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ics,

a r t i c l e i n f o

Keywords:Geographic accessHealth careGISMozambique

a b s t r a c t

in health resource allocation and program planning. Therefore,understanding and evaluating access to health care and its spatialvariation are vital for healthcare planners and policy makers.

en, 1975; Cromleyh & Phillips, 1984;d in two distinct,l and non-spatialts the spatial sep-.) between healthon-spatial access,mical and organi-religion, etc.) thatthcare. From the

perspective of utilization, two types of accessibility can be distin-guished: potential and revealed (Joseph & Bantock, 1982; Joseph &Phillips, 1984). The former describes the opportunity to use healthservices, whereas the latter refers to actual achievement of po-tential access, that is, utilization.

Of interest in this study is potential geographic access to sexualand reproductive health (SRH) services, and in particular to familyplanning in rural Africa. It has been found that geographic access toSRH services is an important factor inuencing health outcomes

* Corresponding author. School of Geography & Geosciences, Irvine Building,University of St Andrews, North Street, St Andrews, KY16 9AL, Fife, Scotland, UK.Tel.: 44 01334 464026; fax: 44 01334 463949.

Contents lists availab

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Social Science & Medicine 96 (2013) 60e68E-mail addresses: jing.yao@st-andrews.ac.uk, jyaoasu@gmail.com (J. Yao).Utilization of health services signicantly impacts health out-comes. It has long been recognized that access to health services isessential to how people utilize such services (Gulliford & Morgan,2003; Higgs, 2009; Joseph & Phillips, 1984; Meade & Emch, 2010).This is especially true for rural areas of resource-limited developingcountries characterized by poor overall health, such as those in ruralsub-Saharan Africa (Stock, 1983; Tanser, Gijsbertsen, & Herbst,2006). Improving accessibility of health services for greater qualityof life, enhanced overall health and well-being, reduced health in-equities and better service to target populations is a central concern

implications in different settings (Aday & Anders& McLafferty, 2011; Gulliford et al., 2002; JosepWang, 2012). Generally, access can be measureyet interacting dimensions: geographic/spatia(Donabedian, 1973). Geographic access highligharation (distance, rivers, forests, mountains, etcfacilities and the population in need of service. Nin contrast, refers to demographic, social-econozational factors (sex, age, education, income,facilitate or hinder the acquisition of healIntroduction Though it is widely acknowledged that access is crucial forhealthcare utilization, access is dened differently and has different0277-9536/$ e see front matter 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.socscimed.2013.07.025services is essential to positive health outcomes, especially in rural areas of developing countries, wherelong distances as well as poor transportation conditions, can be potential barriers to health careacquisition. Improving accessibility of health services for target populations is therefore critical forspecialized healthcare programs. Thus, understanding and evaluation of current access to health care iscrucial. Combining spatial information using geographical information system (GIS) with populationsurvey data, this study details a gravity model-based method to measure and evaluate access to SRHservices in rural Mozambique, and analyzes potential geographic access to such services, using familyplanning as an example. Access is found to be a signicant factor in reported behavior, superior totraditional distance-based indicators. Spatial disparities in geographic access among different populationgroups also appear to exist, likely affecting overall program success.

2013 Elsevier Ltd. All rights reserved.Available online 3 August 2013vertical transmission of infectious diseases like HIV/AIDS. It has long been recognized that access to SRHArticle history: Utilization of sexual and reproductive health (SRH) services can signicantly impact health outcomes,such as pregnancy and birth, prenatal and neonatal mortality, maternal morbidity and mortality, andA geographical perspective on access tohealth care for women in rural Africa

Jing Yao a,*, Alan T. Murray b, Victor Agadjanian c

aCentre for GeoInformatics, School of Geography and Geosciences, University of St AndrbGeoDa Center for Geospatial Analysis and Computation, School of Geographical SciencAZ 85287, USAcCenter for Population Dynamics, T. Denny Sanford School of Social and Family DynamAll rights reserved.exual and reproductive

