metadata: authors: 0: author: Becerra Luna,Laura Natalia
1: author: Lebrand,Mathilde Sylvie Maria
2: author: Pkhikidze,Nino
3: author: Yi Chang,Andres
count: Kenya
docna: 0: docna: Infrastructure Matters : Complementarities
            with the Quality of Health Service Delivery in Kenya
docty: Policy Research Working Paper
abstracts: cdata!: In many low- and middle-income
            countries, the lack of access to essential infrastructure
            such as roads, electricity, and information and
            communications technology may hinder the provision of many
            critical services such as health care. For instance, scarce
            and deficient roads might limit the patients&#x2019;
            access to health facilities, restrict the supply of
            qualified staff, and constrict access to key inputs such as
            medicines and vaccines. Likewise, lack of reliable
            electricity and internet connection may limit the ability of
            health facilities to power essential equipment, have better
            access to information, potentially serve more patients, and
            manage their supply chain efficiently. This paper combines
            exhaustive health facility surveys with geospatial data to
            study the extent to which better access to infrastructure in
            Kenya might improve the functioning of health care
            facilities and the quality of their services. First, the
            paper documents the gap in access to infrastructure in the
            health care sector in Kenya and reviews the literature on
            this topic. Then, using a novel data set, it finds that
            access to electricity and good quality roads is associated
            with more accurate provider diagnostics for both general
            illnesses and those primarily affecting children and
            pregnant women. Additionally, access to electricity is
            associated with (i) higher availability of vaccines for
            children&#x2014;mostly by making it possible to have a
            working fridge, which is essential to store most vaccines;
            and (ii) higher availability of essential and priority
            medicines, by facilitating the use of information and
            communications technology for supply chain management.
            Finally, access to good quality roads, electricity and use
            of information and communications technology for supply
            management are positively related to the availability of
            antenatal care tests for pregnant women. Overall, the
            results suggest that increased investment in infrastructure
            and communications technologies may improve health service
            provision in Kenya.
display_title: Infrastructure Matters :
            Complementarities with the Quality of Health Service
            Delivery in Kenya
listing_relative_url: /research/2022/11/33931178/infrastructure-matters-complementarities-quality-health-service-delivery-kenya
topicv3: Infrastructure and Growth m1326244 1083,Health Services Delivery m1327294 1305,Early Childhood m1582438 4408
publishtoextweb_dt: 2022-11-03T00:00:00Z
dois: 10.1596/1813-9450-10220
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2025899
1: author_key: 1457978
2: author_key: 1816486
3: author_key: 1388215
model_extractions: Policy Research Working Paper 10220

# Infrastructure Matters

Complementarities with the Quality of Health Service Delivery in Kenya _Laura Becerra Luna Mathilde Lebrand Nino Pkhikidze Andres Yi Chang_ Human Development Chief Economist Office & Infrastructure Chief Economist Office November 2022
Policy Research Working Paper 10220

# **Abstract**

In many low- and middle-income countries, the lack of access to essential infrastructure such as roads, electricity, and information and communications technology may hinder the provision of many critical services such as health care. For instance, scarce and deficient roads might limit the patients’ access to health facilities, restrict the supply of qualified staff, and constrict access to key inputs such as medicines and vaccines. Likewise, lack of reliable electricity and internet connection may limit the ability of health facilities to power essential equipment, have better access to information, potentially serve more patients, and manage their supply chain efficiently. This paper combines exhaustive health facility surveys with geospatial data to study the extent to which better access to infrastructure in Kenya might improve the functioning of health care facilities and the quality of their services. First, the paper documents the gap in access to infrastructure in the health care sector in Kenya and reviews the literature on this topic.

Then, using a novel data set, it finds that access to electricity and good quality roads is associated with more accurate provider diagnostics for both general illnesses and those primarily affecting children and pregnant women. Additionally, access to electricity is associated with (i) higher availability of vaccines for children—mostly by making it possible to have a working fridge, which is essential to store most vaccines; and (ii) higher availability of essential and priority medicines, by facilitating the use of information and communications technology for supply chain management. Finally, access to good quality roads, electricity and use of information and communications technology for supply management are positively related to the availability of antenatal care tests for pregnant women. Overall, the results suggest that increased investment in infrastructure and communications technologies may improve health service provision in Kenya.

This paper is a product of the Human Development Chief Economist Office and the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ayichang@worldbank.org, mlebrand@worldbank.org, npkhikidze@worldbank.org, and lbecerraluna@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Infrastructure Matters: Complementarities with the Quality of Health Service Delivery in Kenya<sup>1</sup> Laura Becerra Luna*, Mathilde Lebrand*, Nino Pkhikidze*, and Andres Yi Chang*

*World Bank

JEL: I18, O18 Keywords: health facilities, infrastructure, Kenya, roads, electricity, ICT.

> 1 We thank Vivien Foster and Roberta Gatti for getting this collaborative effort started and their continuous support and comments. We are indebted to Benjamin Steward for carrying out and facilitating some of the GIS analysis. We also thank Keith Hansen, Kathryn Andrews, Ruben Conner, Lydia Nakhone, Anne Margreth Bakilana; Peter Ngwa Taniform, Paul Elvis Onyango Ouma, the World Bank Kenya Human Development Team, and members of the Kenya Technical Working Group for providing useful comments on previous versions of this paper. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# 1. Introduction

Health is an essential component of human capital formation, especially in the early years of the lifecycle.<sup>2</sup> Inadequate infrastructure affects both demand for health care services as well as supply. On the demand side, many people in low- and middle-income countries face barriers that prevent or limit their access to the health care services they need. Excessive travel times to reach facilities, lack of information, and limited health care resources impact remote and vulnerable populations the most. On the supply side, inadequate infrastructure hampers the functioning of health care facilities and the quality of their services. These barriers not only affect access to health services but also the quality of services people receive. Therefore, policies to improve access to health services should also focus on improving the quality of provided services.<sup>3</sup> While many dimensions that affect the quality of health service delivery have been documented in the literature, access to infrastructure remains understudied, especially on the supply side. Investments to improve access to infrastructure such as roads, electricity, and information and communications technology (ICT) hold the potential to enhance the quality of health service provision. This is particularly true in low- and middle-income countries, where access to quality infrastructure remains a challenge in rural and hard-toreach areas. Not only do more and better roads facilitate the access of patients to health facilities, but they may also allow facilities to attract more staff and ease the supply of key inputs such as medicines and vaccines. Likewise, lack of reliable electricity may compromise health care provision as electricity enables lifesaving interventions, the maintenance and use of essential equipment, and the ability of health facilities to operate for extended hours, particularly at night. Similarly, access to the internet allows facilities to have better information, reach more patients, and better manage their supply chains. This paper aims to contribute to the literature by exploring the extent to which more and better infrastructure might improve the quality of health services provision in low- and middle-income countries by studying the Kenyan context.

