND Ensuring that deliveries are conducted with the aid of skilled attendants remains the most critical intervention to reduce maternal mortality in subSaharan Africa (Starrs, 1998; World Health Organization [WHO], 2009). Each year, sub-Saharan Africa accounts for approximately 162,000 maternal deaths during pregnancy and child birth (WHO, United Nations International Children’s Emergency Fund [UNICEF], United Nations Population Fund [UNFPA], & World Bank, 2012). In Kenya, maternal mortality is high, with an estimated 7,900 women dying annually from pregnancy-related complications

Received 16 December 2013; accepted 21 March 2014. Address correspondence to Remare R. Ettarh, Faculty of Medicine, University of British Columbia, 2775 Laurel Street, Vancouver, BC V5Z 1M9, Canada. E-mail: [email protected] 1

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(UNICEF, 2012), and the 2008–2009 Kenya Demographic and Health Survey (KDHS) reports a maternal mortality ratio of 488 deaths per 100,000 live births (Kenya National Bureau of Statistics [KNBS] & ICF Macro, 2010). The approximate lifetime risk of maternal death in Kenya was estimated at 1 in 55 in 2010, and the proportion of deliveries conducted each year by skilled attendants in Kenya is 44% (KNBS & ICF Macro, 2010; WHO et al., 2012). The reasons for the low use of skilled attendants in different parts of the country require further study given the contextual nature of the problem. The findings would be useful in developing effective strategies for addressing the barriers to utilization of skilled attendants at birth in other low-income and middle-income countries. The methodological approach to the study also offers valuable lessons to researchers in other developing countries seeking to examine similar relationships from existing national datasets. The factors associated with use of skilled attendants at delivery are well documented in the literature and have been categorized as sociocultural, related to perceived benefit/need, economic accessibility, and physical accessibility (Gabrysch & Campbell, 2009). There are questions that require contextual analysis, however, including the influences of environmental or service-related factors on use of skilled birth attendants. Based on the conceptual framework on the three delays for emergency care seeking by Thaddeus and Maine (1994), physical accessibility directly affects the second delay. The distance to the health facility serves as both a disincentive to care seeking as well as a physical obstacle to reaching care when a health-seeking decision has been made (Thaddeus & Maine, 1994). There is also interaction between the distance to health facilities and other service-related factors such as cost and quality of care (Adegoke & van den Broek, 2009; Thaddeus & Maine, 1994). Studies have not often included distance to health facilities in analyses of determinants of skilled care during delivery due to the challenges of collecting the data, particularly when the studies are conducted across a country. The availability of population health data at the national level through the Demographic and Health Surveys (KNBS & ICF Macro, 2010) and the development of national databases with georeferenced health service provision data (Kenya Open Data Project, 2011) have created opportunities to merge these large-scale databases to examine some of these relationships spatially using a geographic information system. Our aim for this study was to assess the effects of distance to the nearest registered health facility on the use of skilled care at delivery and to describe the spatial distribution of women’s use of skilled providers across the 47 counties in Kenya. This evidence will be useful in guiding policymakers regarding increasing geographical access to health facilities for rural and remote communities as a way to reduce maternal and newborn deaths in sub-Saharan Africa.

