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A Multifaceted Intervention to Improve the Quality of Care of Children in District Hospitals in Kenya: A Cost-Effectiveness Analysis

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A Multifaceted Intervention to Improve the Quality of Care of Children in District Hospitals in Kenya: A Cost-Effectiveness Analysis
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  A Multifaceted Intervention to Improve the Quality of Care of Children in District Hospitals in Kenya: A Cost-Effectiveness Analysis Edwine W. Barasa 1,2 * , Philip Ayieko 1 , Susan Cleary 2 , Mike English 1,3 1 Kenya Medical Research Institute (KEMRI) Centre for Geographic Medicine Research – Coast, and Wellcome Trust Research Programme, Nairobi, Kenya,  2 HealthEconomics Unit, University of Cape Town, Cape Town, South Africa,  3 Department of Pediatrics, University of Oxford, Oxford, United Kingdom Abstract Background:   To improve care for children in district hospitals in Kenya, a multifaceted approach employing guidelines,training, supervision, feedback, and facilitation was developed, for brevity called the Emergency Triage and Treatment Plus(ETAT + ) strategy. We assessed the cost effectiveness of the ETAT +  strategy, in Kenyan hospitals. Further, we estimate thecosts of scaling up the intervention to Kenya nationally and potential cost effectiveness at scale. Methods and Findings:   Our cost-effectiveness analysis from the provider’s perspective used data from a previouslyreported cluster randomized trial comparing the full ETAT +  strategy ( n =4 hospitals) with a partial intervention ( n =4hospitals). Effectiveness was measured using 14 process measures that capture improvements in quality of care; theiraverage was used as a summary measure of quality. Economic costs of the development and implementation of theintervention were determined (2009 US $ ). Incremental cost-effectiveness ratios were defined as the incremental cost perpercentage improvement in (average) quality of care. Probabilistic sensitivity analysis was used to assess uncertainty. Thecost per child admission was US $ 50.74 (95% CI 49.26–67.06) in intervention hospitals compared to US $ 31.1 (95% CI 30.67–47.18) in control hospitals. Each percentage improvement in average quality of care cost an additional US $ 0.79 (95% CI0.19–2.31) per admitted child. The estimated annual cost of nationally scaling up the full intervention was US $ 3.6 million,approximately 0.6% of the annual child health budget in Kenya. A ‘‘what-if’’ analysis assuming conservative reductions inmortality suggests the incremental cost per disability adjusted life year (DALY) averted by scaling up would vary betweenUS $ 39.8 and US $ 398.3. Conclusion:   Improving quality of care at scale nationally with the full ETAT +  strategy may be affordable for low incomecountries such as Kenya. Resultant plausible reductions in hospital mortality suggest the intervention could be cost-effective when compared to incremental cost-effectiveness ratios of other priority child health interventions. Please see later in the article for the Editors’ Summary  . Citation:  Barasa EW, Ayieko P, Cleary S, English M (2012) A Multifaceted Intervention to Improve the Quality of Care of Children in District Hospitals in Kenya: ACost-Effectiveness Analysis. PLoS Med 9(6): e1001238. doi:10.1371/journal.pmed.1001238 Academic Editor:  Jochen Profit, Baylor College of Medicine, United States of America Received  August 5, 2011;  Accepted  May 3, 2012;  Published  June 12, 2012 Copyright:    2012 Barasa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the srcinal author and source are credited. Funding:  This work was made possible by a Wellcome Trust Msc Fellowship awarded to EWB ( # 090569/Z/09/Z) and a Wellcome Trust Senior Fellowship awardedto ME ( # 076827). The KEMRI Wellcome-Trust programme is supported by core funding from Wellcome-Trust ( # 092654/Z/10/A). The funders had no role in thedesign, conduct, analyses or writing of this study or in the decision to submit for publication. Competing Interests:  ME was responsible for developing the intervention that is the subject of the cost-effectiveness analysis and testing its implementation.All other authors have declared that no competing interests exist. Abbreviations:  CPG, clinical practice guideline; cRCT, cluster randomized trial; DALY, disability adjusted life years; ETAT + , Emergency Triage and Treatment Plus;ICER, incremental cost-effectiveness ratio; WHO, World Health Organization.