Kenya PUBLIC EXPENDITURE TRACKING AND SERVICE DELIVERY INDICATOR SURVEY (PETS+/SDI)

Transcription

1 Kenya PUBLIC EXPENDITURE TRACKING AND SERVICE DELIVERY INDICATOR SURVEY (PETS+/SDI) June 2013 Version 7/1/2013 1:21 PM i

2 CONTENTS ABBREVIATIONS AND ACRONYMS... vii EXECUTIVE SUMMARY... viii INTRODUCTION... 1 Part 1. SERVICE DELIVERY Health Providers Caseload Absence rate Provider knowledge and ability a. Diagnostic Accuracy b. Process Quality: Adherence to clinical guidelines c. Process Quality: Management of maternal and neonatal complications Availability of Inputs a. Drug Availability b. Vaccines and vaccine-related equipment and supplies c. Equipment availability d. Infrastructure availability Part 2. PUBLIC HEALTH EXPENDITURE User fees a. Implementation of the 10/20 Policy b. Price differentials at public and private (non-profit) facilities c. Revenues from user fees d. Waivers and exemptions Health Sector Services Fund (HSSF) a. HSSF transfers: Magnitude, Delays and Share of intended transfers b. HSSF Planning, Oversight and Financial Management Part 3. WHAT DOES THIS MEAN FOR KENYA? Annex A. Methodology Definition of Indicators Sampling Strategy ii

3 Survey Instrument Annex B. Experience with Implementing Health Facility Surveys Annex C. Additional and More Detailed Results References iii

4 FIGURES Figure 1. Infant mortality rate and Under-5 mortality rate in Kenya compared to middle income countries... 1 Figure 2. Association between health spending and outcomes (Africa)... 2 Figure 3. Relationships that influence incentives for service delivery... 5 Figure 4. Caseload per clinician by facility size... 8 Figure 5. Absence by cadre type... 9 Figure 6. Reasons for absence Figure 7. Absence rate and caseload by facility size (health centers only) Figure 8. Diagnostic accuracy by number of cases correctly diagnosed Figure 9. Diagnostic accuracy by condition Figure 10. Share of providers who attained a minimum share of adherence to the clinical guidelines by number of tracer conditions Figure 11. Availability of vaccine-related equipment and supplies by facility level Figure 12. Availability of individual types of vaccines by facility level Figure 13. Purpose of last trip that vehicle or ambulance made Figure 14.Level of awareness and implementation of the 10/20 Policy Figure 15. Prices charged for services exempt under 10/20 Policy Figure 16. Prices charged for other services (Kshs) Figure 17. Revenue forgone from implementation of waivers and exemptions Figure 18. Delays in Facility Receipt of HSSF funds for FY 2011/ Figure 19. Comparison of user fee and HSSF revenues by provider type (Kshs 000) Figure 20. HSSF/HMSF and User Fees in Facilities Figure 21. Availability of work plans and existence of Health Management Committees Figure 22. Facilities with a designated Financial Accounting Officer Figure 23. Staff designated as the Financial Accounting Officer Figure 24. Sharing of financial information with communities Figure 25. Inclusion of Community Representatives in HMC Figure 26. Frequency of Health Management Committee meetings Figure 27. Facility sub-committees in existence and operational Figure 28. Motivations for Facility Surveys Figure 30. Treatment actions prescribed by cadre Figure 31. Diagnostic accuracy by questions asked: Acute diarrhea with severe dehydration Figure 32. Diagnostic accuracy by questions asked: Malaria with anemia Figure 33. Diagnostic accuracy by questions asked: Pneumonia Figure 34. Diagnostic accuracy by questions asked: Diabetes mellitus Figure 35. Diagnostic accuracy by questions asked: Pulmonary tuberculosis Figure 35. Correct Treatment Actions: Post-partum hemorrhage Figure 36. Correct Treatment Actions: Neonatal asphyxia Figure 37. Distribution of Push and Pull Facilities Figure 38. Availability of drugs by facility type Figure 39. Access to various forms of electronic communication Figure 40. Average availability of ambulance services and fuel Figure 41. Power outages over last 3 months Figure 42. Prices for dental services iv

5 TABLES Table 1. Service Delivery Indicators at-a-glance... xiv Table 2. Health SDI/PETS+ sample in Kenya... 3 Table 3. Hours and days of service delivery... 6 Table 4. Caseload per clinician by level of facility... 8 Table 5. Absence by level of facility... 9 Table 6. Diagnostic accuracy by cadre Table 7. Adherence to clinical guidelines by cadre Table 8. Management of maternal and neonatal complications by cadre Table 9. Drug availability (adjusted for facility type) Table 10. Drug availability by level of facility (adjusted for level of facility) Table 11. Tracer drugs for children and mothers in Push and Pull facilities Table 12. Vaccines availability by facility level Table 13. Medical equipment availability (adjusted for level of facility) Table 14. Availability of specific types of medical equipment Table 15. Communication equipment availability Table 16. Availability of specific types communication equipment Table 17. Availability of ambulance services by facility level Table 18. Infrastructure availability Table 19. Availability of specific types of infrastructure Table 20. Services with largest price differentials between prices at public and private (non-profit) facilities Table 21. Aggregate revenue from user fees by facility level (Kshs 000) Table 22. HSSF Disbursements during FY 2011/12 (Kshs 000) Table 23. Availability of financial management tools Table 24. Nomenclature and definition of Health Service Delivery Indicators Table 25. Service delivery activities and staffing norms by level of facility Table 26. Selected counties Table 27. Precision of estimates for selected SDI/PETS+ variable Table 28. Health SDI/PETS+ survey instrument Table 29. Frequency of Service Availability Mapping (SAM) and Service Provision Assessment (SPA) surveys Table 30. Comparison of SDI and SARA surveys Table 31. Distribution of health personnel by provider type Table 32. Distribution of health personnel by facility type Table 33. Distribution of health personnel by gender Table 34. Caseload per clinician by level of facility Table 35. Absence by level of facility Table 36. Correlates of Absence Table 37. Adherence to clinical guidelines by cadre Table 38. Adherence to clinical guidelines by facility type Table 39. Management of maternal and neonatal complications by cadre Table 40. Management of maternal and neonatal complications by facility type Table 41. Diagnostic accuracy cadre Table 42. Availability of specific types of equipment used in the equipment indicator Table 43. Drug availability (adjusted for facility type) Table 44. Drug availability (unadjusted for level of facility) Table 45. Drugs identified in the Service Availability and Readiness Assessment and drugs assessed in the Kenya SDI/PETS+ survey Table 46. Drug availability by level of facility (adjusted for level of facility) v

6 Table 47. Drug availability by level of facility (unadjusted for level of facility) Table 48. Drug availability in Push and Pull facilities Table 49. Vaccines availability by level of facility Table 50. Availability of specific types of equipment used in the equipment indicator Table 51. Equipment availability (adjusted for level of facility) Table 52. Equipment availability (unadjusted for level of facility) Table 53. Availability of individual types of equipment Table 54. Availability of individual types of equipment by facility type Table 55. Purpose of last trip that vehicle or ambulance made by facility level Table 56. Availability of specific types of infrastructure used in the infrastructure indicator Table 57. Infrastructure availability ANNEXES Annex A. Methodology Annex B. Experience with Implementing Health Facility Surveys Annex C. Additional and More Detailed Results vi

7 ABBREVIATIONS AND ACRONYMS BCG GDP HSSF IMCI IMR KEMSA KIPPRA SDI U5MR USAID WHO Bacillus Calmette Guérin (vaccine against tuberculosis) Gross Domestic Product Health Services Support Fund Integrated Management of Childhood Illnesses Infant Mortality Rate Kenya Medical Supplies Agency Kenya Institute of Policy Researchand Analysis Service Delivery Indicators Under-5 Mortality Rate Unites States Agency for International Development World Health Organization vii