Kenya has made great progress in increasing the availability of health care services in the last decade, but much remains to be done (WHO, 2017). A child born today in Kenya is likely to only achieve 55% of her/his potential according to the latest update of the Human Capital Index (World Bank, 2020), which includes measures of child and adult survival rates, stunting and access to and quality of schooling. While Kenyan county governments have prioritized investments in the health sector, especially in the construction of new health facilities and purchase of medical equipment and ambulances, not many efforts have been made to ensure that health facilities have access to reliable infrastructure (Mugo, et al., 2018).<sup>4</sup> According to the latest 2018 Service Delivery Indicators (SDI) report, only 56% of primary-level health care facilities have access to stable electricity and only 15% of health facilities use ICT for supply chain management.

Improving the access and quality of infrastructure remains a challenge in Kenya. Despite an increase in access to energy by 50 percentage points over the past ten years, which has translated into 70% nationwide coverage, 30% of the population still has limited access to reliable electricity (World Bank, 2021). The latest Rural Access Index in Kenya shows that 56.8% or about 13.4 million rural residents are still unconnected to

> 2 See Bleakley, 2010; Case, Fertig, & Paxson, 2005; Conti, Heckman, & Urzua, 2010; Knudsen, Heckman, Cameron, & Shonkoff, 2006.

> 3 Within Millennium Development Goals (MDGs), from 2000 to 2015, there was an increase in health coverage in low- and middle-income countries (LMICs). But health outcomes did not improve at the same rate for every country or everyone within each country. Despite the increase in the coverage achieved, low quality of services persists in many LMICs and high maternal and child mortality rates continue to be of serious concern. In this context, quality health services are essential to better human capital and healthier economies (WHO, 2018). There are many well-documented factors that affect the quality of health service delivery (Di Giorgio, et al., 2020; Gatti R. , et al., 2021; Mosadeghrad, 2014). For instance, providers’ effort and knowledge in primary care vary according to institutional settings (private or public, with their cost structure), and medical training -where the relationship is not strictly positive (Das, Holla, Mohpal, & Muralidharan, 2016; Das, et al., 2012).

> 4 The right to access quality health care is a basic human right guaranteed by the 2010 constitution of Kenya. In line with the law, the national government has made investments in interventions geared towards accelerating UHC for all Kenyans. In 2013, Kenya devolved health services to 47 semi-autonomous counties established in response to the new constitution of 2010, increasing local autonomy for managing health services (Achoki, et al., 2019).

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roads in fair condition (Iimi, et al., 2016). Finally, mobile subscriptions had a penetration close to 100% in 2018, but only 23% of the population in Kenya had access to internet services in 2019 (World Bank, 2021). Given these gaps, improving the access and quality of infrastructure has a potentially important role for improving health service provision in Kenya.

This paper focuses on specific dimensions of infrastructure with data availability and for which we found prior suggestive evidence on their link to the quality of health service delivery. In particular, we study access to and quality of electricity (using stability of the connection as a proxy for quality), road quality and accessibility, cellphone, and internet connectivity (2G and 3G), and use of ICT. Likewise, in this paper we are restricted to study only certain aspects related to the quality of health services for which we have data. While we understand that quality of health service delivery is multi-dimensional and complex to measure, we use level of health services’ readiness (i.e., availability of key medicines, vaccines for children, and ANC tests) and health providers’ medical knowledge to treat certain conditions (i.e., diagnostic accuracy) as proxies of quality of health service delivery. For this analysis, we combine geo-referenced survey data collected at the health facility level with geospatial data on infrastructure and location characteristics in order to document some of the cross-sectoral drivers of the quality of health.

We first find that access to electricity and quality of roads are associated with more accurate provider diagnostics for general illnesses and for those affecting primarily the early development of children and threatening the life of pregnant women. We also find that access to electricity is associated with (i) higher availability of vaccines for children, mostly by making it possible to have a working fridge, which is essential to store most vaccines; and (ii) higher availability of essential and priority medicines for children and pregnant women, partially through the use of ICT for supply chain management. Finally, access to electricity and ICT use for supply management are positively related to the availability of antenatal care (ANC) tests for pregnant women. Test availability also decreases when facilities are further away from fair or good quality roads. Overall, our results suggest that investments in infrastructure and technology may play an important role in improving the quality of health service delivery, including early childhood and maternal health.

Our contributions to the literature are three. First, we focus on the potential impact of access to infrastructure (i.e., electricity, road quality, cellphone and internet coverage, and use of ICT) on the quality of health service provision in a lower-middle-income setting. This is different from the current literature in that (a) we focus on the supply side while most papers focus on the demand side; (b) we use novel data by merging geospatial data with high-quality health service delivery indicators survey data from different sectors; and (c) while our data does not allow to identify causal impacts, we document the initial gaps in access to infrastructure and control for a wide set of characteristics in our correlational analysis to quantify the potential effect of infrastructure on health service provision. Furthermore, we provide insights for the provision of general health services, as well as for early childhood care and pregnant women. Second, we take advantage of the rich set of questions in the health service delivery indicators survey to provide suggestive evidence of the possible channels through which infrastructure affects the quality of health service delivery. Third, we lay the groundwork for future cross-sectoral studies by reviewing the literature and proposing a simple analytical framework on the channels through which infrastructure affects the quality of health service delivery.

The rest of the paper continues as follows. Section 2 summarizes some of the main findings from the literature around a simple framework. Section 3 describes the sources of data used, the construction of the indicators analyzed, and the methodology for the proposed analysis. Section 4 presents the results and policy implications of our findings.

