Child and Maternal Health in Kenya 2011 Report


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This report evaluates access to maternal and child healthcare and health outcomes in Kenya using geographic information systems (GIS), statistical analysis, and a comprehensive review of existing literature.

It seeks to aid in identifying distributions of health facilities and services relative to key maternal and child health indicators (e.g., safe delivery, care and treatment of birth injuries, antenatal and postnatal care, immunization, and nutrition).

It also seeks to contribute a portfolio of geospatial maps for identifying, analyzing, and monitoring health needs in one of the world’s poorest, most densely populated, and most vulnerable regions. In addition to identifying and analyzing information currently available, the report highlights limitations of both Kenya’s existing data sets and overreliance on distance as a measure of “access” and “use.”

This report responds to a request from Direct Relief International (DRI) to identify healthcare access and health outcomes in Kenya as part of its multi-organizational collaborative project to enhance health services in an integrated and efficient manner.

Along with the African Medical Research Foundation (AMREF), Marie Stopes International (MSI), and district-level health ministries in Kenya, Tanzania, and Uganda, DRI is attempting to determine critical gaps in health infrastructure.

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Child and Maternal Health in Kenya 2011 Report

  1. 1. University of Michigan Gerald R. Ford School of Public Policy Applied Policy Seminar, Winter 2012 Produced in Collaboration with Direct Relief InternationalChild and Maternal Health in Kenya A Review and Analysis of Access and Outcomes Valerie Benka, Salvador Maturana, Devi Glick Winter 2012
  2. 2. Table of Contents Executive Summary ......................................................................................................................................... 2 Introduction ....................................................................................................................................................... 3 Background and Research Rationale ........................................................................................................ 4 Overview of the Kenyan Healthcare System .......................................................................................... 6 Background and History ........................................................................................................................... 6 Government health facilities ................................................................................................................... 7 Private health providers ........................................................................................................................... 8 Private-Public collaboration ................................................................................................................... 9 Health providers relevant to maternal and child health in Kenya ........................................ 10 Sector quality ............................................................................................................................................. 11 Literature review .......................................................................................................................................... 13 Use of GIS to analyze health care access and health outcomes .............................................. 13 Use of Spatial Data to analyze maternal/child health care and health outcomes in Kenya/East Africa .................................................................................................................................... 16 Limitations of spatial data/non-geographic factors influencing health care access and use .................................................................................................................................................................. 25 Conclusions ................................................................................................................................................. 27 Overview of data collected and available for analysis .................................................................... 28 Data sets ....................................................................................................................................................... 29 Survey Design ................................................................................................................................................. 33 General Approach ..................................................................................................................................... 33 Kenya SPA Survey Sample Design...................................................................................................... 35 Statistical Analysis ........................................................................................................................................ 36 Spatial analysis of data ................................................................................................................................ 36 Summary and conclusion ........................................................................................................................... 37 Works Cited ..................................................................................................................................................... 38 1
  3. 3. Executive SummaryAccess to maternal and child healthcare as well as health outcomes are of significant importanceto researchers as a measure of equity and health system performance in Kenya. Through bothqualitative and quantitative analysis we evaluated the body of literature and select data availableon maternal and child health access and outcomes in Kenya. From existing studies there is strongevidence that distance does play a role in use of health facilities and obtainment of skilledmedical care, though how big a role varies according to the particular study, the distances inquestion, the facilities available, and the urgency of the medical situation. Overall people in ruralareas will have to travel further than people in urban areas. Studies suggest differential marginalbenefit from adding health facilities based on the geographic area - adding new facilities inremote locations in Coast, Eastern, Rift Valley, and North Eastern will increase access more sothan adding new facilities in Central, Nairobi, Nyanza, and Western provinces. Perceived or actual quality issues, education and perception were also found to affectfacility choice and use. Studies showed a significant drop between women’s use of skilledproviders for antenatal care and for delivery, and call out the need to educate women about thebenefits of giving birth with the help of a skilled provider. Although the DHS and SPA datasets provided a wealth of data on the National andProvincial level, the sampling methodology limited our ability to do analysis at a smallergeographic scale than Province. We conducted a significant amount of statistical and spatialanalysis of the DHS and SPA data at the Provincial level to illustrate what level of analysis iscurrently possible with the data. Our recommendation for DRI is to focus data collection on oneprovince at a time and increase the geographic scale for which the data is representative. 2
  4. 4. Introduction This report evaluates access to maternal and child healthcare and health outcomes inKenya using geographic information systems (GIS), statistical analysis, and a comprehensivereview of existing literature. It seeks to aid in identifying distributions of health facilities andservices relative to key maternal and child health indicators (e.g., safe delivery, care andtreatment of birth injuries, antenatal and postnatal care, immunization, and nutrition). It alsoseeks to contribute a portfolio of geospatial maps for identifying, analyzing, and monitoringhealth needs in one of the world’s poorest, most densely populated, and most vulnerable regions.In addition to identifying and analyzing information currently available, the report highlightslimitations of both Kenya’s existing data sets and overreliance on distance as a measure of“access” and “use.” This report responds to a request from Direct Relief International (DRI) to identifyhealthcare access and health outcomes in Kenya as part of its multi-organizational collaborativeproject to enhance health services in an integrated and efficient manner. Along with the AfricanMedical Research Foundation (AMREF), Marie Stopes International (MSI), and district-levelhealth ministries in Kenya, Tanzania, and Uganda, DRI is attempting to determine critical gapsin health infrastructure. The research team consisted of Valerie Benka (MPP/MS candidate), Devi Glick(MPP/MS candidate), and Salvador Maturana (MPP candidate), who completed this project aspart of the Applied Policy Seminar at the University of Michigan’s Ford School of Public Policy.Devi completed the GIS analysis portion of the project, having taken multiple GIS courses inboth her undergraduate and graduate studies. Valerie focused on research and datacollection. She has experience working in central Kenya on health-related research (Q fever) and 3