8 EXECUTIVE SUMMARY The Service Delivery Indicators provide a set of metrics for benchmarking service delivery performance in education and health in Africa. The overall objective of the indicators is to gauge the quality of service delivery in primary education and basic health services. The indicators enable governments and service providers to identify gaps and to track progress over time and across countries. It is envisaged that the broad availability, high public awareness and a persistent focus on the indicators will mobilize policymakers, citizens, service providers, donors and other stakeholders for action to improve the quality of services and ultimately to improve development outcomes. This report presents the findings from the implementation of the Service Delivery Indicators in the health sector in Kenya in 2012/13. Survey implementation was preceded by extensive consultation with Government and key stakeholders on survey design, sampling, and adaptation of survey instruments. Pre-testing of the survey instruments, enumerator training and field-work took place between September and December The survey was implemented by the Kenya Institute of Policy Research and Analysis (KIPPRA) with support by the World Bank and the USAID-funded Health Policy Project. The World Bank s SDI Team provided quality assurance and oversight. Information was collected from 294 public and non-profit private health facilities and 1,859 health providers. The results provide a representative snapshot of the quality of service delivery and the physical environment within which services are delivered in public and private (non-profit) health facilities at the three levels: dispensaries (health posts), health centers and first-level hospitals. The survey provides information on two levels of service delivery: (i) five measures of provider knowledge/ability and effort, and (ii) five measures of the availability of key inputs, such as drugs, equipment and infrastructure. Summary of Key Findings Availability of Inputs Drugs On average, public facilities had only half (52.3%) of the tracer drugs available. The availability of tracer drugs for children was relatively higher (69.2%) than tracer drugs for mothers (41.0%). Given the national concern about maternal mortality and efforts to improve maternal health outcomes, the availability of tracer drugs for women is of concern. It is commonly reported that rural facilities suffer severe drug shortages compared to their urban counterparts. In Kenya, there was no evidence to support this. In fact, rural public facilities had 25% more of tracer drugs for children compared to urban public facilities. That said, some facilities in deep rural areas may suffer from higher levels of drug shortages, but the rural indicator is not make that distinction. Private (non-profit facilities)

9 performed slightly better than public facilities, with 62.1% of tracer drugs available 19% more than public facilities. Equipment Medical equipment: More than three quarters (77.8%) of public facilities met the requirements that make up the medical equipment indicator. 1 It is an important achievement that refrigeration is available in more than 98% of health centers and hospitals. Of the items of equipment that make up the equipment indicator, the availability of sphygmonometers (blood-pressure meters) and sterilization equipment was the most constraining pieces of equipment. The public-private differences and rural-urban differences were only significant at the level of dispensaries but not at higher levels. Communications equipment: The study found that 83% of public facilities had at least one of the following forms of communication: landline, cellular phone; shortwave radio or internet. The most widely available communications equipment was the mobile phone exceeding landlines as a communication instrument: 75% versus XX% of all facilities. Ambulance services: Less than a tenth (8%) of public facilities had access to ambulance services, compared to nearly a third (31%) of private (non-profit) facilities. In public facilities the most common purpose of ambulances was indeed to transport patients (78.4%), compared to only 50% in private (non-profit) facilities. Infrastructure The infrastructure indicator captures the availability of three inputs: water, sanitation and electricity. More than ninety percent of public facilities had access to an accepted source of sanitation and two thirds had an accepted source of electricity. Water proved to be the main limiting aspect of the infrastructure indicator, with only half of public facilities having access to a clean water source. In the public sector, more than two thirds of health centers (68.1%) and nearly all first level hospitals (96.1%) meet the minimum infrastructure requirements as defined by the infrastructure indicator. At the level of dispensaries the private-public differences were very pronounced: the infrastructure indicator was 29.6% in public dispensaries compared to 74.0% in their private counterparts. Provider Effort Absence Close to a third (29.2%) of providers in public facilities was found to be absent on an announced visit, compared to a fifth (20.9%) among private (non-profit) providers but the difference was not statistically significant. Who were most likely to be absent? The multivariate analysis found: (i) No difference in absence rates among cadre-types; (ii) Absence was more likely among health providers at rural facilities; (iii) Relative to facilities with 1-2 workers, absence in facilities with staff in excess of 6 workers was found to have higher. The survey found that the overwhelming share (88%) of absence was indeed 1 A weighing scale (adult, child or infant), a stethoscope, a sphygmonometer, a thermometer for all facilities and the following is an added requirement for health centers and hospitals: sterilizing equipment and a refrigerator. ix

10 sanctioned absence. But, from the consumer s perspective, these providers are not available to deliver services whether sanctioned or not. Caselaod The average caseload in the public sector is low at 8.7 patients per provider per day and the caseload for half of health providers was less than 7 patients per day. The average caseload in private (non-profit) providers was higher at 10.4 patients per provider per day. Case mix across facility types varies, so it is worth looking at comparisons by level of facility. In the public sector, the highest caseload was found in urban health facilities: about 15.4 patients per day in health centers and first level hospitals. How did caseload vary by facility size? Medium-sized facilities with between 3 and 20 workers (that account for almost half of all facilities), had the lowest caseload levels between 6.4 and 6.6 patients per provider per day. Provider Knowledge and Ability However, absence is not the only measure of service delivery performance. Having health professionals present in a facility is a necessary but not sufficient condition for delivering quality health services. Three indicators of provider knowledge and ability were assessed: adherence to clinical guidelines and diagnostic accuracy using five tracer conditions, and the management of maternal and neonatal complications. 2 providers managed to correctly diagnose close to three quarters (71.6%) of the five tracer conditions, and was not significantly different from their private counterparts (74.2%). providers in Kenya were found to adhere to under half (42.7%) of the clinical guidelines in the management of the five tracer conditions, and 44.2% of the guidelines for management of maternal and neonatal complications. Both measures of process quality progressively declined by cadre type being highest among doctors, followed by clinical officers and nurses. The lowest scores for adherence to clinical guidelines and the management of maternal and neonatal complications were among rural nurses at 39.4% and 44.0% respectively. In both these measures of process quality the study found no significant differences between public and private (non-profit) providers. A unique feature of the Service Delivery Indicator survey is that it looked at the production of services at the frontline. Successful service delivery requires that all the measures of service delivery need to be present in the same facility and at the same time. More than three quarters of public facilities met all the requirements that make up the equipment indicator. Average estimates of infrastructure availability were relatively positive, but the picture was quite bleak when we assessed the availability of inputs at the same time in the same facility only 40 percent of facilities had clean water and sanitation and electricity. Even more disconcerting was the finding that not a single health facility had all the 10 tracer drugs for children or all the 16 tracer drugs for women. Only 16% of providers were 2 The choice of tracer conditions was guided by burden of disease among children (e.g. malaria with anemia; diarrhea with severe dehydration; and pneumonia) and adults, (e.g. pulmonary tuberculosis and diabetes). Two additional cases were considered: post-partum hemorrhage, the most common cause of maternal death during birth, and neo-natal asphyxia, the most common cause of neonatal death during birth. x

11 able to correctly diagnose all five of the tracer conditions. It is disturbing that only 13% of providers adhered to at least half of the guidelines for each tracer condition, and only 20% of providers adhered to at least half of the treatment actions for each of the two complications. Expenditure Tracking Implementation of the 10/20 Policy On average, awareness of the policy was relatively high among public providers (72%) and the policy was implemented at 86% of facilities. Of the 14% of public facilities who were not implementing the policy, the most common reasons cited were: (i) other sources of revenue were insufficient; and (ii) that the community can afford to pay user fees. Among private (non-profit) providers, awareness was low, and 40% reported implementing the policy. When the prices charged for categories of service that were exempted by the policy were assessed it was evident that there completeness of implementation of the 10/20 Policy falls short, and many services (e.g. services for children under 5 years and tubeculosis) that should be exempt are indeed being charged for. Health Sector Services Fund (HSSF) In an attempt to address many of the problems with quality and performance of lower level health facilities the government introduced direct transfers to dispensaries and health centers, called the HSSF. Part of the justification for the introduction of the HSSF was to off-set the loss of revenue facilities experienced following the introduction of user fee reforms. How does user fee revenue and HSSF transfers compare? User fee revenue substantially exceeds the revenue from HSSF transfers on average 177% in public facilities and 100% in private (non-profit) facilities. Magnitude of transfers and delays: With the exception of the first quarter, disbursements are relatively constant across the fiscal year. The disbursements were higher for the first quarter, and may reflect the disbursement of delayed transfers from previous quarters. Some facilities reported experiencing delays in the receipt of the HSSF disbursements, with as much as 139 facilities reporting delays of weeks. That said, this is still a small share of the total number of facilities receiving HSSF transfers. How do the HSSF transfers compare to the earmarked resources? Transfers to public facilities are at or more than the amounts they are eligible for, while the picture for private (non-profit) is very different these facilities are receiving on average only 11% of the transfers they are entitles to. HSSF Planning, Oversight and Financial Management: In order to qualify for HSSF funds, facilities need to demonstrate adequate work planning to inform rational use of resources, oversight by facility management committees and demonstrated financial management capacity. Implementation of workplans was relatively high at 70% of facilities, especially high in urban facilities (84%). Once developed, facilities experienced delays in obtaining approval of the work plans. About 65% of rural facilities experienced substantial delays. About 80% of the delays were associated with delays in the district health office. The use of financial management tools (such as receipt books, payment vouchers and cashbooks) was sub-optimal: over 50% of public and private (non-profit) facilities do not receive the tools xi

