Kenya health system productivity change between 2014 and 2022: progress towards universal health coverage

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In line with these reforms, significant investments have been made across the 47 county governments to strengthen healthcare provision. Methods: We assembled a panel dataset of health system inputs, outputs, and contextual factors for 2014 and 2022 to measure productivity change across Kenya’s 47 counties. Using the Malmquist Productivity Index (MPI), we estimated total factor productivity change (TFPCH), technology change (TECCH), technical efficiency change (TECH), and scale efficiency change (SECH). We further examined the impact of contextual factors on productivity shifts using linear regression analysis. Results: On average, TFPCH was 0.69 for under-5 survival, 0.74 for maternal survival, and 0.67 for healthy life expectancy (HALE). Similarly, TECCH scores were 0.70, 0.71, and 0.67 respectively. While TECH and SECH averaged around 1.0, indicating little net change, there was significant regional and county-level heterogeneity. Notably, counties classified as marginalized showed significantly greater productivity growth across all three outcome measures. Conclusion: Despite considerable investments in decentralization and service expansion, Kenya’s health system exhibited overall productivity declines between 2014 and 2022, largely due to suboptimal adoption and application of health technologies. Enhancing managerial capacity, process optimization, and leveraging proven cost-effective interventions may be more important than large-scale equipment investments alone. Health sciences/Health care/Public health Health sciences/Medical research/Epidemiology Health system productivity change Health systems Productivity change Malmquist productivity Technology change Efficiency change Figures Figure 1 Figure 2 Figure 3 Introduction Productivity is the ratio of outputs to inputs. Meanwhile, productivity change is the change of the ratio that is occasioned by the shifts in the production function (1). Efficiency and productivity improvement are identified as key considerations of a well-functioning health system (2). As the World Health Organization (WHO) explains in its health systems framework, an effective health system is fair and equitable, both in the distribution of health goods and services and in the way it is financed. It emphasizes efficiency and cost-effectiveness as well and responds to the legitimate non-health expectations of those seeking health care, such as respect and compassion (3). Ultimately, an effective health system ensures that anyone in need of a specific health good or service can access it and derive the relevant benefits without undue financial pressure. However, this must be done sustainably, given that resources available for health service delivery are finite (4). Therefore, with increasing demands for healthcare across different populations, health system productivity growth has become a central theme in the global health debates as governments around the world strive to do more with limited resources (5). The pressure to improve access to healthcare is greater in low- and middle-income countries (LMICs) which are witnessing rising health needs. These are occasioned by dual epidemics of increasing burden of noncommunicable diseases and injuries in the backdrop of an unfinished agenda of communicable diseases, maternal and child health conditions (6). This paper focuses on Kenya, a middle-income country in Africa that is faced with increasing population health demands amidst resource constraints. Successive government policies have emphasized decentralization of health services as key to reaching the populations in need (7). In 2010, the country promulgated a new constitution that introduced devolution, transferring several core responsibilities to the 47 county governments in the spirit of accelerating decentralization(8). After the 2013 general elections, healthcare in Kenya was officially devolved to a two-tier governance structure including the national government and the 47 counties. The counties now manage health facilities, staff, and budgets, allowing for more local decision-making and service delivery (9). This marked a major shift in governance of health service delivery, transferring the responsibility from the national government to the county governments. The goal of devolution was to improve access to healthcare services, allowing for more localized decision-making and enhanced accountability and responsiveness to community needs (10). Tackling health disparities has been at the core of Kenya’s health devolution efforts. (11) established that despite the country’s improving health indicators over the last 3 decades, health gains were uneven across counties. For instance, life expectancy in 2016 ranged from 57.0 years in Homa Bay to 71.8 years in Laikipia. Meanwhile, some counties like Siaya saw dramatic improvements in key health indicators post-2006, while others lagged (11). In response to the pre-existing and on-going health disparities across counties, the government launched several initiatives to expand public health and bridge gaps in health service delivery. In 2013, a free maternal health services program was launched in public hospitals to reduce maternal and infant mortality (12). The goal was to eliminate financial barriers to maternal health services in the country and ensure equitable access to skilled care during pregnancy, delivery, and post-natal periods. The government allocated KESH 3.8 billion to fund the program in its first year, with demonstrable results. Health facility deliveries increased from 43% in 2011 to 56.8% in 2015; and maternal mortality dropped from 488 to 362 deaths per 100,000 live births between 2008 and 2014 (13). In 2015, the government of Kenya also launched a landmark program aimed at modernizing healthcare infrastructure across all 47 counties through public private partnership arrangements. Through this program with an estimated cost of USD 432 million, the government leased specialized medical equipment from private companies for use in public hospitals (14). The goal was to bridge the gap in access to critical healthcare services, especially in underserved areas. Similarly, Kenya has continued to expand childhood immunization program by launching new vaccines into the routine schedule, such as Typhoid Conjugate Vaccine (TCV) and Measles-Rubella (MR). Outreach services are also being implemented through mobile clinics and community health workers to remote and informal settlements to ensure no child is left behind (15). In the implementation of these health-focused initiatives, universal health coverage (UHC) has been a guiding principle for Kenya’s health policy making. In 2018, UHC pilot programs were launched in four counties – Kisumu, Machakos, Isiolo and Nyeri, to test models for nationwide implementation. The focus was on expanding access to essential health services and reducing out-of-pocket spending for those seeking healthcare (16). Despite the challenges such as delays in funding disbursements, infrastructure and staffing gaps, the UHC pilot programs witnessed increased health service utilization, community awareness and engagement (17). The pilot program also significantly influenced health sector funding in terms of allocation and resource availability. For instance, previously, in 2018-2019, the funding was done through an input-based financing model, whereby the national government directly supplied commodities and paid for services. By 2020, this shifted to an output-based financing model, where counties were reimbursed based on services delivered (18). After the pilot program implementation, it was also clear that the national government needed to increase its resource allocation to support the national objectives, due to the limited resource base at the county levels (19). Considering all these developments, important questions arise pertaining to the productivity of the decentralized Kenyan health system. For instance, did the additional investments translate to improvements in health outcomes? Was progress realized through adoption of new technologies or through embracing better ways of combining existing resources, or both? Were there differences in performance across the country? These are some of the key questions we address in this paper. As the first study in Kenya to holistically examine productivity change of the health system at the county level, the approach and conclusions presented here are expected to have wider policy implications for the country and the broader region. Methods We assessed the Kenya health system productivity change in terms of reduction of under- 5 mortality, maternal mortality and HALE. These are universally accepted measures of health system performance (20). We compared progress in under-5 survival, maternal survival and HALE between 2014 and 2022 against a set of key health inputs, development health funds and health workforce density for the same period. The year 2014 is critical, since it is around this time that the two-tier health system governance structure was fully operationalized, a year after its adoption in line with the new constitution (21). Our study focuses on the organization and utilization of available resources to deliver healthcare. We have considered county-level health financing and health workforce density, as health system inputs and compared them against the corresponding health system output measures, under-5 survival, maternal survival and HALE. We considered data from 2014 as baseline and compared it with the most recent period of 2022 where data were available. This roughly covers two electoral cycles of 5-year terms in which the county governments have been in place and various reforms and investments were put in place. We measured health system productivity using MPI, an approach that involves constructing an aggregate (country-wide) efficiency frontier based on data for all counties and then estimating the distance of individual counties from the country-wide frontier. Accounting for time, the MPI represents ratios of distance functions from the efficiency frontier for each county. Changes in total productivity may result when a specific county catches up with the country’s frontier due to efficiency gains or adoption of new technologies causing a shift in the production frontier for the country(22). Considering two time periods, (t) and (t+1), the MPI can be algebraically expressed as follows: An MPI of above 1 signifies productivity improvement (i.e. improvement in the rate of return per unit of health input) while values of less than one indicates a regression in productive use of resources in the system (23). The resultant TFPCH from the analysis can also be decomposed further to assess if the observed change is due to TECH or TECCH or both. Further, by modelling the frontier using both constant-return-to-scale (CRS) and variable-returns-to-scale (VRS), TECH can be decomposed into pure technical efficiency change (PTEC) and SECH (24). Whereby in the model: MPI = the productivity of the most recent period relative to the past period x = system inputs TECH = efficiency change d = distance functions TECCH= technology change t = past time period y = system outputs t+1 = recent time period We generated all the respective indices for the 47 counties. To give further context to the county level estimates, we grouped the counties into the provinces (regions) in which they were clustered previously before devolution. Since Nairobi County was previously classified as a province, we group it into the Central province given its proximity to the region. Our MPI analysis employed an output rather than an input-oriented efficiency model because in Kenya, decision makers at the county level normally face a fixed set of core health inputs in the short run, and they can only organize the utilization of the available resources to achieve optimal outcomes. Therefore, an output-oriented model was justified because health systems focus on achieving the highest possible outcome, given their resources, rather than setting a target outcome level and removing resources once they attain their targets. The MPI analysis confers several advantages, making it suitable for health system productivity assessment. These include being suitable for small sample sizes and not requiring information on prices of inputs or outputs, which are not generally available for publicly funded health services. MPI is also flexible as no assumption is required to be imposed for the functional form of the model (25). We used malmq2, a user-written command available on Stata version 16.1 for MPI data management procedures, technical analyses and visualizations (StataCorp. 