3
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Access to reliable electricity has been identified as critical for health care facilities. Without it, many lifesaving interventions cannot be undertaken (WHO, 2014). Having electricity also allows the maintenance of essential equipment for medical tasks, such as sterilizers, refrigerators, and equipment to maintain the cold chain of medicines and vaccines. These practices are related to the quality of the services provided and might reduce mortality (Chen, Chindarkar, & Xiao, 2019; Irwin, Hozha, & Grepin, 2019; World Bank, 2008). Besides the well-functioning of essential equipment, electricity allows facilities to have laboratories and implement reliable tests that impact diagnostic quality (Irwin, Hozha, & Grepin, 2019). Access to electricity has been related to the ability to attract better-skilled providers, which results in better health outcomes such as improved survival rates, particularly for children and pregnant women (Kruk, et al., 2018; Irwin, Hozha, & Grepin, 2019; Lenz, Munyhirwe, Peters, & Sievert, 2016; Chaindhury & Hammer, 2003). Finally, extended hours of operation are only feasible if facilities have access to a source of energy, which contributes to the continuous provision of services as these should be available beyond the daylight hours (WHO, 2014). Extended hours also increase the number of patients that come into facilities and their perceived quality of health service delivery (Chen, Chindarkar, & Xiao, 2019; Irwin, Hozha, & Grepin, 2019).

Long travel times to health facilities as well as transportation restrictions are also significant barriers to health care access, especially for low-income populations. Large distances, the lack of transport availability, and deficient road conditions inevitably delay medical attention, which threatens the lives of people who need it, especially for urgent care (Syed, Gerber, & Sharp, 2014; Kaibung, Mavole, & Okuku, 2017). For instance, pregnant women in remote areas are less likely to receive medical attention, and several maternal deaths might be prevented if access to emergency childbirth care was ensured (Babinard & Roberts, 2006). Remote areas might also face lower availability and retention of medical providers (Ojakaa, Olango, & Jarvis, 2014). In addition, lack of transportation or poor road quality conditions might negatively influence the availability of medicines. Without accessibility, meeting the demands for medicines could be more complicated and, therefore, medicines might be restocked less frequently, affecting the availability of drugs in the health centers (Hall, Radebe, & Roberts, 2006; Kaibung, Mavole, & Okuku, 2017).

Finally, having access to the internet and ICT improves access to medical knowledge, to other peers and experts, and access to patients. Connectivity and mobile and internet infrastructure have been proven pivotal in the COVID-19 context, enabling medical services even at a distance through telemedicine. Furthermore, ICT and internet access also allow facilities to access worldwide information that can be used, from learning opportunities to external consultations to discussing diagnostics with experts (Bukachi & Pakenhan-Walsh, 2007). Finally, given its massification power, ICT also enables the broadcasting of information on a large scale, which can be valuable for health promotion (Dutta, Gupta, & Sengupta, 2019).

ICT might also be beneficial for increasing the efficiency of facilities when used for administrative tasks. For example, using ICT may help to reduce drug stock-out, time spent on paperwork, and patients’ waiting time (Achampong, 2012; Nilseng, et al., 2014; Githinji, et al., 2013; Kaibung, Mavole, & Okuku, 2017). Access to ICT and the internet might then improve the efficiency of medical personnel by ensuring a better supply of needed inputs such as medicines or vaccines, enhancing their knowledge by having access to information and freeing up time previously spent on manual tasks.

The quality of care comprises a large variety of inputs, processes, and outputs. For this work, we focus our attention on the dimensions for which we have data to test our hypotheses, as access to infrastructure and ICT. We acknowledge this scope omits other dimensions of quality that might be equally relevant but expect that future research can explore this further once the data to do so becomes available.

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# 3. Data and Methodology

This section describes the data used in the paper and the construction of the main variables of interest. It then provides summary statistics and discusses some data limitations.

## 3.1. Data Sources

We combined high-quality georeferenced facility survey data with geospatial data on infrastructure and location-specific characteristics for this analysis, including: Service Delivery Indicators Data The Service Delivery Indicators (SDI) initiative gathers comparable international information on service delivery for education and health around the world. These data help to identify gaps, track changes over time, benchmark progress, stimulate evidence-based debate, and ultimately, influence how countries design policies and interventions to accelerate progress in human capital outcomes. Even when service coverage has been shown to have increased in many countries, the quality remains uneven, and the investments in expansion and access have not been matched with human development outcomes (World Bank, 2021). The SDI surveys develop a set of critical indicators that benchmark service delivery performance to provide valuable inputs for direction and policy making, increase public accountability, track progress over time and improve quality and human capital accumulation across countries (World Bank, 2018). Launched by the World Bank in partnership with the African Economic Research Consortium and the African Development Bank in 2010, the SDIs for health and education were piloted in 2010 in Senegal and Tanzania, implemented in Kenya in 2012. Since then, they have been scaled up to cover more than 15 African countries and adapted outside Africa, like in Iraq, Indonesia, Bhutan, and Moldova, where work is ongoing (Gatti R., et al., 2021).

The SDIs are nationally representative surveys that involve three dimensions of health care service provision: (i) providers’ knowledge, (ii) providers’ effort, and (iii) providers’ inputs to work. SDI surveys allow for assessing how these elements come together to produce quality health services in the same facility. Data collection is undertaken by enumerators visiting selected medical facilities in announced and unannounced visits and observing providers’ performance, the availability of inputs, and the infrastructure in place.

For this analysis, we rely on the SDI health survey implemented in Kenya in 2018, the largest to date, for which 3,094 facilities, of which 1,718 public and 1,313 private facilities, were visited between March and July 2018. The survey is representative of each of the 47 counties, the location of facilities (i.e., urban or rural), facility ownership (i.e., public or private), and facility level (i.e., hospital, health center, or dispensary and clinic).