12 from their respective headquarters. Of concern is that 24% of facilities reported receiving none of the three financial management aids. Nearly all facilities had an assigned official responsible for responsible for financial accounting. In the majority of public facilities, the responsibility for financial accounting lies with the officer-in-charge (89%), of the hospital superintendent. Transparency and governance: The majority of public facilities (77%) share financial information with communities compared to their private (non-profit) counterparts (23%). The proportion was particularly low in urban facilities (35%). Most of the facilities used meetings as the preferred mechanism to share the financial information. Very few facilities used posters to share the information. Finally, most facilities report having a community representative on the health management committee, and the majority of facilities used elections (55%) as the preferred means of involvement of a community member. What does this mean for Kenya? Kenya has invested heavily in education but less so in health. Today the government spends more on education than any of its neighbors, both as a share as a share of government spending and as a share of GDP. Conversely, government spending on health is modest in relation to its regional comparators. 3 That said, Kenya has made tangible progress towards the health MDGs. Significant gaps remain gaps which can only partly be explained by lack of resources. The fiscal headroom for a budget increase for any sector in the immediate future is constrained by the past and the present: fiscal expansion over the past few years needed to bolster the economy 4 and likely budgetary pressure posed by the new constitution s county reforms. More than ever before is it true that quality improvements in Kenya s health sector will have to initially come from productivity and efficiency gains. Furthermore, the success of the health sector in attracting a greater budget allocation will strongly bolstered by demonstrating value for money and the effectiveness of existing health spending. There have been some impressive past gains, but what next? Kenya has made some phenomenal gains in recent years. For example, infant mortality rate has fallen by 7.6 percent per year, the fastest rate of decline among 20 countries in the region. A large part of this improvement was attributed to public health improvements such as use of insecticide treated bednets. 5 Arguably, the next set of gains will be more challenging marginal women and children will become harder (and costlier) to reach, and the performance of the frontline health facilities will be a critical determinant of progress. 3 In 2012 government health spending was 8.5% of total government spending, and government health expenditure has remained at a constant 4.8% of GDP since Expansionary fiscal policy years has caused the Kenyan government s 2012 budget to be at about 30 percent of GDP. Kenya public sector debt has doubled between 2007 and Debt as a proportion of GDP has now increased by about 4 percentage points from 39 percent in 2007 to 43 percent at the end of 2012 but it is still below the policy target of 45 percent (World Bank, 2013) 5 Demombynes and Trommlerová (2012). xii

13 The SDI/PETS+ results found that while Kenya does relatively better on the availability of key inputs such as infrastructure and equipment. Regarding the availability of drugs, there are some important gaps: less than half of the priority drugs for children and mothers, and some gaps remain especially in availability of priority drugs for mothers. The greatest challenge is in the area of provider effort (evidenced by the absence and caseload data), and provider ability (evidenced by the assessments of clinicians knowledge and abilities). High provider absence and sub-optimal provider ability suggest room for improvement in efficiency of spending on health outcomes without detracting from service quality. The results presented here are reflective of some systemic problems, such as provider knowledge and ability, provider effort and the availability of priority drugs. xiii

14 Table 1. Service Delivery Indicators at-a-glance All Private Caseload Absence from facility 28% 29% 21% 28% 38% Diagnostic Accuracy 72% 74% 75% 73% 79% Adherence to clinical Guidelines 44% 43% 48% 42% 52% Management of maternal /neonatal complications 45% 44% 46% 43% 49% Drug availability (all) 54% 52% 62% 53% 49% Drug availability (children) 70% 69% 75% 71% 57% Drug availability (mothers) 44% 41% 54% 41% 44% Equipment availability 78% 77% 80% 76% 81% Infrastructure availability 47% 39% 75% 37% 59% xiv

15 INTRODUCTION 1. Globally the microeconomic evidence is clear: good health and nutrition improves the capacity to learn and work, which dramatically improves income and welfare. 6 While the relationship between health and economic growth is harder to discern at the aggregate level, 7 evidence from East Asia, and China in particular, shows very clearly that reductions in child mortality and improvements in nutritional status preceded the country s economic boom. 8 The other side of the coin is the relationship between spending and health outcomes, and here the evidence is much weaker. 9 In Africa, as elsewhere, the weak association between spending and outcomes suggests an unfinished quality agenda (Figure 2). 2. Kenya has made phenomenal gains in health outcomes over the past decade. Over the past decade, the country s infant mortality rate and under-5 mortality rate have decreased by a third. 10 Kenya s Vision 2030 sets out the country s development framework to achieve middle-income country status. The challenge facing the health sector is: what will be the drivers of the next set of health gains to the level of middle-income countries (see Figure 1)? Figure 1. Infant mortality rate and Under-5 mortality rate in Kenya compared to middle income countries IMR Kenya Middle-income countries Vision U5MR Kenya Middle-income countries Vision 2030 Source: World Development Indicators 6 Spence and Lewis (2009). 7 Macroeconomic and microeconomic evidence of the links between education and economic growth is robust. (see for example, Lucas (1988); Barro 1991 and Levine, R. and D. Renalt (1992)). Huffman (2001), Glewwe (2002) and most recently Glewwe et al, 2012, provide recent reviews of the microeconomic literature on the impact of education on income in developing countries. 8 Wagstaff et al. (2009). 9 Filmer and Pritchett (1999). 10 Infant mortality rate decreased by 31% and under-5 mortality rate decreased by 36% (World Development Indicators).

16 3. The literature points to the importance of the functioning of health facilities, and more generally, the quality of service delivery. 11 Nurses and doctors are an invaluable resource in determining the quality of health services. While seemingly obvious, the literature has not always made the links between systems investments and the performance of providers, arguably the ultimate test of the effectiveness of investments in systems. 12 The service delivery literature is, however, clear that, conditional on providers being appropriately skilled and exerting the necessary effort, increased resource flows for health can indeed have beneficial education and health outcomes (see Box 1) This report presents the findings from the implementation of the first Service Delivery Indicator (SDI) survey in Kenya. A unique feature of the SDI surveys is that it looks at the production of health services at the frontline. The production of health services requires three dimensions of service delivery: (i) the availability of key inputs such as drugs, equipment and infrastructure; (ii) providers who are skilled; and (iii) providers who exert the necessary effort in applying their knowledge and skills. Successful service delivery requires that all these elements need to be present in the same facility and at the same time. While many data sources provide information on the average availability of these elements across the health sector, the SDI surveys allow for the assessment of how these elements come together to produce quality health services in the same place at the same time. Figure 2. Association between health spending and outcomes (Africa) U5MR (per 1,000 live births) vs spending as % of Government spending U5MR (per 1,000 live births) vs spending as % of GDP U5MR (per 1,000 live births) U5MR (per 1,000 live births) spending on health, total (% of government exenditure) spending on health, total (% of GDP) Note: The red markers denote Kenya. Source: World Development Indicators. 11 Spence and Lewis (2009). 12 Swanson et al. (2012). 13 Spence and Lewis (2009). 2