, St Andrews, Fife, KY16 9AL, Scotland, UKnd Urban Planning, Arizona State University, Tempe,

Arizona State University, Tempe, AZ 85287, USA

le at ScienceDirect

& Medicine

vier .com/locate/socscimed

& Msuch as pregnancy and birth, prenatal and neonatal mortality,maternal morbidity and mortality, and vertical transmission of in-fectious diseases like HIV/AIDS (Acharya & Cleland, 2000; Rahman,Mosley, Ahmed, & Akhter, 2008; Tanser et al., 2006). As is the case inother types of health care, geographic access to SRH services can bedened in many ways, including travel distance/time/costs (Nemet& Bailey, 2000), gravity-based metrics (Joseph & Bantock, 1982) andmore recently, the two-step oating catchment area (2SFCA) (Luo &Wang, 2003; Wang & Luo, 2005). In the context of rural Africa,current research of geographic access to healthcare primarily relieson distance-based measures (Buor, 2003; Noor, Zurovac, Hay,Ochola, & Snow, 2003; Stock, 1983; Tanser et al., 2006). The valueof alternative measures is worth further exploration.

Given their capability of managing and processing spatial data,geographic information systems (GIS) are well suited for evaluatinggeographic access to health services (Cromley & McLafferty, 2011;Higgs, 2004; Meade & Emch, 2010; Rushton, 2003; Wang, 2012; Yao,Murray, Agadjanian, & Hayford, 2012). Desktop mapping makes iteasy and straightforward to visualize health data in different spatialrepresentations and under various spatial scales. Also, some spatialoperations, suchasdataaggregationandcalculationof traveldistance/time/costs, can be easily implemented using readily available func-tions inGIS. Further, spatialanalysisusingGIScanprovide insights intodisparities in geographic access among a population across space,helping identify insufcient health service access and possible inu-encing factors that otherwise cannot be detected.

The aim of this study is to develop a geographic access index in aGIS environment capable of reecting important spatial inuencesand variability to SRH services in rural Africa, using access to familyplanning in rural areas of Mozambique as an example. Theremainder of the paper is structured as follows. The next sectionprovides an overview of current research on access, especiallygeographic/spatial access, to health services. The study area anddata utilized are then described. We then provide a detaileddescription of the proposed method. An application of the newmethod to examine geographic access to SRH services is thenpresented, focusing on variation over space and the impact onactual health care usage by women in rural Mozambique. Weconclude with a discussion of the results and implications.

Background

Healthcare access is a multidimensional concept, and in recentyears there has been increasing interest and research on access in anumber of elds, including hygiene, economics, geography, soci-ology, and public policy, among others (Cromley & McLafferty,2011; Gulliford & Morgan, 2003; Joseph & Phillips, 1984). As aresult, numerous denitions of access have been proposed in theliterature oriented to different academic specialties. One of theearliest denitions explains access in terms of entry to the healthcare system (Donabedian, 1973). Similarly, Aday and Andersen(1975) suggested that access is more relevant to consumers ofhealth services compared to suppliers, describing whether peoplecan enter the healthcare system, either potentially or actually.Penchansky and Thomas (1981) identied ve dimensions of ac-cess: availability, accessibility, accommodation, affordability andacceptability, highlighting the match between health providers andtheir clients. The rst two are dened in spatial terms, whereavailability implies adequacy of healthcare provision and accessi-bility refers to geographic impedance (travel distance/time) be-tween healthcare supply and demand. It is worth noting thatgeographic access has long been a major concern in rural healthservice systems (Arcury et al., 2005; McGrail & Humphreys, 2009;Stock, 1983). The focus of our paper is on spatial aspects of ac-

J. Yao et al. / Social Sciencecess, so the remainder of this section is limited to specic aspects ofgeographic access, including provider-to-population ratios,distance-based measures, and gravity-based models.