2019. Stata Statistical Software: Release 16.1 College Station, TX: StataCorp LP. (26). We further sought to account for the influence of socioeconomic determinants on health system total productivity change. Various studies have documented that health system performance is influenced by the broader socio-economic environment (27). Therefore, after obtaining measures of productivity change, we sought to determine the effect of contextual factors as determinants of productivity change. This was accomplished by applying a regression model, whereby the TFPCH indices generated for various health outputs were taken as dependent variables, and educational attainment for women aged 15-44 years, household access to clean water, county marginalization status (a binary classification by the Kenyan government), and household access to electricity, were taken as the independent variables. The selection of contextual variables was guided by previous studies and data availability (28). Data sources Health system productivity change analysis requires panel data on health sector inputs and health outputs. We used population-adjusted health workforce and the development of health budget to counties as health systems inputs. Health system outputs comprised of HALE, probability of survival for under-five year olds and maternal survival. Under-5 and maternal survival were calculated as the reciprocal of under-five mortality rate (U5MR) and maternal mortality rate (MMR) for each county. County level data were assembled from multiple in-country and external sources available for the years 2014 and 2022. Data on health workforce density were sourced from surveys conducted by the Kenya National Bureau of Statistics (KNBS), while information on the development health budget was derived from county reports available on the website of the Office of the Controller of Budget (29). HALEs, under five mortality rate and maternal mortality rate were obtained from the Global Burden of Disease Study subnational estimates for Kenya, produced by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (30). Each health system output was considered separately in the productivity change analysis, using the same inputs to gain insight into specific aspects of the health system production. All variables were log-transformed to mitigate skewness and stabilize variance, which improves the robustness of data envelopment analysis (DEA) and MPI analysis. Ethical approval Permission to conduct the study was granted by the Ministry of Health in Kenya and the respective national institutions that provided the data used on the analysis. Since our study used only deidentified publicly available secondary data at the county level, it was exempt from the full institutional board review. Results Table 1 summarizes the main variables used in the study for the 47 counties in Kenya. In 2014, MMR averaged 299.37 (SD 179.58) deaths per 100,000 live births, while in 2022 it averaged 264.69 (SD. 168.38) deaths per 100, 000 live births. U5MR averaged 51.98 (SD 20.83) deaths per 1000 live births in 2014, while in 2022 it averaged 42.53 (SD 11.70) deaths per 1000 live births. Meanwhile, development health funds averaged KESH 388.01 (SD 210.72) million in 2014 and in 2022 increased to KESH 478.72 (SD 382.06) million. The large standard deviations observed for MMR, U5MR and development health funds indicate large county-level variation over the two time points. However, considering trends between 2014 and 2022, all the health system outputs (MMR, U5MR and HALE) showed signs of convergence across the counties, exemplified by the reduction in standard deviations over time. Meanwhile, health system inputs (development health funds and health workforce) trended in the opposite direction showing signs of divergence, as evidenced by increasing standard deviations, over time. Table 1: Descriptive statistics of the main study variables Year 2014 2022 Description Unit Mean Standard Deviation Min Max Mean Standard Deviation Min Max Maternal mortality rate Deaths per 100,000 live births 299.37 (179.58) (78.00) (835.31) 264.69 (168.38) (64.66) (855.95) Healthy life expectancy Years 56.26 (3.28) (44.96) (60.12) 55.88 (2.71) (48.97) (59.39) Under-five mortality rate Deaths per 1000 live births 51.98 (20.83) (22.00) (119.00) 42.53 (11.70) (15.00) (73.00) Development health funds Millions (KESH) 388.01 (210.72) (20.00) (1242.00) 478.72 (382.06) (74.44) (2492.57) Health workforce Health workers per 10,000 population 2.44 (2.02) (0.12) (12.89) 16.40 (8.00) (6.75) (54.74) County level performance Figure 1, panel A shows the spatial distribution of productivity change indices for under-5 survival across Kenya. TFPCH showed a heterogenous picture with generalized low performance across the country with all scores below 1, except in 3 counties. Meanwhile, TECH performance was relatively high, with 51% of the counties reporting scores above 1. All counties in Kenya had TECCH below 1 for under-5 survival, while SECH showed a heterogeneous picture, with only 28% reporting scores above 1. Figure 1, panel B compares the same productivity change indices for under-5 survival and population adjusted funds disbursed to respective counties for healthcare development in the year 2014. The top left quadrants represent high performance where productivity indices increased with a development budget of less than KESH 1000 per capita, while the bottom right quadrants represent low performance segment, where productivity declined despite having a development budget above KESH 1000 per capita. The bottom left quadrant represents declining productivity in the backdrop of a development budget of less than KESH 1000 per capita, while the top right represents increasing productivity with a development budget of more than KESH 1000 per capita. Considering TFPCH, only 2 counties were in the high performance, top left quadrant, while 89% were in the bottom left quadrant. There were only 2 counties in the low performance bottom right quadrant. In terms of TECH, 55% of the counties were in the high performance top left quadrant and 38% in the bottom left quadrant. For TECCH, majority of the counties (~94%) were also in the bottom left quadrant, while SECH showed a mixed picture; 26% were in the high performance top left quadrant, and 68% were in the bottom left quadrant. Appendix 1 ranks the counties according to TFPCH attainment for under-5 survival. Taita Taveta county was leading with overall productivity increase of 27% for under-5 survival, which was largely driven by SECH, at 23% and TECH at 17%. The worst performance was in Wajir county, which reported a decline of 59% in overall productivity in under-5 survival. On average, the country experienced a decline in overall productivity of 31% in under-5 survival, which was mainly caused by a declining TECCH of 30%. TECH only increased marginally by 2% and SECH declined by 4%. Figure 2, panel A shows the productivity change indices for maternal survival across the 47 counties. Considering TFPCH, there was a general trend of low performance across the country with only a few pockets of improvement; only 3 counties reported a score above 1. TECH showed higher performance except in a few parts of the country, particularly in the central and norther regions. An estimated 64% of the counties reported a score above 1. On the other hand, TECCH was low across the country and none of the counties reported a score above 1. SECH had a heterogeneous picture with no specific regional pattern, and only 34% of the counties reported a score above 1. Figure 2, panel B compares the same productivity change indices with population adjusted funds allocated for health development to the respective counties. As described above, the quadrants represent segments of performance, where the top left is the high-performance segment, and the bottom right is the worst performance segment. In terms of TFPCH, only 3 counties were in the top left segment (including Kakamega that reported a score of 1), while 87% of the counties were concentrated in the bottom left quadrant. TECH had a different picture, with 72% of the counties in the high performance top left quadrant and 21% in the bottom left quadrant. TECCH has suboptimal performance with 94% of the counties in the bottom left and the remainder in the bottom right quadrant, the worst performance segment. In terms of SECH, 38% of the counties were in the high-performance top left quadrant, and 55% were in the bottom left quadrant. Appendix 2 ranks the counties according to TFPCH attainment for maternal survival. The top performance was still in Taita Taveta county where overall productivity increased by 47%, mainly driven by TECH which increased by 84%, while TECCH declined by 12% and SECH by 9%. The worst performance was in Marsabit county that reported a decrease in overall productivity of 55%. On average, Kenya’s TFPCH for maternal survival decreased by 26%, which was largely driven by declines in TECCH of 29% and SECH of 5%. Figure 3, panel A shows the productivity change indices for HALE across the Kenyan health system. There was a generalized picture of low performance, with only 1 county reporting TFPCH above 1. Similarly, only 2 counties had a TECH score above 1, while TECCH had none. SECH showed mixed performance with 45% of the counties reporting a score above 1. Figure 3, panel B compares the same productivity change indices with population adjusted funds allocated for health development to the respective counties. As described above, the quadrants represent segments of performance. For TFPCH, 89% of the counties were in the bottom left quadrant, with only 1 in the high performance top left quadrant. In terms of TECH, all the counties were clustered around the point of no change, 1, while for TECCH, 94% of the counties were in the bottom left quadrant. SECH showed a varied picture with most counties distributed between the top (49%) and bottom (45%) left quadrants. Appendix 3 ranks the counties according to TFPCH attainment for HALE. Taita Taveta led in terms of overall productivity change that increased by 12%, largely driven by SECH which increased by 33%, despite a decline of 16% for TECCH and no change in TECH. The worst performance was at Uasin Gishu that reported 55% decline in overall productivity, mainly caused by the 46% decline in TECCH and 20% in SECH. On average the country reported a 33% decrease in overall productivity related to HALE, which was largely driven by TECCH, which also declined by 33%. On an aggregate level, there was no change in TECH and SECH, with a score of 1, although there was county level variation for the latter. Regional