17 5. In Kenya, as in most health systems, a significant majority of people encounter with the health services at dispensaries (also called health posts), health centers and the first level hospitals. In the 2012/13 SDI survey, information was collected from 294 such facilities and 1,859 health providers (see Table 2). The results provide a representative assessment of the quality of service delivery and the environment within which these services are delivered in rural and urban locations, in public and private (non-profit) health facilities. The private (non-profit facilities) include largely facilities owned by faithbased organization and also includes some non-government facilities. 6. Survey implementation was preceded by extensive consultation with Government and key stakeholders on survey design, sampling, and the adaptation of survey instruments. Pre-testing of the survey instruments, enumerator training and fieldwork took place between September and December The survey was implemented by the Kenya Institute of Policy Research and Analysis (KIPPRA) with support by the World Bank and the USAID-funded Health Policy Project. The World Bank s Service Delivery Indicators (SDI) Team provided quality assurance and oversight. Table 2. Health SDI/PETS+ sample in Kenya Variable Sample Weighted Total Share of Total Distribution Facilities % 100% Health posts (dispensaries) % 79% Health centers` % 15% Hospitals (first level) 45 34% 6% Ownership 292 b % 79% Private (non-profit) % 21% Location 292 b % 85% 86 29% 15% public 46 34% 9% public 88 34% 70% Healthcare workers 1, % 100% Nurses and midwives 1,016 55% 61% Clinical officers % 10% Doctors 47 3% 2% Paraprofessionals % 27% Notes: a. erent weights were applied where the unit of analysis was facilities and where unit of analysis was health providers. b. The totals for location and ownership sum to 292 as they exclude two refusals. 7. The survey used a multi-stage, cluster sampling strategy which allowed for disaggregation by geographic location (rural and urban); by provider type (public and private non-profit) and facility type (dispensaries/health posts, health centers and first 3

18 level hospitals) (see Table 2). 14 Annex A provides details of the methodology and sample for the Kenya Service Delivery Indicators survey. The modules of the survey instrument are shown in Table 28 (in Annex B). 8. The report is organized as follows: Part I presents the findings on service delivery and Part II reports the findings on expenditure tracking. Part I focuses especially on health providers, specifically, provider effort and this is followed by findings on provider knowledge and ability. Part I concludes with an assessment of the availability of key inputs, such as drugs, equipment and infrastructure. Part II is divided into three main themes: (i) the implementation of the 10/20 Policy on user fees; and (ii) the implementation of a new transfer mechanism to health facilities the Health Sector Services Fund (HSSF), looking at funding flows as well as management arrangements. The report concludes with a summary of the overall findings and some implications for Kenya as the country looks to realizing the goals of Vision 2030, the country s blueprint for economic and human development. 14 Using the Kenya designation, levels 2, 3 and 4 were included in the sample (Table 25 in Annex A). 4

19 Box 1. Service Delivery Indicators-Data for Results and Accountability The Service Delivery Indicators initiative is a partnership of World Bank, the African Economic Research Consortium (AERC) and African Development Bank to develop and institutionalize the collection of a set of indicators that would gauge the quality of service delivery within and across countries and over time. The ultimate goal is to sharply increase accountability for service delivery across Africa, by offering important advocacy tool for citizens, governments, and donors alike; toward the end of achieving rapid improvements in the responsiveness and effectiveness of service delivery. The 2004 World Development Report titled: Making Services Work for Poor People focused attention on frontline service providers and the relationships of accountability between providers, policymakers clients/citizens (). 1 Figure 3. Relationships that influence incentives for service delivery The Service Delivery Indicators (SDI) provide robust evidence on the quality of education and health services that can be used for benchmarking service delivery performance in education and health in Africa. It is envisaged that the broad availability, high public awareness and a persistent focus on the indicators will mobilize policymakers, citizens, service providers, donors and other stakeholders for action to improve the quality of services and ultimately to improve development outcomes. The perspective adopted by the Service Delivery Indicators is that of citizens accessing a service. The i ndicators can thus be viewed as a service delivery report card on education and health care. Instead of using citizens perceptions to assess performance, however, the indicators assemble objective and quantitative information from a survey of schools and health clinics. The service delivery indicators are identified at two levels: (i) the knowledge and effort of service providers, i.e. what frontline service providers know and do; and (ii) the availability of key inputs for effective service provision. These indicators are designed to hone in on the links in the results chain between financial inputs and human development outcomes. The measurement of these indicators is based on survey instruments underpinned by rigorous research and embrace latest innovations in measuring provider competence and effort. The survey instruments were piloted in Tanzania and Senegal, and Kenya is the first country where the SDI is being rolled out, using a standardized methodology, but with adaptation to each country s context. The countries where implementation is currently happening are: Mozambique, Nigeria, Togo and Uganda. More information on the SDI survey instruments and data, and more generally on the SDI initiative can be found at: and or by contacting sdi@worldbank.org. 5

20 Part 1. SERVICE DELIVERY 9. One of the most basic factors in assessing service delivery is the hours of operation. Table 3 shows the number of days per week and the number of hours per day services are available in Kenya s public and private (non-profit) health providers. On average, dispensaries are open for just over 5 days a week and more than 10 hours per day. Health centers and hospitals are open for between 6 and 7 days a week, and the number of hours per day ranges between 18 and 23 hours per day. Table 3. Hours and days of service delivery All Private (non-profit) Number of days per week facility is open All facilities Dispensaries 5.6 Health Centers 6.5 First level hospitals 6.9 Hours outpatient consultation offered per day All facilities Dispensaries 10.3 Health Centers 17.8 First level hospitals Health Providers 10. The health sector is a labor intensive sector. For this and other reasons, special attention was afforded to providers in this study: (i) The inability of central ministries (and also communities) to monitor precisely what the frontline providers do; (ii) The highly discretionary nature of work effort determining whether a nurse presents for work 24/7 often in tough working conditions; 15 and (iii) The asymmetry of information particularly acute in the health sector between policymakers and providers, as well as between communities and providers. These factors make other services, like water and sanitation or housing (that are technology or infrastructure intensive) fundamentally different from health service provision (and also education). 11. Another reason for the emphasis on observed behavior of providers is due recognition of the importance of health systems and particularly, the fact that the influence of health systems converge at frontline health facilities and health providers. A point of departure of this study is that nurses and doctors are intrinsically motivated, but that institutional incentives attenuate or undermine this motivation. More generally, are the 15 The transaction intensive nature of service delivery in health and education makes the cost of the inability to monitor extremely costly. 6

21 incentives for performance (that are shaped by the underlying health systems) aligned with health sectoral objectives? 2. Caseload 12. The caseload indicator is defined as the number of outpatient visits (recorded in outpatient records) in the three months prior to the survey, divided by the number of days the facility was open during the 3-month period and the number of health workers who conduct patient consultations (i.e. paramedical health staff such as laboratory technicians or pharmacists assistants are excluded from the denominator). In hospitals, the caseload indicator was measured using out-patient consultation records; only providers doing outpatient consultations were included in the denominator. The term caseload rather than workload is used to acknowledge the fact that the full workload of a health provider includes work that is not captured in the numerator, notably administrative work and other non-clinical activities. From the perspective of a patient or a parent coming to a health facility, caseload while not the only measure of workload is arguably a critically important measure. 13. The average caseload in the public sector was relatively low at 8.7 patients per provider per day (Table 4). The distribution of this variable was quite skewed, and the median caseload in public facilities was even lower the caseload for 50 percent of health providers was 7 patients per day or less. The average caseload among private (non-profit) providers was 10.4 patients per provider per day, slightly higher than the average in public facilities, although the differences were not statistically significant. Case mix across facility types may vary, so it is worth looking at comparisons by level of facility. In the public sector, the highest caseload was found in urban health facilities: 15.4 per provider for health centers and 15.3 per provider for urban hospitals, significantly higher than rural public facilities 16. Figure 4 shows the caseload by size of facility. Health centers with between 3 and 20 workers account for 86% of all health centers and just under half (47%) of all facilities. These facilities were also the ones with the lowest caseload levels between 6.4 and 6.6 patients per provider per day. 14. Caseload is usually of concern because a shortage of health workers may cause caseload to rise and potentially compromise service quality. The data for Kenya suggests that a large share of health providers, especially those in moderately sized facilities, have very low caseload levels. It is worth noting that the caseload indicator did not take into account the staff absence rates. This may explain why health staff members who are present at work feel that their true workload is higher than these numbers suggest. 16 The rural-urban difference in caseload for public health centers, p= 0.015; and for public hospitals, p=