* E-mail: edwine.halton@gmail.com Introduction  An estimated 7.6 million children die globally every year beforethe age of five [1]. 99% of these deaths occur in developing countries; 50% in sub-Saharan Africa [2]. Most of these deaths aredue to a few treatable and preventable diseases, for which effectiveinterventions are already available [3,4]. Delivering these inter-  ventions is essential to achieving the 4th Millennium DevelopmentGoal (MDG), which aims to reduce the under-five mortality rateby two-thirds by 2015. In Kenya, the under-five mortality rate hasto be reduced by 50% from its 2008 level to meet the MDG target.Improving case management of serious illness might help achievethis goal [5,6], and we have recently described one possible approach to this in Kenyan district hospitals [7]. That approachincluded the development and implementation of evidence-basedclinical practice guidelines (CPGs) linked to health workertraining, follow-up supervision, performance feedback, andfacilitation (for brevity referred to as the ETAT +  strategy) [8,9]. However, while the strategy was effective, would scaling up theapproach be a good use of limited resources? Addressing thisquestion demands a rigorous evaluation of costs and consequenceswith such data used to estimate the costs and effects of scaling upthe intervention to reach the population in need. This paperpresents a cost-effectiveness analysis performed alongside thepreviously reported cluster randomized trial of the effects of the PLoS Medicine | www.plosmedicine.org 1 June 2012 | Volume 9 | Issue 6 | e1001238  ETAT +  strategy. We also present an assessment of the costs of scaling up the intervention to the national level and speculate on,using a simple model that assumes the strategy reduces inpatientmortality, the possible costs per disability adjusted life year(DALY) averted. Methods Study Design This was a cost-effectiveness analysis alongside a clusterrandomized controlled trial (cRCT). The time horizon selectedwas 18 mo (September 2006–April 2008), which was the periodduring which the intervention was implemented and evaluated.Costing took a provider’s perspective. While this is oftenconsidered narrow [10,11], for the purpose of this analysis, we considered it sufficient as it encompasses the relevant range of costsand effects of interest to policy makers responsible for budgeting and planning for scale-up in Kenya. To account for differentialtiming and time preference, we discounted costs and outcomesusing a 3% annual discount rate [12]. Costs were adjusted forinflation using gross domestic product (GDP) deflators for Kenya[13] and are valued and presented in 2009 US $ . Effects aremeasured in terms of process indicators of quality of care thatinclude important measures of child assessment, diagnosis,classification, and treatment on admission. Probabilistic sensitivityanalysis using Monte Carlo simulation was used to assess theimpact of uncertainty around hotel costs, development costs,medicine costs, staff salaries, and effectiveness estimates. Data Collection and Sample Sizes In this cRCT (described in full elsewhere [7]), eight rural districthospitals in four provinces in Kenya were randomized into fourfull and four partial intervention hospitals, hereafter termedintervention and control hospitals [7]. Resource use data werecollected via clinical record reviews conducted at baseline and at6-monthly intervals over an 18-mo period (four surveys in total).During each survey, these reviews were conducted on 400randomly selected pediatric admissions. Admissions were includedif children, aged between 2 and 59 mo, were admitted for acuteillnesses during the preceding 6-mo time period. The total sampleincluded 6,199 and 5,115 record reviews of pediatric admissionsfor intervention and control hospitals, respectively. The clinicalperformance indicators used as the measure of effectiveness in thisanalysis were extracted from 1,158 and 1,157 records at 18 mopost implementation in the intervention and control hospitals,respectively. The Intervention The intervention was a package of care intervention that wasdelivered in the form of evidence-based CPG dissemination [8],health worker training, job aids, follow-up supervision, and local(health facility) facilitation by a nurse or diploma level clinician[7,9]. The role of the local facilitator was to offer local oversightand on-site problem solving to support facilities implementing