  5. 5. with qualitative research methods. Finally, Salvador analyzed population data and healthsurveys. He has previously worked with such international datasets as the Integrated Public UseMicrodata Series (IPUMS)-International Database, the Organisation for Economic Co-operationand Development (OECD) Social and Economic Indicators, the World Health Organisation(WHO) Mortality Database, the OECD Programme for International Student Assessment (PISA)Survey, and several national household surveys in Latin AmericaBackground and Research Rationale In 2000, the United Nations established eight Millennium Development Goals tied toeradicating poverty by 2015. Improving child and maternal health are two of the eight goals.The delivery of “equitable” health care in developing countries is key to achieving these goals,and an important indicator of a health system’s equity is a population’s access to and use ofservices (Noor et al. 2006, citing Daniels et al. 2000, Macinko and Starfield 2002). “Equity in service provision is increasingly being used as a measure of health systemperformance,” Noor et al. (2003) assert, and equity “manifests itself in the distribution, access toand utilization of health services between population groups.” The statement complementsresearch showing that in sub-Saharan Africa, a person’s distance from a health facility hassignificant bearing on his or her “access” to health care (Noor et al. 2006). Distance, the authorsargue, contributes to the time required to obtain health care, to delays in pursuing treatment, andto increases in both treatment expenditure and opportunity costs. In 1997, the Kenyan government’s health policy strategic framework stated that allcitizens should have access to health services within a five-kilometer range of their home. Thiscame as part of the government’s overall plans to restructure the health sector to “make all health 4
  6. 6. services more effective, accessible, and affordable” (Noor et al. 2003, citing Ministry of Health1997). This metric was based on the assumption that improved physical access to health carecould reduce delays in citizens’ decisions to seek health care and time traveled to obtain it (Nooret al. 2003, Moïsi et al. 2010, citing Kenya Ministry of Health 1997). The five-kilometerbenchmark has been broadly utilized in subsequent literature analyzing Kenyans’ access tohealth care and health outcomes, and across the nation, indications are that a majority of thepopulation does now have a health facility within 5 kilometers of their home (Moïsi et al. 2010). The following analysis focuses specifically on maternal and child health care “access” (aterm whose complexity is discussed below) and health outcomes. There is extensive literatureciting how both children and mothers living in developing countries experience extremeconsequences due to inadequate medical care. In Kenya, most maternal deaths occur during labor,delivery, and the immediate postpartum period (Wanjira et al. 2011). The majority of maternaldeaths in Kenya are due to obstetric complications, many of which could have been preventedwith adequate medical care during and after delivery (Wanjira et al. 2011). Lack of adequatemedical care can be attributed in part to the fact that only about 83 percent of expectant mothersaccess facilities during delivery, and in part to the fact that the care available at some facilities issub-standard (Wanjira et al. 2011). Both of these issues are addressed below. Turning to children, it has been estimated that 41 percent of the 9.7 million global deathsof persons under five occur in Sub-Saharan Africa (Rutherford et al. 2010, citing Black et al.2003). The United Nations (2012) estimates 55 infant (aged under one year) mortalities per onethousand live births. Although this number is both lower than in many other African countriesand steadily decreasing, it still speaks to a failure on the part of the health system to adequatelyprovide for Kenyan children; it has been estimated that between 41 and 72 percent of newborn 5
  7. 7. deaths in Sub-Saharan Africa could be avoided through adequate access to preventive health careand treatment and/or treatment (Rutherford et al. 2010, citing Haines et al. 2007). This report begins to disaggregate national-scale statistics and look at maternal healthcare access spatially, while also addressing some of the shortcomings and traps of using spatialdata to identify so-called health care “access” disparities.Overview of the Kenyan Healthcare SystemBackground and History Kenya’s health care system is comprised of public facilities with multiple tiers of careprovision and specialization, as well as private and faith-based general hospitals and clinics.There are complexities with terminology insofar as some “public” health facilities are actuallyoperated by non-government entities but referred to by this name because their services areavailable to all at low or no cost. In addition to stationary facilities, mobile clinics are relativelycommon in Kenya. Mobile clinics are oftentimes run by nongovernmental organizations andprovide care to populations ranging from residents of Nairobi slums to rural pastoral populationsin the country’s northern regions. Kenya’s centralized government health care system was blamed for yielding regional andprovincial disparities in health services distribution and quality, inequities in resource allocationand access, and inconsistent indicators of health across regions (Ndavi et al. 2009). In response,Kenya’s Ministry of Health (MOH) has sought to strengthen management of district-level healthcare (Ndavi et al. 2009). This entails local District Health Management Boards (DHMBs) andDistrict Health Management Teams (DHMTs) gradually assuming responsibility for theoperation of facilities under their jurisdiction (Ndavi et al. 2009). In addition, the Kenya Medical 6
  8. 8. Research Institute (KEMRI), a national government body established through the Science andTechnology (Amendment) Act of 1979, is responsible for carrying out health research in thecountry. Although it does not manage health facilities, its existence and activities haveimplications for the quality and sophistication of care provided within Kenya’s health system.KEMRI offices are located in the urban centers of Nairobi, Mombasa, and western Kenyaalongside Lake Victoria.Government health facilities Approximately 41 percent of Kenya’s health care facilities are run by the government(Wamai 2009). The government health care system is organized according to “steps” (Muga et al.2005). An overview of government health establishments, beginning with the least sophisticated,is below. Given what the various health providers are expected to provide, for purposes ofproviding cost-effective maternal and newborn government health services, it makes sense tofocus on dispensaries and health centers. The government operates most of the country’s healthcenters and dispensaries (as well as hospitals) (Wamai 2009). Dispensaries are the most basic (i.e., least sophisticated) health facilities within Kenya’spublic health system. They are designed to be the first contact for patients, offering preventivehealth care, basic outpatient curative care, and referrals to a higher-level health provider ifnecessary (Muga et al. 2005). Dispensaries are staffed by “enrolled” nurses (entry-level nurseswho practice under the supervision of a registered nurse), public health technicians, and medicalassistants (Muga et al. 2005, Riley et al. 2007). Enrolled nurses are trained to provide antenatalcare and treatment for simple pregnancy-related health problems (e.g., anemia). Dispensary staffwill occasionally conduct normal deliveries. 7
  9. 9. Health centers are one step above dispensaries and staffed by midwives, nurses, clinicalofficers, and occasionally doctors. They offer preventive and curative services “mostly adaptedto local needs” (Muga et al. 2005). The scope of health centers is wider than that of dispensaries;in addition to providing services offered by the former, they offer reproductive health servicesand perform minor surgical services (e.g., drainage). Severe and complicated conditions arereferred to a higher level of care provider. Sub-district hospitals and nursing homes, district hospitals, provincial hospitals, andnational referral hospitals, respectively, offer increasingly specialized and high-level care. Sub-district and district hospitals are the lowest level of care with specialized Maternal Child Health(MCH) Clinics (Wanjira et al. 2011). The cost of care at government-run health facilities is likely to be less than that at privatefacilities (with the exception of nonprofit- and/or mission-driven providers that offer care forfree). In 2004, the government removed high and variable user fees for its health facilities. Itestablished a flat fee of Ksh10 (US$0.15) at dispensaries and Ksh20 (US$0.30) at health centers(CREHS 2009).1 The government more recently determined that pregnant women should not paydelivery fees at any government run health facilities (Wanjira et al. 2011).Private health providers Privately owned and operated hospitals and clinics, both for-profit and non-profit andincluding those run by faith-based organizations, provide between 30 and 40 percent of thehospital beds and over 40 percent of health services in Kenya (Muga et al. 2005). In one study,1 These conversions were given in the CREHS (2009) policy brief. As of June 2012, Ksh10 isequivalent to approximately US$0.12, and Ksh20 to approximately US$0.24. 8
  10. 10. Noor et al. (2004) identified 6,674 services providers in Kenya, of which 3,355 (over 50 percent)were private sector, employer-provided, or specialist facilities. These numbers make privatesector facilities a major supplement and complement to government providers. There arecorresponding private providers at most of the levels offered by government providers. Ofcritical importance, however, private sector providers offer mainly curative health services andvery few preventive ones (Muga et al. 2005). This has implications for achieving public healthobjectives, particularly among Kenya’s most disadvantaged maternal and child populations.Clinics and nursing homes, in contrast, are private sector institutions (Wamai 2009). One private sector service pertinent to maternal health is maternity homes. Althoughprivate, they nonetheless collaborate with Kenya’s Reproductive Health and Child HealthDivisions of the Ministry of Health. This makes them more likely to offer reproductive andfamily planning services to clients. Private maternity homes fall under the governance of theKenya Registered Midwives Association (Muga et al. 2005).Private-Public collaboration Collaboration between Kenya’s private and public health systems is varied. As mentionedabove, Noor et al. (2004) calculated that 3,355 of Kenya’s 6,674 identified services providerswere private sector, employer-provided, or specialist facilities. Only 39 percent of these privateservice providers were registered in the Kenyan Ministry of Health database (versus 84 percentof public health facilities supported by the Ministry of Health, missions, not-for-profitorganizations and local authorities). 9