Distance and Use of Skilled Attendants in Kenya

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METHODS This study is cross sectional and was conducted using population data obtained from the 2008–2009 KDHS. The KDHS collects nationally representative data on women of child-bearing age (15–49 years) and their children. A national sample of 10,000 households was drawn for the survey from 400 clusters across the country. Analysis was based on 5,852 live births in the 5 years preceding the survey. Details on the methodology of the 2008–2009 KDHS are available in the final survey report (KNBS & ICF Macro, 2010). Health facility data were obtained from the Kenya Open Data Initiative (Kenya Open Data Project, 2011), an open government data portal that makes core government, demographic, expenditure, and public services data available in a useful format for researchers, policymakers, and the general public. The dataset with details on registered health facilities in each county was downloaded on November 15, 2011. A total of 3,406 facilities with valid geographical coordinates were included in the study and merged with data from the KDHS using ArcGIS (ESRI, 2006). The approach to delivery of health services in Kenya is outlined in the KEPH (Ministry of Health [Kenya], 2007). It defines six levels of service delivery, namely, villages/households/individuals (Level 1); dispensaries and clinics (Level 2); health centers, maternities, and nursing homes (Level 3); primary hospitals (Level 4); secondary hospitals (Level 5); and tertiary hospitals (Level 6). Of the 3,150 public health facilities that were providing services in 2010, 76% were dispensaries (Level 2) and 15% were health centers (Level 3; National Coordinating Agency for Population and Development, Ministry of Medical Services, Ministry of Public Health and Sanitation, & Kenya National Bureau of Statistics, 2011). The distances between the health facilities and the households represented by the cluster coordinates were determined with ArcGIS 9.2 using the method employed by Ettarh and Kyobutungi (2012) Briefly, buffers of differing distances were created around the location of all health facilities in order to identify households within those distances. The households were categorized based on whether they were located less than 2 km, 5 km, 10 km, 20 km, and 50 km from the nearest registered health facility. County health facility density was determined as the ratio of the number of registered health facilities in the county per 10,000 population and categorized based on the mean county density (5.8 per 10,000 population) for all the 47 counties in the country (Ettarh & Kyobutungi, 2012). County-level data on the health facility density were assigned to each birth in the county. The outcome variable in this study was a binary variable showing whether a live birth had assistance from a skilled provider (coded as “1”) or not (coded as “0”). Other covariates that were included in the analysis follow: maternal age, maternal educational level, household wealth quintile, place of residence, province of residence, and type of health facility where

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delivery was taken. Household wealth quintiles were obtained from a KDHS index computed using data on characteristics related to a household’s socioeconomic status (KNBS & ICF Macro, 2010). The main predictors tested were the distance to the nearest health facility and the county health facility density. Multivariate logistic regression was conducted to investigate the influence of distance to health facilities on the use of a skilled provider during delivery while controlling for other factors. The survey protocol for the 2008–2009 KDHS had been previously approved by the Scientific and Ethical Review Committee of Kenya Medical Research Institute. All participants in the survey gave informed consent and were deidentified by ICF Macro before the dataset was shared.

RESULTS We present the unadjusted odds ratios to demonstrate that having primary school education, secondary education and higher, residing in households ranked higher in the wealth index, and residing in Nairobi province were significantly associated with a higher likelihood of delivering with a skilled attendant compared with having no education, residing in households in the lowest wealth index, and residing in Central Province, respectively (Table 1). The likelihood of delivery by skilled attendants was lower among women who were married and formerly married, among women residing in rural areas, and among women residing in Coast, Eastern, North Eastern, Nyanza, Rift Valley, and Western provinces, compared with that in the respective reference categories. The crude odds ratios also showed that these sociodemographic characteristics were significantly associated with a lower probability of using skilled care at delivery. Results of association between use of skilled care at delivery and type of facility, county facility density, and distance to the health facility are shown in Table 2. Health centers, hospitals, and maternity facilities and counties with equal or higher facility density than the national average were significantly associated with use of skilled care at delivery. The unadjusted logistic regression model shows that distance to the health facility was also a significant predictor of use of skilled care at delivery. As the distance to the health facility increased, the odds of seeking skilled care during delivery decreased. Figure 1 shows the results of the relationship between proximity to different health facility levels and use of skilled care at delivery. The proportion of deliveries by a skilled attendant increased as the distance to the health facility decreased. Most skilled deliveries occurred when the distance to the health facility was 5 km or less, with the highest deliveries occurring in maternity facilities (90%) that were situated less than 2 km from where the women lived. The proportion of deliveries by skilled care at maternity

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TABLE 1 Relationship Between the Sociodemographic Characteristics of Births and Delivery by Skilled Birth Attendants

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Variable Maternal age (years) 15–19 20–29 30–39 40–49 Maternal education No education Primary Secondary Higher Household wealth quintile Lowest Second Middle Fourth Highest Marital status Never married Married Formerly married Place of residence Urban Rural Province Central Coast Eastern North Eastern Nairobi Nyanza Rift Valley Western ∗p

N

Deliveries in category (%)

Deliveries by skilled attendant (%)

