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Research Article

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

• edwine.halton@gmail.com

Affiliations: Kenya Medical Research Institute (KEMRI) Centre for Geographic Medicine Research – Coast, and Wellcome Trust Research Programme, Nairobi, Kenya, Health Economics Unit, University of Cape Town, Cape Town, South Africa

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• Affiliation: Kenya Medical Research Institute (KEMRI) Centre for Geographic Medicine Research – Coast, and Wellcome Trust Research Programme, Nairobi, Kenya

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• Affiliation: Health Economics Unit, University of Cape Town, Cape Town, South Africa

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• Affiliations: Kenya Medical Research Institute (KEMRI) Centre for Geographic Medicine Research – Coast, and Wellcome Trust Research Programme, Nairobi, Kenya, Department of Pediatrics, University of Oxford, Oxford, United Kingdom

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• Published: June 12, 2012
• DOI: 10.1371/journal.pmed.1001238

## 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 the costs of scaling up the intervention to Kenya nationally and potential cost effectiveness at scale.

### Methods and Findings

#### Data Collection and Sample Sizes

In this cRCT (described in full elsewhere [7]), eight rural district hospitals in four provinces in Kenya were randomized into four full and four partial intervention hospitals, hereafter termed intervention and control hospitals [7]. Resource use data were collected via clinical record reviews conducted at baseline and at 6-monthly intervals over an 18-mo period (four surveys in total). During each survey, these reviews were conducted on 400 randomly selected pediatric admissions. Admissions were included if children, aged between 2 and 59 mo, were admitted for acute illnesses during the preceding 6-mo time period. The total sample included 6,199 and 5,115 record reviews of pediatric admissions for intervention and control hospitals, respectively. The clinical performance indicators used as the measure of effectiveness in this analysis were extracted from 1,158 and 1,157 records at 18 mo post implementation in the intervention and control hospitals, respectively.

#### The Intervention

The intervention was a package of care intervention that was delivered 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 oversight and on-site problem solving to support facilities implementing the intervention. The training course was developed from the existing World Health Organization (WHO) Emergency Triage, Assessment and Treatment (ETAT) course with the addition of new materials on newborn resuscitation and case management of common causes of serious illness in the newborn or child and with the CPGs spanned: emergency pediatric care, malaria, pneumonia, asthma, diarrhea and dehydration, meningitis, malnutrition, and neonatal care [8]. This new training was therefore given the name “Emergency Triage Assessment and Treatment Plus Admission Care (ETAT+).”

In intervention hospitals, the intervention was delivered over 18 mo as a combination of ETAT+ training for health care workers conducted over 5.5 d, dissemination of CPG booklets, job aids, and pediatric admission record (PAR) forms [7]. The PAR is a structured form used by clinicians to document key symptoms and signs of a sick child's clinical information on admission [14]. This was followed by 2–3-monthly supervisory visits that sometimes included short, ad hoc follow-up training and appointment of a local facilitator in each facility linked to hospital supervisors by regular phone calls from the study team [9],[15]. Results and feedback reports of the surveys conducted in the facilities were disseminated in face-to-face meetings in intervention hospitals.

In the control hospitals, a partial version of the intervention was delivered in the form of CPG booklet distribution, a 1.5-d seminar, and provision of written survey feedback based on written reports only. Control hospitals did not receive any follow-up supervisory support or local facilitation.

#### Evaluating Effectiveness

We used process indicators of quality of care to estimate the effectiveness of the intervention. In total 14 pre-specified indicators that span three broad areas were considered as primary outcomes [7]: assessment of a severely ill child, therapeutic care, and supportive care on admission. These indicators cover the diseases (malaria, pneumonia, and diarrhea/dehydration) that result in 60% of inpatient deaths in children under five in Kenya. Effectiveness was then estimated as the between-group (intervention and control) difference using logistic or linear regression analyses for each of the 14 process measures while adjusting for hospital-level covariates (all-cause pediatric mortality, malaria transmission, and hospital size). This procedure is described in detail elsewhere [7]. Further details of the effectiveness analysis are also provided in Figure S1.

#### Evaluating Costs

Costs were categorized as intervention development, intervention implementation, and inpatient pediatric treatment costs. The latter were included in order to capture any change in resource use associated with the implementation of best practice pediatric care. Costs were summed across all categories to obtain the total cost per hospital and per hospital admission in intervention and control hospitals. Each cost category is further described in the following sections. Costs were collected using clinical and accounting record reviews and interviews with those involved in developing and implementing the intervention.

