Private Sector Innovation Programme for Health

Transcription

1 What Do We Know About the Kenyan Poor and Their Use of the Private Health Sector? A synthesis of the literature Prepared for DFID Submitted by Cardno Emerging Markets (UK) Ltd Oxford Policy Management (OPM) In association with IHA and MannionDaniels Private Sector Innovation Programme for Health PO 6240 February 2014

2 Table of Contents Acronyms... i Foreword... ii 1 Introduction Background Purpose of Literature Review Methodological Approach Organization of the Literature Review Defining the Poor What is Poor? Approaches to Identify the Poor Household Assets Assessment Monetary/Income and Consumption Expenditure Assessment Subjective Measures of Poverty Food Security Assessment Progress Out of Poverty Index (PPI) Who are the Poor in Kenya? Using the Kenya Poverty Incidence / Headcount Index Using Median Household Expenditures Key Findings on the Poor in Kenya Strategies to Define the Poor Health-Seeking Behaviour of the Kenyan Poor Health-Seeking Behaviour Frameworks Factors Influencing Health-Seeking Behaviour of the Poor in Kenya Implications for PSP4H Bibliography Figures Figure 1.1 Overview of the Analytical Process under PSP4H Figure 3.1 Percentage of Population below the Poverty Line by Region Figure 3.2 Headcount Index by County Figure 3.3 Percentage of Population below the Poverty Line Figure 3.4 Mean Household Expenditure per Adult Equivalent (in thousand) by Counties Figure 3.5 Consumption Expenditure by Income Quintiles, National and by Rural/Urban Figure 3.6 Market Segmentation Figure 3.7 Poverty Profiles Annexes Annex 1: Hybrid Poverty Assessment Tool

3 Acronyms DFID KNSB KAIS KDHS KSh M4P MEDS PPI PSP4H VfM Department for International Development Kenya National Statistics Bureau Kenya AIDS Indicator Survey Kenya Demographic and Health Survey Kenya shillings Making Markets work for the Poor Mission for Essential Drugs and Supplies Progress Out of Poverty Index Private Sector Innovations Programme for Health Value for money Cardno Emerging Markets (UK) Ltd P a g e i

4 Foreword The Private Sector Innovation Programme for Health (PSP4H) was commissioned by DFID Kenya in late 2013 to explore the markets in which poor people pay for-profit providers for healthcare. The overall objective of the PSP4H programme is to learn lessons of how a market systems approach might benefit pro-poor health interventions. Although PSP4H is primarily an action research programme aimed at producing empirical evidence that will inform future health programme design, sound analysis constructs the foundation for specific market interventions. To underpin our action research, PSP4H first conducts pertinent primary and secondary research. The programme began by synthesizing the existing data, knowledge and experience of for-profit health markets used by the poor in Kenya. Our researchers divided this synthesis into two reports, one exploring the demand side of the market and one exploring the supply side. This report What Do We Know About the Kenyan Poor and Their Use of the Private Health Sector? explores the demand side and is the first research report issued by PSP4H. I draw attention to what may be areas of primary interest, the chapters addressing Who Are the Poor in Kenya? and the Health-Seeking Behaviour of the Kenyan Poor. Kenya s working poor have nominal disposable income which must be shared amongst all household needs. This report explores the willingness and ability of the poor to pay for health services, given that most pay out-of-pocket for health care at present. The reasons they seek health care from the private sector are clearly identified in this study. A companion report completes the picture; it depicts the supply side of the Kenyan health care market and explores four market segments in depth. Forthcoming primary research probes the economic behaviour of working poor health consumers across Kenya through interviews, focus group discussions and open forums. We trust the research reports and publications stemming from PSP4H will focus private sector activity on the market presented by Kenya s working poor. This segment is currently underserved and offers opportunities to introduce sustainable health care business models that are win-win profitable for business while delivering better value to the working poor. Ron Ashkin Team Leader PSP4H Nairobi February 2014 Cardno Emerging Markets (UK) Ltd P a g e ii