theintervention. The training course was developed from the existing World Health Organization (WHO) Emergency Triage, Assess-ment and Treatment (ETAT) course with the addition of newmaterials on newborn resuscitation and case management of common causes of serious illness in the newborn or child and withthe CPGs spanned: emergency pediatric care, malaria, pneumo-nia, asthma, diarrhea and dehydration, meningitis, malnutrition,and neonatal care [8]. This new training was therefore given thename ‘‘Emergency Triage Assessment and Treatment Plus Admission Care (ETAT +  ).’’In intervention hospitals, the intervention was delivered over18 mo as a combination of ETAT +  training for health careworkers conducted over 5.5 d, dissemination of CPG booklets, jobaids, and pediatric admission record (PAR) forms [7]. The PAR isa structured form used by clinicians to document key symptomsand signs of a sick child’s clinical information on admission [14].This was followed by 2–3-monthly supervisory visits thatsometimes included short, ad hoc follow-up training andappointment of a local facilitator in each facility linked to hospitalsupervisors by regular phone calls from the study team [9,15]. Results and feedback reports of the surveys conducted in thefacilities were disseminated in face-to-face meetings in interventionhospitals.In the control hospitals, a partial version of the intervention wasdelivered in the form of CPG booklet distribution, a 1.5-d seminar,and provision of written survey feedback based on written reportsonly. Control hospitals did not receive any follow-up supervisorysupport or local facilitation. Evaluating Effectiveness We used process indicators of quality of care to estimate theeffectiveness of the intervention. In total 14 pre-specified indicatorsthat span three broad areas were considered as primary outcomes[7]: assessment of a severely ill child, therapeutic care, andsupportive care on admission. These indicators cover the diseases(malaria, pneumonia, and diarrhea/dehydration) that result in60% of inpatient deaths in children under five in Kenya.Effectiveness was then estimated as the between-group (interven-tion and control) difference using logistic or linear regressionanalyses for each of the 14 process measures while adjusting forhospital-level covariates (all-cause pediatric mortality, malariatransmission, and hospital size). This procedure is described indetail elsewhere [7]. Further details of the effectiveness analysis arealso provided in Figure S1. Evaluating Costs Costs were categorized as intervention development, interven-tion implementation, and inpatient pediatric treatment costs. Thelatter were included in order to capture any change in resource useassociated with the implementation of best practice pediatric care.Costs were summed across all categories to obtain the total costper hospital and per hospital admission in intervention and controlhospitals. Each cost category is further described in the following sections. Costs were collected using clinical and accounting recordreviews and interviews with those involved in developing andimplementing the intervention. Guideline Development Costs Development costs included the staff costs incurred in thedevelopment of ETAT +  guidelines and training, the costs of course training materials, and costs of organizing and running meetings and workshops. Staff costs were calculated by interview-ing key staff involved in guideline development in order to estimatethe amount of time spent on these activities. The opportunity costof this time was then assumed to be equivalent to the associatedcost of employment. The costs of course training materials wereassumed to be equivalent to the market prices of these items.Development costs were annualized over 4 y, which was assumedto be the useful life of the clinical guidelines. Guideline Implementation Costs Implementation costs included the costs of initial ETAT + training of health workers, follow-up training, supervisory visits Cost Effectiveness of Hospital Quality ImprovementPLoS Medicine | www.plosmedicine.org 2 June 2012 | Volume 9 | Issue 6 | e1001238  and phone calls, feedback meetings, and on-site local facilitatorcosts. The opportunity costs of resources used in these activities,e.g., staff time used in attending trainings, were evaluated byestimating the number of days spent at each training workshopand calculating the costs on the basis of the equivalent cost of employment. The costs of the initial training were considered to