Provider-to-population ratio, or physician-to-population ratio, haslong been used to measure geographical access to health services(Guagliardo, 2004; Wang, 2012). Usually, the ratio can be calculatedusing population/physician data aggregated by administrative unitssuch as county or city. This traditional measure has raised a lot ofcriticismmainly because it fails to account for the variation in spatialaccesswithin administrative boundaries and the interaction betweenprovider and population (Guagliardo, 2004). Also, it might not beappropriate to dene the catchment area of health facilities using pre-specied spatial units because health service areas usually overlaprather than are separated bydistinct boundaries. Further, provider-to-population ratios derived on various spatial scales can lead to quitedifferent conclusions on spatial disparities in geographic access,which is well known in geography as the modiable areal unit prob-lem (MAUP) (Openshaw, 1984).

Distance-based measures can avoid some of the problems associ-atedwith provider-to-population ratios (Cromley&McLafferty, 2011).In fact, theyare increasinglyemployed ingeographic accessevaluationlargely thanks to the advance of GIS and increased availability ofdigitized spatial data (Higgs, 2004; Rushton, 2003). In principle, suchmeasures can be dened using Euclidean distance, distance alongroadnetwork, travel timeorcosts. Thoughstraightforwardandeasy tocalculate, Euclidean distance has been considered less than idealbecause it ignores physical barriers (e.g. rivers and mountains) andother factors (e.g. road types and transportation modes) that mightaffect the actual travel distance (Martin, Jordan, & Roderick, 2008).Some studies, however, found it adequate for explaining spatialimpedance in healthcare seeking in rural areas (Stock, 1983) and alsobeing a valid proxy for actual travel distance (Cudnik, Yao, Zive,Newgard, & Murray, 2012). To account for actual transportation con-ditions, distance along roadnetworks and travel time/costs have beenusedas surrogates for geographic access (Lovett,Haynes, Sunnenberg,& Gale, 2002). Some studies incorporated more complex factors suchas transportationmodes (e.g. public orprivate) andtimetables (Arcuryet al., 2005; Martin et al., 2008). Fortney, Rost, and Warren (2000)compared various distance-based access measures and examinedthe sensitivity of results obtained from different measures.

Though it has been recognized that distance represented byvarious measures has a signicant impact on the utilization of healthservice as discussed above, most research fails to consider the char-acteristics of either health providers (e.g. size of health facility andservice quality) or populations, which these providers serve (e.g.access to transportation). In fact, people usually trade off distance anddesired health services when making decisions on health care utili-zation (Rosero-Bixby, 2004). Gravity-based models are methods thatcan account for such trade-offs. The gravity model originated fromNewtonian physics and was extended in economic geography todelineate trade areas (Huff, 1963, 1964). Joseph and Bantock (1982)modied it to measure geographic access to health services, incor-porating interaction between supply and demand and considering anearby healthcare facility more accessible than a distant one. Sincethen, many extensions of the gravity model have been proposed,such as the two-step oating catchment area (2SFCA) (Luo & Wang,2003). One limitation of 2SFCA lies in its reliance on the availabilitymeasure that is based on provider-to-population ratio. Also, theconstant catchment radius used in the model might not reect thevariation among health service provision or community character-istics. Many improvements have beenmadewith regard to the 2SFCAmethod, such as adoption of varying catchment areas (Luo&Whippo,2012) and application of different distance decay functions (Luo & Qi,2009; McGrail & Humphreys, 2009).

In summary, the reviewabove shows thatwhile all the geographic

edicine 96 (2013) 60e68 61access measures used to date are important in evaluating spatial

inequities in access and understanding spatial patterns of healthservice utilization, they all are subject to limitations in one or morerespects. Moreover, in Africa, most recent work on geographic accessto health care has employed distance-based measures (Buor, 2003;Noor et al., 2003; Stock, 1983; Tanser et al., 2006). More sophisti-cated measures for access to health services, such as gravity-basedmodels have not been considered, with the exception of a study byWilson and Blower (2007) that proposed a gravity model to assessHIV/AIDS treatment accessibility in South Africa. The intent of thisstudy is to develop a geographic access index based on the gravitymodel for SRH services, which is also adjusted to a specic researchcontext.