5 1 Introduction 1.1 Background The range of strategies to harness the private sector to advance public health goals, including improving access to quality health care for the poor, is extremely diverse. Historically, developing country governments have not pursued many strategies to increase participation of the private health sector and support from international Aid Agencies aiming to increase engagement of the private health sector has been limited. Indeed, lack of government and donor awareness of the private sector and understanding of how it operates have occasionally undermined the effectiveness and sustainability of aid-supported actions aimed to leverage the private health sector. That being said, over the last 10 years interest and experience implementing strategies to engage the private sector to achieve national health goals has grown. In most cases, donor aid has focused on direct intervention e.g. donation of inputs, supplies and subsidies. This assistance aimed to provide incentives for the private sector to expand its role in the provision of health care as well as increase its efforts to target services towards the poorer segments of the market. However, targeting the poor has been implicit, not explicit, in these aid programmes. Consequently, there is limited experience in working through the markets and designing pro-poor interventions to harness the private health sector to increase access to health care for the poor. DFID/Kenya is exploring a new approach to aid assistance in health. Through the Private Sector Innovation Programme for Health (PSP4H), DFID/Kenya is adapting a market-based approach to address the health needs of the Kenyan poor. Making markets work for the poor (often referred to as M4P) focuses on the working poor who pay for health services in the private, commercial health sector and aims to ensure that they receive value for the money (VfM) they spend on health care. The PSP4H programme will serve as an innovation centre for private sector health care businesses and will encourage private health sector businesses to invest in providing health care and products in underserved markets that have significant growth potential and reach the working poor. The programme will partner with private sector partners to identify innovative approaches and pilot test them. For example, PSP4H will assist with market research, pilot testing, generating demand, and risk sharing as means to motivate private sector health care providers to enter into markets that primarily serve the working poor. PSP4H will then identify successful and viable pilot projects and work to scale up and replicate these market-based approaches. PSP4H will play an instrumental role in documenting the process; analysing what works, doesn t work, and why; and sharing lessons learned with Kenyan stakeholders working in the health sector as well as the international donor and wider M4P communities. 1.2 Purpose of Literature Review PSP4H s Mission Foster sustainable health care business models that are a win-win profitable for business while delivering better quality services that offer value to the working poor. The PSP4H Programme will carry out a series of analyses to identify the market interventions that have the greatest potential for success -- for increasing access to the poorer segments of the market and for advancing Kenya s national health goals. The analyses are designed to answer three basic questions that will guide the Programme s activities: MARKET SELECTION Which value chains or markets have the greatest potential for reaching the poor and for growing over time? (E.g. Where do the poor seek healthcare? Which services and products are delivered to the poor? What are the opportunities for building upon these existing programmes?) Cardno Emerging Markets (UK) Ltd P a g e 1

6 STRATEGIES FACILITIATION What are the potential areas to improve health outcomes of the working poor? (E.g. health systems such as drug supply, diagnostics, malaria, etc.) What are the constraints in the market and operating environment that prevent the private health sector from realizing these opportunities? (E.g. regulations, unfair competition, consumer inability to pay, etc.) What interventions are needed with both public and private sector stakeholders to unlock this potential market and increase engagement of private sector partners? (E.g. market interventions to create more favourable environment and minimize/share market risks) The three core research areas of the PSP4H analytical process (Figure 1.1) include: 1) an assessment of the private health sector through a review of the literature and key informant stakeholder interviews; 2) definition and analysis of the poor to improve understanding of the poor through literature review and focus group discussions; and 3) selection of potential markets through market analysis, stakeholder open forums, and private health sector dialogue. While the figure presents the analytical process as linear, in fact it is interactive and the different research areas, through triangulation, inform one another and sharpen the focus and understanding of the poor and their utilization of private sector health care services; strengthens the analysis of the market potential as well as barriers to market entry; and, improves the design of the pilot market interventions. Figure 1.1 Overview of the Analytical Process under PSP4H As shown in Figure 1.1, this report is one of several analytical initiatives to guide PSP4H selection and design of market interventions. This report focuses on understanding the poor in Kenya and their use of the health services, including private ones. It provides: i) an analysis of the poor in Kenya; ii) recommendations on how to define the poor for the purposes of the PSP4H Programme; and iii) an overview of health-seeking behaviours of the working poor. In the coming month, PSP4H will undertake two other major research initiatives. The second one focuses on providing a snap shot of the private health sector, its size and scope as well as assessment of key health markets. The third research project is formative research of consumers and private providers who serve them to learn about the working poor s health seeking behaviour and private providers interest and capacity to serve this population group. This suite of analyses will guide PSP4H s selection of a limited number of private sector health markets that merit further research and hold the greatest likelihood for potential growth and sustainability of health services and products delivered to the working poor. Cardno Emerging Markets (UK) Ltd P a g e 2

7 1.3 Methodological Approach To carry out the literature review, the PSP4H staff: Conducted a desk review that included government of Kenya statistics, documents and research; health research conducted by Kenyan policy institutions and Kenyan development agencies; and international research on the Kenyan private health sector Conducted key informant interviews with a wide array of private sector stakeholders representing different health private sector entities (e.g. pharmaceutical manufacturing, wholesalers/ distributors, pharmaceutical retailers, and healthcare providers at different facility levels) Conducted two Forums in Nairobi and Mombasa (a third is planned for January in Kisumu) with organizations representing private sector organizations and consumers in the poorer segment of the market (e.g. Kenya Healthcare Federations, Kenyan Private Hospital Association, Jawabu). 1.4 Organization of the Literature Review The Literature Review on Consumer Demand is organized into four sections. The first section focuses on better understanding the poor in Kenya. This section reviews the different approaches commonly used in Kenya to assess poverty. The second section delves into an analysis of the poor in Kenya - who are they, where do they live and their income levels. The third section offers three approaches to define the poor in Kenya for the purpose of the PSP4H programme. Health seeking behaviour of the poor in Kenya is the focus of the fourth section, offering a preliminary look at the factors a private health market intervention will need to address. The last section concludes with a summary of the working poor, key characteristics that need be considered in programme efforts and further research needs. Cardno Emerging Markets (UK) Ltd P a g e 3