#### Guideline Development Costs

Development costs included the staff costs incurred in the development 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 interviewing key staff involved in guideline development in order to estimate the amount of time spent on these activities. The opportunity cost of this time was then assumed to be equivalent to the associated cost of employment. The costs of course training materials were assumed to be equivalent to the market prices of these items. Development costs were annualized over 4 y, which was assumed to 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 and phone calls, feedback meetings, and on-site local facilitator costs. The opportunity costs of resources used in these activities, e.g., staff time used in attending trainings, were evaluated by estimating the number of days spent at each training workshop and calculating the costs on the basis of the equivalent cost of employment. The costs of the initial training were considered to be capital costs as the effects of the training were expected to be realized over a period of more than 1 y. These costs were annualized over a useful life of 2.5 y, which was the length of time over which the practice change effects were seen to be sustained [7]. Follow-up activities and supervision were considered to be recurrent costs.

#### Treatment Costs

Treatment costs were computed as the sum of “hotel,” medicine, and laboratory costs per admission. Resource use data for patient length of stay in hospital, medicines, and laboratory tests were collected from patient clinical records. Estimates of the utilization of these resources were then multiplied against the unit cost of each item. Per diem “hotel” unit costs were derived from the WHO, “Choosing Interventions that are Cost Effective” (WHO-CHOICE) estimates and recent work on the economic costs of inpatient care in Kenya [16],[17]. Medicine unit costs were derived from 2009 market prices while unit costs of diagnostic tests were based on non-profit cost recovery prices from a Kenyan district hospital [16]. Given the skewed nature of cost data, treatment costs are presented as both means (with confidence intervals) 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 with the partial intervention as delivered in the control hospitals. The partial intervention was chosen as a comparator because it mirrors practice that would be considered a basic, standard approach to dissemination of guidelines that does not typically include active follow-up or supervision and for ethical reasons (withholding new national guidelines was deemed unreasonable). While “no intervention” is an alternative counterfactual, this assumes, somewhat unrealistically to us, that no national or international body will produce guidelines or disseminate them or make attempts to improve poor hospital services.

The summary measure of effect was the mean of the adjusted differences between control and intervention hospitals at 18 mo. This was calculated as the mean percentage improvement in the 14 process of care indicators in intervention compared to control hospitals (Equation 1), with 95% CIs obtained by bootstrapping with 2000 iterations.(1)
Where: Q, mean percentage improvement in process of care; Ei, adjusted difference of each process of care between control and intervention hospitals at 18 mo; n, number of processes of care.

#### Assessing Cost Effectiveness

The incremental cost-effectiveness ratio (ICER) was defined as the incremental cost per percentage gain in mean quality based on the 14 indicators. This is the ratio of the difference in the total admission cost per child between intervention and control hospitals, and the difference in mean quality improvement (Equation 2).(2)
Where: Ci, child admission costs in intervention hospitals; Cc, child admission costs in control hospitals; Ei, percentage improvement in process measures of quality in intervention hospital; Ec, percentage improvement in process measures of quality in control hospitals.

The cRCT was not designed to examine effects on health outcomes, therefore we explored the potential incremental cost per DALY averted on the basis of conservative assumptions of the effect of improving quality of care on inpatient childhood mortality. We assumed relative reductions in the mortality rate of 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, and diarrhea) were assumed to be equivalent to the proportions of the contribution of each of these diseases to under-five childhood deaths in Kenya [18]. In this “what-if” analysis, the intervention was compared to common practice where guidelines are developed and disseminated with no accompanying training and/or follow-up supervision. DALYs were calculated using standard methods [19]. DALYs are generic measures of health outcomes 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 respiratory infection episode (0.28), and diarrheal diseases (0.11) [22]. Separate DALY calculations for each of these diseases where made and summed up to yield total DALYs averted by the intervention.

#### Total Costs of Scale-up

Kenya has 121 hospital facilities with estimated median annual pediatric admissions of 2,000 per facility across this group, representing a total for pediatric admissions of 242,000 per annum (there are a larger number of smaller hospitals not considered in this analysis). We estimated the cost of scaling up this intervention with a number of assumptions: (1) Development costs do not vary with scale-up; given that they are only incurred once, they are not a function of the scale of the intervention; (2) That training, supervision, and follow-up costs (implementation) vary as a function of the number of hospitals; (3) That treatment costs vary as a function of the number of pediatric admissions; (4)That the intervention would reach all 121 hospitals when at scale. It is however difficult to estimate potential economies of scale and scope, for example for supervision, that might lessen costs or specific, higher, travel costs for hard to reach areas during scale up. Given the skewness of treatment costs, their scale-up component costs were calculated on the log-scale and then back transformed to the original scale.