becapital costs as the effects of the training were expected to berealized over a period of more than 1 y. These costs wereannualized over a useful life of 2.5 y, which was the length of timeover which the practice change effects were seen to be sustained[7]. Follow-up activities and supervision were considered to berecurrent costs. Treatment Costs Treatment costs were computed as the sum of ‘‘hotel,’’medicine, and laboratory costs per admission. Resource use datafor patient length of stay in hospital, medicines, and laboratorytests were collected from patient clinical records. Estimates of theutilization of these resources were then multiplied against the unitcost of each item. Per diem ‘‘hotel’’ unit costs were derived fromthe WHO, ‘‘Choosing Interventions that are Cost Effective’’(WHO-CHOICE) estimates and recent work on the economiccosts of inpatient care in Kenya [16,17]. Medicine unit costs werederived from 2009 market prices while unit costs of diagnostic testswere based on non-profit cost recovery prices from a Kenyandistrict hospital [16]. Given the skewed nature of cost data,treatment costs are presented as both means (with confidenceintervals) and medians (with interquartile ranges). Evaluating Cost Effectiveness The cost-effectiveness analysis compared the implementation of the ETAT +  strategy as delivered in the intervention hospitals withthe partial intervention as delivered in the control hospitals. Thepartial intervention was chosen as a comparator because it mirrorspractice that would be considered a basic, standard approach todissemination of guidelines that does not typically include activefollow-up or supervision and for ethical reasons (withholding newnational guidelines was deemed unreasonable). While ‘‘nointervention’’ is an alternative counterfactual, this assumes,somewhat unrealistically to us, that no national or internationalbody will produce guidelines or disseminate them or makeattempts to improve poor hospital services.The summary measure of effect was the mean of the adjusteddifferences between control and intervention hospitals at 18 mo.This was calculated as the mean percentage improvement in the14 process of care indicators in intervention compared to controlhospitals (Equation 1), with 95% CIs obtained by bootstrapping with 2000 iterations. Measure of effectiveness :  Q ~ X 14 i  ~ 1  Ei  = n  ð 1 Þ Where:  Q   , mean percentage improvement in process of care;  Ei  ,adjusted difference of each process of care between control andintervention hospitals at 18 mo;  n , number of processes of care. Assessing Cost Effectiveness The incremental cost-effectiveness ratio (ICER) was defined asthe incremental cost per percentage gain in mean quality based onthe 14 indicators. This is the ratio of the difference in the totaladmission cost per child between intervention and controlhospitals, and the difference in mean quality improvement(Equation 2). ICER ~ ( C  i  { C  c ) = E  i  { E  c )  ð 2 Þ Where:  C  i ,  c  hild admission costs in intervention hospitals;  C  c  ,  c  hildadmission costs in control hospitals;  E  i , percentage improvementin process measures of quality in intervention hospital;  E  c  ,percentage improvement in process measures of quality in controlhospitals.The cRCT was not designed to examine effects on healthoutcomes, therefore we explored the potential incremental cost perDALY averted on the basis of conservative assumptions of theeffect of improving quality of care on inpatient childhoodmortality. We assumed relative reductions in the mortality rateof between 1% and 10%, equivalent to absolute reductions of between 0.07% and 0.7% with median inpatient mortality,derived from the eight hospitals, equal to 7%. The proportion of lives saved from the respective diseases (malaria, pneumonia, anddiarrhea) were assumed to be equivalent to the proportions of thecontribution of each of these diseases to under-five childhooddeaths in Kenya [18]. In this ‘‘what-if’’ analysis, the interventionwas compared to common practice where guidelines aredeveloped and disseminated with no accompanying training and/or follow-up supervision. DALYs were calculated using standard methods [19]. DALYs are generic measures of healthoutcomes derived by adding the years of life lost due to disease(YLL) and the years of life lived with disability (YLD) [19 – 21]. DALYs were calculated using a discount rate of 3%, age weighting and disability weights for malaria episode (0.19), lower respiratoryinfection episode (0.28), and