Data on service delivery performance, including the availability of inputs and medical providers’ knowledge, are aggregated at the facility level. We focus on questions related to the availability of resources, such as medicines and vaccines, and providers’ knowledge, focusing on early childhood and maternal health. For the former, enumerators verify firsthand the availability of non-expired medicines and vaccines from a list of essential pharmaceuticals and maternal and reproductive products. For the latter, Patient Case Simulations (PSC, also called vignettes) are performed to assess the providers’ diagnostic and treatment accuracy for specific illnesses of wide prevalence in the country. A random sample of staff is selected in each facility<sup>5</sup> with one of the enumerators presenting different cases of a sick patient to each health provider while the other acts as an observer. Section 3.2 describes the variables built based on SDI questions.

> 5 Up to ten medical personnel from those who regularly treated patients.

6
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of facilities lacked both services. Figures 2 and 3 demonstrate the evidence of the gap in access to infrastructure for health facilities in Kenya, with some rural facilities far from the most populated cities lacking electricity and internet connectivity.

# _Road connectivity_

We use two sources of road data: the Global Friction Surface 2015 and georeferenced road network data from government sources (Kenya Road Fund 2018). The Global Friction Surface 2015 enumerates land-based travel speed for all land pixels between 85 degrees north and 60 degrees south for a nominal year 2015.<sup>7</sup> It was produced through a collaboration between the University of Oxford Malaria Atlas Project (MAP), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying data sets used to produce the map include roads (comprising all Open Street Map and Google roads data sets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. A speed of travel in terms of time to cross is allocated to each pixel. The final map represents the travel speed from this allocation process, expressed in units of minutes required to travel one meter. We also use a road network from the Kenya Road Board for 2018 that relies at least partially on actual road surveys. The road network data set features surface type and road conditions.

We use the Global Friction Surface 2015 to calculate the catchment area of population for each health facility, which represents the number of people that can be reached in a certain amount of time given the time allocated on the Global Friction Surface. We use the road network to calculate the orthogonal distance of each facility to the nearest fair- or good-quality road.<sup>8</sup>

# _Socioeconomic factors_

We use local estimates of deprivation scores, a proxy for poverty, calculated by the Poverty Global Practice at the World Bank. These calculations were done in small areas for the whole country, providing helpful information for comparisons within counties in Kenya. The methodology used and leveraged the "Small Area Estimation of Poverty under Structural Change" by Lange, Pape, & Putz, 2018. Poverty is estimated for each sub-location in two stages by estimating household consumption using census data.<sup>9</sup> In the first one, a consumption model is developed using survey data<sup>10</sup> but including non-variable household characteristics from the census. In the second stage, the estimated parameters are applied to census data, providing estimates for welfare at the sub-location level. In Kenya, 7,110 sub-locations were used in the analysis; on average, each had 120 households. The measure of poverty is included in the analysis as a control and proxy for the facility's resources.

- 3.2. Key Variables and Summary Statistics

We first merge the SDI data set and the Master List of facilities which results in a subset of 2,578 facilities. This means that 15% of the 2018 SDI facilities could not be found in Kenya's Master List of facilities. This final sample was complemented with information on road, electricity, and cellphone internet access, with additional data described in the previous subsection.<sup>11</sup>

> 7 D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181.

> 8 For quality road we followed the definition of the Rural Access Index: “ _Good roads are paved roads that look dark and are not eroded. Fair roads are either paved roads in fair condition or good gravel roads that are motorable_ ” (Iimi, et al., 2016).

> 9 10% of the Kenya Population and Housing Census (KPHC), randomly selected.

> 10 The Kenya Integrated Household Budget Survey (KIHBS) conducted between 2015 and 2016.

> 11 The restricted sample works with the facilities that merges with the reviewed Master List. Moreover, an initial inclusion of “Distance to the nearest Facility” made it necessary to work with facilities in the 10,131 list.

8
We focus on four indicators of quality for health service delivery: (i) provider’s knowledge, (ii) availability of vaccines for children, (iii) availability of essential medicines; and (iv) availability of antenatal care (ANC) tests. All service delivery variables are calculated from the SDI data. Providers' knowledge is measured using the diagnostic accuracy rate aggregated at the facility level for general illnesses and diseases of children and pregnant women. Availability of vaccines is the percentage of vaccines available at the facility as a proportion of the seven essential vaccines asked in the survey: Rubella, Polio (OPV/IPV), Rotavirus, Pentavalent, Pneumonia, Bacille Calmette-Guerin (BCG), and Vitamin A. Likewise, availability of medicines is defined as the proportion of medicines available at the time of the survey in relation to the 19 essential medicines asked in the survey according to the standards of the World Health Organization (WHO). Finally, ANC tests refer to the number of the type tests available at the facility out of four test types (see Appendix for complete list of medicines and ANC tests).

Access to infrastructure is captured by five variables at the facility level: (i) access to electricity, differentiated by stable and unstable access,<sup>12</sup> (ii) the use of ICT for supply management, (iii) the availability of fridges for vaccine storage, (iv) distance to a primary or secondary road of at least fair quality<sup>13</sup> and (v) cellphone internet access (2G or 3G). Extensive description of the construction of the variables is provided in the Appendix. To refer to the specific questions used to construct the indicators, see Table A4.

Table 1 presents descriptive statistics for the main variables used, indicating the source of each of them (whether SDI or Geospatial information). A large fraction of health facilities is located in rural areas (76%), and over half of the sample are public institutions (63%). Most facilities have access to electricity (89%), mainly because of their connection to the national grid. Still, there is variation regarding the connection quality: only 55% of facilities have stable access to electricity. Likewise, most facilities have access to improved sources of water and sanitation as defined by the WHO,<sup>14</sup> and have basic medical equipment<sup>15</sup> (83%). There is variation in poverty levels, catchment areas, and access to other services (e.g., 2G/3G) for health facilities in Kenya.

> 12 Stable access to electricity: without interruptions in the last three months, or with interruption but access to a secondary source. Unstable access to electricity: with interruptions in the last three months and without access to a secondary source.

> 13 Several distance and quality variables were analyzed, and “Distance from a health facility to at least “fair” quality Primary Road (Trunk) or Secondary Road (Km)” was the best fitted variable.

> 14 <u>https://www.who.int/water_sanitation_health/monitoring/jmp04_2.pdf</u>

> 15 Basic equipment is a dummy variable indicating whether the facility has a thermometer, a stethoscope, a sphygmomanometer, and a weighing scale.