22 Table 4. Caseload per clinician by level of facility All Private (non-profit) All facilities Dispensaries Health Centers First level hospitals Notes: See Table 34 in Annex C for more details (including standard errors). Figure 4. Caseload per clinician by facility size Health centers Hospitals workers 3-5 workers 6-10 workers workers > 20 workers 3. Absence rate 15. The average rate of absence at a facility is measured by assessing the presence of at most ten randomly selected clinical health staff at a facility during an unannounced visit. Only workers who are supposed to be on duty are considered in the denominator. The approach of using unannounced visits is regarded best practice in the service delivery literature. 17 Health workers doing fieldwork (mainly community and public health workers) were counted as present. The absence indicator was not estimated for hospitals because of the complex off-duty arrangements, interdepartmental shifts etc. 16. Close to a third (29.2%) of providers in public facilities was found to be absent, compared to a fifth (20.9%) among private (non-profit) providers but the difference was not statistically significant(p=0.254) (Table 5). Absence was particularly high in urban public facilities where just under four in ten health providers (37.6%) were absent; 9.3% points higher than in rural public facilities (p=0.177) In any workplace setting, absence may be sanctioned or not sanctioned. The survey found that the overwhelming share (88%) of absence was indeed sanctioned absence (Figure 6). But, from the consumer s perspective, these providers are not available to 17 Rogers, H. and Koziol M. (2012). 18 Dropping the hospital observations from the absence rate variable reduces the number of observations, but when included, the p=

23 deliver services whether sanctioned or not. It is possible that absence can be improved by more prudent sanctioning of absence. This suggests that management improvements and better organization and management of staff can potentially improve the availability of staff for service delivery. 18. The caseload of health workers is to some degree influenced by service utilization and demand-side factors, and may be a contributor to lower caseload in rural areas. But we also see that absence in some rural facilities, especially rural health centers, is quite high (41.9%) (Figure 7). Taken together the findings on absence and caseload are suggests there is some room for improvement in the levels of productivity in health service delivery. 19. In sum, who are most likely to be absent? The multivariate analysis presented in Table 36 in Annex C confirmed these findings: (i) Absence rates were similar across cadretypes; (ii) Absence was more likely among health providers in rural facilities; (iii) Absence in facilities with staff in excess of six workers relative to facilities with 1-2 workers were found to have higher absence rates; and (iv) While absence in private (non-profit) facilities was 40% lower than public facilities, this was not statistically significant after controlling for other factors. Table 5. Absence by level of facility All Private (non-profit) All facilities 27.5% 29.2% 20.9% 26.9% 31.2% 28.3% 37.6% Dispensaries 25.5% 26.9% 20.1% 24.8% 31.5% 25.9% 38.1% Health Centers 37.5% 41.1% 24.8% 39.2% 30.4% 41.9% 36.1% Figure 5. Absence by cadre type 37.6% 36.1% 30.0% Doctors Clinical Officers Nurses 9

24 Figure 6. Reasons for absence Sick and maternity leave Traning and seminar attendance Official mission 7.1% 10.1% 19.8% 88.0% Other approved absence 51.0% On strike 3.2% 12.0% Not specified 5.4% Sanctioned Not Sanctioned Unapproved absence 3.4% Figure 7. Absence rate and caseload by facility size (health centers only) Caseload Absence rate % % 37% 35% % 1-2 workers 3-5 workers 6-10 workers workers > 20 workers

25 4. Provider knowledge and ability 20. Having health professionals present in facilities is a necessary but not sufficient condition for delivering quality health services. For this reason, quality was also assessed using two process quality indicators (the adherence to clinical guidelines in seven tracer conditions and the management of maternal and newborn complications) and an outcome quality indicator, diagnostic accuracy in five tracer conditions. 21. The choice of tracer conditions was guided by the burden of disease among children and adults, and whether the condition is amenable to use with a simulation tool, i.e., the condition has a presentation of symptoms that makes it suitable for assessing provider ability to reach correct diagnosis with the simulation tool. Three of the conditions were childhood conditions (malaria with anemia; diarrhea with severe dehydration, and pneumonia), and two conditions were adult conditions (pulmonary tuberculosis and diabetes). Two other conditions where included: post-partum hemorrhage and neonatal asphyxia. The former is the most common cause of maternal death during birth, and neonatal asphyxia is the most common cause of neonatal death during birth. The successful diagnosis and management of these seven conditions can avert a large share of child an adult morbidity and mortality. 22. These indicators were measured using the patient case simulation methodology, also called clinical vignettes. Clinical vignettes are a widely used teaching method used primarily to measure clinicians (or trainee clinicians) knowledge and clinical reasoning. A vignette can be designed to measure knowledge about a specific diagnosis or clinical situation at the same time gaining insight as to the skills in performing the tasks necessary to diagnose and care for a patient. According to this methodology, one of the fieldworkers acts as a case study patient and he/she presents to the clinician specific symptoms from a carefully constructed script while another acts as an enumerator. The clinician, who is informed of the case simulation, is asked to proceed as if the fieldworker is a real patient. For each facility, the case simulations are presented to up to ten randomly selected health workers who conduct outpatient consultations. If there are fewer than ten health workers who provide clinical care, all the providers are interviewed The results of the measures used to assess provider knowledge and ability are presented below. There were similar trends observed across the various measures of provider knowledge and ability. First, there was little variation in measures of provider knowledge and ability across public and private (non-profit) providers. Second, provider ability scores progressively declined among the three cadre types: doctors, clinical officers 19 For more information on the methodology, see There are two other commonly used methods to measure provider knowledge and ability, and each has pros and cons. The most important drawback in the patient case simulations is that the situation is a not a real one and that this may bias the results. The direction of this potential bias makes this issue less of a concern the literature suggests that the direction of the bias is likely to be upward, suggesting that our estimates can be regarded as upper bound estimates of true clinical ability. The patient case simulation approach offers key advantages given the scope and scale of the Service Delivery Indicators methodology: (i) A relatively simple ethical approval process is required given that no patients are observed; (ii) There is standardization of the case mix and the severity of the conditions presented to the clinician; and (iii) The choice of tracer conditions is not constrained by the fact that a dummy patient cannot mimic some symptoms. 11

26 and nurses. Finally, these performance measures were generally the worst among rural public nurses. a. Diagnostic Accuracy 24. Diagnostic accuracy was measured as the unweighted average of the number of cases correctly diagnosed, as a proportion of all five cases diagnosed. Table 6 shows that providers arrived at the correct diagnosis in three quarters (72.2%) of the tracer conditions. 20 As with process quality, there was little variation across public-private (nonprofit) providers, and the highest scores were among doctors and among clinical officers. Only 15.6% of providers were able to correctly diagnose all five of the tracer conditions, and only 42.1% could diagnose four out of the five cases (Figure 8). Table 6. Diagnostic accuracy by cadre All Private (non-profit) All cadres 72.2% 71.6% 74.2% 70.8% 77.7% 74.8% 71.1% Doctors 85.4% 88.3% 78.4% 88.9% 82.6% 83.9% 92.9% Clinical officers 80.2% 79.6% 81.1% 80.1% 80.3% 75.9% 82.6% Nurses 69.8% 70.1% 68.7% 69.3% 74.0% 72.3% 69.9% Figure 8. Diagnostic accuracy by number of cases correctly diagnosed 1 case 0.5% 2 cases 11.5% 3 cases 30.3% 4 cases 42.1% 5 cases 15.6% 25. The diagnostic accuracy rate varied across case conditions, ranging from 35% for low for malaria with anemia to 97% for pulmonary tuberculosis. Two in every ten clinicians were not able to offer correct diagnosis of relatively common conditions such as acute diarrhea, pneumonia and diabetes. For malaria with anemia only three in every ten clinical officers were able to give correct diagnosis. Due to the significance of malaria in Kenya s burden of disease a closer look was taken at the malaria case. The diagnosis of malaria 20 Figure 30 to Figure 31 in Annex C shows the history taking and examination questions providers asked who provided the correct diagnosis. 12