8 2 Defining the Poor A hallmark of the Making Markets work for the Poor approach is its focus on understanding and targeting the poor. In this and the next sections, existing literature on methodologies to define the poor as well as Kenyan data on the size, scope, and selected characteristics of the Kenyan poor are reviewed. This analysis will inform a dialogue that will define, describe and identify the poor who will be the beneficiaries of PSP4H market interventions. 2.1 What is Poor? According to a report by the World Bank [1], in 2005/6 nearly half of the Kenyan population (47% - 17 million people) were unable to meet the cost of buying a basket of goods that would provide the amount of calories needed to meet their recommended daily nutritional requirements as well as minimal non-food needs. Further, the report notes that the majority of the poor (14 million) live in rural areas. So, who are the poor? According to the United Nations, absolute poverty is a condition characterized by severe deprivation of basic human needs including food, safe drinking water, sanitation facilities, health, shelter, education and information [2]. This definition is premised on the basis that human development comprises more than economic factors, it also include access to shelter, education and information. Falkingham and Namazie in their review of approaches to identifying the poor discuss two main ways of defining poverty and who the poor are [3]. The first is absolute poverty that focuses on defining a minimum standard of living based on a person s physiological needs for water, clothing and shelter, essentially, their basic needs. The second is relative poverty that defines poverty in relation to a generally accepted standard of living in a specific society at a specific time, and goes beyond basic physiological needs. According to Falkingham and Namazie, the two concepts of absolute and relative poverty capture different, but equally important, dimensions of the poverty problem. In order to fully understand the dimensions of poverty in a society, it is essential to identify how many people, and which people, are living in households that are unable to purchase or access a fixed minimum amount of goods and services, in other words, are living in absolute poverty, as well as identify those with resources that are so limited as to exclude them from achieving a defined standard of living and life-style that is enjoyed by the rest of the society. 2.2 Approaches to Identify the Poor Due to the different dimensions of poverty, a range of measures have been developed to capture the multidimensionality of poverty [4]. The following is a brief overview of different tools and methodologies used to measure poverty Household Assets Assessment One of the approaches used for defining the poor is the use of data from household ownership of basic amenities, such as the source of drinking water (e.g., buying, piped into residence, public taps, other); type of sanitation facilities (e.g., flush toilet, traditional pit latrine, ventilated pit latrine, trench toilet/other, no facility/bush); materials used for the floor of the house (e.g., mud/sand/dung, wood/vinyl/tiles, cement, other); property ownership (e.g., ownership of a car, television, refrigerator, radio, bicycle, and motorcycle); and other characteristics that relate to a household s socioeconomic status. This data is then used to compute a wealth index by assigning a weight (factor score) generated through statistical tools such as principal components analysis or factor analysis. The resulting indices are used to rank the households and group them into wealth quintiles ranging from one (lowest) to five (highest). The assets index is considered a good measure of long-term wealth. This approach is commonly used by national sample surveys such as the Kenya Demographic and Health Survey (KDHS) [5] and the Kenya AIDS Indicator Survey (KAIS) [6]. Cardno Emerging Markets (UK) Ltd P a g e 4

9 2.2.2 Monetary/Income and Consumption Expenditure Assessment Expenditure data (money-metric measure) is another metric used to determine poverty status. The proponents of this approach argue that poverty is more than a lack of material resources [3, 7]. Household poverty measures based on household income and expenditures are related to current employment circumstances. According to the World Bank, household income or expenditure estimates are usually weighted by household size to calculate household per capita expenditure based on consumption or household income per person. Household incomes are difficult to measure and do not always accurately reflect actual income, however, household consumption expenditure is usually a reliable indicator. Consequently, expenditure data is usually used as a proxy for estimating incomes. Expenditure-related data is likely to provide some insights into a household s current ability to cater for basic food needs. This approach is often regarded as a more robust indicator of poverty than other measures [8]. However, in the context of developing countries, it is very challenging to gather data that accurately measure household expenditures Subjective Measures of Poverty Subjective measures of poverty (subjective poverty index) are another means of determining who is poor. This involves asking the head of the household to rank the household welfare status [9]. Subjective measures of wellbeing have been reported to be associated with various markers of socioeconomic status and are thought to better reflect the household s social position by accounting for past and future prospects Food Security Assessment Poverty status has also been assessed through the use of food security measures, also known as the food poverty index. This approach entails asking respondents to describe the food availability in the household and whether the household experienced periods of food shortages [10, 11]. These measures were developed to gain a better understanding of household food availability and intrahousehold resource allocation that is, how food purchases are allocated amongst various members of the household Progress Out of Poverty Index (PPI) The Progress Out of Poverty Index (PPI) is comprised of ten questions about a household s characteristics and asset ownership that are scored to compute the likelihood that the household is living below the poverty line or above by only a narrow margin. With the PPI, it is possible to identify the segments of the population that are most likely to be poor or vulnerable to poverty. The PPI is country-specific. There are PPIs for forty-five countries [12]. Cardno Emerging Markets (UK) Ltd P a g e 5