Crude OR

352 3359 1963 401

5.8 55.3 32.3 6.6

46.0 46.3 42.8 38.2

1.00 1.01 0.88 0.72

— 0.81 0.70 0.54

— 1.26 1.10 1.20

1297 3429 1024 325

21.3 56.4 16.9 5.3

21.8 40.7 71.2 92.6

1.00 2.46∗ 8.85∗ 44.94∗

— 2.12 7.33 29.05

— 2.86 10.69 69.50

1773 1079 985 985 1253

29.2 17.8 16.2 16.2 20.6

19.8 32.7 44.2 56.3 81.1

1.00 1.97∗ 3.20∗ 5.23∗ 17.37∗

— 1.66 2.70 4.40 14.46

— 2.34 3.80 6.21 20.86

382 5178 515

6.3 85.2 8.5

53.1 44.1 43.1

1.00 0.70∗ 0.67∗

— 0.57 0.51

— 0.86 0.87

1467 4608

24.1 75.9

74.2 35.2

1.00 0.19∗

— 0.17

— 0.22

499 840 704 537 411 1070 1144 736

8.4 14.1 11.9 9.0 6.9 18.0 19.3 12.4

73.8 43.7 39.9 32.8 86.6 46.2 34.4 31.7

1.00 0.28∗ 0.24∗ 0.17∗ 2.30∗ 0.31∗ 0.19∗ 0.16∗

— 0.22 0.18 0.13 1.62 0.24 0.15 0.13

— 0.35 0.30 0.23 3.25 0.39 0.24 0.21

95% CI

< .95; OR: odds ratio.

facilities decreased to less than 10% when the distance to facility increased to between 2 km and 5 km. Table 3 shows the results of factors associated with use of skilled care at delivery after controlling for other variables. The odds of delivering using a skilled attendant increased with the level of maternal education. Mothers with the highest level of education had a higher likelihood of using skilled care at delivery compared with those with no education (OR = 14.8; p < .001). As the level of household wealth status increased, the likelihood of delivery by a skilled attendant also increased. Women residing in households ranked higher in the wealth index were over four times more likely

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TABLE 2 Relationship Between Proximity to Health Services and Delivery by Skilled Attendants

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Variable Type of facility Dispensary Health center Hospital Maternity County facility density Lower than average Equal to or higher than average Distance to nearest facility (km) Less than 2.0 2.0–5.0 5.1–10.0 10.1–20.0 20.1–50.0 ∗p

N

Deliveries by Deliveries in skilled category (%) attendant (%) Crude OR

95% CI

442 1632 3604 261

60.7 27.5 7.4 4.4

38.3 50.7 57.9 78.5

1.00 1.66∗ 2.21∗ 5.90∗

— 1.47 1.82 4.36

— 1.86 2.71 7.99

4625 1450

76.1 23.9

38.1 65.4

1.00 3.08∗

— 2.72

— 3.49

2335 2221 790 290 305

39.3 37.4 13.3 4.9 5.1

59.7 43.9 26.1 23.8 7.9

1.00 0.53∗ 0.24∗ 0.21∗ 0.06∗

— 0.47 0.20 0.16 0.04

— 0.60 0.28 0.28 0.09

< .95; OR: odds ratio.

FIGURE 1 Relationship between distance to different health service levels and delivery by skilled attendants in Kenya.

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TABLE 3 Multivariate Logistic Regression Showing Determinants of Delivery by Skilled Attendants

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Variable

Adjusted OR

Maternal education (Ref: No education) Primary Secondary Higher Household wealth quintile (Ref: Lowest) Second Middle Fourth Highest Place of residence (Ref: Urban) Rural Province (Ref: Central) Coast Eastern North Eastern Nairobi Nyanza Rift Valley Western Type of facility (Ref: Dispensary) Health center Hospital Maternity ∗p

< .05;

∗∗ p

< .01;