level performance Table 2, panel A shows the regional productivity change indices for under-5 survival following the former provincial governance arrangements. The Coast province was leading and reported the lowest decline in overall productivity growth of 14%, while the worst regional performance was in the North Eastern province that reported overall productivity decline of 42%. Most of the productivity decline across the provinces was driven by decreases in TECCH, which averaged 30%. Similarly, table 2, panel B shows the regional productivity indices for maternal survival. Still the Coast province was leading by reporting the least overall productivity decline of 10% and the worst performance was in Central province that reported a decline of 34% in overall productivity. The declining trend in productivity was driven by suboptimal performance in TECCH, which on average declined by 29%. Table 2, panel C focuses on overall productivity in HALE. The Coast province reported the lowest decline of 23%, while the worst performance was at both Rift Valley and Central provinces that declined by 37%. On average the productivity decline was driven by TECCH that on average declined by 33%. Table 2: Provincial Trends Panel A: Productivity c hange for under-5 s urvival by r egion TFPCH TECH TECCH SECH Counties Central 0.62 (0.44-0.86) 0.98 (0.84-1.20) 0.69 (0.59-0.79) 0.92 (0.83-1.03) (6.00) Coast 0.86 (0.57-1.27) 1.11 (0.99-1.23) 0.74 (0.59-0.94) 1.04 (0.93-1.23) (6.00) Eastern 0.75 (0.50-1.09) 1.04 (0.91-1.13) 0.72 (0.59-0.81) 0.99 (0.81-1.19) (8.00) North Eastern 0.58 (0.41-0.66) 0.97 (0.79-1.11) 0.62 (0.58-0.65) 0.96 (0.90-1.00) (3.00) Nyanza 0.70 (0.66-0.78) 1.01 (0.97-1.04) 0.73 (0.64-0.81) 0.95 (0.90-0.99) (6.00) Rift Valley 0.63 (0.44-0.85) 0.98 (0.85-1.10) 0.69 (0.56-0.87) 0.94 (0.84-1.15) (14.00) Western 0.72 (0.54-0.88) 1.04 (1.00-1.11) 0.71 (0.60-0.80) 0.96 (0.90-1.04) (4.00) National 0.69 (0.41-1.27) 1.02 (0.79-1.23) 0.70 (0.56-0.94) 0.96 (0.81-1.23) (47.00) Panel B: Productivity change for maternal survival by region TFPCH TECH TECCH SECH Counties Central 0.66 (0.48-0.91) 1.25 (0.77-2.52) 0.70 (0.62-0.79) 0.87 (0.36-1.21) (6.00) Coast 0.90 (0.58-1.47) 1.26 (0.83-1.84) 0.75 (0.61-0.96) 0.96 (0.74-1.14) (6.00) Eastern 0.76 (0.45-1.04) 1.05 (0.73-1.50) 0.72 (0.61-0.80) 1.00 (0.74-1.26) (8.00) North Eastern 0.67 (0.49-0.86) 1.15 (0.74-1.42) 0.63 (0.60-0.65) 0.95 (0.75-1.09) (3.00) Nyanza 0.79 (0.62-0.91) 1.13 (1.03-1.21) 0.73 (0.64-0.82) 0.95 (0.87-1.00) (6.00) Rift Valley 0.68 (0.46-0.89) 1.00 (0.79-1.15) 0.70 (0.58-0.88) 0.98 (0.85-1.18) (14.00) Western 0.80 (0.61-1.00) 1.36 (0.90-2.13) 0.71 (0.62-0.80) 0.90 (0.53-1.09) (4.00) National 0.74 (0.45-1.47) 1.13 (0.73-2.52) 0.71 (0.58-0.96) 0.95 (0.36-1.26) (47.00) Panel C: Productivity change for HALE by region TFPCH TECH TECCH SECH Counties Central 0.63 (0.50-0.80) 1.00 (0.99-1.00) 0.65 (0.57-0.75) 0.96 (0.86-1.10) (6.00) Coast 0.77 (0.51-1.12) 1.00 (0.99-1.00) 0.70 (0.56-0.91) 1.09 (0.90-1.33) (6.00) Eastern 0.72 (0.45-1.00) 1.00 (0.99-1.01) 0.68 (0.57-0.76) 1.05 (0.79-1.31) (8.00) North Eastern 0.57 (0.48-0.66) 0.99 (0.99-1.00) 0.59 (0.56-0.61) 0.98 (0.86-1.08) (3.00) Nyanza 0.69 (0.59-0.77) 1.00 (0.99-1.01) 0.69 (0.59-0.78) 1.00 (0.94-1.04) (6.00) Rift Valley 0.63 (0.44-0.88) 1.00 (0.99-1.00) 0.66 (0.52-0.83) 0.96 (0.80-1.24) (14.00) Western 0.68 (0.49-0.85) 1.00 (1.00-1.00) 0.67 (0.57-0.76) 1.01 (0.86-1.11) (4.00) National 0.67 (0.44-1.12) 1.00 (0.99-1.01) 0.67 (0.52-0.92) 1.00 (0.79-1.33) (47.00) Determinants of performance Table 3 presents the results of three separate linear regressions examining the association between selected county-level determinants and TFPCH for under-5 survival, maternal survival and HALE. Across all three models, county marginalization status was positively and significantly associated with improved health outcomes. Specifically, a county classification as marginalized was associated with a 0.0685 (p < 0.05), 0.114 (p < 0.05), and 0.0519 (p < 0.05) increase in TFPCH for under-5 survival, maternal survival, and healthy life expectancy, respectively. Other covariates, including household access to clean water, education attainment for women aged 15–44, and household access to electricity, did not show statistically significant associations with any of the health outcomes. While the coefficients for women's education were positive across all models, they did not reach conventional levels of significance. Table 3: Determinants of Total Factor Productivity Change (Under-5 Survival) (Maternal Survival) (Healthy Life Expectancy) TFPCH TFPCH TFPCH Household access to clean water 0.000768 -0.000584 -0.000914 (0.38) (-0.28) (-0.65) Education attainment for women of 15-44 years 0.000678 0.00435 0.00210 (0.13) (0.75) (0.52) County marginalization status 0.0685* 0.114* 0.0519* (2.42) (2.72) (2.52) Household access to electricity -0.000170 -0.00359 -0.00112 (-0.04) (-0.88) (-0.35) Constant 0.615 * 0.683 ** 0.663 ** (2.75) (3.82) (4.88) N 47 47 47 t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 Discussion The national average TFPCH for under-5 survival was 0.69 during the study period, indicating a 31% decline in productivity relative to the baseline of 2014. Similarly, for maternal survival and HALE the national average was 0.74 and 0.67, pointing to a 26% and 33% decline in productivity respectively. In other words, the country’s health system produced less health output in 2022 in comparison to the year 2014, when using the same level of health system inputs. This was despite spirited efforts to decentralize health system management and expansion of the scale of operations by procuring new medical equipment and construction of infrastructure to improve access. The loss in overall productivity was primarily linked to declines in TECCH, signifying a deterioration in the health system’s ability to adopt and use appropriate technologies to improve health outcomes. For under-5 and maternal survival, decreases in SECH also contributed marginally, declining by 4% and 5% respectively, while TECH increased by 2% and 13% respectively. This points to the fact that despite the modest improvements in the management of health inputs, the healthcare system was not operating at the optimal scale in the utilization of available resources and might benefit from structural resizing of some health programs at the county level. The impact of appropriate use of health technology to improve population health outcomes such as under-5 and maternal survival has been widely documented. For example, (31) indicated that BCG vaccination was associated with an increased likelihood of child survival in Uganda. (32) gave further evidence on the benefits of vaccination, extending beyond disease prevention and increased survival, to include economic returns to countries. Similarly, antenatal care (ANC) and skilled birth attendance (SBA) which involves the use of various health technologies has been demonstrated as vital in the detection and effective management of complications that might arise during pregnancy and childbirth, improving both under-5 and maternal survival (30). Preterm birth conditions, intrapartum complications and infections that contribute to health loss could easily be tackled by skilled health personnel equipped with the right health technologies (33). The declining trend in TECCH at a time when Kenya has invested heavily in programs to equip county health facilities with modern and often expensive medical equipment needs to be reexamined. There is need to focus more on targeted innovation, process optimization and quality improvement as part of the technology adoption process. Leveraging existing and proven cost-effective health technologies to tackle the prevailing burden of disease and injuries at the county level would be vital for the country to make progress. Considering under-5 and maternal survival, TECH displayed a heterogeneous picture across the country with aggregate modest improvements of 2% and 13% respectively, while HALE showed no significant change. This showed that there were marginal improvements in the utilization of the available resources to deliver healthcare services, particularly targeting maternal health. This is particularly evident when considering the counties that reported overall productivity growth and yet had been allocated less than KESH 1000 per capita for health development. It shows that for some specific counties, additional resources would be needed to make progress, particularly those that were experiencing declining productivity in the context of low financial resources (less than KESH 1000 per capita) allocated for health development. The impressive productivity growth in some of the previously marginalized counties like Taita Taveta and Turkana shows that the government policy to address the inequalities and bridge the health gaps caused by marginalization is prudent (33). This is further illustrated when considering the determinants of productivity growth, whereby there is a significant positive relationship between county marginalization status and the overall productivity growth for all the health outputs considered. Therefore, to make progress, the country should not only focus on scaling up access to priority health technologies and infrastructure but also seek to address more broader structural and socio-economic determinants that affect the demand and utilization of health services in respective counties. However, low TECH in some counties could also be indicative of the weak managerial capacity to optimally combine outputs with the inputs available in the health system (34). In some cases, the rapid increase in funding for health might outpace the essential build-up of managerial and technical capacity to effectively implement health programs (35). Further, in the process of rapid decentralization, health administrative units could be formed without effective human resources and stewardship. This would result in low absorption capacity, limited coordination and weak accountability systems, further curtailing effective implementation to ensure productivity. In interpreting our results, we recognize the limitations associated with this study. First, we have had to rely on the most recent available data for the country to develop a consistent analytical framework. Some of these data were collected for different purposes and archived in various sources of variable quality. Further, we did not have data on private (and non-governmental) health spending as an input and only relied on the public health spending, assuming that this was the predominant source of health funding for most counties in Kenya. However, the inability to include the private health expenditure in our analysis could artificially inflate the productivity of counties where private spending is significant, since we might have underestimated the input side of the equation. Second, we have only included health financing resources and health workforce as our model inputs, but we acknowledge that there are other important health system inputs that should be considered for a comprehensive analysis. For example, infrastructure, medical and health technologies which are critical components for the functioning of any health system could be considered. The impact of these other additional factors on health systems’ productivity needs further investigation as more data becomes available. Third, the data available for human resources was aggregated and included all health workers in the county and did not distinguish them according to their specific functions in healthcare delivery to facilitate a more nuanced analysis. Fourth, our analysis has not accounted for the health system shocks that were occasioned by the Covid-19 