Data and study area

The data used in this study are from a survey conducted in 2009 inrural areas of four districts (Chibuto, Chokw, Guj andMandlakaze)of Gaza province in southern Mozambique, an impoverished nationin southeast Africa. The study region covers an area of approximately5900 square miles with a population of about 625,000, served byfty-three state-run primary health clinics. The local economy islargely dependent on subsistence agriculture. Due to poor agricul-tural yields and the proximity to the border of Mozambique withSouth Africa, Gaza province has a high volume of male labor out-migration to that neighboring country, which can be traced back tocolonial times (De Vletter, 2007). Fertility in the area is high, and soarematernalmortality andmorbidity. SRH services, such as provisionof family planning, are relatively limited. Also, Gaza province has the

women married to migrants and 15 women married to non-migrants aged 18e40 were selected from village household ros-ters through probability sampling. In both survey waves, thegeographic location of each respondent was recorded as a spatialpoint represented by latitude and longitude, along with informa-tion on age, educational level, household characteristics, husbandsmigration status, and reproductive health and behavior-relatedinformation, etc. In addition, some community-based informationwas also collected, including cost of travel to the nearest town bypublic transportation. The share of households with a migrant in acommunity was derived on the basis of household rosters used inthe 2006 sampling. Finally, additional community characteristics,such as percent of women with ve or more years of schooling,percent afliated to a formal religion, and percent of householdswith a radio, were created as aggregate averages of the surveysample. Fig. 1 depicts the spatial distribution of respondents resi-dences, health service units, and towns (district centers) of the fourdistricts. As can be seen from Fig. 1, most women in the sample, asthe majority of the population, lived in the southern part of thestudy area whereas the northern part is relatively sparsely popu-lated. A similar pattern can be observed for health facilities, most ofwhich are located in the south.

The SRH outcome used in this study is current use of a modernfamily planningmethod. Family planning programs in high-fertilityAfrican countries have been credited for improving SRH andreducing maternal and childhood deaths (Benefo & Pillai, 2005;Caldwell & Caldwell, 2002; Cleland et al., 2006). In Mozambique,family planning programs have existed since the early 1980s, with

J. Yao et al. / Social Science & Medicine 96 (2013) 60e6862highest HIV prevalence in Mozambique, 25% of adult population(Ministry of Health, 2010), and improvement in access to SRH ser-vices is an urgent task for the local health care system.

The 2009 survey sample included 1867 women from 56 villages(14 villages per district). The survey was a second wave of a lon-gitudinal survey for which the sample was originally drawn in2006. In that year, within each of the 56 villages, approximately 15Fig. 1. Study arethe primary goal to enhance the health of women and childrenthrough birth spacing (Ministry of Health, 2010). Currently, familyplanning services in rural areas are offered almost entirely throughstate-run maternal and child health clinics, where both consulta-tions and methods are provided completely free of charge. Clientsare typically offered a choice among injectable contraceptives(Depo-Provera), oral contraceptives, the intra-uterine device (IUD),a and data.

primarily concerns interpersonal, technical, environment and

& Madministrative qualities. For the application here, the choice ofrelevant factors is also driven by context-specic considerations aswell as data availability. Specically, the variables describing clinicsinclude number of nurses (variable 1), number of rooms (variable 2),whether or not the clinic has piped water (variable 3), whether ornot the clinic electricity (variable 4), and whether or not the clinichas received aid from any non-governmental organization (NGO)(variable 5). Because the last three variables are binary (either 0 or1), the data for the rst two variables are standardized so that allthe values range from 0 to 1. By denition, higher values of the HSQindex reect better health services.