27 with anemia was least accurate at 27%, although a relatively larger share (59%) of providers arrived at the diagnosis of malaria (without specifying the additional diagnosis of anemia). Figure 9. Diagnostic accuracy by condition Pulmonary tuberculosis 97% Pneumonia 83% Acute diarrhea 81% Diabetes 80% Malaria with anemia 35% b. Process Quality: Adherence to clinical guidelines 26. The assessment of process quality is based on two indicators: (i) clinicians adherence to clinical guidelines in five tracer conditions and (ii) clinicians management of maternal and neonatal complications. The former indicator is an unweighted average of the share of relevant history taking questions, and the share of relevant examinations performed for the five tracer conditions. The set of questions is restricted to core or important questions as expressed in the Integrated Management of Childhood Illnesses (IMCI) and the Kenya National Guidelines for the tracer conditions. 27. providers in Kenya were found to adhere to under half (42.7%) of the clinical guidelines in the management of the five tracer conditions. This relatively modest performance was not significantly different between private (non-profit) and public providers (Table 7). This measure of process quality progressively declined by cadre type, being highest doctors, followed by clinical officers and nurses (Table 7). 21 It is notable that the highest process quality scores were found among rural doctors where roughly three quarters of clinical guidelines were adhered to. The lowest scores were among rural nurses at 39.4% for adherence to clinical guidelines. This implies that when a child or adult receives treatment from a rural nurse only about two fifths of the country s clinical guidelines are followed, yet nurses constitute the larger proportion (75%) of health workers who regularly conduct outpatient consultations in rural areas. 21 The disaggregation of the two process quality indicators by facility type is shown in Table 38 and Table 40 in Annex C) 13

28 Table 7. Adherence to clinical guidelines by cadre All Private (non-profit) All cadres 43.7% 42.7% 47.6% 41.7% 52.0% 41.1% 51.2% Doctors 61.2% 60.9% 61.7% 69.2% 54.6% 72.5% 49.7% Clinical officers 54.3% 52.4% 57.2% 53.9% 54.8% 51.7% 53.3% Nurses 40.3% 40.4% 39.6% 39.4% 47.9% 39.7% 48.9% 28. How many providers adhered to all the guidelines for the five tracer cases? Figure 10a shows that less than 1% of providers adhered to at least 75% of the guidelines for every tracer condition. Using a lower threshold of 50%, Figure 10a shows that only 13% of providers adhered to at least half of the guidelines for each tracer condition. c. Process Quality: Management of maternal and neonatal complications 29. The second process quality indicator is clinicians ability to manage maternal and neonatal complications. This indicator reflects the unweighted share of relevant treatment actions proposed by the clinician. The set of questions is restricted to core or important questions as expressed in the Integrated Management of Childhood Illnesses (IMCI). providers adhered to only 44.2% of the clinical guidelines for managing maternal and newborn complications, was not significantly different between private (non-profit) and public providers. This process quality was also found to progressively decline by cadre type (Table 8) and by facility level (Table 40in Annex C). Table 8. Management of maternal and neonatal complications by cadre All Private (non-profit) All cadres 44.6% 44.2% 45.8% 43.6% 48.3% 43.4% 48.7% Doctors 57.4% 57.1% 58.1% 72.0% 45.4% 75.3% 39.4% Clinical officers 46.4% 45.6% 47.7% 45.4% 47.5% 43.1% 48.6% Nurses 44.5% 44.5% 44.3% 43.8% 49.9% 44.0% 50.9% 14

29 Figure 10. Share of providers who attained a minimum share of adherence to the clinical guidelines by number of tracer conditions Tracer conditions Maternal and newborn complications 78% At least 50% At least 75% 92% At least 50% At least 75% 48% 34% 33% 14% 10% 13% 20% 3% 4% 11% 13% 1% 1% 7% 20% 1% Number of cases Number of cases 30. It is disturbing that less than 1% of providers adhered to at least 75% of the guidelines for the two maternal and neonatal complications (Figure 10b). Using a lower threshold of 50%, Figure 10b shows that only 20% of providers adhered to at least half of the treatment actions for each of the two complications. 15

30 5. Availability of Inputs a. Drug Availability 32. This indicator is defined as the number of drugs of which a facility has one or more available, as a proportion of all the drugs on the list. The drugs had to be unexpired and had to be observed by the enumerator. The drug list contains tracer medicines for children and mothers identified by the World Health Organization (WHO) following a global consultation on facility-based surveys. 22 The 10 tracer drugs for children and 16 tracer drugs for women are listed in Table 45 in Annex C. 23 Some drugs are not dispensed at the lowest level facilities (dispensaries) and the estimates of drug availability adjusted for level of facility are presented in Table 9 and Table On average, public facilities had only half (52.3%) of the tracer drugs available. The availability of tracer drugs for children was relatively high (69.2%). Given the national concern about maternal mortality and efforts to improve maternal health outcomes, the availability of tracer drugs for women was unsettlingly lower at 41.0%. It is commonly reported that rural facilities suffer severe drug shortages compared to their urban counterparts. In Kenya, there was no evidence to support this. In fact, rural public facilities had 25% more (p=0.01) of tracer drugs for children compared to urban public facilities. 34. Private (non-profit facilities) performed slightly better than public facilities, with 62.1% of tracer drugs available (19% more than public facilities; p=0.005). This was largely driven by public-private differences in the share of tracer drugs for women (30 %, p=0.001). The most disconcerting finding is that not a single health facility including first level hospitals had all the tracer drugs for children and women. Table 9. Drug availability (adjusted for facility type) All Private (non-profit) All tracer drugs 54.3% 52.3% 62.1% 54.0% 56.0% 52.7% 48.5% Tracer drugs for mothers Tracer drugs for children 43.6% 41.0% 53.5% 42.3% 51.4% 40.6% 43.8% 70.4% 69.2% 75.1% 71.5% 63.8% 70.8% 56.7% 22 WHO (2011). Priority medicines for mothers and children Geneva World Health Organization Note, the two lists overlap by three drugs, so a total of 21 drugs are in the SARA list. Three additional drugs were added to the list of tracer drugs for women in the adaptation of the instrument for the Kenyan clinical guidelines. See Table 45 in Annex C. 24 The unadjusted estimates are shown in Table 44 and Table 47 in Annex C. 16

31 Table 10. Drug availability by level of facility (adjusted for level of facility) All Private (non-profit) Dispensaries 53.3% 51.6% 59.9% 53.5% 52.2% 52.4% 42.8% Health centers 54.4% 51.4% 64.9% 54.5% 54.1% 52.3% 46.3% First level hospitals 66.9% 62.9% 79.8% 63.9% 71.1% 60.5% 66.2% Drug Availability by Push and Pull Facilities 35. Drug supplies are distributed to health facilities under the Kenya Medical Supplies Agency (KEMSA) using two mechanisms: the Push system and the Pull system. Under the Push system, supplies are pre-packaged by KEMSA in structured universal drug kits, which are delivered to facilities against their drug budgetary allocations of drawing rights. 25 In the Pull system, facilities requisition specific supplies which are checked off against their budget allocation, their drawing rights, lodged with KEMSA. The Pull system was conceptualized to enable facilities to focus only on needed drugs while also avoiding the wastefulness of the traditional drug kit. 36. How widespread is the implementation of the Pull system? Figure 37 (in Annex C) shows that the pull system is implemented in the overwhelming share of facilities on average 81% of all facilities use the pull system, and the same is true across all facility types. 37. The survey explored the comparative availability of the drugs for mothers and children across the two systems, as shown in Table 11. The results suggest the effects were mixed and that there were no significant differences across the two mechanisms. On the one hand, drugs for children had slightly greater availability in public Push facilities (72% versus 69%), while in drugs for mothers, there was no difference 39% for both Push and Pull public facilities). Given the relatively short period since the full implementation of the two systems, it may be too soon to assess statistically significant differences. 25 For a discussion, see Aronovich, DG and S Kinzett (2001), Kenya Assessment of the Commodity Supply Chain and the Role of KEMSA. Arlington VA; DELIVER/John Snow Inc. for USAID. Available at Accessed 29/05/