10 3 Who are the Poor in Kenya? As the prior section discussed, there are several approaches to assessing poverty and identifying the poor. In Kenya, two of the most common approaches used are the consumption expenditure assessment approach known as the Poverty Incidences or Headcount Index and the Median Monthly Expenditures. This section uses both approaches, examining absolute and relative poverty, to gain a more complete understanding of the dynamics of poverty and who are the poor in Kenya. 3.1 Using the Kenya Poverty Incidence / Headcount Index Poverty in Kenya is multifaceted and can vary across space, time and various socio-economic groups. According to the Kenya National Bureau of Statistics (KNBS), the poverty line in Kenya is determined by the household consumption expenditure required to purchase a food basket that fulfils the minimum nutritional requirements (set at 2,250 calories per adult equivalent per day) and to meet the costs of basic nonfood items such as schooling, health, transportation and rent [13]. Regional price adjustments are applied to reflect differences in the cost of living between different areas, especially between urban and rural areas. Adjustments are also made to compute an adult equivalence scale that accounts for differences in needs among household members (e.g. children consume less food relative to adults). In 2005/06, the poverty line was estimated to be Kshs 1,562 and Kshs 2,913 per adult equivalent per month for rural and urban households, respectively. The proportion of the total population whose consumption is below this poverty line is deemed poor (also known as the poverty incidence or headcount index). In 2009, it was estimated that 45.2% of all Kenyans were living below the poverty line. This level is below the 46 % of the population that was estimated to be living below the poverty line in 2005/06. It is estimated that half of the people in rural areas and a third in urban areas live below the poverty line. Poverty by Geographic Region Incidence of Poverty In 2009, 45.2% of Kenyan s were living below the poverty line Half of Kenya s rural population live in poverty One third of Kenya s urban residents live below the poverty line The Kenya Poverty Incidence/Headcount Index shows that the incidence of poverty varies by geographic region (see Figure 3.1). The Central Region is the wealthiest with less than one third (31%) living in poverty compared to North Eastern and Nyanza Regions with more than two thirds (64 and 65%, respectively) of their total populations living in poverty. The regional averages of the Kenya Poverty Incidence / Headcount Index conceal the concentration of poverty in specific geographic areas. To identify these pockets of poverty, one needs to examine the Kenya Poverty Incidence / Headcount Index by county. The North Eastern Region has two of the poorest counties in Kenya - Mandare and Wajeri with 86 and 84% of their total populations living on less than Kshs 1,600 monthly. Yet, the poorest county, Turkana (88%) is located in the Rift Valley Region, one of the more affluent regions in the country. Not surprisingly, the most affluent counties, Kiambu and Nairobi, are in the same geographic area, located in the contiguous Central and Nairobi Regions, respectively. Cardno Emerging Markets (UK) Ltd P a g e 6

11 Figure 3.1 Percentage of Population below the Poverty Line by Region Source: SID and KNBS, 2013 Figures 3.2 and 3.3 provide a ranking and map of the poverty incidence by county (Society for International Development (SID) & Kenya National Bureau of Statistics, 2013) [14]. A discussion of poverty incidence across the former eight regions/provinces of Kenya from wealthiest to poorest is presented below. Figure 3.2 Headcount Index by County Source: SID and KNBS, 2013 Central Province: The Central Province a highly urban region - is one of the wealthiest regions in Kenya. In 2005, Central Province had an average poverty incidence of 31%. The majority of the poor (41%) were concentrated in eight of the 29 constituencies, namely Juja, Gatanga, Mwea, Kandara, Kinangop, Kiharu, Ol-Kalou and Kieni. Despite its affluence, there are constituencies with high levels of poverty in both urban and rural areas in the Central Province. The average urban poverty rate was 49%, ranging from a low of 9% in Maragwa to a high of 85% in Kangema Constituency. In contrast, the average rural poverty rate was 31% in the Central Region. The 29 political constituencies in Central Province contributed almost 8 % to total national poverty. Cardno Emerging Markets (UK) Ltd P a g e 7

12 Figure 3.3 %age of Population below the Poverty Line Source: SID and KNBS, 2013 Nairobi Province: Nairobi Province is the most populous region in Kenya (estimated at 1,991,724 based on 1999 Census). The average poverty incidence in Nairobi Province is estimated to be 44%. Almost half of the poor (49%) were concentrated in three of the eight constituencies, namely: Embakasi (41%), Kasarani (47%), and Makadara (59%). The eight political constituencies in Nairobi Province contributed 6% to total national poverty. Rift Valley Province: Even though the Rift Valley Province is one of the more affluent regions, nearly half of the people in Rift Valley Province (48%) were considered poor. Nearly half (24) of the 49 constituencies in the province had a poverty incidence that were above the provincial average. A third of poor people in Rift Valley Province were concentrated in ten of the 49 constituencies, namely: Eldoret North, Saboti, Narok South, Molo, Nakuru Town, Kilgoris, Turkana Central, Turkana North, Naivasha and Bomet. The urban poverty rate was 54%, with poverty rates in urban areas as high as 71% in Turkana South Constituency. The average rural poverty rate of 48%, varied from a low of 32% in Subukia Constituency to a high of 64% in Turkana Central Constituency. The Rift Valley Province contributed just over 22% to total national poverty. Coast Province: Poverty incidence in Coast Province as a whole was 58%. Half of the 71 constituencies had a poverty incidence that was above the provincial average. 60% of the poor in Cardno Emerging Markets (UK) Ltd P a g e 8