Hedonic models are usually estimated using regression methods.In this case, the weight of each variable is determined with a Poissonandmale condoms (female condoms are rarely available). However,modern contraceptive prevalence among married women in ruralareas of Mozambique remains low e about 11% on average, ac-cording to the 2011 Mozambique Demographic and Health Survey(Instituto Nacional de Estatstica and Macro International, 2013).The low contraceptive prevalence is partially attributed to thephysical barriers in accessing these types of services. In our study,the patient ow for family planning services in each clinic is used toestimate a hedonic model, and the percent of women that usefamily planning in each community (derived from the surveysample) is employed as a proxy for the utilization of family plan-ning services to evaluate the impact of geographic access.

Methods

In this study, spatial information and attribute variables arecombined in the denition of geographic access. Specically,characteristics of health facilities are integrated in a hedonic modelto evaluate the overall service quality of health providers, which isthen incorporated into a gravity-based model to dene access.Spatial variation of geographic access is further explored by Kerneldensity estimation, a GIS-based spatial analysis technique.Regression analysis is utilized to investigate the impact of potentialgeographic access on the actual pattern of family planning serviceuse. GIS is employed to manage spatial information as well as toderive geographic measures in order to facilitate above procedures.

Essential here is the measure of geographic access. The geo-graphic access index is derived in two steps. First, a health servicequality (HSQ) index is derived for each clinic in the study area. Spe-cically, the HSQ index is the weighted sum of attributes for healthfacilities:

aj X5

i1wivi (1)

where

i: index of variable, i 1,2,3,4,5j: index of clinic, j 1,2,.,53vi: ith variablewi: weight for ith variableaj: HSQ index of jth clinic

Hedonic models are widely applied in value estimation (e.g.market or property price) by combining the contributory values ofcomposite variables (Freeman, 2003). Similar to access, HSQ isalso a complex concept, which involves many aspects of healthservice (Kenagy, Berwick, & Shore, 1999; Taylor & Cronin, 1994). Forexample, Dagger, Sweeney, and Johnson (2007) suggested that HSQ

J. Yao et al. / Social Scienceregression analysis of the actual health care utilization data in thesample. In our case, the explanatory factors are the ve attributes ofthe clinic dened above. The dependent variable is the patient ow(average number of monthly patients) of each clinic for familyplanning services.

In the second step, the HSQ index is incorporated into a gravitymodel to calculate the potential geographic access. This is done toexplicitly account for spatial proximity and the well-known dis-tance decay effects observed for service utilization. In this process,the sample data are aggregated to the community level because thehealth care seeking behavior in the study area largely relies onpublic transportation or walking, and can be considered invariantfor the people in the same village. Suppose dkj is the distance ortravel time/costs from community k to the clinic j, generally, thepotential access of community k to health services, Ak, can bedened by the following gravity model (Cromley & McLafferty,2011; Hansen, 1959):

Ak Xj

aj=fdkj

(2)

which can be considered the sum of potential access to each clinic j,aj/f(dkj). In healthcare access research, the numerator also canrepresent service availability dened by the physician-to-population ratio (Luo & Wang, 2003; Wang & Luo, 2005) insteadof service quality, aj, used in this study. The denominator f(dkj) isusually some function representing the tendency for reduced ac-tivity rates with increasing distance, known as distance decay ingeography (Haggett, Cliff, & Frey, 1977). In the traditional gravitymodel, f(dkj) has the following form:

fdkj dbkj (3)

where b is the impedance or travel/distance friction coefcient (avalue of 2 is used in the Newtonian gravity model). In practice, thevalue varies depending on the context under consideration. Highervalues of Ak represent better geographic access, implying a greaterlikelihood of using health services.