diarrheal diseases (0.11) [22].Separate DALY calculations for each of these diseases wheremade and summed up to yield total DALYs averted by theintervention. Total Costs of Scale-up Kenya has 121 hospital facilities with estimated median annualpediatric admissions of 2,000 per facility across this group,representing a total for pediatric admissions of 242,000 perannum (there are a larger number of smaller hospitals notconsidered in this analysis). We estimated the cost of scaling up thisintervention with a number of assumptions: (1) Development costsdo not vary with scale-up; given that they are only incurred once,they are not a function of the scale of the intervention; (2) Thattraining, supervision, and follow-up costs (implementation) vary asa function of the number of hospitals; (3) That treatment costs varyas a function of the number of pediatric admissions; (4)That theintervention would reach all 121 hospitals when at scale. It ishowever difficult to estimate potential economies of scale andscope, for example for supervision, that might lessen costs orspecific, higher, travel costs for hard to reach areas during scaleup. Given the skewness of treatment costs, their scale-upcomponent costs were calculated on the log-scale and then back transformed to the srcinal scale. Sensitivity Analysis Uncertainty was addressed by specifying distributions aroundcost and effectiveness parameters and conducting probabilisticsensitivity analysis using Crystal Ball software (Decisioneering).Triangular distributions with plausible ranges were fitted aroundthe effectiveness estimate, development costs, salaries, medicines,and ‘‘hotel’’ components of costs (Table 1). Intervention effective-ness was varied to reflect the range of process of careimprovements between intervention and control hospitals acrossthe 14 indicators; from 3.54%, to 52.10%, the smallest and Cost Effectiveness of Hospital Quality ImprovementPLoS Medicine | www.plosmedicine.org 3 June 2012 | Volume 9 | Issue 6 | e1001238  greatest reported difference between control and interventionhospitals’ indicators. Development costs were varied to reflect ascenario where a ‘‘ready made’’ intervention was adopted hencehaving zero intervention costs, and a scenario where the fulldevelopment costs were incurred. The ‘‘hotel’’ unit cost estimateused in the base case, which was also used to compute the lowerrange in the sensitivity analysis, was the WHO-CHOICE estimatefor district hospitals in Kenya, inflated to 2009 (US $ 6.96 per day)[23], while the upper range was computed from an estimate froma Kenyan study inflated to 2009 (US $ 15.05 per day) [24]. Thelower range of salary costs was derived by assuming theintervention implementers were compensated at government of Kenya salary scales while the upper limit, which was also theestimate used in base case, assumed that intervention implemen-ters were compensated at the salary scales of the researchorganization that implemented the intervention. The range of medicine costs was derived from variations in market prices in the2010 Kenya drug prices catalogue. Confidence intervals aroundthe mean ICER were derived from 1,000 Monte Carlosimulations. Results Changes in Process of Care Measures The mean of the adjusted differences of the 14 process measuresbetween control and intervention hospitals was 25.01% (95% CI17.87%–32.18%). The findings of performance changes across allprocess measures in both control and intervention hospitals arepresented in Table 2. Intervention Costs Total intervention costs and admission costs per child inintervention and control hospitals are presented in Table 3. Anaverage of 32 health workers underwent the initial ETAT + training at a cost of US $ 8,069.32 per intervention hospital orUS $ 252.16 per trainee. Follow-up training, supervision, and localfacilitator costs were 19.89% of total intervention costs inintervention hospitals. The annual costs of a local facilitator perfacility were US $ 5,697.87, 5.62% of total intervention costs inintervention hospitals. Treatment Costs  An ordinary linear (OLS) regression of (log transformed)treatment costs revealed that costs did not significantly vary withchild diagnosis, hospital, and time (i.e., across the four surveys)(unpublished data). We therefore pooled treatment costs acrosssurveys and diagnoses within each study arm to increase samplesizes. The mean and median treatment costs were US $ 28.15 (95%CI 27.61–28.70) and US $ 22.47 (interquartile range [IQR] 14.33– 