13 Coast Province were concentrated in a third of the 21 constituencies, namely: Bahari, Kaloleni, Msambweni, Kisauni, Malindi, Kinango and Ganze. The average rural poverty rate was 63%. Rural poverty incidence among constituencies varied from a low of 30% in Bura Constituency to a high of 81% in Ganze Constituency. Compared to the Coast Province average urban poverty rate of 47%, the urban poverty incidence among constituencies varied widely from a low of 34% in Mvita Constituency to a high of 66% in Garsen Constituency. The Coast Province contributed almost 10% to total national poverty. Eastern Province: In Eastern Province the average poverty incidence was 58%. 24 of the 36 constituencies had a poverty incidence that was above the provincial average. 42% of poor people in Eastern Province were concentrated in ten of the 36 constituencies, namely: Makueni, Nithi, Kitui Central, Mbooni, Kangundo, Kibwezi, Igembe, Mwingi North, Mwala, and Machakos Town. In Eastern Province, the average rural poverty rate was 63%. Rural poverty incidence varied from a low of 34% in Ntonyiri Constituency to a high of 76% in Kitui South Constituency (the poorest in the North Eastern Province). The average urban poverty rate was 51%, ranging from 19% in Saku Constituency to a high of 95% in Laisamis Constituency. The Eastern Province contributed just over 18% to total national poverty. Western Province: The poverty incidence in Western Province was 61%. Out of the 24 constituencies in the province, eleven had a poverty incidence that was above the provincial average. Nearly two-thirds of poor people in Western Province (63%) were concentrated in half of the constituencies, namely: Lurambi, Kimilili, Malava, Lugari, Mumias, Sirisia, Webuye, Nambale, Emuhaya, Kandunyi, Shinyalu and Amogoro. The average rural poverty rate was 60%, rising to a high of 72% in Ikolomani Constituency in Kakamega district. The average urban poverty rate was 68%, with even higher levels of poverty, 84 %, in Funyula Constituency. The Western Province contributed almost 14% to total national poverty. North Eastern Province: The average poverty incidence in North Eastern Province was 64%. Nearly two-thirds (64%) of poor people in North Eastern Province were concentrated in six constituencies, namely: Mandera Central, Wajir East, Dujis, Wajir South, Mandera East and Lagdera. The poverty rate in rural areas did not vary much from the average rural poverty rate of 64%. Similarly, the urban poverty rate did not vary much from the average level of 61%. The North Eastern Province contributed less than 4% to total national poverty. Nyanza Province: Nearly two-thirds (65%) of the population in Nyanza Province were considered poor. Out of the total 32 constituencies, half had a poverty incidence that was above the provincial average. 43% of poor people in Nyanza Province were concentrated in ten constituencies, namely: Kisumu Town West, Kasipul-Kabondo, Kuria, Kitutu-Chache, Alego, Kitutu-Masaba, Rangwe, Bomachoge, Ugenya and Bomasi. Rural poverty levels varied widely from the rural average of 65%, ranging from a low of 44% to a high of 80%. The difference was even greater in urban constituencies: the average urban poverty rate was 63% ranging from a low of eight % in Kitutu Masaba Constituency to a high of 99% in Nyaribari Masaba Constituency. The Nyanza Province contributed 19% to total national poverty. 3.2 Using Median Household Expenditures Another way to assess poverty is to use Median Household Expenditures and disposable income. While household welfare is highly correlated with income, income is difficult to measure. Consequently, household expenditures are often used as a proxy for estimating income. The SID/KNBS 2013 Report on poverty provides estimates of household expenditures by counties based on 2005 data. More recent data on both household expenditures and disposable income will be released in early Cardno Emerging Markets (UK) Ltd P a g e 9

14 Figure 3.4 shows the mean Household Expenditures per Adult Equivalent in Kenya by county. The national average Household Expenditure per Adult Equivalent 1 per month is Ksh 3,440. As the Headcount Poverty Index revealed, the national average Household Expenditure per Adult Equivalent masks huge regional differences. The average rural Household Expenditure per Adult Equivalent per month is estimated to be Ksh 2,270 compared to the average urban Household Expenditure per Adult Equivalent per month of Ksh 6,010. As can be seen in Figure 3.5, the average Household Expenditure per Adult Equivalent per month ranges from a high of Ksh 7,200 in Nairobi to low of Ksh 1,300 in Wajir. The 2013 report notes that counties with mean Household Expenditures per Adult Equivalent per month below Ksh 2,200 are among the poorest in Kenya, namely: Makueni, Kwale, Tana River, West Pokrot, Marisabit, Mandera, Turkana and Wajir. Figure 3.4 Mean Household Expenditure per Adult Equivalent per month (in thousands) by Counties Source: SID and KNBS, 2013 Figure 3.5 shows the %age of households in income quintiles based on the median Household Expenditure per Adult Equivalent per month. Using the median household, fewer rural residents are classified as poor. In rural areas, slightly more than 38% live below the rural average of Ksh 2,270 when using median household expenditure. In contrast, 50% of the rural population is classified as poor when using the headcount index: this is almost a 12 percentage age point difference. This pattern does not hold true, however, when comparing median household expenditures and the headcount index at the national level and with urban areas. In these comparisons, the percentage of poor is approximately the same. Figure 3.5 Consumption Expenditures Shares by Quintiles and Number Households (National and Rural/Urban) by Counties Source: SID and KNBS, Adult equivalent is a concept based on the assumption that each person needs different levels of consumption based on age, gender, height, weight etc. Cardno Emerging Markets (UK) Ltd P a g e 10