This study made two adjustments to the potential geographicaccess measure given in (2). First, considering the actual publictransportation situation in the study area, the distance decay effect ismitigated by the ease of getting to the nearest town because eachcommunity is directly connected by public transportation to nearbytowns, making travel possible if necessary. In other words, livingcloser to a town implies better transportation opportunities. Specif-ically, the distance friction is multiplied by a weight dened as theEuclidean distance from a community to the nearest town divided bythe sum of all such distances. Thus, all the weights add up to 1. In theabsence of data on actual travel time, Euclidean distance is consid-ered a reasonable proxy for proximity in rural Africa (Stock, 1983;Tanser et al., 2006). In addition, b 1 is used as the impedance co-efcient as it is considered sufcient for the spatial context understudy. The distance decay function therefore is:

fdkj wkdkj (4)

where wk weight for access to town for kth community.Another modication concerns the maximum distance that

people are willing to travel to acquire healthcare services, whichhas long been known in geography as range (Christaller, 1933). Asthe study area is rural, it is common for people to visit only thehealth services within a certain spatial extent. In other words,people rarely visit a clinic beyond a distance threshold. Here thisthreshold is assumed to be approximately 10 km. The developed

edicine 96 (2013) 60e68 63potential geographic access index is:

population method would assume. On the whole, women livingin the south of the study area have better access to SRH servicesthan those living in the north, which can be attributed to fewerclinics and larger distances from the towns in the northern areas.Also, the population around the towns has more opportunities tobe served, especially in the southwestern part of the study area. Theexception is an area near the town of Chibuto, largely due to the factthat it does not contain any survey data.

To further investigate the impact of geographic access, regres-sion analysis was carried out. The results are shown in Table 2. Apronounced association between potential geographic access andactual service use is observed. That is, the utilization ratio of family

& Mclinic. Given the fact that the regression is carried out on sampledata and considering the potential impact that other attributesAk X

jfj:dkjd0gaj=wkdkj

1wk

Xjfj:dkjd0g

aj=dkj (5)

where d0 distance threshold for visiting a health care unit.Once the index Ak is derived for each community, GIS-based

techniques can help to explored spatial patterns of geographic ac-cess over the study area. This enables communities having poorergeographic access to local SRH services to be identied, providinguseful information for future improvement programs targetingthese areas. In this study, assessment is achieved through Kerneldensity estimation (Diggle, 1985). Of course, desktop mapping canoffer a straightforward view of the differences among communitiesbased on the values of access indices. The advantage of the Kerneldensity method is that it can t a continuous surface using a niteset of samples, providing insights into continuous variation ingeographic access across the study area. Kernel density estimationis a non-parametric smoothing technique that calculates proba-bility densities at any location in continuous space using a kernelfunction. In health research, it is usually employed to estimate theprobability that an event or a disease will occur (Waller & Gotway,2004). In this case, a continuous surface is estimated from discretepoints of clinics with value Ak. The attribute value associated with apoint on the surface represents the probability that a location hasgood access to health services.

However, actual inequities in health service utilization can bemore complicated than simple physical access. Regression analysishelps to understand the impact of geographic access relative to thatof other factors on the utilization of SRH services. Service utilizationis clearly important, and health outcomes are likely dependent onusage. Thus, the share of respondents that utilize family planningservices in each community is used as the dependent variable in ageneral linear regression model, representing actual utilization ofSRH services. The regression model accounts for a variety of factorsthat are known to affect family planning use. For example, it is wellrecognized that education and economic status have great inu-ence on healthcare utilization, and use of family planning is noexception (Martin, 1995; Obare, van der Kwaak, & Birungi, 2012;Riyami, A, & Mabry, 2004). Likewise, migration has beenshown to affect health care utilization between migrant and non-migrant populations (Newbold & Willinsky, 2009; Ostrach, 2013;Pavlish, Noor, & Brandt, 2010). Religion afliation can also inu-ence fertility regulation (Adongo, Phillips, & Binka, 1998;Agadjanian, 2001; Agadjanian, Yabiku, & Fawcett, 2009). Giventhe potential inuences of non-spatial factors, other community-level explanatory variables, in addition to geographic access, arealso taken into account in the regression analysis, including percentof respondents with at least ve years of schooling, percent of thosewith migrant husbands, percent of those with religious afliation,the average number of households owning a radio set (a proxy forcommunity economic status), and typical cost of public trans-portation to the nearest town.