  11. 11. Health providers relevant to maternal and child health in Kenya In addition to the two sectors (public and private) of health care and tiers of facilities,there are also two overarching categories of medical caretakers relevant to maternal care: skilledbirth attendants and unskilled birth attendants. Skilled birth attendants are defined by the World Health Organization (WHO) as persons“trained to proficiency in the skills needed to manage normal (uncomplicated) pregnancies,childbirth and the immediate postnatal period, and in the identification, management and referralof complications in women and newborns” (Harvey et al. 2007). In Kenya, skilled birthattendants’ definition is restricted to doctors, nurses and midwives (Wanjira et al. 2011). Skilledproviders exist in larger numbers in urban areas than in rural, a trend likely related to the higherconcentration of health facilities in urban areas than rural (Wanjira et al. 2011). Maternal mortality is identified as a “sensitive” marker of disadvantage because maternalmortality cannot be directly measured for population subgroups in most data-poor settings (Wirthet al. 2008). Consequently, the percentage of births attended by a skilled birth attendant (SBA) isan intermediate indicator of maternal mortality, reflecting “the distribution of human resourcesappropriately skilled in delivery care and accessible at a health facility or in the community”(Wirth et al. 2008). The Kenya Demographic Health Survey has found that the percentage ofmedically assisted deliveries has fallen consistently from 50 percent of births in 1993 to 44percent of births in 2008 (Wirth et al. 2008; Wanjira et al. 2011). Traditional birth attendants are worth noting because they are used so widely in Kenya.At present, they are excluded from the title of “skilled birth attendant” because 80 percent ofKenyan traditional birth attendants lack formal training in pregnancy and delivery (Wanjira et al. 10
  12. 12. 2011). This exclusion followed a policy shift in the late 1990s.2 Both DHS and regional studiesindicate that while many women receive antenatal care from a skilled provider at a health facility,a large proportion deliver at home, often with assistance from a traditional birth attendant. The2008-09 DHS study found that while 92 percent of Kenyan women receive some antenatal carefrom a skilled provider (most often a nurse/midwife, and often late in their pregnancy), less thanhalf this number (43 percent) give birth in a health facility, and 28 percent of all Kenyan birthsare assisted by a traditional birth attendant (Kenya National Bureau of Statistics and ICF Macro2010). Smaller-scale studies, particularly in rural areas, show much higher rates of assistancefrom a traditional birth attendant, even among mothers who receive antenatal care from skilledproviders (see Mwaniki et al. 2002, Cotter et al. 2006, Van Eijk et al. 2006).Sector quality It is worth noting that the aforementioned services offered by government health facilitiesand presence of skilled birth attendants reflect ideal circumstances. The actual qualifications andcapabilities of skilled birth attendants have been called into question (Harvey et al. 2007).Wanjira et al. (2011) argue that only 15 percent of all Kenyan health workers providing maternalhealth services have received any type of in-service training in treating delivery-relatedcomplications. The studies’ authors tie these meager qualifications to the fact that Kenya’sgovernment-run health facilities are underfinanced and lacking basic resources.2 Although traditional birth attendants are currently excluded from categorization as “skilledbirth attendants,” this has not always been the case. In the 1970s, the World Health Organizationand other funding bodies supported training traditional birth attendants (Kruske et al. 2004). By1997, Kruske et al. (2004) report, these entities shifted their focus on “skilled” attendants, whichexcluded traditional birth attendants and ended funding for traditional birth attendants’ training. 11
  13. 13. Other studies corroborate arguments about substandard care, particularly within thepublic sector. Ndavi et al. (2009) analyzed data from the District Health Management module ofthe 2004 Kenya Service Provision Assessment (KSPA) survey to identify the efficacy ofDHMBs and DHMTs that are the result of health care decentralization. The authors found thatDHMTs most often cited lack of funds and transports as the reason for their failure to meetsupervision targets. Wamae et al. (2009) cite insufficiently equipped facilities, again in the public sector.Combining 2004 KSPA data with interviews, they concluded that the full range of essentialequipment was lacking in almost all facilities. They also reported that of all facilities, hospitalswere the most likely to be stocked with essential equipment, followed by dispensaries, maternityfacilities (“maternities”), and clinics, respectively. This means that the most accessible facilitiesfor pregnant mothers are the least equipped medically. Research also points to the fact that medical professionals are not doing all they can toeducate patients in ways that would lead to the best health outcomes. Cotter et al. (2006)identified missed opportunities to counsel women on the value of delivering with a skilledattendant. They performed a case study of the use of a Kikoneni Health Centre (KCH), located inKwale district in Coast province. Using retrospective data, Cotter et al. (2006) found that of 994women who attended the antenatal care clinic, just 74 (7.4 percent) presented for deliveryservices. This study will be discussed further below. Wamae et al. (2009) also speak to medical providers’ missed “critical opportunities” toboth conduct full assessments of sick children seeking care and to counsel caretakers onchildren’s illnesses. Their survey found that one in every five caretakers was counseled in publicclinics, and one in every ten caretakers was counseled in public health centers. Medical 12
  14. 14. professionals in private facilities were twice as likely to counsel caretakers as professionals intheir public counterparts.Literature review This literature review focuses on three key themes: first, the use of spatial data to addresshealth care access and health outcomes; second, the use of spatial data to analyze maternal childhealth care and health outcomes in Kenya and East Africa specifically; and third, limitations ofspatial data and non-geographic factors influencing access to and use of health services.Use of GIS to analyze health care access and health outcomesThe evolution of GIS to evaluate health access “A fundamental premise of health geography is that illness and health are unequallydistributed across space and time,” writes Graves (2008). The author continues, “Spatial patternsof illness have been associated with many factors, including climate, microbes, exposures,culture, race/ethnicity, geography, and distribution of healthcare services.” GIS can aid inidentifying and analyzing all of these variables, as well as highlighting areas with the highestgeographic barriers to access (Kruk and Freedman 2008). This makes it a promising mechanismfor analyzing health and illness, particularly in combination with other, more conventionalqualitative methods. Although much spatial analysis to data has focused on rural and disenfranchisedpopulations in developed countries, the last decade has seen an expanding focused on developingnations (see, e.g., McCray 2000, Tanser and le Soeur 2002, Graves 2008, Gjesfjeld and Jung2011, Chakrabarti 2009, Langford and Higgs 2006; Charreire and Combier 2009; McLaffertyand Grady 2004). Early work in less developed countries focused largely on epidemiology and 13
  15. 15. the spread of disease, particularly diseases with an environmental component, rather than accessto care (Tanser and le Soeur 2002). To a certain extent, this trend remains. Nonetheless, there isenough research on developing countries, including East Africa and specifically Kenya, to beginto make inroads in analyzing gaps in health care access and outcomes. Frank Tanser, aninfectious disease epidemiologist and Associate Professor of Health and Population Studies atSouth Africa’s University of KwaZulu-Natal, and Abdisalan M. Noor, a graduate of theUniversity of Oxford who is currently affiliated with KEMRI, are the most prolific writers todate on the use of spatial data in analyzing health access and health issues in sub-Saharan Africa(see Noor et al. 2003, Noor et al. 2004, Noor 2005, Noor et al. 2006, Noor et al. 2009, Tanser etal. 2001, Tanser 2006a, Tanser 2006b, Tanser and le Soeur 2002).Distance measures and “access” One critical caveat—and potential limitation—to the value of spatial analyses is thatcertain distance measures do not accurately reflect the time required for travel. Shahid et al.(2009) assessed the efficacy of various GIS methodologies to estimate distance betweenresidence and the nearest hospital: Euclidian, Manhattan, and Minkowski distance.3 The studywas motivated by the fact that actual road distances and travel time cannot be directlyimplemented in spatial analytical modeling. Their findings have bearing on how scholars shouldaccount for this distance-time measure, even though many do not do so. Shahid et al. (2009)found that the Minkowski coefficient best approximates road distance between a person’s homeand hospital, whereas Euclidian distances underestimates road distance and travel time andManhattan distance overestimates these measures. The Minkowski coefficient, the authors3 The Euclidian distance metric uses a straight line to measure the distance between two points(“as the crow flies”) and yields the shortest distance. The Manhattan metric measures distancebetween points along a rectangular path with right angle turns, resembling grid-like city roadsystems. Minkowski distance assumes a curvilinear trajectory (Shahid et al. 2009). 14