9
_Table 1. Descriptive statistics_

|**Variable**|**SDI**|**GEO**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|---|---|
|General Diagnostic accuracy|x||65.739|20.055|0|100|2578|
|Children and mothers’ cases diagnostic accuracy|x||67.471|19.984|0|100|2575|
|%Medicine Availability|x||44.686|23.547|0|94.737|2578|
|%Vaccine Availability|x||89.641|25.608|0|100|2023|
|ANC tests available forpregnant women|x||2.08|1.595|0|4|2230|
|Access to electricity|x||0.893|0.31|0|1|2578|
|Stable Access to electricity|x||0.555|0.497|0|1|2301|
|Unstable Access to electricity|x||0.445|0.497|0|1|2301|
|Access to improved sources of water|x||0.888|0.315|0|1|2578|
|Access to improved sources of sanitation|x||0.718|0.45|0|1|2578|
|Having a working fridge|x||0.925|0.264|0|1|1826|
|Having basic equipment|x||0.831|0.375|0|1|2578|
|Use ICT for supply management|x||0.147|0.354|0|1|2569|
|Access to 2G network||x|0.877|0.328|0|1|2578|
|Access to 3G network||x|0.173|0.379|0|1|2578|
|Catchment population area (x 100,000 inhabitants)||x|7.089|12.482|0|62.601|2577|
|Distance to at least “fair” quality Primary Road or||x|1.982|5.415|0|95.213|2578|
|SecondaryRoad(Km)||||||||
|Public|x||0.63|0.483|0|1|2578|
|Rural|x||0.766|0.423|0|1|2578|
|Deprivation score||x|38.179|17.378|1.388|92.915|2549|

# 3.3. Methodology

To analyze the relationship between quality of health services and infrastructure variables, we use linear regressions controlling for characteristics at the facility-level. Fixed effects at the county level are included in the specifications as Kenya devolved health services to its 47 semiautonomous counties in 2013. Equation 1 describes the model used in the analysis: Where Qualityfac are the health service quality variables at facility level (i.e., provider's knowledge or medicines/inputs availability), and INFfac are the infrastructure access variables (i.e., electricity, distance to a fair/good quality road, access to internet/cellphone coverage, or ICT use). Controlsfac are control variables at the facility level, including rural/urban, public/private, facility-level (hospital, health center, or dispensary), and a proxy for poverty small-area estimations. Finally, αcounty are county fix effects and εi is the random error term. In general, the large set of controls at the facility level that measure surrounding their characteristics allow us to clean our correlations between quality of health service delivery and infrastructure variables to some extent.<sup>16</sup>

# 3.4. Limitations of the Study

This section describes the limitations of the proposed approach for the analysis and each data source used and the discrepancies found between different sources.

> 16 Interactions between infrastructure variables (i.e., road connectivity and electricity access) were considered but the results did not explain the relationships better than the proposed model. Future research can explore this interdependencies more deeply.

10
Restricted dimensions of quality of health care and infrastructure The analysis is restricted to the relationship between health services quality and infrastructure dimensions for which we have data. As health care quality is a multidimensional concept, we are not able to explain all its variability with the proposed approach and the available data sets. However, our approach allows us to explore the associations between the variables of interest and provide suggestive evidence on the relevance of the access to infrastructure to the quality of health services in Kenya. We based our discussion and recommendations acknowledging these limitations of the methodology.

Longitudinal and cross-sectoral dimensions of SDI Despite SDI surveys being a rich source of information, these data did not allow us to pursue a causal analysis due to lack of an exogeneity source. Therefore, this study only presents correlations between access to infrastructure and health service quality. These are still useful benchmarks for illustrating the potential that investments in infrastructure might have for improving the quality of health service delivery.<sup>17</sup> Infrastructure is a cross-sectoral and typically long-term investment that might influence services simultaneously in multiple sectors over many years. Ideally, the education SDI data would also have been exploited in this paper, but the 2012 education data was several years more outdated and done at a smaller scale (i.e., only representative at the country level, not at the county level). Moreover, it was impossible to perform an intertemporal analysis using the 2012 and 2018 health SDI data sets due to several methodological changes that made data not fully comparable over the years (e.g., different sample methodologies, different facilities, and different levels of representativeness).

Finally, as mentioned before, the last SDI data set for Kenya is from 2018. Although it is not completely outdated, in the past years, Kenya has made important improvements both in infrastructure and health service delivery, which might not be captured by the present analysis. Nevertheless, for the purpose of the analysis, we use the most up-to-date data available.

Limitations of geospatial data The main limitation of geospatial data is that the information is not as granular as for the SDI data set. For example, the Master Facility List, although the most updated list of health centers in Kenya, contains very limited information about each facility (i.e., level of the facility and ownership). Additionally, the calculations for other geospatial data are often predictions for small areas based on multiple data sources, and in some cases the calculations based on sources like MAP might be challenging because of the geospatial coverage range capacity. This makes it less precise for the analysis as compared to SDI data collected directly from health facilities and more susceptible to capture other geographical aspects such as urban/rural status. Also, the information estimated with geospatial data is less specific than SDI data; for instance, it is only possible to study access to electricity (a dummy variable) instead of the quality of the connection at this stage.

> 17 Other dimensions of quality of health service delivery were also considered but not included in this paper. For instance, one attractive aspect of the 2018 SDI is that it included an exit interview for users, capturing the supply side of the services and the perceptions of the demand. Nevertheless, the sample of users is small (up to five patients per facility, with many facilities having only one patient interviewed), questions were only asked about family planning services, and there is no variation in the inpatients' perceptions of quality.

11
systemic differences were discovered during the analysis. We carry out a more nuanced comparison between the type of information each data source provides, as well as the magnitude of these differences and their causes in Appendix II. Due to the granularity of SDI, which includes information on source and quality of electricity, as well as differences in definitions and on expected accuracy from direct observations versus small-area estimations, this paper favors the use of SDI data on access to electricity. Nonetheless, we note that the GEP data might still provide useful information for other studies when no first-hand data is available, particularly when using electrification as a control that might also capture other socioeconomic variables.