15 3.3 Key Findings on the Poor in Kenya The 2013 Poverty Report reveals the following general trends on poverty that are useful for the purposes of the PSP4H Programme: There are extreme income inequalities in Kenya. The average median income in the richest county Nairobi is more than five times greater than the poorest county Wajir. Income disparity is highly varied between urban and rural areas, as well as within urban and rural areas. There are pockets of extreme poverty in urban settings that are comparable to those found in rural areas. These patterns of income disparity not only persist, but are magnified, as one further disaggregates the data by geographic level: the difference between poor and rich at the county level is four times; at the constituency level is nine times; and at the ward level it is thirty times. The data challenges common perceptions. A common belief is that Kibera is the poorest slum in Nairobi but, in fact, data show that Mathare and Ruarka are actually poorer. The data also confirms common beliefs. Social services and benefits are highly concentrated in urban areas. Consequently, the urban populations have one and a half times greater access to water and two times greater access to sanitation as compared to the rural population. Urban areas have almost ten times (43%) the electricity coverage than rural areas (5%). Being poor in urban areas is not the same as being poor in rural areas. Kenyans living in urban areas have almost ten times more income to expend than their counterparts in rural areas. They also have greater access to social services, such as education, health, water and sanitation. However, this comes at a price: the cost of living is much higher and the urban poor do not have access to resources commonly available in rural areas, such as food and, at times, better housing. Also, individuals without education living in urban areas are twice as likely to be unemployed as compared to their rural counterparts. Cardno Emerging Markets (UK) Ltd P a g e 11

16 4 Strategies to Define the Poor Given the problems in assessing poverty status using existing approaches, the PSP4H Programme proposes three options outlined below - to define the poor and identify target populations for programming purposes. The approach to define the poor will inform the next stage of project analysis as defined in Figure 1.1 Option #1: Create a hybrid approach that includes additional indicators (see text box). The World Bank Consultative Group to Assist the Poorest (CGAP) in collaboration with the International Food Policy Research Institute (IFPRI) developed a hybrid Poverty Assessment Tool (Falkingham J and Namazie C 2002). Country field tests indicate that this CGAP tool captures a wider concept of poverty than simple material or economic poverty and that is more akin to a measure of capability poverty. While the PSP4H may not have sufficient funds to apply the CGAP tool, it can consider creating its own composite index based on weighted averages of selected, available indicators. Indicators for a Hybrid Measurement Demographic composition of household Place of residence- rural vs. urban Education levels of household members Occupation of household members Household consumption expenditures Household assets and basic amenities Access to healthcare Food Security Option #2: Consult representatives of the private health sector to better understand how they segment the market. PSP4H staff have conducted two stakeholder forums in Nairobi and Mombasa as well as several one-on-one meetings. The PSP4H team used Figure 4.1 to start the dialogue. As the diagram illustrates, the upper income levels are well served by the commercial health sector while the lower income levels struggle to receive basic health services. Private health sector stakeholders shared that it is a challenge for them to serve the absolute poor (bottom income quintile) since this income group, as the prior discussion on poverty demonstrated, do not have the means to pay for services rendered. Private sector feedback with PSP4H stated it is unrealistic to expect the commercial health sector to serve the poorest of the poor unless they receive some form of provider payment to reimburse them for their services (e.g. contracting, vouchers, etc.). They also stated that there is ample room for the commercial sector to expand health services and products to the working poor, that is second and third income quintiles. Figure 4.1 Market Segments Demonstrating how large this potential market can be, a representative from one of the majority pharmaceutical companies shared how they segment the market (see text box). They acknowledge that the top end of the market is saturated and well served by the private health sector. They also said that they are actively exploring strategies to sell products to the middle segment of the market, or the working poor. They anticipate that this market can be profitable as it is big enough to allow them to make a Market Segments 5% Have a lot 65% Have less 35% Have nothing Cardno Emerging Markets (UK) Ltd P a g e 12

17 profit by selling a high volume of cheaper goods and services. Option #3: Consult representatives of organizations that work with the poor to better understand the health-seeking behaviour of the poor. The PSP4H is in the process of conducting consumer research and has also invited several community leaders working with the poor to the open stakeholder forums. The discussions with both private providers and community leaders have proposed yet another strategy to define the poor in Kenya. This group proposed creating a ranking and profile of different types of income groups that would capture, based on reality, the nuances of being poor in an urban and/or rural setting (See Figure 4.2). The profiles are classified by income levels as well as by the nature of employment formal or informal, skilled or unskilled. To ground-truth the profiles, the stakeholders offered examples in each of the levels. Although the economic status improves the higher the income level, it is important to note that there is considerable churning or movement between the income levels. In fact, regular income earners skilled or unskilled are just a paycheck and/or a catastrophic health event away from poverty. Moreover, there are some casual and unskilled labourers, such as artisans and tradesmen, whose incomes are comparable to those of more skilled labourers. Figure 4.2 Poverty Profiles Cardno Emerging Markets (UK) Ltd P a g e 13