  16. 16. conclude, increases the reliability of spatial analytical models and outweighs the cost of thecomputational procedure required for the Minkowski coefficient. Nonetheless, Shahid et al.(2009) warn, the appropriate coefficient for a given study must be calculated based on theregional geography and road network. Noor et al.’s (2006) research supports Shahid et al.’s (2009) study findings in the contextof Kenya. Using high resolution spatial and epidemiological data, Noor et al. (2006) evaluate“access” to medical care in four Kenyan districts—Greater Kisii, Bondo, Kwale, and Makueni—for parents seeking care for malaria and/or fever for their children. Within the context of the fourdistricts, Noor et al. (2006) found that the Euclidean distance model overestimates by 19 percentthe population within one hour of a health facility. Extrapolating to the entire country, this wouldmean that 19 million people are within one hour of government health services, rather than the25 million estimated by the commonly used Euclidian distance model. The authors had two additional important findings. First, as distance from health servicesincreased, the difference in travel time assigned to the patients by the adjusted and Euclidianmodel increased, indicating that the disparities between persons in close proximity to a healthfacility and those further away from a facility are greater than expected. Second, Noor et al.(2006) found that patients did not always seek the closest facility and instead sought higher-orderfacilities over lower ones, a trend that might speak to the quality differences inherent in the “step”system; this finding has significant implications for time-sensitive obstetrical care. Shahid et al. (2009) and Noor et al.’s (2006) analyses highlight an important caveat tospatial analyses of health care “access.” National and international comparisons of health careaccess and equity commonly use the Euclidian distance model to evaluate the proportion ofpeople who live within one hour of health care (Noor et al. 2006, citing Ministry of Health 1999, 15
  17. 17. World Bank 2001). “These measures,” Noor et al. (2006) explain, “assume that people alwaysuse the nearest health service, with little regard for patients’ actual use characteristics and almostexclusively use Euclidean or straight-line definitions of distance” (Noor et al. 2006, citingGething et al. 2004, Guargliardo et al. 2004). As more detailed studies have discovered, thesemeasures can significantly misinterpret actual access, thus demonstrating the need for spatialanalyses to take into account factors such as movement barriers, topography, cost of travel,access to transportation, road or path patterns and composition, and socioeconomic andbehavioral determinants of health service access and use (see, e.g., McLafferty 2003, Noor et al.2006, Shahid et al. 2009).Use of Spatial Data to analyze maternal/child health care and health outcomesin Kenya/East AfricaChild health services A number of studies have looked at the relationship between distance and health careobtainment, illness, and/or mortality among Kenyan children. Feikin et al. (2009) researchedmedical care for children in rural western Kenya. Noor et al. (2003) and Molyneux (1999)focused on treatment for childhood fever and/or malaria, the latter being the principal cause ofchildhood mortality in coastal Kenya and a major cause of death among children in many areasof the country (Molyneux 1999). Moïsi et al. (2010) investigate the effects of distance to healthcare facilities on child mortality. These studies are briefly discussed below. Feikin et al. (2009) analyzed the impact of distance on utilization of peripheral healthfacilities for sick children aged five and under in the rural village of Asembo, where walking isthe most common means of transportation. The authors relied on demographic surveillancesystem (DSS) data collected from households and outpatient clinics, as well the GPS position of 16
  18. 18. each household, the latter being linkable to a child’s unique DSS identification number. 4Controlling for the nearest DSS clinic, maternal education, clustering at the household level, andhousehold socioeconomic status, the authors performed Poisson regression to model the numberof clinic visits made as related to the distance a child lived from the nearest DSS clinic. Theauthors found that the rate of clinic visits decreased linearly at 0.5-kilometer intervals up to 4kilometers, after which the rate of visits stabilized. For every one-kilometer increase in residencefrom a DSS clinic, Feikin et al. (2009) found, the rate of clinic visits decreased by 34 percentfrom the previous kilometer. The authors attribute this trend to the distance-decay effect (i.e., theinteraction between two physical spaces declines as the distance between them increases). Theyalso note, however, that infants and children with more severe illnesses traveled further for clinicvisits, indicating that the distance-decay effect was less pronounced among infants and childrenin more dire condition. Noor et al. (2003) analyzed potential and actual use of government health facilitiesamong pediatric patients seeking care for malaria and/or fever in four Kenyan districts withvaried ecological, population, and health services conditions: Kwale, Makueni, Greater Kisii, andBondo. (This research is also discussed in depth in the aforementioned Noor et al. [2006] articleabout the limitations of the Euclidian distance measure.) They used GIS and proximity-spiderdiagrams to analyze data obtained from provincial district-specific database lists as well asreports and maps from a large number of public, private, and religious-affiliated sources. (Thehighest spatial resolution data readily accessible in the public domain are provided at the sub-location level.) The authors found that more than 60 percent of patients utilized health facilitieswithin 5 kilometers of their home. However, there was significant diversity among districts in4 Not all children and adults have DSS identification numbers. The authors acknowledge thattheir exclusive reliance on DSS numbers biases the study. 17
  19. 19. terms of overall average travel distances; patients in Kwale and Makueni districts, where accessto government health facilities was relatively poor, traveled greater mean distances than those inGreater Kisii and Bondo. The analysis also showed specific areas of poor access and largedifferences between rural and urban settings, with residents of the latter traveling longerdistances to health facilities. The difference in access between urban and rural communities waslarger in relatively large, low-population density districts than in relatively small, high-densityones. Notably, Noor et al. (2003) found that distance was not the exclusive factor shapingchoice of health facility. The authors found that between 65 and 74 percent of respondents used amore distant health center or hospital for care. Noor et al. (2006) report that not only do patientsoften choose a health facility other than the one nearest to their home for treating children withfever/malaria, but also that Euclidian distance between home and health facility oftenunderestimates time required to travel between home and health facility.5 Molyneux (1999) studies urban and rural residents in coastal Kenya to explore thechoices that mothers make to treat their child’s “uncomplicated” fever. The urban subpopulationis located within a close three kilometers of the Mombasa District Hospital and five privateclinics, and even the rural study area subsection is transected by a large paved road.Consequently, although the central point of the rural subsection is approximately 10 kilometersfrom the district hospital, over 66 percent of rural residents are within two kilometers of a bus5 The percentage of people who use a facility other than the one nearest their home per Noor et al.(2003) and Noor et al. (2006) is markedly lower than what Tanser et al. (2001) found to be thecase among residents of Hlabisa district, South Africa, seeking primary care. Hlabisa district hasa central community hospital, 11 satellite fixed clinics, and several mobile clinics that provideprimary care. Tanser et al. (2001) found that 87 percent of homesteads use the nearest clinic andtravel an average Euclidian distance of 4.72 kilometers to attend a clinic. There are multiplepossible explanations for this difference, beginning with the fact that the studies occurred indifferent countries with different health care systems. 18