# 4. Results

This section presents and discusses the main results for each quality indicator. All specifications include controls at the facility level and county fixed effects.

## 4.1. Provider's Knowledge

Diagnostic errors are a source of avoidable illness and death, now more than ever since many previously incurable conditions now have a treatment (Khullar, Jha, & Jena, 2015). We explore here two indicators for diagnostic accuracy, one related to children and adults' illnesses and another one focusing on children and pregnant women-related ailments. As shown in Table 2, access to a stable electricity connection is positively related to diagnostic accuracy at the facility level, while longer distance to the nearest at least fair quality road is negatively related with this measure of provider's knowledge.

Column 1 in Table 2 displays the results for general cases (diarrhea with dehydration, pneumonia, diabetes, and tuberculosis). Here, access to electricity service is related to an increase of 4.20 percentage points in the diagnostic accuracy compared to facilities without access to electricity. Even though unstable access to electricity is also positively related to diagnostic accuracy, the relationship is not significant. Column 2 shows diagnostic accuracy for childhood and pregnant women cases (diarrhea with dehydration, pneumonia, postpartum hemorrhage, and neonatal asphyxia); the results follow the same logic, with a positive and significant relationship between access to a stable electricity connection and diagnostic accuracy of 5.03 percentage points.

The specification includes controls for a comprehensive set of facility-level covariates, including access to improved sources of water or sanitation and small area poverty and population density estimations to control for possible sorting. Given the broad set of controls used, the results suggest that facilities with better infrastructure might attract and/or retain better-skilled providers, which is in line with evidence from Lenz, L. et al., 2016, where mainly for rural areas, access to grid electricity facilitates recruiting trained staff in Rwanda.

On the other hand, increasing distance to at least a fair quality primary or secondary road is negatively related to provider's diagnostic accuracy at facility level. Using the quantile distribution of this geospatial variable by distance, it is seen that the further the facility is from fair or good quality roads, the worse the results of provider knowledge. While there seems not to be any significant differences between quintiles one and two, facilities in the third quintile of the distance variable have 3.7 and 2.5 percentage points lower diagnostic accuracy than at the facilities which are in the first quantile - the closest to the fair or good quality roads. This 12
relationship intensifies with distance, and the farthest facilities are estimated to have 4.9 and 4 percentage points lower diagnostic accuracy than those closest to fair or good quality roads.

Our results may indicate that, despite the country's efforts to make hard-to-reach areas more attractive to health workers (Ministry of Health, 2009; Ministry of Health, 2015), health centers in remote areas still face transportation barriers for the recruitment and retention of top-performance medical providers, as documented in Ojakaa, Olango, & Jarvis, 2014 for Kenya. The findings on the relationship between providers’ knowledge and the access to electricity and distance to the nearest fair or good quality road highlight the importance of facilities' infrastructure quality for health provider retention (Ojakaa, Olango, & Jarvis, 2014; Rockers, et al., 2012; Rowe, de Savigny, FLanata, & Victora, 2005; WHO, 2010; Lenz, Munyhirwe, Peters, & Sievert, 2016).

_Table 2. Linear regression for diagnostic accuracy_

||(1)<br>|(2)<br>|
|---|---|---|
||Diagnostic|Children and mother illness|
||Accuracy|Diag. Accuracy|
|Stable Electricity vs No Access|4.20***|5.03***|
||(1.44)|(1.50)|
|Unstable Electricity vs No Access|2.04<br>|1.88<br>|
||(1.43)|(1.47)|
|2G vs No Access|2.66|-2.08|
||(1.83)|(1.86)|
|3G vs No Access|2.19|-2.22|
||(2.42)|(2.45)|
|Second quantile road distance|-1.76|-0.89|
||(1.13)|(1.15)|
|Third quantile road distance|-3.71***|-2.51**|
||(1.16)|(1.21)|
|Fourth quantile road distance|-4.93***|-4.05***|
||(1.20)|(1.24)|
|Public|3.70***|5.46***|
||(1.01)|(1.00)|
|Rural|-2.14*|-1.69|
||(1.28)|(1.28)|
|Deprivation Score|-0.09*|-0.06|
||(0.05)|(0.05)|
|Catchment pop.|-0.03|0.01|
||(0.08)|(0.07)|
|Hospital vs Dispensary|2.94**|0.62|
||(1.28)|(1.45)|
|Health Center vs Dispensary|2.49**|-0.97|
||(0.97)|(1.07)|
|% of Male-Facility|2.33*|1.41|
||(1.32)|(1.34)|
|Average providers age -Facility|-0.14***|-0.23***|
||(0.05)|(0.05)|
|% Post-secondary education-Facility|10.63***|7.37***|
||(1.62)|(1.47)|
|Constant|58.00***|66.09***|
||(4.42)|(4.36)|
|Observations|2,548|2,545|
|R-squared|0.17|0.15|
|Controls|Yes|Yes|
|Fixed Effects|County|County|
|Mean|65.74|67.47|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 13
# 4.2. Availability of Vaccines

Vaccines are pivotal and cost-effective solutions for individual and collective well-being (Owais, Hanif, Siddiqui, Agha, & Zaidi, 2011; Ramalingaswami, 1989; Schuchat, 2011). In Kenya, more than one out of seven children die before the age of five, largely from causes related to preventable diseases for which the best response is to promote immunization (CDC Kenya, 2017). From our sample, 78.45% of the facilities provide vaccination services, and only these are asked about the availability of vaccines. The survey also gathered information about whether the vaccines were stored at the facility and whether the center used a fridge for storing vaccines.<sup>18</sup> All the vaccines mentioned in the survey questions require refrigeration (i.e., Rubella, Polio (OPV/IPV), Rotavirus, Pentavalent, Pneumonia, Bacille Calmette-Guerin (BCG), and Vitamin A). The relationship between the percentage of available vaccines and access to electricity and fridge is expected to be positive and significant, and it is indeed the case.