32 Table 11. Tracer drugs for children and mothers in Push and Pull facilities All Private (non-profit) Tracer drugs for children Push' facilities 75% 72% 86% 75% 78% Pull' facilities 70% 69% 73% 70% 68% Tracer drugs for mothers Push' facilities 40% 39% 47% 40% 48% Pull' facilities 41% 39% 45% 40% 48% b. Vaccines and vaccine-related equipment and supplies 38. The availability of equipment and supplies for preparation and storage of vaccine stocks was very high across all facility types (Figure 11). The rate of availability by facility type was also relatively high, ranging from 76.9% for dispensaries to 92.9% for hospitals. (see Table 12 with more detail presented in Table 49 in Annex C). Vaccination is often implemented on a campaign basis, and may explain why the availability of some critical vaccines (such as BCG vaccine) appears to be low. Finally, Error! Reference source not found. in Annex C presents the availability across facilities of two non-pharmaceutical supplies, disposable gloves being available in an average 98% of facilities while condoms were available in 89% of facilities, on average. Figure 11. Availability of vaccine-related equipment and supplies by facility level Functional refrigerator 95.2% 99.2% 100% Vaccine carrier Vaccine packs 93% 100.0% 100.0% 100% 98.0% 99.3% Hospitals Health centers Dispensaries Sharp containers 96.4% 97.3% 99% 18

33 Table 12. Vaccines availability by facility level All Private (non-profit) All facilities 80.8% 83.4% 71.7% 79.9% 85.9% 83.4% 84.0% Dispensaries/Health posts 76.9% 79.3% 68.6% 77.2% 73.3% 80.3% 58.0% Health Centers 90.9% 93.7% 77.9% 89.6% 94.5% 93.3% 94.8% First level hospitals 92.8% 95.0% 86.5% 90.3% 95.9% 93.8% 96.6% Figure 12. Availability of individual types of vaccines by facility level Measles vaccine 79% 98.0% 95.7% Oral polio vaccine Diptheria BCG vaccine Pneumonia conjugate 93.0% 90.1% 77% 98.0% 95.2% 85% 79.3% 71.3% 66% 93.0% 93.2% 84% Hospitals Health centers Dispensaries 19

34 c. Equipment availability Medical Equipment 39. The equipment indicator focuses on the availability (observed and functioning by the enumerator) of minimum equipment expected at a facility. The pieces of equipment expected in all facilities are: a weighing scale (adult, child or infant), a stethoscope, a sphygmonometer and a thermometer. In addition, it is expected that the following pieces of equipment be available at health centers and hospitals: sterilizing equipment and a refrigerator. Table 14 shows the availability of each of these types of equipment and Table 13 presents availability of minimum equipment adjusted by level of facility More than three quarters (77.0%) of public facilities met the above mentioned requirements that make up the equipment indicator (Table 13). The public-private differences were especially large for dispensaries: private (non-profit) facilities exceeded public facilities by a third (31 percent; p=0.146) on the availability of equipment. Similarly, the public rural-urban differences were pronounced at the dispensary level (p=0.2538), but not at other levels of facility. Table 13. Medical equipment availability (adjusted for level of facility) 27 All Private (non-profit) All facilities 77.8% 77.0% 80.4% 76.7% 80.7% 75.8% 81.4% Dispensaries 76.1% 71.2% 94.9% 74.0% 92.3% 69.4% 91.2% Health centers 75.9% 75.2% 78.0% 73.4% 86.0% 73.1% 88.1% First level hospitals 82.5% 81.4% 86.5% 88.4% 74.9% 85.5% 75.6% Table 14. Availability of specific types of medical equipment All Private (non-profit) Any scale (adult, child, infant) 98.7% 98.4% 99.6% 98.5% 99.4% 98.2% 100.0% Thermometer 92.0% 90.8% 96.5% 91.2% 96.8% 90.1% 96.2% Stethoscope 94.3% 92.9% 99.4% 93.8% 97.5% 92.4% 97.3% Sphygmonometer 86.3% 83.1% 98.1% 84.5% 96.8% 81.6% 94.8% Refrigerator (Health centers and First level hospitals only) Sterilization equipment (Health centers and First level hospitals only) 98.0% 98.2% 97.3% 99.2% 94.6% 100.0% 91.8% 84.8% 85.3% 83.3% 83.0% 90.1% 83.2% 92.5% 26 Table 52 shows the equipment indicator using only the following equipment: weighing scale (adult, child or infant), a stethoscope, a sphygmonometer and a thermometer. 27 See Table 54 in Annex C for availability of individual pieces of equipment across the various facility levels. 20

35 41. There was no significant difference in the aggregate equipment indicator between public and private (non-profit) facilities (p=xxx). The availability of sphygmonometers and sterilization equipment is the most constraining pieces of equipment comprising the aggregate equipment indicator (Table 14). It is an important achievement that refrigeration is available in more than 98.2% of public health centers and first level hospitals, and in 100% of public rural health centers and hospitals. Communications Equipment 42. The study explored the availability of a composite set of functioning communications equipment (radio; phone; computer) and found that 83% of public facilities and 65% of private (non-profit) facilities had at least one of the three forms of communication equipment, with hospitals scoring 100% (Table 15). Interestingly, only XX% of facilities had all three types of communications equipment. Table 15. Communication equipment availability All Private (non-profit) Communications Indicator 79.0% 83.0% 65.0% 80.0% 75.0% Dispensaries 76.0% Health Centers 89.0% Hospitals 100.0% 43. The availability of individual types of communication equipment in public facilities is given in Figure 39.The most widely available communications equipment was the mobile phone exceeding landlines as a communication instrument: XX% versus XX% of facilities. Many public hospitals cited availability of a functioning computer (XX%) and the availability of functional internet is XX%. The availability of internet access in public facilities is far exceeded by the private (non-profit) facilities by more than three times: 9% of public facilities versus 33% of private (non-profit) facilities (p=0.0163). Table 16. Availability of specific types communication equipment All Dispensaries Health centers Hospitals Communications Indicator Internet Computer Shortwave radio Cellular phone Landline 21

36 Ambulance and Fuel 44. An effective referral system requires the availability of ambulance services. Note, this need not be ownership of a dedicated emergency vehicle, but that the facility merely has access to an emergency vehicle. On average less than a tenth (8%) of public facilities had access to ambulance services, compared to nearly a third (31%) of private (non-profit) facilities. The availability was highest among hospitals (92%). 28 Fuel is often identified as an important constraining factor. Of those facilities that had an ambulance, fuel did not pose to be a constraint as 96% of facilities with an emergency vehicle had access to fuel (see Figure 40 in Annex C). Table 17. Availability of ambulance services by facility level All Private (non-profit) All facilities 13% 8% 31% 9% 38% Dispensaries 6% Health centers 19% First level hospitals 92% 45. In public facilities the most common purpose of ambulances was indeed to transport patients (78.4%), compared to 50% in private (non-profit) facilities (Figure 13 and Table 55 in Annex C). In the public sector non-patient uses (such as transporting medicines and supplies or personnel) accounted for less than a quarter (21.6%) of the most recent use. Figure 13. Purpose of last trip that vehicle or ambulance made Transporting patients Transporting personnel Collecting medicines and supplies Other 0% 20% 40% 60% 80% 100% Private All (non-profit) 28 Given the emphasis was on functional availability, it is possible that some facilities might have ambulances that are out of service. 22