pandemic, that could have adversely affected performance. Lastly, the MPI approach used in our analysis is based on Farrell radial efficiency distance metrics, which means that any gain or loss which is not captured by the radial efficiency measures will not be captured by our results. This has led to some criticism of the MPI, but to date there has been no widely accepted solution to this problem (36). Conclusion This study is particularly informative to LMICs that are implementing decentralization of health systems to improve health outcomes. It underscores the need for health system decision makers to appreciate the various determinants of health system performance, paying particular attention to subnational disparities to tailor effective solutions to drive productivity growth. The analysis in Kenya indicates that opportunities to expand output may have been missed, due to inappropriate adoption and deployment of health technologies. Hence, county-level decision makers must not only advocate for more resources and investment in expensive modern technologies but also embrace innovation and adoption of appropriate and cost-effective health technologies to drive productivity growth. This would also require strengthening the managerial and implementation capacity of health systems so that they are able to effectively utilize the technologies and resources available to address prevailing population health needs. Declarations Competing Interests The authors declare no competing interests Funding source This research was done as part of the routine assignments of the Africa Institute for Health Policy. No funding source was available Authors’ contributions TA conceptualized the study. NR, DB and JT collated the data and did the preliminary analysis. TA, WO, MS and LW did the data analysis. TA wrote the first draft and WO, LW, NR, JT, DB, MS did the detailed review and provided comments. Acknowledgements The authors are grateful to the Kenya National Bureau of Statistics that collected and shared some of the data that has been used in this project. Further, the authors appreciate the contribution of the Institute of Health Metrics and Evaluation, University of Washington, that provided the subnational data on health burden. Data Sharing The main data sets supporting the conclusions of this article are available on request and with written permission from the Kenya National Bureau of Statistics. The data from IHME are publicly available and can be accessed through this publication: https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30472-8/fulltext Patient and Public Involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. References Syverson C. What determines productivity? J Econ Lit. 2011;49(2):326–65. Mills A, Rasheed F, Tollman S. 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Bull World Health Organ. 2008 Feb 1;86(2):140–6. usaidheroku. New Sector Working Group in Taita Taveta and Its Advocacy Focus-USAIDHERO [Internet]. Available from: https://usaidhero.ku.ac.ke/elementor-2153 Barasa E, Musiega A, Hanson K, Nyawira L, Mulwa A, Molyneux S, et al. Level and determinants of county health system technical efficiency in Kenya: two stage data envelopment analysis. Cost Eff Resour Alloc . 2021 Dec;19(1):78. Adjagba AO, Oguta JO, Akoth C, Wambiya EOA, Nonvignon J, Jackson D. Financing immunisation in Kenya: examining bottlenecks in health sector planning and budgeting at the decentralised level. Cost Eff Resour Alloc. 2024 Oct 29;22(1):76. Algammal AM, Hetta HF, Elkelish A, Alkhalifah DHH, Hozzein WN, Batiha GES, et al. Methicillin-Resistant Staphylococcus aureus (MRSA): One Health Perspective Approach to the Bacterium Epidemiology, Virulence Factors, Antibiotic-Resistance, and Zoonotic Impact. Infect Drug Resist . 2020 Sep;Volume 13:3255–65. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7607328","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":515319314,"identity":"4eb2c1e9-399b-46ea-9282-4efeea90a733","order_by":0,"name":"Tom 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survival\u003c/p\u003e","description":"","filename":"productivitychildcombinedgraph.png","url":"https://assets-eu.researchsquare.com/files/rs-7607328/v1/c37ff832baa513c0d5a2ba75.png"},{"id":91396641,"identity":"e4683a87-7e6b-483f-a237-cae147ca5a24","added_by":"auto","created_at":"2025-09-16 06:08:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166470,"visible":true,"origin":"","legend":"\u003cp\u003eProductivity change for maternal survival\u003c/p\u003e","description":"","filename":"productivityhalecombinedgraph.png","url":"https://assets-eu.researchsquare.com/files/rs-7607328/v1/2140f7910193266bf3f6e6bd.png"},{"id":91396628,"identity":"229ce777-0e86-47e9-9540-249ad48fc268","added_by":"auto","created_at":"2025-09-16 06:08:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174201,"visible":true,"origin":"","legend":"\u003cp\u003eProductivity change for healthy life expectancy\u003c/p\u003e","description":"","filename":"productivitymumcombinedgraph.png","url":"https://assets-eu.researchsquare.com/files/rs-7607328/v1/b44ecb4bf0f98622c6c940bd.png"},{"id":91396746,"identity":"3b0810c7-ab75-431f-a524-23aa83b5dfde","added_by":"auto","created_at":"2025-09-16 06:08:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1258043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7607328/v1/a9924261-82d7-452e-b1a3-77a0986e4b6f.pdf"},{"id":91396647,"identity":"49efd4ad-295e-407d-8bb3-844b3c8dbc7e","added_by":"auto","created_at":"2025-09-16 06:08:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34952,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7607328/v1/9349e163e2532876d515c37f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Kenya health system productivity change between 2014 and 2022: progress towards universal health coverage","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProductivity is the ratio of outputs to inputs. Meanwhile, productivity change is the change of the ratio that is occasioned by the shifts in the production function (1). Efficiency and productivity improvement are identified as key considerations of a well-functioning health system (2). As the World Health Organization (WHO) explains in its health systems framework, an effective health system is fair and equitable, both in the distribution of health goods and services and in the way it is financed. It emphasizes efficiency and cost-effectiveness as well and responds to the legitimate non-health expectations of those seeking health care, such as respect and compassion (3).\u003c/p\u003e\n\u003cp\u003eUltimately, an effective health system ensures that anyone in need of a specific health good or service can access it and derive the relevant benefits without undue financial pressure. However, this must be done sustainably, given that resources available for health service delivery are finite (4). Therefore, with increasing demands for healthcare across different populations, health system productivity growth has become a central theme in the global health debates as governments around the world strive to do more with limited resources (5). The pressure to improve access to healthcare is greater in low- and middle-income countries (LMICs) which are witnessing rising health needs. These are occasioned by dual epidemics of increasing burden of noncommunicable diseases and injuries in the backdrop of an unfinished agenda of communicable diseases, maternal and child health conditions (6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis paper focuses on Kenya, a middle-income country in Africa that is faced with increasing population health demands amidst resource constraints. Successive government policies have emphasized decentralization of health services as key to reaching the populations in need (7). \u0026nbsp;In 2010, the country promulgated a new constitution that introduced devolution, transferring several core responsibilities to the 47 county governments in the spirit of accelerating decentralization(8). After the 2013 general elections, healthcare in Kenya was officially devolved to a two-tier governance structure including the national government and the 47 counties. \u0026nbsp; The counties now manage health facilities, staff, and budgets, allowing for more local decision-making and service delivery (9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis marked a major shift in governance of health service delivery, transferring the responsibility from the national government to the county governments. The goal of devolution was to improve access to healthcare services, allowing for more localized decision-making and enhanced accountability and responsiveness to community needs (10). Tackling health disparities has been at the core of Kenya\u0026rsquo;s health devolution efforts. (11) established that despite the country\u0026rsquo;s improving health indicators over the last 3 decades, health gains were uneven across counties. For instance, life expectancy in 2016 ranged from 57.0 years in Homa Bay to 71.8 years in Laikipia. Meanwhile, some counties like Siaya saw dramatic improvements in key health indicators post-2006, while others lagged (11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn response to the pre-existing and on-going health disparities across counties, the government launched several initiatives to expand public health and bridge gaps in health service delivery. In 2013, a free maternal health services program was launched in public hospitals to reduce maternal and infant mortality (12). The goal was to eliminate financial barriers to maternal health services in the country and ensure equitable access to skilled care during pregnancy, delivery, and post-natal periods. The government allocated KESH 3.8 billion to fund the program in its first year, with demonstrable results. Health facility deliveries increased from 43% in 2011 to 56.8% in 2015; and maternal mortality dropped from 488 to 362 deaths per 100,000 live births between 2008 and 2014 (13).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn 2015, the government of Kenya also launched a landmark program aimed at modernizing healthcare infrastructure across all 47 counties through public private partnership arrangements. Through this program with an estimated cost of USD 432 million, the government leased specialized medical equipment from private companies for use in public hospitals (14). The goal was to\u0026nbsp;bridge the gap in access to critical healthcare services, especially in underserved areas. Similarly, Kenya has continued to expand childhood immunization program by launching new vaccines into the routine schedule, such as Typhoid Conjugate Vaccine (TCV) and Measles-Rubella (MR). Outreach services are also being implemented through mobile clinics and community health workers to remote and informal settlements to ensure no child is left behind (15).\u003c/p\u003e\n\u003cp\u003eIn the implementation of these health-focused initiatives, universal health coverage (UHC) has been a guiding principle for Kenya\u0026rsquo;s health policy making. In 2018, UHC pilot programs were launched in four counties \u0026ndash; Kisumu, Machakos, Isiolo and Nyeri, to test models for nationwide implementation. The focus was on expanding access to essential health services and reducing out-of-pocket spending for those seeking healthcare (16). Despite the challenges such as delays in funding disbursements, infrastructure and staffing gaps, the UHC pilot programs witnessed increased health service utilization, community awareness and engagement (17).