  20. 20. stop. Despite relative easy geographic, if not economic, access to medical facilities for bothpopulations, they were not mothers’ first choice for care. Government or private clinics werecontacted in 49 percent of cases. Sixty-nine percent of rural and urban respondents turned first orexclusively to over-the-counter antimalarial/antipyretic medicines without contacting a physician.Government or private clinics were contacted in 49 percent of cases. The only significantdifference between rural and urban mothers was the use of private/government clinics: ruralmothers were more likely to seek government services, and urban mothers to consult privatepractitioners. Moïsi et al. (2010) investigated the effects of distance to health care facilities on childmortality in Kilifi District, Kenya. The authors used data collected on over 220,000 peoplethrough the District’s Epidemiological and Demographic Surveillance System (Epi-DSS). Theauthors also used GIS to estimate walking and vehicular travel times to hospitals, vaccine clinics,and 100 other public, private, or NGO-operated health facilities, and they developed models toevaluate the effects of travel time on the likelihood of mortality for children under five years ofage (accounting for such variables as sex, maternal education, and rainfall). Euclidian distancemeasures showed no clear trends of increasing or decreasing mortality with increased pedestrianor vehicular travel time to a hospital or clinic so long as the child lived within two hours’ drivingtime of the facility. The authors additionally noted that while significant spatial variations inmortality were observed across the area, they were not correlated with distance to healthfacilities. As such, the authors concluded, “our data did not lend support to the widely heldnotion that mortality increases with distance to hospitals and vaccine clinics,” and “given thepresent density of health facilities in Kenya, geographic access to curative services does notinfluence population-level mortality.” 19
  21. 21. The authors suggests that the Kilifi DSS study is representative of Kenya as a whole vis-à-vis health care access insofar as approximately two-thirds of the country’s population liveswithin a one-hour walk of a primary health facility. Notably, although this finding is consistentwith another DSS study from the Gambia, it contrasts with multiple analyses conducted in othercountries that show a strong relationship between mortality and distance to health facilities.Obstetric and maternal health services Noor et al. (2004) and Noor et al. (2009) use GIS mapping software to analyze Kenyans’proximity to health providers. These studies provide the most comprehensive look at overallgeographic access to health facilities, and are closely tied to Noor’s (2005) doctoral dissertationon the development of spatial models of Kenyan health service access and use to define healthequity. The analyses by Noor et al. (2004) and Noor et al. (2009) give no attention to facilitiesspecializing in care for mothers and/or children. However, assuming that all public healthfacilities are equipped to provide routine antenatal, obstetric, and pediatric care, these overallanalyses should reflect the access that mothers and children have to basic health care. Noor et al. (2004) built a health services providers database from multiple governmentand nongovernmental sources and positioned these facilities spatially. Of 6,674 service providersidentified, 3,319 were public (supported by Kenya’s Ministry of Health, missions, nonprofitorganizations, and local authorities). The remaining 3,355 were private-sector, employer-provided, or specialist facilities serving higher-income persons. The authors were able tospatially place 92 percent of the 3,319 public service facilities. Eighty-two percent of the Kenyanpopulation is within 5 kilometers of a public health facility, 12 percent are between 5 and 10 20
  22. 22. kilometers, and 6 percent are more than 10 kilometers away.6 Noor et al. (2004) also note thatalthough 82 percent of people live within 5 kilometers of a facility, they occupy only 20 percentof the country—a statistic that speaks to the relative concentration of much of Kenya’spopulation and the dispersal and relative isolation of the country’s rural residents. Noor et al. (2009) updated the analysis from five years prior, compiling a list of 5,334public health facilities, an increase of 1,862 facilities over 2003.7 Sixty-seven percent of healthfacilities were operated by the Ministry of Health, followed by 28 percent mission and NGOs, 2percent local authorities, and 3 percent employers and other ministries. The authors spatiallyreferenced 93 percent of these facilities. Using 2008 health facility and population data, Noor etal. (2009) found that 89 percent of the population was within 5 kilometers Euclidian distance ofa public health facility, and that 80 percent of the population outside 5 kilometers of publichealth service providers was in the sparsely settled pastoralist areas. Using this data, Noor et al. (2009) concluded that new health facilities are unlikely toyield significant improvements in geographic access to health facilities in Central, Nairobi,Nyanza, and Western provinces. Additional facilities in Coast, Eastern, and Rift Valleyprovinces could increase access in remote locations. Finally, Noor et al. (2009) cite that NorthEastern province is significantly behind others in terms of resident proximity to a health facility. While Noor et al. (2004) and Noor et al. (2009) looked at proximity to health facilitiesnationwide, Cotter et al. (2006) targeted Kikoneni Health Centre (KHC) in Coast province. Their6 Noor et al. (2009) report that in 2003, 72 percent of Kenya’s population lived within 5kilometers Euclidian distance of a public health facility, indicating that the analysis published inNoor et al. (2004) used a different distance measurement.7 For the updated list, Noor et al. (2009) appear to supplement the 2004 study with additionalhealth facilities obtained through antimalarial and antiretroviral services. This indicates that thefive-year increase is not due to an actual increase in facility numbers, but rather more extensiveidentification methods. 21
  23. 23. study sought to estimate maternal use of skilled attendants for delivery at KHC, as well asavailability of skilled attendants for the general Kikoneni population. Data was retrospectivelyreviewed, and spatial analysis was not performed. The findings nonetheless have bearing onanalyses of access to maternal healthcare. Of 994 women who attended KHC’s antenatal careclinic, a paltry 74 (7.4 percent) presented for delivery services. A comparison of deliveries athealth facilities with expected births in the population found that 5.4 percent of expected births inthe population occurred at health facilities (Cotter et al. 2006). Not only are both percentageswell below the proportion of mothers who give birth in a health facility across the province (perKenya DHS data), but they also indicate that receipt of skilled prenatal care does not translateinto use of a skilled practitioner when giving birth. The relatively limited reliance on skilled care for delivering is corroborated by studieselsewhere in Kenya. Mwaniki et al. (2002) studied mothers in Mbeere District, Eastern Province,and found that 97.5 percent of mothers received antenatal care at a health facility, but only 52percent gave birth at one. Van Eijk et al. (2006) studied mothers in rural western Kenya. Theyfound that 90 percent of the 635 women interviewed visited an antenatal clinic at least onceduring their last pregnancy (albeit often only in the third trimester), but a much lower 17 percentdelivered at a health facility. Reasons for this drop-off vary, and relate in varying degrees to distance factors.However, consistent across studies is the fact that proximity to a clinic is not the sole predictor ofits use for giving birth. Cotter et al. (2006) reported four reasons why the use of an SBA was solow, one of which was the combination of logistic and geographic barriers: the distance is toosignificant, and transportation is often unavailable or unaffordable. The other three relate toknowledge and perceptions and are discussed below. Mwaniki et al. (2002) found that utilization 22
  24. 24. of health facilities for maternity care was heavily correlated with distance; mothers living under5 kilometers from the facility better utilized its services. However, the authors also found astrong correlation between number of children and facility use (mothers with more children usedits less); mothers additionally cited dissatisfaction with facilities’ services, cleanliness, and staffas reasons for not using them. Meanwhile, Van Eijk et al. (2006) reported that although lack oftransport, especially at night, was the most common reason why mothers did not deliver at ahealth facility (49 percent of respondents), fast progression of labor (47 percent) and expense (28percent) also factored in heavily. In combination these studies indicate that distance from ahealth facility can be a major factor in whether or not its skilled caretakers are used for delivery,but there are several other variables that influence mothers’ actions. It is worth noting that the trend of greater use of skilled care and facilities for antenatalcare than actual birth is by no means limited to Kenya. Looking at studies from other Africancountries may help put Kenya in context and illuminate factors affecting maternal care andhealth outcomes. Van den Broek et al. (2003) conducted a study of reproductive health in rural Malawi.With nearly 60,000 persons surveyed, it was considered the largest community-basedreproductive study in Africa at that time. The population survey was also deemed to be typical ofmany other sub-Saharan countries in terms of population composition, dependency ratio,occupation, and educational attainment. Almost all pregnant women (94.9 percent) receivedclinical prenatal care despite living an average of 5 kilometers form the health center. The meannumber of visits was 5.2. Distance only came into play with whether a mother made six or moreantenatal clinic visits. 23