∗∗∗ p

95% CI

1.79∗∗∗ 4.88∗∗∗ 14.79∗∗∗

1.44 3.75 8.98

2.22 6.35 24.37

1.38∗∗∗ 1.99∗∗∗ 2.47∗∗∗ 4.89∗∗∗

1.14 1.64 2.00 3.70

1.68 2.43 3.05 6.47

0.80∗

0.64

0.99

0.47∗∗∗ 0.51∗∗∗ 0.97 0.56∗∗ 0.34∗∗∗ 0.31∗∗∗ 0.19∗∗∗

0.35 0.38 0.66 0.37 0.25 0.23 0.14

0.63 0.69 1.43 0.84 0.46 0.41 0.27

1.28∗∗ 1.29∗∗ 1.53∗∗

1.10 1.02 1.09

1.48 1.63 2.14

< .001; OR: odds ratio.

to seek skilled care at delivery compared with their counterparts residing in households at the bottom of the index (OR = 4.9; p < .001). Type of facility was a significant determinant of delivery by skilled attendants. Women who delivered at a health center (OR = 1.3; p < .01), hospital (OR = 1.3; p < .01), and maternity facility (OR = 1.5; p < .01) were significantly more likely to be attended by a skilled attendant during delivery compared with women who gave birth at a dispensary. Women who resided in rural areas were significantly less likely to seek skilled care during delivery compared with their counterparts in urban areas (OR = 0.8; p < .05). Province was also associated with lower odds of skilled care at delivery. In the unadjusted logistic regression model, Nairobi province was significantly associated with the use of skilled care at delivery; however, after controlling for confounders, it was observed that the odds of delivering with a skilled attendant decreased compared with Central Province. Results on the associations of distance to the health facility and the likelihood of skilled care at delivery are shown in Figure 2. After adjusting for confounders, the effect of distance to the health facility on the probability of seeking skilled care at delivery still remained. The odds of seeking skilled care at delivery decreased as the distance to the nearest health facility increased. The spatial patterns of distance to the health facility and use of

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FIGURE 2 Effect of distance to the nearest facility on likelihood of delivery by a skilled attendant.

skilled birth attendants show that a higher proportion of households that were more than 5 km from the nearest health facility were mostly found in counties situated in the North Eastern, Eastern, Nyanza, Western, and Rift Valley regions in Kenya. Further, these regions also represented counties that had fewer deliveries that were attended by skilled attendants (Figure 3).

DISCUSSION The increased availability of data from national surveys and open databases in Kenya for use in research and planning is a significant development that has allowed us to examine the effect of distance on the use of skilled birth attendants across the country. We found that proximity to a registered health facility was a significant determinant of delivery by skilled attendants after controlling for other important sociodemographic factors. The percentage of deliveries that were conducted by skilled birth attendants was 44% nationally, well below the internationally agreed-upon target of 90% coverage by 2015 (WHO, 2008). This estimate is similar to that reported from demographic and health surveys in neighboring countries in the region that face similar cultural, socioeconomic, and geographical challenges (Mpembeni et al., 2007; Uganda Bureau of Statistics & ICF International Inc., 2012).

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FIGURE 3 Spatial patterns of proximity to health facilities and use of skilled birth attendants across the 47 counties in Kenya.

Most of the previous researchers who have investigated the determinants of delivery service in Kenya have not included distance or proximity to health facilities as a predictor, despite the acknowledgment that distance exerts a significant influence on this outcome (Thaddeus & Maine, 1994). This is probably associated with the challenges with collecting or accessing reliable geographical data as well as the absence of location data for health facilities. This study examined the relationship between proximity to registered health facilities and utilization of skilled birth attendants across the 47 counties thereby providing policy-relevant findings for national stakeholders. The significant effects of maternal education, household wealth, place of residence, province, and type of health facility located nearest to residence on use of skilled birth attendants found in this study have been reported in other studies in Kenya and parts of sub-Saharan Africa (Cotter, Hawken, & Temmerman, 2006; Mpembeni et al., 2007; Van Malderen et al., 2013). These results highlight the extent of social, ethnic, wealth, and geographical inequalities in the utilization of safe delivery services in Kenya. The significantly greater likelihood of use of skilled care at delivery by women with higher education has been reported in other studies (Ahmed, Creanga, Gillespie, & Tsui, 2010; Magadi, Diamond, & Madise, 2001) and may be associated with greater understanding of messages regarding safe motherhood, better health-seeking behavior in general, and greater empowerment compared with the less-educated women (Yanagisawa, Oum, & Wakai, 2006).