18 5 Health-Seeking Behaviour of the Kenyan Poor Health-seeking behaviour plays an instrumental role in determining a consumer s access and use of health services and products. It also informs the types of services they perceive as being acceptable. Proper understanding of health-seeking behaviour can reduce delays in diagnosis; improve treatment compliance; and inform health promotion strategies so they can be more effective in a variety of contexts. Literature review findings about the health-seeking behaviour of the poor in Kenya are presented below. 5.1 Health-Seeking Behaviour Frameworks A review of the literature on health-seeking behaviour shows that the several factors determine consumer behaviours and utilization of a health care system, public or private, formal or non-formal (MacKian S 2003). The factors that influence the way people utilize health care can be broadly categorized into five key characteristics geographical, social, economic, cultural and organizational. The three delays model of Thadeus and Maine can also help policymakers and programme managers better understand how a wide range of factors can directly affect access to healthcare (Thadeus S and Maine D 1994). The three delays model organizes these factors along three action points in the process to seek and receive healthcare: delay #1 in deciding to seek care; delay #2 in reaching the health facility; and, delay #3 in receiving quality care once at the health facility. 5.2 Factors Influencing Health-Seeking Behaviour of the Poor in Kenya Geographical Factors. One of the key factors contributing to greater burden of illness among the poor is distance to a facility (Kiwanuka SN et al. 2008; Mwaniki PK, Kabiru EW, and Mbugua GG 2002; Buor D 2003; Duff P et al. 2010; Posse M et al. 2008; Kunihira NR et al. 2010; Futures Group Health Policy Initiative 2009). Access is a barrier for Kenyans, particularly those residing in rural areas. Nationally, according to the Kenya Integrated Household Budget Survey approximately 11% (11.3%) of Kenyans travel one kilometre or less to reach a health facility, while about half (48%) travel five kilometres or more. Rural-urban differentials show large disparities in distance to a health facility: only 7.4% of rural dwellers travel for one kilometre or less to reach a health facility compared to 49% for urban residents. In addition, more than half of rural dwellers travel five or more kilometres to reach a health facility, while only 12% of urban dwellers travel similar distances (Central Bureau of Statistics Ministry of Planning and National Development 2005/06). Socio-cultural Factors. Socio-cultural factors such local beliefs, women s status and ethnicity have been associated with delays in receiving treatment. Local beliefs regarding aetiology of diseases strongly influence health-seeking patterns. For instance, people are likely to consult traditional healers when they believe that an illness is due to witchcraft. Previous studies on barriers to HIV treatment and care in Kenya found considerable use of traditional and faith healers (Izugbara CO and Wekesa E 2011;Nagata JM et al. 2011; Kiragu K et al. 2008). Research has shown that women s autonomy is an important determinant of utilization of health services (Chibwana AI et al. 2009). Kenyan men play a paramount role in determining the health needs of a woman. The unwillingness of husbands to give money and facilitate their spouses to seek care and/or lack of cooperation in decision making have been cited as barriers to women s access to health care services (Essendi H, Mills S, and Fotso JC 2010; Tey NP and Lai SL 2013; Shaikh BT and Hatcher J 2005). Ethnicity has also been found to be an important determinant of uptake of health care services. The Luhya and minor ethnic groups in the urban slums are more likely than the Kikuyu to have delayed measles vaccination (Ettarh RR, Mutua MK, and Kyobutungi C 2012). These findings substantiate previous research which showed that ethnicity was a key predictor of parental health-seeking behaviour for childhood illnesses (Teerawichitchainan B and Phillips JF 2007). Cardno Emerging Markets (UK) Ltd P a g e 14