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe pilot program also significantly influenced health sector funding in terms of allocation and resource availability. For instance, previously, in 2018-2019, the funding was done through an input-based financing model, whereby the national government directly supplied commodities and paid for services. By 2020, this shifted to an output-based financing model, where counties were reimbursed based on services delivered (18). After the pilot program implementation, it was also clear that the national government needed to increase its resource allocation to support the national objectives, due to the limited resource base at the county levels (19).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering all these developments, important questions arise pertaining to the productivity of the decentralized Kenyan health system. For instance, did the additional investments translate to improvements in health outcomes? Was progress realized through adoption of new technologies or through embracing better ways of combining existing resources, or both? Were there differences in performance across the country? These are some of the key questions we address in this paper. As the first study in Kenya to holistically examine productivity change of the health system at the county level, the approach and conclusions presented here are expected to have wider policy implications for the country and the broader region.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe assessed the Kenya health system productivity change in terms of reduction of under- 5 mortality, maternal mortality and HALE. These are universally accepted measures of health system performance (20). We compared progress in under-5 survival, maternal survival and HALE between 2014 and 2022 against a set of key health inputs, development health funds and health workforce density for the same period. The year 2014 is critical, since it is around this time that the two-tier health system governance structure was fully operationalized, a year after its adoption in line with the new constitution (21).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study focuses on the organization and utilization of available resources to deliver healthcare. We have considered county-level health financing and health workforce density, as health system inputs and compared them against the corresponding health system output measures, under-5 survival, maternal survival and HALE. \u0026nbsp; We considered data from 2014 as baseline and compared it with the most recent period of 2022 where data were available. This roughly covers two electoral cycles of 5-year terms in which the county governments have been in place and various reforms and investments were put in place.\u003c/p\u003e\n\u003cp\u003eWe measured health system productivity using MPI, an approach that involves constructing an aggregate (country-wide) efficiency frontier based on data for all counties and then estimating the distance of individual counties from the country-wide frontier. Accounting for time, the MPI represents ratios of distance functions from the efficiency frontier for each county. Changes in total productivity may result when a specific county catches up with the country\u0026rsquo;s frontier due to efficiency gains or adoption of new technologies causing a shift in the production frontier for the country(22). Considering two time periods, (t) and (t+1), the MPI can be algebraically expressed as follows:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\" style=\"width: 640px; height: 161.151px;\" width=\"640\" height=\"161.151\"\u003e\u003c/p\u003e\n\u003cp\u003eAn MPI of above 1 signifies productivity improvement (i.e. improvement in the rate of return per unit of health input) while values of less than one indicates a regression in productive use of resources in the system (23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe resultant TFPCH from the analysis can also be decomposed further to assess if the observed change is due to TECH or TECCH or both. \u0026nbsp;Further, by modelling the frontier using both constant-return-to-scale (CRS) and variable-returns-to-scale (VRS), TECH can be decomposed into pure technical efficiency change (PTEC) and SECH (24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\" style=\"width: 641px; height: 86.6964px;\" width=\"641\" height=\"86.6964\"\u003e\u003c/p\u003e\n\u003cp\u003eWhereby in the model:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cem\u003eMPI =\u0026nbsp;\u003c/em\u003ethe productivity of the most recent period relative to the past period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cem\u003ex =\u0026nbsp;\u003c/em\u003esystem inputs\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cem\u003eTECH =\u003c/em\u003e efficiency change\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cem\u003ed =\u0026nbsp;\u003c/em\u003edistance functions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cem\u003eTECCH=\u0026nbsp;\u003c/em\u003etechnology change\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cem\u003et =\u0026nbsp;\u003c/em\u003epast time period\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cem\u003ey =\u003c/em\u003e system outputs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cem\u003et+1 =\u0026nbsp;\u003c/em\u003erecent time period\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe generated all the respective indices for the 47 counties. \u0026nbsp;To give further context to the county level estimates, we grouped the counties into the provinces (regions) in which they were clustered previously before devolution. Since Nairobi County was previously classified as a province, we group it into the Central province given its proximity to the region.\u003c/p\u003e\n\u003cp\u003eOur MPI analysis employed an output rather than an input-oriented efficiency model because in Kenya, decision makers at the county level normally face a fixed set of core health inputs in the short run, and they can only organize the utilization of the available resources to achieve optimal outcomes. Therefore, an output-oriented model was justified because health systems focus on achieving the highest possible outcome, given their resources, rather than setting a target outcome level and removing resources once they attain their targets.\u003c/p\u003e\n\u003cp\u003eThe MPI analysis confers several advantages, making it suitable for health system productivity assessment. These include being suitable for small sample sizes and not requiring information on prices of inputs or outputs, which are not generally available for publicly funded health services. MPI is also flexible as no assumption is required to be imposed for the functional form of the model (25). We used malmq2, a user-written command available on Stata version 16.1 for MPI data management procedures, technical analyses and visualizations (StataCorp. 2019. Stata Statistical Software: Release 16.1 College Station, TX: StataCorp LP. (26). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe further sought to account for the influence of socioeconomic determinants on health system total productivity change. Various studies have documented that health system performance is influenced by the broader socio-economic environment (27). Therefore, after obtaining measures of productivity change, we sought to determine the effect of contextual factors as determinants of productivity change. This was accomplished by applying a regression model, whereby the TFPCH indices generated for various health outputs were taken as dependent variables, and educational attainment for women aged 15-44 years, household access to clean water, county marginalization status (a binary classification by the Kenyan government), and household access to electricity, were taken as the independent variables. The selection of contextual variables was guided by previous studies \u0026nbsp;and data availability (28).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealth system productivity change analysis requires panel data on health sector inputs and health outputs. We used population-adjusted health workforce and the development of health budget to counties as health systems inputs. \u0026nbsp; Health system outputs comprised of HALE, probability of survival for under-five year olds and maternal survival. Under-5 and maternal survival were calculated as the reciprocal of under-five mortality rate (U5MR) and maternal mortality rate (MMR) for each county.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCounty level data were assembled from multiple in-country and external sources available for the years 2014 and 2022. Data on health workforce density were sourced from surveys conducted by the Kenya National Bureau of Statistics (KNBS), while information on the development health budget was derived from county reports available on the website of the Office of the Controller of Budget (29). HALEs, under five mortality rate and maternal mortality rate were obtained from the Global Burden of Disease Study subnational estimates for Kenya, produced by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (30). \u0026nbsp; Each health system output was considered separately in the productivity change analysis, using the same inputs to gain insight into specific aspects of the health system production. All variables were log-transformed to mitigate skewness and stabilize variance, which improves the robustness of data envelopment analysis (DEA) and MPI analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePermission to conduct the study was granted by the Ministry of Health in Kenya and the respective national institutions that provided the data used on the analysis. Since our study used only deidentified publicly available secondary data at the county level, it was exempt from the full institutional board review.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 summarizes the main variables used in the study for the 47 counties in Kenya. In 2014, MMR averaged 299.37 (SD 179.58) deaths per 100,000 live births, while in 2022 it averaged 264.69 (SD. 168.38) deaths per 100, 000 live births. U5MR averaged 51.98 (SD 20.83) deaths per 1000 live births in 2014, while in 2022 it averaged 42.53 (SD 11.70) deaths per 1000 live births. Meanwhile, development health funds averaged KESH 388.01 (SD 210.72) million in 2014 and in 2022 increased to KESH 478.72 (SD 382.06) million. \u0026nbsp;The large standard deviations observed for MMR, U5MR and development health funds indicate large county-level variation over the two time points. However, considering trends between 2014 and 2022, all the health system outputs (MMR, U5MR and HALE) showed signs of convergence across the counties, exemplified by the reduction in standard deviations over time. Meanwhile, health system inputs (development health funds and health workforce) trended in the opposite direction showing signs of divergence, as evidenced by increasing standard deviations, over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Descriptive statistics of the main study variables \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eUnit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMaternal mortality rate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDeaths per 100,000 live births\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e299.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(179.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(78.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(835.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e264.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(168.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(64.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(855.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHealthy life expectancy\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e56.