  25. 25. Distance from a clinic played a more significant role when it came to delivery. Van denBroek et al. (2003) found that the use of a trained health care worker decreased as distance froma health center increased, and that proximity of any household to a health center (regardless ofother household/maternal variables such as education and income) has an effect on use of a clinicfor delivery. It also impacted delivery outcomes. For households situated within one kilometer ofthe health center, 79.1 percent of pregnancies resulted in a currently living child; for householdssituated seven kilometers or more from the health center, 73.3 percent of households resulted in acurrently living child. The authors also found a statistically significant correlation betweenmaternal education and use of a skilled birth attendant, with more educated mothers more likelyto receive professional care.8 Walker and Vajjhala (2009) address the links between spatial exclusion, transport access,and Millennium Development Goals for women living in Lesotho, Ethiopia, and Ghana. Thestudy seeks to illustrate the implications of these links for understanding the underlyingdynamics of spatial exclusion. In addition to quantifying nationwide distances to health careusing DHS clusters, they also survey women on the major factors that hinder access to healthservices.9 In contrast to Van den Broek et al.’s (2003) study of Malawi, which points to distanceas the key factor in use of a medical facility for giving birth, Walker and Vajjhala’s (2009) workindicates that reducing distance is not the “magic bullet” to increasing medical access and healthoutcomes. The findings suggest that while distance and transport impediments are substantial,addressing these in isolation will not necessarily yield hoped-for increases in health care8 The authors do not speculate on why education correlates with use of a professional clinic, andwhat interplay there might be between variables—e.g., whether more educated mothers are moreknowledgeable about the benefits of giving birth in a medical facility, more able to afford thecost of skilled obstetrical care, or simply living closer to medical facilities.9 Notably, this study focused on all women’s health services, not just antenatal or obstetrical care.It is possible that reasons for not accessing health services differ when it comes to delivering. 24
  26. 26. important community-level contributions to improving rural access, particularly for women.5.2 Barriers to Access Evaluating the types of spatial relationships and constraints addressed in the previoussections is even outcomes. As when 1 shows, distance and transport were significant factors in obtainment andmore important Table considering investments to reduce barriers to transport andhealth servicenot a woman received professionalquestionscare. three DHS questionaires for whether or delivery. Based on a full set of DHS health on all However, getting money(Ghana, Ethiopia, and Lesotho), Table 3 and the maps in this section illustrate the extent towhich transport barriers, on average across the clusters,reason that womenmajority of women as treatment, not geographic factors, was all major are identified by a did not access healthaservices. big problem relative to other barriers to health service access. Because each of these barrierswas evaluated separately by survey participants and not ranked in order of difficulty or priority,the data1: Percentages of women by cluster of barriers to one another. Instead, “big problem” Table do not allow for a direct comparison who identified each barrier as a we highlighteach barrier separately in the maps of Lesotho that follow to show the spatial variation. for accessing health services (Source: Walker and Vajjhala 2009). Table 3. Summary Table of Country Averages: Percentage of Women b y Cluster Who Identified Each Barrier as a “Big Problem” for Accessing Health Serv ices Barrier to health service Ghana Lesotho Ethiopia 2000 Ethiopia 2005 Knowing where to go 11% 3% NA NA Getting permission to go 9% 2% NA 30% Getting money for treatment 57% 40% NA 73% Distance to health facility 37% 29% NA 63% Having to take transport 37% 31% NA 65% Not wanting to go alone 21% 12% NA 55% No female service provider 16% 7% NA 66% Limitations of spatial data/non-geographic factors influencing health care access 19 and use One overarching issue with both spatial and non-spatial analyses of health care access and outcomes is how we define “access.” Rutherford et al. (2010) assert that the “concept of access is not well understood” and cite several definitions from existing work that speak to a variety of interpretations of the term. The authors build on the claim by Andersen (1995) that access could be assessed by both health service use and outcomes, which speaks to the necessity of incorporating use into the access concept. In considering maternal and child health access and outcomes in Kenya and the broader region, this definition seems apt, particularly given differences in quality among facilities, research findings that a large percentage of Kenyans do 25
  27. 27. not use the facility closest to their home, and, looking more broadly, the non-spatial barriers tohealth care acquisition cited by Walker and Vajjhala’s (2009) study subjects. Although distance factors into the access to and use of health services in Kenya, evenauthors who focus on physical access acknowledge that overall “access” is influenced by otherfactors as well (see, e.g., Noor et al. 2003). Behavior, culture, education, income, and servicecost all become considerations in evaluating overall use of health facilities and resultant healthoutcomes.Demographic components of SBA use in Kenya Studies have found that use of skilled birth attendants is highly stratified by poverty andother social determinants of health. Wealth, education, and ethnicity have been found to have thegreatest impact on use of a skilled birth attendant, in that order (Wirth et al. 2008). Region has amuch lower correlation. Wirth et al. (2008) found that the use of an SBA correlates most significantly with wealth,education, and ethnicity. The stepwise gradient for use of an SBA is greater by wealth quintilethan by region. Education and ethnicity were also found to have significant correlations withSBA use. Seventy-two percent of mothers with a secondary education use an SBA, comparedwith 36 percent of mothers with a primary school education, and 27 percent of mothers with noeducation. Kikuyu, Kenya’s majority ethnic group, were found to use SBAs most frequently,with 71 percent of mothers delivering with their help, as compared to 27 percent ofMijikenda/Swahili (Wirth et al. 2008). Knowledge of and attitudes toward various birth attendant “groups” also had stronginfluences on the persons mothers chose to assist with childbirth. Based on findings from ahospital-based cross-sectional survey among women who had recently given birth, albeit notnecessarily in a medical facility, Wanjira et al. (2011) suggest that some mothers view birth 26
  28. 28. attendants at home (unskilled) as similar to birth attendants in health facilities (skilled) in theway they attend to deliveries. Women and communities that view traditional birth attendants asequally skilled as professionals with medical training choose traditional attendants over skilled(Wanjira et al. 2011). Wanjira et al. (2011) interpret this statistic and suggest that the perceptionsof mothers on birth attendants could be greatly influenced by the interpersonal relations with theattendants during labor and delivery. Cotter et al.’s (2006) case study of women in Coastprovince corroborates this finding. Respondents noted that many mothers believe that becausethe previous generation delivered at home, the current generation will do so as well. Healthcareproviders may not be actively working to sensitize women to the importance of delivering withskilled assistance; small minorities of pregnant patients reported being counseled on planning forthe location of delivery or being told of the benefits of delivering in a health facility (Cotter et al.2006). The tendency for Kenyan health providers to neglect key opportunities to counsel patientsis also addressed by Wamae et al. (2009) in the context of care for infants and children.Conclusions Existing studies provide insight into the factors that do and do not influence “access” tomedical care for mothers and children. Distance most certainly plays a role in use of healthfacilities and obtainment of skilled medical care, though how big a role varies according to theparticular study, the distances in question, the facilities available, and the urgency of the medicalsituation. Studies suggest that adding health care facilities will yield a greater benefit in some areasover others. As discussed above, Noor et al. (2009) conclude that adding new facilities in remotelocations in Coast, Eastern, Rift Valley, and North Eastern would increase access more so thanadding new facilities in Central, Nairobi, Nyanza, and Western provinces. On the whole, it is 27
  29. 29. likely that rural residents of any province will have to travel further for medical care than theirmore urban counterparts. However, given the extreme isolation of much of Kenya and dispersalof the population in these isolated areas, the cost-benefit ratio of adding new facilities in ruralcommunities is an issue. In all cases, when evaluating persons’ distance from health facilities, itis important to pay attention to the distance measure being used; the commonly used Euclidiandistance measure has major shortcomings. It is also critical to pay attention to other variables shown to factor into “access.” Studiesshow that people often do not use the closet medical facility, likely because of perceived oractual quality issues, and instead go to higher-level care providers. Studies also show asignificant drop between women’s use of skilled providers for antenatal care and for delivery.This can be attributed in part to distance and the time-sensitivity inherent to giving birth, but alsoto women’s education and perceptions about giving birth in a health facility rather than with theassistance of a traditional birth attendant. Studies point to the need to educate women about thebenefits of giving birth with the help of a skilled provider and make the experience a morepositive one. It is worth noting that use of mobile clinics were omitted from the literature review, yetthey are present in the country and an important source of health care to both urban and ruralpopulations. They are worth considering as a way to address distance factors for ruralpopulations, although they are not a viable option when it comes to providing time-sensitivehealth care. Their value is mainly in providing regularly spaced preventive and curative care.Overview of data collected and available for analysis 28