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The findings also confirm the influence of wealth inequality on use of skilled care at delivery, which is well documented in a study of 77 countries (WHO, 2013). The urban–rural differential in skilled delivery coverage has been reported in other studies in sub-Saharan Africa (Say & Raine, 2007; Zere, Oluwole, Kirigia, Mwikisa, & Mbeeli, 2011) and is likely associated with general inequality in health care utilization and health outcomes between these places of residence. The greater availability and influence of traditional birth attendants in rural areas in Kenya and the associated costs of seeking skilled care during labor make the use of skilled delivery care less likely among women in these areas. The difference in skilled delivery coverage between health facility types is likely associated with the predominance of lower-level facilities across the counties. About 9 of every 10 deliveries were taken in a dispensary (Level 2) or health center/maternity (Level 3), both of which account for 91% of the registered health facilities in Kenya. The likelihood of skilled care at delivery increased, however, as the level of the health care facility increased. This suggests that the quality of care at Levels 2 and 3 may not be adequate, and women may resort to traditional delivery care even where the facility is not too far away. We found that deliveries at maternities (Level 3) dropped sharply once the distance to the nearest maternity exceeded 2 km from the woman’s residence. The proportion of deliveries taken at other facility types when these were located between 2 km and 5 km, however, was not much lower than that in these facilities when located less than 2 km from the woman’s residence. The Kenya National Health Sector Plan for 2013–2017 requires that all households in the country are located within 5 km, or a 1-hour travel time equivalent, to a public health facility (Ministry of Health [Kenya], 2012). It is however not clear what the influence of the quality of care offered at the nearest health facility may be on the decision to have skilled attendance at delivery, particularly when the health facility is located less than 5 km away. The geographical inequalities in skilled birth attendance coverage between provinces in Kenya have been reported in other reports (Van Malderen et al., 2013), with the likelihood of use of skilled attendance being higher in the Central Province than the other provinces after controlling for other factors. Numerous studies have shown better health indicators in the Central Province, which is predominantly populated by the Kikuyu ethnic group (Ettarh & Kyobutungi, 2012; Ettarh, Mutua, & Kyobutungi, 2012; KNBS & ICF Macro, 2010; Mutua, Kimani-Murage, & Ettarh, 2011). This has been attributed to the health-seeking behaviors and attitudes among this ethnic group (e.g., low desired number of children, high uptake of antenatal care, and high immunization utilization), which contribute to the better health outcomes seen in the Kikuyu communities compared with other ethnic populations (Bauni, Gichuhi, & Wasao, 2000; Ettarh et al., 2012).

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Our findings on the spatial patterns of geographical access were similar to those reported by Noor and colleagues (Noor, Alegana, Gething, & Snow, 2009), in which they show that the 83% (3.3 million) of people residing beyond 5 km from a public health service provider were from the Rift Valley, North Eastern, and Eastern provinces and also note an increase in the number of health facilities in the North Eastern province in recent years. The intention has been to address the severely limited geographical access to health service centers in the rural areas caused by the topography of parts of the province, which are mostly arid and inaccessible by motorized vehicles, and the pastoralist activities of the sparse population. This study provides an example of the opportunities for generating policy-relevant evidence using multiple national data sources. The use of national population health data obtained from the 2008–2009 KDHS and merged with georeferenced health service provision data (Kenya Open Data Project, 2011) enabled county- and province-level analyses of determinants of skilled care use during delivery. This approach is relevant to other lowand middle-income countries that also have similar data sources and offers significant research opportunities to guide national policy on maternal health issues.

LIMITATIONS The study is limited in a number of respects. First, we were unable to determine whether the health facility closest to a respondent actually provided delivery care. Women may have to travel over 5 km to a maternity even though a health center or dispensary is located less than 5 km from their residence if these Level 3 facilities do not conduct deliveries. Second, our use of Euclidean distances implies the assumption that women go to the nearest health facility and that they travel to it in a straight line. This was necessitated by the absence of nationwide data on travel routes and times to the nearest health facility for each woman at delivery. Another limitation of the study is the absence of data on the quality of service in health facilities for inclusion in the multivariate regression analysis. Finally, the study is influenced by the limitations of the KDHS due to its cross-sectional nature and, therefore, causal analysis could not be conducted.