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(44.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(60.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e55.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(48.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(59.39)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eUnder-five mortality rate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eDeaths per 1000 live births\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e51.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(119.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e42.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(11.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(73.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDevelopment health funds\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eMillions (KESH)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e388.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(210.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(1242.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e478.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(382.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(74.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(2492.57)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHealth workforce\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eHealth workers per 10,000 population\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(12.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e16.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e(54.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCounty level performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1, panel A shows the spatial distribution of productivity change indices for under-5 survival across Kenya. TFPCH showed a heterogenous picture with generalized low performance across the country with all scores below 1, except in 3 counties. Meanwhile, TECH performance was relatively high, with 51% of the counties reporting scores above 1. All counties in Kenya had TECCH below 1 for under-5 survival, while SECH showed a heterogeneous picture, with only 28% reporting scores above 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1, panel B compares the same productivity change indices for under-5 survival and population adjusted funds disbursed to respective counties for healthcare development in the year 2014. \u0026nbsp;The top left quadrants represent high performance where productivity indices increased with a development budget of less than KESH 1000 per capita, while the bottom right quadrants represent low performance segment, where productivity declined despite having a development budget above KESH 1000 per capita. \u0026nbsp;The bottom left quadrant represents declining productivity in the backdrop of a development budget of less than KESH 1000 per capita, while the top right represents increasing productivity with a development budget of more than KESH 1000 per capita.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering TFPCH, only 2 counties were in the high performance, top left quadrant, while 89% were in the bottom left quadrant. There were only 2 counties in the low performance bottom right quadrant. \u0026nbsp; In terms of TECH, 55% of the counties were in the high performance top left quadrant and 38% in the bottom left quadrant. For TECCH, majority of the counties (~94%) were also in the bottom left quadrant, while SECH showed a mixed picture; 26% were in the high performance top left quadrant, and 68% were in the bottom left quadrant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAppendix 1 ranks the counties according to TFPCH attainment for under-5 survival. Taita Taveta county was leading with overall productivity increase of 27% for under-5 survival, which was largely driven by SECH, at 23% and TECH at 17%. The worst performance was in Wajir county, which reported a decline of 59% in overall productivity in under-5 survival. On average, the country experienced a decline in overall productivity of 31% in under-5 survival, which was mainly caused by a declining TECCH of 30%. \u0026nbsp;TECH only increased marginally by 2% and SECH declined by 4%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2, panel A shows the productivity change indices for maternal survival across the 47 counties. Considering TFPCH, there was a general trend of low performance across the country with only a few pockets of improvement; only 3 counties reported a score above 1. TECH showed higher performance except in a few parts of the country, particularly in the central and norther regions. An estimated 64% of the counties reported a score above 1. On the other hand, TECCH was low across the country and none of the counties reported a score above 1. \u0026nbsp;SECH had a heterogeneous picture with no specific regional pattern, and only 34% of the counties reported a score above 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2, panel B compares the same productivity change indices with population adjusted funds allocated for health development to the respective counties. As described above, the quadrants represent segments of performance, where the top left is the high-performance segment, and the bottom right is the worst performance segment. In terms of TFPCH, only 3 counties were in the top left segment (including Kakamega that reported a score of 1), while 87% of the counties were concentrated in the bottom left quadrant. TECH had a different picture, with 72% of the counties in the high performance top left quadrant and 21% in the bottom left quadrant. TECCH has suboptimal performance with 94% of the counties in the bottom left and the remainder in the bottom right quadrant, the worst performance segment. In terms of SECH, 38% of the counties were in the high-performance top left quadrant, and 55% were in the bottom left quadrant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAppendix 2 ranks the counties according to TFPCH attainment for maternal survival. The top performance was still in Taita Taveta county where overall productivity increased by 47%, mainly driven by TECH which increased by 84%, while TECCH declined by 12% and SECH by 9%. The worst performance was in Marsabit county that reported a decrease in overall productivity of 55%. On average, Kenya\u0026rsquo;s TFPCH for maternal survival decreased by 26%, which was largely driven by declines in TECCH of 29% and SECH of 5%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3, panel A shows the productivity change indices for HALE across the Kenyan health system. There was a generalized picture of low performance, with only 1 county reporting TFPCH above 1. Similarly, only 2 counties had a TECH score above 1, while TECCH had none. SECH showed mixed performance with 45% of the counties reporting a score above 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3, panel B compares the same productivity change indices with population adjusted funds allocated for health development to the respective counties. As described above, the quadrants represent segments of performance. For TFPCH, 89% of the counties were in the bottom left quadrant, with only 1 in the high performance top left quadrant. In terms of TECH, all the counties were clustered around the point of no change, 1, while for TECCH, 94% of the counties were in the bottom left quadrant. SECH showed a varied picture with most counties distributed between the top (49%) and bottom (45%) left quadrants. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAppendix 3 ranks the counties according to TFPCH attainment for HALE. Taita Taveta led in terms of overall productivity change that increased by 12%, largely driven by SECH which increased by 33%, despite a decline of 16% for TECCH and no change in TECH. The worst performance was at Uasin Gishu that reported 55% decline in overall productivity, mainly caused by the 46% decline in TECCH and 20% in SECH. \u0026nbsp;On average the country reported a 33% decrease in overall productivity related to HALE, which was largely driven by TECCH, which also declined by 33%. \u0026nbsp;On an aggregate level, there was no change in TECH and SECH, with a score of 1, although there was county level variation for the latter. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional level performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2, panel A shows the regional productivity change indices for under-5 survival following the former provincial governance arrangements. The Coast province was leading and reported the lowest decline in overall productivity growth of 14%, while the worst regional performance was in the North Eastern province that reported overall productivity decline of 42%. Most of the productivity decline across the provinces was driven by decreases in TECCH, which averaged 30%. \u0026nbsp;Similarly, table 2, panel B shows the regional productivity indices for maternal survival. Still the Coast province was leading by reporting the least overall productivity decline of 10% and the worst performance was in Central province that reported a decline of 34% in overall productivity. The declining trend in productivity was driven by suboptimal performance in TECCH, which on average declined by 29%. Table 2, panel C focuses on overall productivity in HALE. The Coast province reported the lowest decline of 23%, while the worst performance was at both Rift Valley and Central provinces that declined by 37%. On average the productivity decline was driven by TECCH that on average declined by 33%.\u003c/p\u003e\n\u003cp\u003eTable 2: Provincial Trends\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 646px;\"\u003e\n \u003cp\u003ePanel A: \u003cem\u003eProductivity\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ec\u003cem\u003ehange for under-5\u0026nbsp;\u003c/em\u003es\u003cem\u003eurvival by\u0026nbsp;\u003c/em\u003er\u003cem\u003eegion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTFPCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTECH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTECCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eSECH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCounties\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003cp\u003e(0.44-0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.84-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e(0.59-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e(0.83-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCoast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e(0.57-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.99-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e(0.59-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(0.93-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.50-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(0.91-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003e(0.59-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.81-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNorth Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003cp\u003e(0.41-0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.79-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003cp\u003e(0.58-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.90-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNyanza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; (0.66-0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e(0.97-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003cp\u003e(0.64-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.90-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRift Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.44-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.85-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e(0.56-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.84-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003e(0.54-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(1.00-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003cp\u003e(0.60-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.90-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNational\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.69\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.41-1.