  30. 30. Beyond a literature review, we performed statistical analysis of existing data and mappedthem using ArcGIS. This section describes four key health-related Kenyan data sets (the Census,Demographic and Health Survey, National Health Facility Inventory, and Service ProvisionAssessment Sample) and explains why we selected some data sets and omitted others fromanalysis. The section then turns to a discussion of the administrative districts available foranalysis.Data setsCensus The Kenyan Census is conducted by the Kenya National Bureau of Statistics. The mostrecent national census was conducted in 2009. Each census contains a complete count of thecountry’s population and provides information on its size, distribution, composition, and othersocial and economic characteristics. This data can be aggregated to various geographic levels andused to connect demographic factors with health indicators and facility access. The Kenya National Bureau of Statistics appears to have the 2009 census data availableupon special request; however, we were not able to gain access to it. We therefore used a subsetof the 1999 census available from the World Resources Institute (WRI), which had already beengeo-located, and population density information from This data-set containspopulation density information, poverty rates, and other indicators. More recent census data isavailable with the data broken down by various administrative districts; however geo-locateddata in the form of geographic coordinates was not available for the majority of the datasets.The Demographic and Health Survey (DHS) The Kenya DHS began in 1984. Its design was based on World Fertility Surveys andContraceptive Prevalence Surveys, with the addition of an expanded set of indicators in the areasof population, health, and nutrition. Demographic and Health Surveys are designed to collect 29
  31. 31. data on marriage, fertility, family planning, reproductive health, child health, and HIV/AIDS.Due to the subject matter, women of reproductive age (15–49) are the focus of the survey.Women eligible for an individual interview are identified through the households selected in thesample. Consequently, all DHS surveys utilize a minimum of two questionnaires—a HouseholdQuestionnaire and a Women’s Questionnaire (KNBS and ICF Macro). Measure DHS is fundedby United States Agency for International Development (USAID) and executed by ICFInternational. Rural areas were oversampled in this survey to compensate for likely oversampling inurban areas. Responses were collected based on clusters of households (neighborhoods) andaggregated into a single point for each cluster. The clusters are representative at the provincialand national level; therefore the data is representative at the provincial and national level. Thecluster data is available as a point shape file, and we were able to access and use the 2008version of this dataset. As indicated above, the sampling technique used to collect the DHS data is representativeat the national and provincial level, but not at any scale below that. We had intended to do local-level analysis, but to reiterate, the sampling technique used to select the clusters of householdsthat were sampled was only intended to be representative at the provincial and national level.This means that any analysis that is done below the provincial level will not be statisticallyaccurate because the data was not collected to be representative at that level. This gap in data atthe local level presents a significant limitation on the level of analysis that anyone is able toperform. All previous analysis of the DHS data that we found had been limited to the provincialand national level as well. 30
  32. 32. National Health Facility Inventory The National Health Facility Inventory is an inventory of all health facilities in Kenya.The inventory was collected and published most recently in February, 2010 by the Ministry ofMedical Services and the Ministry of Public Health and Sanitation. Facilities are characterizedby level, type, and ownership (public, faith-based, private, and NGO) to give an overall pictureof the health infrastructure available throughout the country. Unlike the Service ProvisionSample discussed below, this dataset is intended to be an inventory of all health facilities in thecountry, not a sample. The dataset has 8044 data points and is available at The data should be representative at any level of spatialanalysis. The data manual clearly outlines that geo-location data is available; however, thepublicly available version of the dataset does not contain geographic information. Attempts tocontact the Ministry of Health and procure access to the full dataset with the geo-locatedinformation were unsuccessful. In place of this dataset, we found a different National Health Facility Inventory fromKenya Open Data (, which didhave geolocation information. There are 6070 data points in this data file, although only 4865data points have geographic information. It is not clear how carefully this data-set was preparedto avoid duplicate entries or omit facilities. The source of the data was listed as the KenyaNational Bureau of Statistics. This dataset is broken down slightly differently than the officialNational Health Facility Inventory. The facilities are broken down into nine categories: 1. Hospitals MOH and Mission; Districts, sub-districts (smaller – less specialized) 2. Referral Hospitals and Provincial Hospitals (larger – less specialized) 3. Health Centres (NGO – basic) 4. Dispensaries (only basic maternal services) 31
  33. 33. 5. Private Hospitals 6. Private Clinics and Medical Centres (specialist) 7. Nursing Homes and Maternity Hospitals 8. Special Treatment Hospitals 9. Institutions Health Facilities - schools, Universities, Employer, Police, Prisons, Other Ministries, Airport & Port Authorities, Armed forcesService Provision Assessment (SPA) Sample The Service Provision Assessment Sample is a sample of health facilities in the country.The survey was designed to gather information on the provision of reproductive and child healthservices in Kenya. The assessment was undertaken to provide a comprehensive picture of thefunctioning and quality of health services in four areas: maternal health, child health, familyplanning, and sexually transmitted infections and HIV/AIDS. The KSPA’s aim was to assess thestrengths and weaknesses of the delivery of these health services and to providerecommendations on how to improve the provision of services in the future (NCAPD, 2011). The SPA contains more detailed information on the provisions at each facility than theNational Health Facility Inventory does. Just like with the DHS, this data is representative at theregional and national level. The SPA was conducted by the Ministry of Health and the NationalCouncil for Population and Development and we were able to access and use the most recentversion of the dataset from 2010. There are 695 data points in the SPA dataset.Administrative districts Kenya is broken down into five sub-national administrative levels: province, division,district, location, and sub-location. There is near-universal agreement of how to define andgeographically delineate the provinces (there are eight: Central, Coast, Eastern, Nairobi, North-Eastern, Nyanza, Rift Valley, and Western), but below that there is significant variability in how 32
  34. 34. divisions, districts, locations, and sub-locations are defined and how they are geographicallydelineated. This made it challenging to conduct analysis or merge data sets (where there were notgeographic coordinates) based any of the sub-province levels. The administrative layers that wechoose to use were obtained from, an open data site with geolocated spatiallayers.Survey DesignGeneral ApproachThe DHS and SPA surveys provide a large quantity of data that we hoped to use for small scalespatial and statistical analysis of maternal health indicators in Kenya. Our goal was to map therelationship between health indicators and facilities, population, and various other factors at themost refined scale possible. The DHS data was collected in clusters, stratified units selected tosample individuals across the entire country. The clusters themselves were composed ofhouseholds from a neighborhood or small village. The SPA data was collected on the individualfacility level. Our preliminary plan was to extrapolate up from the cluster level and facility levelto one of the refined geographic units, but we still did not know at what levels the data wasrepresentative.Evaluating the statistical feasibility of such data analysis strategy required careful review ofKenya DHS and Kenya SPA sample designs. We knew that our ability to extrapolate zone-bounded survey information would be methodologically determined by survey’s datarepresentativeness and thus sample design. 33