Results

The Poisson regression analysis results on patient utilization offamily planning for each clinic using relevant variables are sum-marized in Table 1. It turns out that only one clinic attribute, thenumber of nurses, has a signicant inuence on the patient ow ofthe clinics for family planning services. Thus, this attribute isconsidered most important in dening the service quality of the

J. Yao et al. / Social Science64could have on family planning seeking behavior, weights are alsoassigned to the other four attributes, but these weights are muchsmaller than that of the number of nurses. Based on Equation (1),the rst variable is given the largest weight, and the other variablesare equally weighted, with the sum of all the weights being one, asshown in Equation (6).

aj 0:6v1 0:1v2 0:1v3 0:1v4 0:1v5 (6)The spatial variation in HSQ is illustrated in Fig. 2 based on

symbol size. The three clinics in the towns of Chibuto, Chokw andMandlakaze (the headquarters of the respective districts) havehigher service quality indices than the other clinics, while themuch smaller town of Guj (the headquarters of Guj district) haslower service quality. The best clinic in Guj is very close to thetown of Chokw. It is not surprising that the health facilities closeto these towns have better resources. In contrast, most clinicslocated further away from these towns have much lower servicequality.

The potential geographic access index is derived using Equation(5). The spatial pattern of the values of the index can be observed inFig. 3. Each community has a circle whose size reects its relativelevel of potential geographic access to local SRH services. By visualinspection, it is not difcult to conclude that the two communitieshaving best potential geographic access are not closest to the bestclinic or the town centers. This is no doubt a function of both thefacility characteristics and the distance factor. The two commu-nities with the highest access do have a clinic nearby. However,these two clinics are different in terms of service quality, possiblyattributed to available services, which reects a trade-off betweenservice quality and distance associated with actual access. Again,most communities that haveworse access are far away from towns,especially the four villages in the north and those near the southernborder of the study area.

When a surface based on Kernel density estimation is generatedusing derived index values, the continuous spatial variation ofpotential geographic access over space can be observed (see Fig. 4).The access potential is shown in ve classes using differentrendering, with the darker color representing better access. It isclear that the extent to which potential access differs is notconsistent with administrative boundaries, as the provider-to-

Table 1Results of Poisson regression of the clinic patient ow, parameter estimates andstandard errors. Sample size: 53.

Variables Estimate Std. error

(Intercept) 2.030** 0.074Number of nurses in a clinic 0.439** 0.030Number of rooms in a clinic 0.129 0.025Clinic has piped water (1) 0.629 0.099Clinic has electricity (1) 0.582 0.065Clinic has received assistance from an NGO (1) 0.075 0.063

Signicance codes: p < 0.01: **.

edicine 96 (2013) 60e68planning services declines as geographic access decreases. In ad-dition, some non-spatial factors also affect utilization of family

vice

J. Yao et al. / Social Science & Medicine 96 (2013) 60e68 65planning. For example, Table 2 indicates that utilization increaseswith higher educational level and lower cost of public trans-portation to the nearest town. The reasonably strong linear t(51.8%) implies that additional factors help explain actual familyplanning service use. The most important implication of the results

Fig. 2. Health serpresented in Table 2 is that, in terms of public policy, to further

Fig. 3. Potential geographicimprove utilization of SRH services such as family planning, moreeffort should be devoted to improving education, public trans-portation as well as geographic access. According to the measuredenition, (5), developing the public transportation system andenhancing relevant health services can be efcient ways to in-

quality of clinics.crease potential geographic access.

access of communities.

f pot

& Medicine 96 (2013) 60e68Discussion

Fig. 4. Spatial variation o

J. Yao et al. / Social Science66As is well known, access is a critical factor impacting healthservice utilization. Quantitative measures can help to evaluate ac-cess and identify deciencies in service coverage as well as disad-vantaged population in subsequent analysis. However, mostresearch has been limited to urban areas (e.g. Lovett et al., 2002;Luo & Wang, 2003; Schuurman, Berube, & Crooks, 2010) or ruralareas in developed countries (e.g. Arcury et al., 2005; Joseph &Bantock, 1982; McGrail & Humphreys, 2009; Nemet & Bailey,2000). The existing measures are not effective for rural areas indeveloping nations due to different health policies, health deliverysystems, or health seeking behavior. Thus, addressing access forpeople living in such areas continues to pose serious challenges.Using GIS-based techniques, this study developed a gravity-basedmodel to measure geographic access to SRH services in ruralMozambique and investigated spatial inequities in access amongpopulation groups.