CONCLUSION Across the 47 counties of Kenya, there is substantial variation in the coverage of skilled delivery care, with 87% of counties having coverage levels that are less than the International Conference Population and Development (ICPD+5) target of 80% (UNFPA, 1999). Greater proximity to the nearest registered health facility was found to be significantly associated with

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increased likelihood of skilled assistance at delivery. This relationship was not the same for all levels of health facilities, indicating that other facility attributes may influence the use of skilled delivery care when the distance to health facility was less than 5 km. We recommend that interventions such as outreach maternity services be introduced in counties with remote and rural communities in order to improve access to skilled attendants during deliveries in these areas. In addition to addressing geographical access to health facilities, all aspects of quality delivery care at maternities and other primary health care facilities must be guaranteed to ensure a reduction in the lifetime risk of maternal death in sub-Saharan Africa.

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determinants of skilled care during delivery in Southern Tanzania: Implications for achievement of MDG-5 targets. BMC Pregnancy and Childbirth, 7, 29. Mutua, M. K., Kimani-Murage, E., & Ettarh, R. R. (2011). Childhood vaccination in informal urban settlements in Nairobi, Kenya: Who gets vaccinated? BMC Public Health, 11(1), 6. National Coordinating Agency for Population and Development, Ministry of Medical Services, Ministry of Public Health and Sanitation, & Kenya National Bureau of Statistics. (2011). Kenya service provision assessment survey 2010. Nairobi, Kenya: Authors. Noor, A. M., Alegana, V. A., Gething, P. W., & Snow, R. W. (2009). A spatial national health facility database for public health sector planning in Kenya in 2008. International Journal of Health Geographics, 8, 13. Say, L., & Raine, R. (2007). A systematic review of inequalities in the use of maternal health care in developing countries: Examining the scale of the problem and the importance of context. Bulletin of the World Health Organization, 85, 812–819. Starrs, A. (1998). The safe motherhood action agenda: Priorities for the next decade. New York, NY: Inter Agency Group for Safe Motherhood and Family Care International. Thaddeus, S., & Maine, D. (1994). Too far to walk: Maternal mortality in context. Social Science and Medicine, 38, 1091–1110. Uganda Bureau of Statistics & ICF International Inc. (2012). Uganda demographic and health survey 2011. Kampala, Uganda: Author. United Nations International Children’s Emergency Fund (UNICEF). (2012). Kenya country profile. Maternal, newborn and child survival. New York, NY: Author. United Nations Population Fund (UNFPA). (1999). Key actions for the further implementation of the program of action of the International Conference on Population and Development. New York, NY: Author. Van Malderen, C., Ogali, I., Khasakhala, A., Muchiri, S. N., Sparks, C., Van Oyen, H., & Speybroeck, N. (2013). Decomposing Kenyan socio-economic inequalities in skilled birth attendance and measles immunization. International Journal for Equity in Health, 12, 3. World Health Organization (WHO). (2008). Proportion of birth attended by a skilled health worker. Geneva, Switzerland: Author. World Health Organization (WHO). (2009). WHO recommended interventions for improving maternal and newborn health. Geneva, Switzerland: Author. World Health Organization (WHO). (2013). Handbook on health inequality monitoring: With a special focus on low- and middle-income countries. Geneva, Switzerland: Author. World Health Organization (WHO), United Nations International Children’s Emergency Fund (UNICEF), United Nations Population Fund (UNFPA), & World Bank. (2012). Trends in maternal mortality: 1990 to 2010. Geneva, Switzerland: Author. Yanagisawa, S., Oum, S., & Wakai, S. (2006). Determinants of skilled birth attendance in rural Cambodia. Tropical Medicine and International Health, 11(2), 238–251. Zere, E., Oluwole, D., Kirigia, J. M., Mwikisa, C. N., & Mbeeli, T. (2011). Inequities in skilled attendance at birth in Namibia: A decomposition analysis. BMC Pregnancy and Childbirth, 11, 34.

Influence of Distance to Health Facilities on the Use of Skilled Attendants at Birth in Kenya.

We sought to determine the spatial variation in the use of skilled providers during deliveries across Kenya and the relationship between distance to h...
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