19 Socio-demographic Characteristics. Kenyan studies on determinants of utilization of maternal and child health services have shown that factors such as educational level, age, place of residence, birth parity, wealth status, exposure to media, and knowledge of pregnancy risk factors are associated with use of health facilities for delivery (Kenya National Bureau of Statistics (KNBS) and ICF Macro 2010; Mpembeni RNM et al. 2007). The most significant socio-demographic factor is a women s education level, including both formal education and health knowledge. Education is strongly associated with improved maternal and child health outcomes due to increased awareness of the benefits of preventive and curative care. Increased knowledge health-seeking and increases the likelihood of adopting healthy practices. Maternal age is another factor influencing health-seeking behaviour. In a KNBS and IFC Macro Study, a mother s age and the child s birth order are associated with a mother s decision to seek healthcare or deliver at home. Births to older women and those of higher birth order are more likely to occur with no assistance, compared with births to younger women and those of lower birth order (Kenya National Bureau of Statistics (KNBS) and ICF Macro 2010). Economic factors. Countless studies in Kenya and other developing countries highlight the significant barrier that healthcare costs present to utilization of health services in both private and public sectors. According to the 2007 Kenya Household Health Expenditure Survey (KHHES), over a third of people who did not seek care (38%) identified lack of money as the reason for not seeking care [39]. This is consistent with evidence from the KDHS which showed that women in Nairobi who did not deliver in a facility were more likely to cite high cost as a factor than are women in other provinces (Kenya National Bureau of Statistics (KNBS) and ICF Macro 2010). Data from the national health accounts show that more than a third of the poor who were ill did not seek care compared to only 15% of the rich (Abt Associates 2005). Due to financial constraints, poor people resort to self-medication because they cannot afford to pay for health care services. The Kenya Integrated Household Budget Survey showed that there is a higher incidence of self-diagnosis and visiting of kiosks to purchase medicines, especially in rural areas (10%) than in the urban areas (4%). The proliferation of pharmacy shops and the common tendency to visit a hospital only when faced with a serious illness are the two most frequent reasons cited. Significant indirect costs of obtaining care create barriers to utilization. A study investigating barriers to HIV treatment and care among children found that indirect costs were a major barrier to paediatric treatment, even though the Kenyan government provides ART for free. Clients bear significant indirect costs including transport, consultation fees, and medications to treat opportunistic infections. As a result respondents reported delaying or deferring care for their children (Kiragu K et al. 2008). Lack of money to pay for transport or hire a vehicle to transport a pregnant woman to a health facility is also a frequently cited barrier to accessing care poor urban dwellers in Nairobi city who received referral to the health facility (Essendi H, Mills S, and Fotso JC 2010). Despite the direct and indirect costs, an important %age of low-income households consistently seek care in private facilities. Significant numbers of Kenyans receive health care in private facilities. Evidence from the Kenya Integrated Household Budget Survey shows that among the former eight provinces, 47% of the sick in Nairobi visited private health facilities (private hospitals/dispensaries and clinics). In Coast (26%), Eastern (22%), and North Eastern (26%) provinces the proportion of the population visiting dispensaries tends to be higher than the national average; while in Nyanza (20.5%) and Western (22.4%) Provinces, pharmacists/chemists are the preferred providers. At the district level, in Makueni, Bondo, Trans Nzoia, and Mt Elgon Districts about half of the population sought health care from private pharmacists/chemists and kiosks (Central Bureau of Statistics Ministry of Planning and National Development 2005/06). Organizational factors. How health services are organized, managed and delivered also influence health-seeking behaviour. Organizational factors include supplies; equipment; staffing; training and qualifications; and overall quality of care (MacKian S 2003). Studies conducted among the urban poor in Kenya found that poorly equipped health facilities in the slums and lack of essential drugs and supplies often discouraged women from accessing health facility delivery care(essendi H, Mills S, and Fotso JC 2010; Fotso JC and Mukiira C 2011; Izugbara CO, Kabiru CW, and Zulu EM 2009). Cardno Emerging Markets (UK) Ltd P a g e 15

20 Other studies state many poor women are reluctant to seek healthcare because of provider biases and the ill-treatment that women receive because they are poor. When poor women presented at facilities, providers reportedly abandoned them, did not listen to them, and did not ask them important questions. The providers were also accused of being insensitive to the respondents' cultural beliefs, especially during the birthing process (Macha J et al. 2012; Kiragu K et al. 2008). A study conducted in Kenya, Tanzania and Ghana shows that poor clients choice of a private facility over a public facility appears to be influenced by the presence of a trained provider at all times (Agha S and Keating J 2009). In Kenya, almost one third (29%) of providers were absent in a public health facility. Providers at public health centres were three times more likely to be absent than those at private, non-profit health centres (Martin GH and Pimhidzai O July 2013). The 2010 KSPA survey found that the prospect of being attended to quickly was an important reason for seeking care in private facilities rather than in public facilities where patents often faced longer lines and waiting times. Cardno Emerging Markets (UK) Ltd P a g e 16

21 6 Implications for PSP4H Poverty in Kenya is a tale of two cities given the wide income disparities and profound regional differences. The data shows that any pro-poor health programme should focus on the pockets of poverty found in both urban and rural areas. The existing approaches for defining and identifying the poor - Kenya Poverty Incidence / Headcount Index and the median Household Expenditures - provide sufficient information to identify potential counties and wards with the greatest incidence of poverty. Given the limitations of the existing poverty approaches, the PSP4H team proposes three options: 1) a hybrid approach that includes additional indicators, 2) a private health sector approach to segmenting the market, and 3) poverty risk profile. No matter the approach, all lead the team to conclude that targeting the working poor is the most practical approach for M4P activities. Consultation with multiple private sector stakeholders revealed that private sector providers, particularly for-profit ones, are sceptical that there is sufficient market opportunity with the population group that live before the poverty line. Using the poverty risk profiles, the PSP4H team define the working poor to earn above the poverty line (Kshs 1,450) and above Kshs 5,500 for skilled and unskilled workers in the formal sector. The PSP4H developed criteria to select counties for the normative research needed to supplement the poverty data. Criteria include: 1) pockets of poverty that are within the income range, 2) concentration of private, for-profit providers, and 3) high numbers of population. The criteria eliminated the poorest counties (Turkana, Marsabit, Wajir, and Mandera, Marsabit, Sambura, Tana River, Turkana and Wajir). But included some of the wealthiest counties (Kiambu, Kisumu, Mombasa and Nairobi) because of the concentration of working poor in large peri-urban neighbourhoods. The map illustrates the twelve selected counties. The literature also demonstrates the characteristics the poor look for in a private provider that will inform the PSP4H market interventions. To be responsive to the working poor s needs and preferences, market interventions will need to: Source: SID and KNBS, 2013 Include mechanisms that remove economic barriers (e.g. insurance, medical saving plan, vouchers, contracting); Be geographically accessible and located in the communities where the working poor work and live; Compete on quality issues, ensuring adequate supply of affordable drugs, offering appropriate diagnostics, attending clients quickly and providing well trained staff; Cardno Emerging Markets (UK) Ltd P a g e 17