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.79-1.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.70\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.56-0.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.81-1.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(47.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 646px;\"\u003e\n \u003cp\u003ePanel B: \u003cem\u003eProductivity\u003c/em\u003e\u003cem\u003e\u0026nbsp;change for maternal survival by region\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTFPCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTECH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTECCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eSECH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCounties\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e(0.48-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003cp\u003e(0.77-2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003cp\u003e(0.62-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e(0.36-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCoast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e(0.58-1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(0.83-1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.61-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.74-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003cp\u003e(0.45-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e(0.73-1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003e(0.61-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.74-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNorth Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e(0.49-0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003cp\u003e(0.74-1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.60-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.75-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNyanza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003cp\u003e(0.62-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003cp\u003e(1.03-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003cp\u003e(0.64-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003cp\u003e(0.87-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRift Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e(0.46-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.79-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003cp\u003e(0.58-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.85-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003cp\u003e(0.61-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003cp\u003e(0.90-2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003cp\u003e(0.62-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e(0.53-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNational\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.45-1.47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.73-2.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.71\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.58-0.96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.95\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.36-1.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(47.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 646px;\"\u003e\n \u003cp\u003ePanel C: \u003cem\u003eProductivity change for HALE by region\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTFPCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTECH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTECCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eSECH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCounties\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.50-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e(0.57-0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.86-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCoast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003cp\u003e(0.51-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003cp\u003e(0.56-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e(0.90-1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003e(0.45-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.99-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e(0.57-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e(0.79-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNorth Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003cp\u003e(0.48-0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003cp\u003e(0.56-0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.86-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNyanza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e(0.59-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.99-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e(0.59-0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.94-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRift Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.44-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e(0.52-0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.80-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e(0.49-0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e(0.57-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003cp\u003e(0.86-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e(4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNational\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.67\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.44-1.12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.99-1.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.67\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.52-0.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.79-1.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(47.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDeterminants of performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 presents the results of three separate linear regressions examining the association between selected county-level determinants and TFPCH for under-5 survival, maternal survival and HALE. Across all three models,\u0026nbsp;county marginalization status\u0026nbsp;was positively and significantly associated with improved health outcomes. Specifically, a county classification as marginalized was associated with a 0.0685 (p \u0026lt; 0.05), 0.114 (p \u0026lt; 0.05), and 0.0519 (p \u0026lt; 0.05) increase in TFPCH for under-5 survival, maternal survival, and healthy life expectancy, respectively.\u003c/p\u003e\n\u003cp\u003eOther covariates, including\u0026nbsp;household access to clean water,\u0026nbsp;education attainment for women aged 15\u0026ndash;44, and\u0026nbsp;household access to electricity, did not show statistically significant associations with any of the health outcomes. While the coefficients for women\u0026apos;s education were positive across all models, they did not reach conventional levels of significance.\u003c/p\u003e\n\u003cp\u003eTable 3: Determinants of Total Factor Productivity Change\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(Under-5 Survival)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(Maternal Survival)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e(Healthy Life Expectancy)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eTFPCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eTFPCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eTFPCH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHousehold access to clean water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.000768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.000584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-0.000914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(-0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e(-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eEducation attainment for women of 15-44 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.000678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.00435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.00210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e(0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eCounty marginalization status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.0685*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.114*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.0519*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e(2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHousehold access to electricity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e-0.000170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e-0.00359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-0.00112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(-0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e(-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eConstant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.615\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.683\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.663\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e(2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e(4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003et\u003c/em\u003e statistics in parentheses\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe national average TFPCH for under-5 survival was 0.69 during the study period, indicating a 31% decline in productivity relative to the baseline of 2014. Similarly, for maternal survival and HALE the national average was 0.74 and 0.67, pointing to a 26% and 33% decline in productivity respectively. \u0026nbsp;In other words, the country’s health system produced less health output in 2022 in comparison to the year 2014, when using the same level of health system inputs. This was despite spirited efforts to decentralize health system management and expansion of the scale of operations by procuring new medical equipment and construction of infrastructure to improve access. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe loss in overall productivity was primarily linked to declines in TECCH, signifying a deterioration in the health system’s ability to adopt and use appropriate technologies to improve health outcomes. \u0026nbsp;For under-5 and maternal survival, decreases in SECH also contributed marginally, declining by 4% and 5% respectively, while TECH increased by 2% and 13% respectively. This points to the fact that despite the modest improvements in the management of health inputs, the healthcare system was not operating at the optimal scale in the utilization of available resources and might benefit from structural resizing of some health programs at the county level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impact of appropriate use of health technology to improve population health outcomes such as under-5 and maternal survival has been widely documented. \u0026nbsp; \u0026nbsp;For example, (31) indicated that BCG vaccination was associated with an increased likelihood of child survival in Uganda. \u0026nbsp;(32) gave further evidence on the benefits of vaccination, extending beyond disease prevention and increased survival, to include economic returns to countries. Similarly, antenatal care (ANC) and skilled birth attendance (SBA) which involves the use of various health technologies has been demonstrated as vital in the detection and effective management of complications that might arise during pregnancy and childbirth, improving both under-5 and maternal survival (30). Preterm birth conditions, intrapartum complications and infections that contribute to health loss could easily be tackled by skilled health personnel equipped with the right health technologies (33). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe declining trend in TECCH at a time when Kenya has invested heavily in programs to equip county health facilities with modern and often expensive medical equipment needs to be reexamined. There is need to focus more on targeted innovation, process optimization and quality improvement as part of the technology adoption process. Leveraging existing and proven cost-effective health technologies to tackle the prevailing burden of disease and injuries at the county level would be vital for the country to make progress. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering under-5 and maternal survival, TECH displayed a heterogeneous picture across the country with aggregate modest improvements of 2% and 13% respectively, while HALE showed no significant change. \u0026nbsp;This showed that there were marginal improvements in the utilization of the available resources to deliver healthcare services, particularly targeting maternal health. This is particularly evident when considering the counties that reported overall productivity growth and yet had been allocated less than KESH 1000 per capita for health development. It shows that for some specific counties, additional resources would be needed to make progress, particularly those that were experiencing declining productivity in the context of low financial resources (less than KESH 1000 per capita) allocated for health development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe impressive productivity growth in some of the previously marginalized counties like Taita Taveta and Turkana shows that the government policy to address the inequalities and bridge the health gaps caused by marginalization is prudent (33). This is further illustrated when considering the determinants of productivity growth, whereby there is a significant positive relationship between county marginalization status and the overall productivity growth for all the health outputs considered. Therefore, to make progress, the country should not only focus on scaling up access to priority health technologies and infrastructure but also seek to address more broader structural and socio-economic determinants that affect the demand and utilization of health services in respective counties.\u003c/p\u003e\n\u003cp\u003eHowever, low TECH in some counties could also be indicative of the weak managerial capacity to optimally combine outputs with the inputs available in the health system (34). In some cases, the rapid increase in funding for health might outpace the essential build-up of managerial and technical capacity to effectively implement health programs (35). Further, in the process of rapid decentralization, health administrative units could be formed without effective human resources and stewardship. This would result in low absorption capacity, limited coordination and weak accountability systems, further curtailing effective implementation to ensure productivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn interpreting our results, we recognize the limitations associated with this study. First, we have had to rely on the most recent available data for the country to develop a consistent analytical framework. Some of these data were collected for different purposes and archived in various sources of variable quality. Further, we did not have data on private (and non-governmental) health spending as an input and only relied on the public health spending, assuming that this was the predominant source of health funding for most counties in Kenya. However, the inability to include the private health expenditure in our analysis could artificially inflate the productivity of counties where private spending is significant, since we might have underestimated the input side of the equation. Second, we have only included health financing resources and health workforce as our model inputs, but we acknowledge that there are other important health system inputs that should be considered for a comprehensive analysis. For example, infrastructure, medical and health technologies which are critical components for the functioning of any health system could be considered. The impact of these other additional factors on health systems’ productivity needs further investigation as more data becomes available. \u0026nbsp; \u0026nbsp;Third, the data available for human resources was aggregated and included all health workers in the county and did not distinguish them according to their specific functions in healthcare delivery to facilitate a more nuanced analysis. Fourth, our analysis has not accounted for the health system shocks that were occasioned by the Covid-19 pandemic, that could have adversely affected performance. Lastly, the MPI approach used in our analysis is based on Farrell radial efficiency distance metrics, which means that any gain or loss which is not captured by the radial efficiency measures will not be captured by our results. \u0026nbsp;This has led to some criticism of the MPI, but to date there has been no widely accepted solution to this problem (36).\u003c/p\u003e\n\n"},{"header":"Conclusion","content":"\u003cp\u003eThis study is particularly informative to LMICs that are implementing decentralization of health systems to improve health outcomes. It underscores the need for health system decision makers to appreciate the various determinants of health system performance, paying particular attention to subnational disparities to tailor effective solutions to drive productivity growth. The analysis in Kenya indicates that opportunities to expand output may have been missed, due to inappropriate adoption and deployment of health technologies. Hence, county-level decision makers must not only advocate for more resources and investment in expensive modern technologies but also embrace innovation and adoption of appropriate and cost-effective health technologies to drive productivity growth. This would also require strengthening the managerial and implementation capacity of health systems so that they are able to effectively utilize the technologies and resources available to address prevailing population health needs.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was done as part of the routine assignments of the Africa Institute for Health Policy. No funding source was available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTA conceptualized the study. NR, DB and JT collated the data and did the preliminary analysis. TA, WO, MS and LW did the data analysis. TA wrote the first draft and WO, LW, NR, JT, DB, MS did the detailed review and provided comments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Kenya National Bureau of Statistics that collected and shared some of the data that has been used in this project. Further, the authors appreciate the contribution of the Institute of Health Metrics and Evaluation, University of Washington, that provided the subnational data on health burden.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main data sets supporting the conclusions of this article are available on request and with written permission from the Kenya National Bureau of Statistics. The data from IHME are publicly available and can be accessed through this publication:\u003c/p\u003e\n\u003cp\u003ehttps://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30472-8/fulltext\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and Public Involvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSyverson C. 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Methicillin-Resistant Staphylococcus aureus (MRSA): One Health Perspective Approach to the Bacterium Epidemiology, Virulence Factors, Antibiotic-Resistance, and Zoonotic Impact. \u003cem\u003eInfect Drug Resist\u003c/em\u003e. 2020 Sep;Volume 13:3255\u0026ndash;65.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Health system productivity change, Health systems, Productivity change, Malmquist productivity, Technology change, Efficiency change","lastPublishedDoi":"10.21203/rs.3.rs-7607328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7607328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eThe decentralization of health services is a central pillar of Kenya’s constitutionally mandated devolution, intended to promote equity, accountability, and more effective service delivery. In line with these reforms, significant investments have been made across the 47 county governments to strengthen healthcare provision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We assembled a panel dataset of health system inputs, outputs, and contextual factors for 2014 and 2022 to measure productivity change across Kenya’s 47 counties. Using the Malmquist Productivity Index (MPI), we estimated total factor productivity change (TFPCH), technology change (TECCH), technical efficiency change (TECH), and scale efficiency change (SECH). We further examined the impact of contextual factors on productivity shifts using linear regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOn average, TFPCH was 0.69 for under-5 survival, 0.74 for maternal survival, and 0.67 for healthy life expectancy (HALE). Similarly, TECCH scores were 0.70, 0.71, and 0.67 respectively. While TECH and SECH averaged around 1.0, indicating little net change, there was significant regional and county-level heterogeneity. Notably, counties classified as marginalized showed significantly greater productivity growth across all three outcome measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eDespite considerable investments in decentralization and service expansion, Kenya’s health system exhibited overall productivity declines between 2014 and 2022, largely due to suboptimal adoption and application of health technologies. Enhancing managerial capacity, process optimization, and leveraging proven cost-effective interventions may be more important than large-scale equipment investments alone.\u003c/p\u003e","manuscriptTitle":"Kenya health system productivity change between 2014 and 2022: progress towards universal health coverage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 06:08:20","doi":"10.21203/rs.3.rs-7607328/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-health","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nathealth","sideBox":"Learn more about [Nature Health](https://www.nature.com/naturehealth/)","snPcode":"44360","submissionUrl":"https://mts-nathealth.nature.com","title":"Nature Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e6e80f23-34d4-4bc9-8510-0d1d9039f1fd","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54738158,"name":"Health sciences/Health care/Public health"},{"id":54738159,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2025-11-13T16:06:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 06:08:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7607328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7607328","identity":"rs-7607328","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}