32.78), respectively, in intervention hospitals and US $ 25.10 (95%CI 24.56–25.65) and US $ 19.25 (IQR 13.01–29.04) in controlhospitals. ‘‘Hotel’’ costs were the key driver of treatment costs andcontributed between 73.18% and 79.98% of treatment costs.Treatment costs disaggregated by category are presented in TableS1, while treatment costs per admission episode are presented inTable S2. Incremental Costs, Effects, and Cost-EffectivenessAnalysis The incremental cost per admission in intervention hospitalscompared to control hospitals was US $ 19.68 (95% CI 5.31– 31.92). The incremental cost per percentage improvement inquality of care was US $ 0.79 (95% CI 0.19–2.31) per childadmission. These results are presented in Table 4. Estimated Costs of Scale-up and Budget Impact For an estimated coverage of 121 district hospitals and 242,000annual under-five admissions, the estimated costs of scale-up werefound to be US $ 3,559,328.78. This amount is estimated to beequivalent to 0.60% of the estimated 2010 annual budget forformal provision of care to children under five in Kenya (Table 5). Incremental Cost per DALY Estimates Given ProbableReductions in Mortality The mean baseline inpatient child mortality rate in the eighthospitals was 7% [7]. Assuming the intervention produces a 1%– 10% relative reduction in this mortality rate (absolute reductionsbetween 0.07% and 0.7%), the incremental cost per DALYaverted would range between US $ 398.3 and US $ 39.8, respective-ly. Figure 1 explores the relationship between reduction inmortality and intervention cost effectiveness at different baselinemortality rates while Table 6 compares the range of potentialICERs with those from other key child health interventions thatare considered cost effective. Sensitivity Analysis The incremental cost-effectiveness ratios were robust to changesin most of the variables included in the sensitivity analysis. Fourfactors (intervention effectiveness, hotel costs, medicine costs, andstaff salaries) contributed 99% of the total variance in the ICER(Figure 2). The major contributors to this variance were Table 1.  Parameter ranges and distributions. Parameter (Costs per Child Admission) Base Case (US $ ) Range (US $ ) Distribution Full intervention development costs 8.11 0–8.11 TriangularPartial intervention development costs 4.95 0–4.95 TriangularFull intervention salary costs 12.46 11.42–12.46 TriangularFull intervention hotel costs 20.68 20.68–39.93 TriangularFull intervention medicine costs 2.30 0.66–8.06 TriangularPartial Intervention salary costs 3.65 1.67–3.65 TriangularPartial intervention hotel costs 20.15 20.15–38.89 TriangularPartial intervention medicine costs 1.74 0.50–6.09 TriangularIntervention Effectiveness 25.01 3.54–52.10 Triangulardoi:10.1371/journal.pmed.1001238.t001 Cost Effectiveness of Hospital Quality ImprovementPLoS Medicine | www.plosmedicine.org 4 June 2012 | Volume 9 | Issue 6 | e1001238      T   a    b    l   e    2 . 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     1     7 .     8     7     3     2 .     1     8       a      A     d     j    u    s    t    e     d     d     i     f     f    e    r    e    n    c    e     b    e    t    w    e    e    n     i    n    t    e    r    v    e    n    t     i    o    n    a    r    m    s    o     b    t    a     i    n    e     d     f    r    o    m     l     i    n    e    a    r    o    r     l    o    g     i    s    t     i    c    r    e    g    r    e    s    s     i    o    n    a    n    a     l    y    s     i    s    o     f     h    o    s    p     i    t    a     l    s    u    m    m    a    r    y     d    a    t    a    a     d     j    u    s    t     i    n    g     f    o    r    c     h     i     l     d     ’    s    s    e    x    a    n     d     h    o    s    p     i    t    a     l     f    a    c    t    o    r    s     (    s     i    z    e ,    m    a     l    a    r     i    a    e    n     d    e    m     i    c     i    t    y ,     H     I     V    p    r    e    v    a     l    e    n    c    e ,    a     l     l    c    a    u    s    e    m    o    r    t    a     l     i    t    y     ) .     d    o     i   :     1     0 .     1     3     7     1     /     j    o    u    r    n    a     l .    p    m    e     d .     1     0     0     1     2     3     8 .    t     0     0     2 Cost Effectiveness of Hospital Quality ImprovementPLoS Medicine | www.plosmedicine.org 5 June 2012 | Volume 9 | Issue 6 | e1001238
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