37 d. Infrastructure availability 46. The infrastructure indicator captures the availability of three inputs: water, sanitation and electricity. The indicator is an unweighted average of these three components. 47. In the public sector, more than two thirds of health centers (68.1%) and nearly all first level hospitals (96.1%) meet the minimum infrastructure requirements. (Table 18). In the public sector, water is an important infrastructure constraint: only 49.3% of public facilities had a clean water source. The difference between public urban public was substantial (98.7% versus 42.9%; p<0.001) (Table 19). This was also a key difference with the private (non-profit) facilities the share of private (non-profit) facilities with a clean water source was 72% greater than public facilities (p<0.001). Over ninety percent of private (non-profit) facilities meet the minimum infrastructure requirements and, as do more than two thirds of public facilities (68.4%). While the average estimates of infrastructure availability are relatively positive, when we assess the availability of all of the three inputs at the same time in the same facility, we find that only 40 percent of facilities have clean water and sanitation and electricity. Table 18. Infrastructure availability All Private All facilities 46.8% 39.3% 74.9% 43.4% 66.9% 36.7% 59.3% Dispensaries 38.8% 29.6% 74.0% 36.3% 57.4% 28.5% 41.1% Health centers 68.1% 68.1% 68.3% 68.0% 68.6% 67.2% 73.3% First level hospitals 97.0% 96.1% 100.0% 96.6% 97.7% 95.5% 96.9% Table 19. Availability of specific types of infrastructure All Private (non-profit) Clean water 56.7% 49.3% 84.5% 50.0% 95.9% 42.9% 98.7% Toilet 95.3% 94.8% 97.2% 98.9% 73.9% 98.7% 64.3% Electricity 73.0% 68.4% 90.1% 69.2% 73.9% 65.2% 93.7% 48. Error! Reference source not found. to Error! Reference source not found.shows the breakdown of the source of water, electricity and sanitation. Under half of public facilities (42.7%) and 80.7% of private (non-profit) facilities were on the power grid. facilities were also more likely to be on the power grid (94.6% compared to 43.2% of rural facilities). Other most common source of electricity is solar power, accounting for a quarter (25.2%) of public facilities and a much smaller share of private (non-profit) facilities (7.4%). It is notable that private (non-profit) facilities are most likely to suffer power outages (11.9%) compared to public facilities (8.9%) (see Figure 41 in Annex C). 23

38 Part 2. PUBLIC HEALTH EXPENDITURE 49. Part II documents public expenditure on health services for the financial year 2011/2012, as well as processes related to financial management. This study focused specifically on facility-level expenditure tracking. In addition to tracking sources of revenue and expenditures at the facility level, this study sought to answer some specific questions on: (i) The implementation of the 10/20 Policy on user fees; (ii) Facilities access to and utilization of the a new transfer to facilities, the Heath Sector Services Fund (HSSF) and the Hospital Management Services Fund (HMSF); and (iii) The impact of Push and Pull systems on facilities drug expenditure patterns. 50. In this part of the report specific governance issues are also explored, for example, the involvement of communities in the management of health facilities, functioning of the facility management committees, and transparency in the utilization of funds etc. 51. health facilities are financed through various mechanisms. This includes revenue that accrues to the facilities in a non-monetary form, for example, funding of staff salaries and benefits, and the provision of drugs from KEMSA using special drawing rights, or an allocated amount of the drug budget (although the facility is not the fund holder for these drug allocations). This PETS did not collect data on these categories of funding, but focused only on the monetary sources of revenue, for example: i. User fees ii. HSSF iii. HMSF iv. Donor organizations v. NGOs vi. National Health Insurance Fund (NHIF) vii. Other MOH transfers 52. If one considers only non-monetary revenues (i.e. excluding drugs and salaries) user fees, HSSF and HMSF contribute the largest proportion of monetary revenues for the public facilities while user fees and donor funds provide the bulk of private (non-profit) facility revenues. 6. User fees 53. One of the major recommendations from earlier PETS was the need to reduce the emphasis on user charges because they are the most regressive form of health revenues, and in order to reduce the deterrent effect of user fees on the healthcare seeking behavior of poor users of health services. In 2005 the government introduced reforms that reduced user fees for public dispensaries and public health centers and only allowing these facilities 24

39 to retain a registration fee of Kshs 10 and Kshs for dispensaries and health centers, respectively hence the name, 10/20 Policy. This survey assessed the levels of awareness and implementation of the 10/20 Policy in both government dispensaries and health centers. The private (non-profit) facilities were included in this assessment because some of these facilities benefit from some HSSF transfers, and in order to be eligible for the HSSF transfer, they need to comply with the 10/20 Policy. In addition to awareness, the study reports on the implementation of the policy and the reasons for not implementing this policy. a. Implementation of the 10/20 Policy 54. On average, awareness was relatively high among public providers (72%), and the policy was implemented at 86% of facilities (Figure 14a). Of the 14% of public facilities who were not implementing the policy, the reasons for non-implementation was assessed (Figure 14b). The most common reasons cited were: (i) other sources of revenue were insufficient; and (ii) that the community can afford to pay user fees. Figure 14.Level of awareness and implementation of the 10/20 Policy 30 Level of awareness (all) Dispensaries Health Centers Implementation 72% 86% 59% 65% 71% 86% Advised from the local leaders or DHMT, 3.7% Other, 14.8% Money raised not enough for needs, 18.5% Private (non-profit) 17% 40% 71% 86% 71% 86% Commun ity can afford to pay more, 63.0% Note: Hospitals are excluded from this analysis because the 10/20 Policy only applies to dispensaries and health centers. 55. Among private (non-profit) facilities, awareness was relatively low 17% of these facilities reported being aware of the policy. As mentioned previously, implementation of the 20/10 Policy among these facilities are of interest, as private (non-profit) facilities that are receiving HSSF funds are expected to comply with the 10/20 Policy. Figure 14 shows the as much as 40% of private (non-profit) facilities report implementation of the 10/20 Policy. 29 Equivalent to about US$ 0.20 or US$ Paradoxically, according to this figure, it appears that more facilities are implementing the policy than are aware of the policy. This is possibly explained by the fact that the interviewees may not be aware of the specific name of the policy. 25

40 56. The fees (per unit of service) charged by public and private (non-profit) dispensaries and health centers are shown in Figure 15. According to the 10/20 Policy the services are exempted from user fees are: (i) consultation and services for children under 5; and (ii) consultation and treatment of tuberculosis. Contrary the policy, public dispensaries and health centers are indeed charging for many services. Figure 15. Prices charged for services exempt under 10/20 Policy Under 5 consultation Under 5 services Private (non-profit) Tuberculosis b. Price differentials at public and private (non-profit) facilities 57. Figure 16 shows the prices charged for a variety of out and in-patient services at public and private (non-profit) facilities. The prices for dental services are shown in Figure 42 in Annex C. Unsurprisingly, prices at the public facilities were lower than private (nonprofit) facilities for all services. The services for which the private prices exceeded the public prices by more than 100% (i.e. more than double) are listed in Table 20. In absolute terms the largest differences is for Caesarean section (Kshs 22,982) and specialized surgery (Kshs 24,982). Interestingly, the difference I n charges for in-patient bed say was the largest in relative terms (1,300%)! Table 20. Services with largest price differentials between prices at public and private (non-profit) facilities Private % erence (non-profit) erence Minor surgery 833 2,520-1, % Under 5 consultation % General surgery 4,312 15,003-10, % Normal delivery 594 2,520-1, % Specialized surgery 4,671 28,961-24, % Caesarean section 3,888 26,870-22, % Bed charges per day 163 2,282-2, % Note: General surgery refers ro XXX. Special surgery refers to XXX. 26

41 Figure 16. Prices charged for other services (Kshs) Private (non-profit) Registration Family planning Drugs: Amoxycillin syrup Consultation (over 5 yrs) Laboratory: Urinalysis Laboratory: Malaria test Malaria treatment Laboratory: Hemoglobin Laboratory: Other Antenatal visit Laboratory: Blood sugar Medical examination Radiological examination 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Bed charges per day Minor surgery General surgery Specialized surgery Normal delivery Ceasarean section c. Revenues from user fees 58. Despite the introduction of the 10/20 Policy whose aim was to reduce the household burden on healthcare, the survey found that income from user fees remained a significant source of facility revenue, and the largest source as a share of monetary 27

42 revenue. As expected the average user fee revenue per facility level differs substantially (Table 21). The average for public health centers was Kshs XXX million, and Kshs XXX million for public first level hospitals Kshs XXX million for FY 2011/2012. Table 21. Aggregate revenue from user fees by facility level (Kshs 000) Dispensaries Health centers Hospitals Private (non-profit) d. Waivers and exemptions 59. Some categories of patients and services are either exempted as a rule (Children under-5; HIV drugs; maternity fees; etc.) or waived at the discretion of the facility management based on health ministry guidelines. The survey found that facilities implemented the policy with some seemingly legitimate beneficiaries the very poor people, street children and children under-5, and some not so legitimate beneficiaries facility staff, relatives of staff, and members of the health management boards. The extent of the waivers and exemptions can be measured in terms of revenues forgone, and is reflected in Figure 17. As expected, hospitals had the largest more forgone revenue than health centers and dispensaries. Figure 17. Revenue forgone from implementation of waivers and exemptions Facilities Private Facilities Source: PETs++ Survey data 28