In a previous study focused on the same setting, Yao et al. (2012)found that geographic access has no signicant impact on the uti-lization of SRH services like family planning when using Euclideandistance. The access index developed in this study, in contrast,showed a much higher explanatory power (and more signicanteffects) than in previous work. That is, a strong association betweenpotential geographic access and actual service use was identied.Therefore, the proposed access measure is superior to moresimplistic measures of access such as Euclidean distance.

The proposed measure can be viewed as a generalization of the2SFCA method (Luo & Wang, 2003) in the sense that initially theservice quality of health facilities is dened, which is then miti-gated by distance decay effects. In this case, the service quality oflocal health clinics is dened using a series of attributes which arecombined by different weights calculated from actual survey data;whereas in 2SFCA, as well as many similar models (e.g. Luo & Qi,2009) it is described by provider-to-population ratios. In addition,a threshold of travel distance of 10 km is applied according to the

ential geographic access.underlying research context whereas 2SFCA uses a 30 min traveltime to dene the catchment area for both supply and demand.Further, the developed index can also be thought of as a special caseof the enhanced 2SFCA (E2SFCA) (Luo & Qi, 2009) because bothweights are considered in the distance decay functions. E2SFCAassigned different weights for each travel time zone, which in theproposed approach is calculated using distance to town to ac-count for access to public transportation, highlighting the under-lying spatial context.

One limitation of the geographic access measure derived fromaggregated data is that it can mask variations among individuals inthe same community. Health care utilization can be affected bymany personal characteristics, such as age, social class, economicstatus, transportation opportunities, or activity space. Thus,geographic access can have different implications to different in-dividuals. For example, Nemet and Bailey (2000) found that dis-tance is a signicant physical barrier for rural elderly. Perry andGesler (2000) and Haynes (2003) suggested activity space is a po-tential aspect of geographic access as people having health facilities

Table 2A general linear model of the utilization ratio of family planning services in eachcommunity, parameter estimates and standard errors. Sample size: 56.

Variables Estimate Std.error

(Intercept) 0.235 0.152Percent in community with 5 or more years of education 0.174* 0.075Percent in community with religious afliation 0.124 0.108Percent in community with migrant husband 0.175 0.106Average number of radios possessed in community 0.161 0.091Typical cost of public transportation to nearest town 0.148** 0.051Potential geographic access 0.090** 0.028

Signicance codes: p < 0.01: **, p < 0.05: *, p < 0.1: .

& Mwithin their activity space are more likely to obtain health services.More detailed data on the study setting than what are currentlyavailable are needed to account for these characteristics.

Conclusion

Improving access to health services is a great concern of gov-ernment and policy makers striving to enhance overall publichealth. Spatial patterns of access to health services are complexproducts of distance effects, environmental inuences, socioeco-nomic factors, individual and community characteristics, etc. Theindex proposed in our study is effective in revealing potentialgeographic inuences on access to SRH services in ruralMozambique, which cannot be detected by traditional distanceindicators. The results of this study therefore can help future healthprogram development and service deployment in sub-SaharanAfrica, especially in poor rural settings. As increasing efforts aredevoted to improving SRH service utilization in such settings,geographic dimensions of access to these services should be takeninto full consideration.

Acknowledgments

We thank the support of the Eunice Kennedy Shriver NationalInstitute of Child Health & Human Development (grants#R21HD048257; R01HD058365; R01HD058365-03S1).

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A geographical perspective on access to sexual and reproductive health care for women in rural AfricaIntroductionBackgroundData and study areaMethodsResultsDiscussionConclusionAcknowledgmentsReferences