_Table 3. Linear regression for vaccines availability_

||(1)<br>% Vaccines|(2)<br>% Vaccines|(3)<br>% Vaccines|
|---|---|---|---|
|Stable Electricity vs No Access|10.47***|1.18|1.07|
||(2.63)|(1.32)|(1.25)|
|Unstable Electricity vs No Access|11.21***|0.98|0.90|
||(2.66)|(1.30)|(1.23)|
|Having a fridge||5.46**<br>(2.70)||
|Having a working fridge|||10.93***<br>(2.71)|
|Uses ICT for supply chain||0.01|0.01|
|||(1.21)|(1.22)|
|Second quantile road distance|5.52***|1.56|1.61|
||(1.75)|(1.17)|(1.18)|
|Third quantile road distance|4.80**|1.57|1.55|
||(1.88)|(1.15)|(1.14)|
|Fourth quantile road distance|3.09|1.32|1.35|
||(1.96)|(1.14)|(1.13)|
|# Outpatient last 3 months|0.00**|0.00**|0.00**|
||(0.00)|(0.00)|(0.00)|
|Catchment pop.|0.09|0.03|0.03|
||(0.06)|(0.04)|(0.04)|
|Constant|71.60***|88.67***|84.53***|
||(7.05)|(4.19)|(4.54)|
|Observations|1,994|1,794|1,794|
|R-squared|0.11|0.09|0.12|
|Controls|Yes|Yes|Yes|
|Fixed Effects|County|County|County|
|Mean|89.64|95.67|95.67|
|Robust standard errors in paren|theses *** p_<_0|.01, ** p_<_0.05,|* p_<_0.1|

Column 1 in Table 3 shows the importance of access to electricity for the availability of vaccines. Having access to electricity is associated with an increase of 10 to more than 11 percentage points in the percentage

> 18 A description of the scheme of questions is in the appendix, Figure A1 .

14
of types of vaccines available at the facility. Note that already 89% of facilities in Column 1 have vaccines available. Column 2 shows the results when the variable for having a fridge (working or not) is included. Here, the relation of electricity and the availability of vaccines is captured through the use of a refrigerator for storage with an associated increase of 5.46 percentage points in the percentage of vaccines available. Moreover, when only a working fridge is included in the specification, the association is higher, stating that a functioning refrigerator is associated with the full availability of vaccines.

On the supply side, the availability of the vaccines _in situ_ is key for achieving higher immunization levels. Ensuring a reliable connection to electricity, and the necessary equipment for storage in all facilities might then increase the coverage of vaccination plans. Here, it is relevant to highlight that vaccine coverage might also depend on factors like financial autonomy of vaccine programs and supply chain capacity, but our results indicate infrastructure _in situ_ is also associated with the availability of vaccines at facility level. Finally, it is relevant to mention that in reducing infant mortality related to preventable diseases, motivating the demand side is also relevant, especially after the COVID-19 crisis has interrupted the vaccination schemes of many children around the world.

# 4.3. Availability of Medicines

Medicines are essential components of patient care, and their availability is commonly cited as one of the most crucial elements of quality by health care consumers (Ministery of Health, 2013). Therefore, medicines are critical for the health service provision and pivotal to attend to the population's health care needs (WHO, 2010). Here we analyze two sets of medicines: (i) the list of 19 tracer medicines for children and pregnant women identified by the WHO, and (ii) the list of 10 medicines fundamental for health care systems also identified by the WHO. Results for both lists of medicines are consistent, so here we focus on (i) and present the results for (ii) in Table A5 in the Appendix.

Table 4 shows the results for the list of medicines for children and pregnant women. A stable connection to electricity is related to an increase of 4.5 percentage points in availability of medicines. Furthermore, ICT use for supply management is also positively related to the availability of medicines with a magnitude of 6.79 percentage points. Our transport variable, the distance of the facility to a primary or secondary road in at least fair condition seems not to be correlated with the medicines stock. While a significant difference arises between the first two quintiles of the variable, there is no significant difference in the availability of the selected essential medicines from the closest and further facilities. On the other hand, access to cellphone internet is negatively associated with the percentage of essential drugs in the facilities. While the latter result is surprising, we interpret the cellphone internet access as a proxy for the degree of rural/urban, suggesting the most remote facilities have more medicine availability.

This group of essential medicines need to be available at all facilities to meet the basic health care needs of the population, and there is room for improvement. Only 15% of our sample of facilities use ICT for supply management and, even when almost 90% of the facilities have access to electricity, only half of them have a stable connection. Therefore, as stable electricity and ICT for supply management are positively related to the availability of essential drugs, these two might be leveraged to effectively manage the stock of medical supplies and increase its availability for patients.

15
_Table 4. Linear regression for essential medicines for children and pregnant women availability_

||(1)<br>|(2)<br>|(3)<br>|
|---|---|---|---|
||% Medicine available|% Medicine available|% Medicine available|
|Stable Electricity vs No Access|4.88***||4.50***|
||(1.34)||(1.33)|
|Unstable Electricity vs No Access|0.55||0.76|
||(1.29)||(1.29)|
|Uses ICT for supply chain||7.33***|6.75***|
|||(1.56)|(1.55)|
|# Outpatient last 3 months|0.00***|0.00***|0.00***|
||(0.00)|(0.00)|(0.00)|
|Second quantile road distance|2.93**|2.98**|2.91**|
||(1.26)|(1.24)|(1.24)|
|Third quantile road distance|0.58|0.61|0.76|
||(1.26)|(1.26)|(1.25)|
|Fourth quantile road distance|0.81|0.98|0.93|
||(1.21)|(1.20)|(1.20)|
|2G vs No Access|-5.32***|-5.61***|-5.37***|
||(1.62)|(1.61)|(1.61)|
|3G vs No|-11.97***|-11.97***|-12.07***|
||(2.51)|(2.49)|(2.50)|
|Catchment pop.|0.18**|0.18**|0.18**|
||(0.09)|(0.09)|(0.09)|
|Constant|45.63***|45.71***|42.76***|
||(4.75)|(4.62)|(4.69)|
|Observations|2,539|2,530|2,530|
|R-squared|0.40|0.41|0.41|
|Controls|Yes|Yes|Yes|
|Fixed Effects|County|County|County|
|Mean|44.69|44.69|44.69|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# 4.4. Availability of Antenatal Care Tests

Promptly detecting potential risks to children's health in utero and/or pregnant women is essential to avoid fatal consequences. In Kenya, Maternal Mortality Ratio (MMR) is 362 maternal deaths per 100,000 live births (DHS Program, 2014),<sup>19</sup> and the stillbirth rate is between 18.32 and 21.09 per 1,000 births according to UNICEF estimates (UNICEF, 2020). Although the indicators have indeed improved in the last decade, they remain far from their targets, which are 147 and 12, respectively for maternal deaths per 100,000 live births and stillbirth rate per 1,000 births (Lang’at, Mwanri, & Temmerman, 2019). Antenatal care (ANC) tests are essential to diagnose possible complications that require special attention during pregnancy, which is critical to attain those goals. Therefore, facilities must provide ANC tests to pregnant women and have them readily available.