  35. 35. Kenya DHS Survey Sample DesignThe 2008-09 KDHS survey was designed to cover the entire country, including the arid andsemi-arid districts. The survey collected information on demographic and health issues from asample of women at the reproductive age of 15-49 and from a sample of men age 15-54 years ina one-in-two subsample of households (Kenya National Bureau of Statistics and ICF Macro,2010). To be representative, the sample was drawn from 10,000 households. Most importantly, itwas constructed to allow for separate estimates for key indicators for each of the eight provincesin Kenya, as well as for urban and rural areas separately.The Kenya DHS used master sampling frames developed on the platform of a two-stage sampledesign. The first stage involved selecting data collection points (‘clusters’) from the nationalmaster sample frame. A total of 400 clusters, 133 urban and 267 rural, were selected. The secondstage involved the systematic sampling of households from an updated list of households in theselected cluster. Then, all women age 15-49 years who were either usual residents or visitorspresent in sampled households on the night before the survey were eligible to be interviewed inthe survey. In addition, in every second household selected for the survey, all men age 15-54years were also eligible to be interviewed (Kenya National Bureau of Statistics and ICF Macro,2010).Finally, a total of 9,936 households were selected in the sample, of which 9,268 were occupiedduring fieldwork and thus eligible for interviews, among which 9,057 were successfullyinterviewed, yielding a response rate of 98 percent. From interviewed households, 8,767 womenwere eligible and 8,444 were interviewed, yielding a 96 percent response rate. Interviews withmen covered 3,465 of the eligible 3,910 men, yielding 89 percent response rate (Kenya NationalBureau of Statistics and ICF Macro, 2010). 34
  36. 36. Kenya SPA Survey Sample DesignThe 2010 Kenya SPA is a facility-based survey designed to provide information on thepreparedness of health facilities. It provides national and provincial-level representativeinformation for hospitals, health centers, maternity and nursing homes, clinics, and stand-alonevoluntary counseling and testing (VCT) facilities (National Coordinating Agency for Populationand Development (NCAPD) [Kenya], Ministry of, 2011).The sample of facilities included was randomly selected from a Master Facility List (MFL) of6,192 functioning health facilities in Kenya at the time of the survey. A sample of 703 facilitieswas designed to allow for key indicators to be presented at national and provincial levels, by typeof facility, and by the different managing authorities. Interviewers were not able to survey eightof the sampled facilities for various reasons, including inaccessibility due to poor roads.Consequently, data were successfully collected from 695 facilities. (National CoordinatingAgency for Population and Development (NCAPD) [Kenya], Ministry of, 2011).Data assessmentAs described previously, both for KDHS and KSPA, households and respondents wereselected in order to produce representative population estimates at the national andregional level only. The survey design for DHS is not conducive for smaller area estimation, aswas the primary data mapping strategy. Indeed, any sub-regional estimates are highly unreliableand likely to result in large standard errors and so it is not methodologically feasible to do spatialanalysis at the individual cluster level. Moreover, the GPS data for the cluster can be used toextract additional information based on location but are not representative of the populationliving at that exact place (Measure DHS, 2011). 35
  37. 37. Giving the statistical constraint, we decided not to use cluster to map the data at any level belowprovinces. We instead mapped the data at the level for which it was representative: the Provinciallevel.Statistical AnalysisAlthough we were not able to perform the extensive statistical analysis at the fine scale that wehad hopped, we did run a few regressions on the Provincial level with the DHS data. Althoughthe results were not all that surprising, they are worth mentioning. The main things we saw werethat:1) The denser the area the more facilities there are (.51)2) Denser (population) areas tend to have more private facilities than public (.02 / -.01), thoughboth relationships are pretty weak.3) Denser (facilities) areas tend to have more private facilities than public (.16 / -.08), thoughboth relationships are weak.4.) From the coefficient between both dummies (.21) we observe coexistence which means thatthe facilities are serving as compliments, not substitutes.See Appendix A for the stata output and the original analysis.Spatial analysis of data Based on the data constraints discussed in the previous section, we were only able toperform spatial analysis of the SPA and DHS at the Provincial and National level. The attachedmaps contain population and facility level analysis at the location level, as well as DHS and SPAanalysis at the Provincial level. 36
  38. 38. See Appendix B for maps.Summary and conclusion This research and analysis yielded two overarching conclusions. The first is the need toreevaluate data collection if it is to be accurate below provincial and national levels. Ourrecommendation is to focus future data collection efforts on being representative at the districtlevel. While this would require a significant increase in the quantity of sampling needed but it ispossible to focus on only one or a few provinces as a starting point. The second is the need toconsider variables beyond distance when evaluating access to and use of medical care. Althoughdistance from a health facility is shown to factor into use of that facility to some degree, it isunlikely that merely adding health facilities to the Kenyan landscape will drastically increasematernal and child use of facilities and enhance health outcomes. Variables such as facilityquality, cost of services, and knowledge about and attitudes toward skilled health care can playstrikingly significant roles in whether a facility gets used. 37
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  46. 46. Appendix A. cd "M:KENYA". insheet using "density_population_facility_este si.csv". corr private_count public_count population_density facility_density privat~t public~t popula~y facili~yprivate_co~t 1.0000public_count 0.2885 1.0000population~y -0.0055 -0.0005 1.0000facility_d~y 0.2308 0.1826 0.5144 1.0000Where private_co~t = counts of private facility in the districtpublic_count = counts of public facility in the districtpopulation~y = population density in the districtfacility_d~y = facility density in the districtHere the same correlation matrix but using dummies for private/public (private_dum, public_dum). Thisis more useful to look for the relationships:. gen private_dum=0. replace private_dum=1 if private_count=~0. gen public_dum=0. replace public_dum=1 if public_count~=0. corr private_dum public_dum privat~m public~m popula~y facili~y private_dum 1.0000 public_dum 0.2181 1.0000population~y 0.0267 -0.0193 1.0000facility_d~y 0.1639 0.0849 0.5144 1.0000Indeed, 1. The denser the area the more facilities there are (.51) 2. Denser (population) areas tend to have more private facilities than public (.02 / -.01), though both relationships are pretty weak. 3. Denser (facilities) areas tend to have more private facilities than public (.16 / -.08), though both relationships are weak. 4. From the coefficient between both dummies (.21) we observe complementarity/coexistence
  47. 47. Liz comments on the correlations:The strong positive correlation between population density and facilities density is not too surprisinggood to demonstrate. On the types of facilities, I think the counts actually provide more interestinginformation - they give a sense of the number of each type in a district, rather than simply their presence.The rather strong positive correlation suggests that higher number of private are co-located with highernumbers of public. Therefore, they are not serving as substitutes but rather as complements.
  48. 48. Appendix B: MapsSources- Administrative Districts Shapefile: Map Library Population Density Raster File: Afri Pop Health Facilities Data: Kenya Open Data Maternal Health and Child Health Data: Measure DHS. Kenya Demographic andHealth Survey (DHS) 2008-09. Maternal Health Facilities and Child Health Facilities Data: Measure DHS. KenyaService Provision Assessment (SPA) 2010. 1
  49. 49. List of MapsPopulation Density - Map 5.2: Postnatal Care- Map 1: Population Density in Kenya - Map 5.3: Basic Antenatal Care Supplies - Map 5.4: Sexually Transmitted Infection TreatmentHealth Facilities by Sub-location - Map 5.5: Pregnancy Complications- Map 2.1: Health Facilities in Kenya - Map 5.6: Normal Delivery- Map 2.2: Health Facilities in Kenya: Density - Map 5.7: Caesarean Section - Map 5.8: Home Delivery ServicesHealth Facilities - Map 5.9: Essential Delivery Services- Map 3.1: All Health Facilities - Map 5.10: Serious Delivery Complications- Map 3.2: Government Dispensaries in Kenya- Map 3.3: Government Facilities in Kenya Child Health- Map 3.4: Government Health Centers in Kenya - Map 6.1: Vaccination- Map 3.5: Public and Private Facilities in Kenya - Map 6.2: No Vaccinations - Map 6.3: Fever TreatmentMaternal Health - Map 6.4: Diarrhea- Map 4.1: Antenatal Care - Map 6.5: Diarrhea Treatment- Map 4.2: Tetanus Protection - Map 6.6: Diarrhea Treatment 2- Map 4.3: Delivery in a Health Facility- Map 4.4: Delivery in Health Facility 2 Child Health Facilities- Map 4.5: Delivery by a Skilled Provider - Map 7.1: Outpatient Care- Map 4.6: Postnatal Check-up - Map 7.2: Childhood Immunizations - Map 7.3: First-line ServicesMaternal Health Facilities- Map 5.1: Antenatal Care 2
  50. 50. Map 1 This map shows the average population density (people per square km) for each sub-location. 3
  51. 51. Map 2.1 This map shows how many health facilities fall within the geographic bounds of each sub- location. 4
  52. 52. Map 2.2 This map displays the density of health facilities per 5 square km in each sub-location. Border facilities, that is facilities near the border of another sub- location, and which could therefore be within 5 km of someone within that sub- location are not accounted for. 5
  53. 53. Map 3.1 All health facilities include both government and private facilities which can be broken down into dispensaries, hospitals, health centers, clinics, nursing homes and maternity hospitals, and other specialty or institutional facilities. 6
  54. 54. Map 3.2 Only dispensaries run by the government are included here 7
  55. 55. Map 3.3 Central Government Facilities are run by the Ministry of Health (majority), Other Ministries, and the Armed Forces. Local Authority facilities are run by local governments 8
  56. 56. Map 3.4 Only Health Centers run by the government are included here 9