19 The latest available data is from 2014.

16
This analysis involves four ANC tests in line with Kenyan national guidelines: HIV, Urinalysis, Hemoglobin, and Syphilis. Table 5 displays the results for the number of tests provided at the facility, the variable being ANC test between 0 and 4. Similar to what has been found before, electricity service is positively related to the provision of tests, but in this case, the relationship is significant for both stable and unstable connections. The use of ICT for supply management is also positively correlated with the number of tests provided, which might be explained by the need for inputs to perform the rapid tests (strips, containers, etc.). Farther distance to the nearest road of fair or good quality is negatively related to the number of ANC tests provided for the farthest facilities compared to the ones at the facilities with the closest distance to the nearest fair or good quality toad; this might indicate that farther the facilities, more barriers they face in getting the needed inputs. It is worth noting that better infrastructure might also increase the demand for medical services. This may be the underlying reason for the statistically significant relationships between ANC tests and infrastructure variables such as electricity.

_Table 5. Linear regression for Antenatal Care test availability_

||(1)<br>ANC test|(2)<br>ANC test|(3)<br>ANC test|
|---|---|---|---|
|Stable Electricity vs No Access|0.56***||0.55***|
||(0.09)||(0.09)|
|Unstable Electricity vs No Access|0.49***||0.50***|
||(0.09)||(0.09)|
|Uses ICT for supply chain||0.27**|0.26**|
|||(0.11)|(0.11)|
|Second quantile road distance|0.14|0.14|0.13|
||(0.09)|(0.09)|(0.09)|
|Third quantile road distance|-0.16*|-0.18*|-0.15|
||(0.09)|(0.09)|(0.09)|
|Fourth quantile road distance|-0.25**<br>(0.10)|-0.27***<br>(0.10)|-0.25**<br>(0.10)|
|# Outpatient last 3 months|0.00***|0.00**|0.00**|
||(0.00)|(0.00)|(0.00)|
|2G vs No Access|0.27**<br>|0.26**<br>|0.26**<br>|
||(0.13)|(0.13)|(0.13)|
|3G vs No Access|0.23|0.24|0.22|
||(0.20)|(0.20)|(0.20)|
|Catchment pop.|0.01**|0.01**|0.01**|
||(0.01)|(0.01)|(0.01)|
|Constant|2.45***|2.78***|2.33***|
||(0.36)|(0.35)|(0.36)|
|Observations|2,201|2,193|2,193|
|R-squared|0.34|0.33|0.34|
|Controls|Yes|Yes|Yes|
|Fixed Effects|Yes|Yes|Yes|
|Mean|2.080|2.080|2.080|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 17
# 5. Discussion

Overall, while this study does not present causal inferences, it does document the evidence of existing correlations between different infrastructure variables and the quality of health services provided in Kenya after controlling for a wide set of other characteristics. These results are supported with and complement other existing literature to study possible channels and/or corroborate findings.

In determining the quality of health services, many factors come into play. We acknowledge that health care quality and infrastructure have several dimensions that cannot be covered by the available data or the scope of this work, which might have influenced the variability or quality of the variables explained by our model. Nonetheless, with the existent information we have relevant evidence to positively associate infrastructure access to our analyzed dimensions of health quality. For the case of Kenya, this study finds suggestive evidence that infrastructure matters, and that investments in infrastructure and technology may play an important role in improving the quality of health service provision, including the services for early childhood and maternal health. These results contribute to the understanding of quality in the health services from a supply side, by exploring dimensions of infrastructure to which it might be related and providing evidence on the relevance of the relationship. More work is needed to explore the quality of health services from a demand side perspective and, for both sides, more frequent and up to date data is needed to perform relevant and timely evaluations of the service and its components.

In terms of policy recommendations, in order to ensure that everyone, especially households in poorer and more isolated areas, has access to high-quality basic health services, it is important to prioritize electrifying the facilities that do not have access to electricity. While most facilities have access to electricity (89%), half do not have stable access, which is crucial for delivering health services, even in a broader scope than those analyzed here. In addition to the shown relationship between access to electricity and provider’s knowledge, the availability of drugs, vaccines and medical inputs, a stable electricity connection is also key to perform tests (e.g., laboratory test), treat patients (e.g., use ventilators), be able to perform medical procedures from the simplest to surgeries, among others. Therefore, providing the health facilities with a nationwide access to stable electricity access remains a priority to be addressed in Kenya. Needless to say, this would be an investment that would benefit many other sectors and households directly.

Likewise, it is equally important to invest in and improve the accessibility to roads and enhance their quality. Our findings suggest that more and better roads might help facilities eliminate access barriers that allow health facilities to access inputs, patients, and providers with more ease. Making facilities more accessible, however, may not contribute to addressing these issues if other complementary measures are not put into place. For instance, the 2018 SDI data suggests that urban-based health care workers had similar levels of diagnostic accuracy scores as their rural counterparts. Therefore, just increasing access will not necessarily translate into better providers if the pool of qualified providers is not large enough.

Moreover, the evidence presented suggests that ICT may be a valuable ally for improving the efficiency of supply chains and distribution of inputs to their end users. Enabling facilities to access and use ICT may positively impact the availability of medicines and inputs and also save time and resources. There is a substantial potential to increase the use of ICT for supply chain management in Kenya since only 15% of the facilities reported using it for this purpose.

Overall, these results show that investments in infrastructure might have significant positive effects for the quality of health service delivery and might be a relevant factor for improving health (and possibly many other) outcomes in the population as a whole.

18