#### Sensitivity Analysis

Uncertainty was addressed by specifying distributions around cost and effectiveness parameters and conducting probabilistic sensitivity analysis using Crystal Ball software (Decisioneering). Triangular distributions with plausible ranges were fitted around the effectiveness estimate, development costs, salaries, medicines, and “hotel” components of costs (Table 1). Intervention effectiveness was varied to reflect the range of process of care improvements between intervention and control hospitals across the 14 indicators; from 3.54%, to 52.10%, the smallest and greatest reported difference between control and intervention hospitals' indicators. Development costs were varied to reflect a scenario where a “ready made” intervention was adopted hence having zero intervention costs, and a scenario where the full development costs were incurred. The “hotel” unit cost estimate used in the base case, which was also used to compute the lower range in the sensitivity analysis, was the WHO-CHOICE estimate for district hospitals in Kenya, inflated to 2009 (US$6.96 per day) [23], while the upper range was computed from an estimate from a Kenyan study inflated to 2009 (US$15.05 per day) [24]. The lower range of salary costs was derived by assuming the intervention implementers were compensated at government of Kenya salary scales while the upper limit, which was also the estimate used in base case, assumed that intervention implementers were compensated at the salary scales of the research organization that implemented the intervention. The range of medicine costs was derived from variations in market prices in the 2010 Kenya drug prices catalogue. Confidence intervals around the mean ICER were derived from 1,000 Monte Carlo simulations.

### Results

#### Changes in Process of Care Measures

The mean of the adjusted differences of the 14 process measures between control and intervention hospitals was 25.01% (95% CI 17.87%–32.18%). The findings of performance changes across all process measures in both control and intervention hospitals are presented in Table 2.

#### Incremental Cost per DALY Estimates Given Probable Reductions in Mortality

The mean baseline inpatient child mortality rate in the eight hospitals was 7% [7]. Assuming the intervention produces a 1%–10% relative reduction in this mortality rate (absolute reductions between 0.07% and 0.7%), the incremental cost per DALY averted would range between US$398.3 and US$39.8, respectively. Figure 1 explores the relationship between reduction in mortality and intervention cost effectiveness at different baseline mortality rates while Table 6 compares the range of potential ICERs with those from other key child health interventions that are considered cost effective.

#### Sensitivity Analysis

The incremental cost-effectiveness ratios were robust to changes in most of the variables included in the sensitivity analysis. Four factors (intervention effectiveness, hotel costs, medicine costs, and staff salaries) contributed 99% of the total variance in the ICER (Figure 2). The major contributors to this variance were intervention effectiveness (49%) and hotel costs (43%). Salary and medicine costs contributed to 4% of ICER variance each, while development costs contributed 1% of the variation. The higher the intervention effectiveness, the lower the ICER (increased intervention cost effectiveness), while the higher the costs (hotel, salary, medicine, development costs) the higher the ICER (reduced intervention cost effectiveness).

### Discussion

This analysis compares the costs and effects of a guideline-based intervention aimed at improving the quality of care of children admitted to district hospitals in Kenya. In analyzing the costs, we included the costs of developing clinical guidelines that are often left out in such analyses [25]. Our analysis revealed that these costs formed 16% of the overall intervention costs, suggesting that this component is an important cost driver that should not be ignored. The development costs are however minimal at scale-up, given that they are incurred once and do not vary with the scale of the intervention. The average training cost per health worker was US$252.16, which is significantly lower than training costs for similar interventions such as Integrated Management of Childhood Illness (IMCI) where the reported median cost was US$633 [26]. One of the unique components of this intervention was the appointment of a local facilitator in intervention hospitals. The annual cost of this facilitator per facility was US$5,698. The local facilitator worked at the facility for the entire intervention period. This provided continuity and helped to keep the “quality agenda” on the table [27]. There are many challenges in undertaking cost-effectiveness analyses of interventions targeting hospitals and multiple diseases. Basic challenges include the lack of high quality data on hotel costs. These represent between 73% and 80% of total treatment costs for children admitted with common diseases even though inpatient stays are typically 3 d or less. We used the WHO-CHOICE hotel cost estimates applicable to district hospitals in Kenya. In effect, the use of these data amounts to an assumption that there are no major differences in the intensity of staffing per patient. While this assumption was justified as we did not expect that our intervention would require different levels of health worker input within the different hospital settings, we acknowledge that poor primary data is a potential shortcoming and suggest that addressing this information gap is a priority. Specifying summary measures that capture intervention effects is also a challenge [28]. We used a mean percentage improvement in quality assessed across 14 indicators for the three major causes of admission and inpatient death in children [7]. By this measure, intervention hospitals outperformed control hospitals by 25%. However, such a measure gives equal weight to each indicator, with which some might disagree. This assumption is, however, consistent with findings from an international Delphi study conducted with pediatric experts where the respondents rated these 14 process of care indicators as having similar priority for improvement [29]. It can be argued however that certain process measures, for example those targeting appropriate dosing of medicines, are more important than others, such as processes that target patient assessment. Despite the lack of an agreed weighting for these processes, we explored a weighting procedure (unpublished data) that gives higher weight to treatment processes (weight 3), followed by diagnosis processes (weight 2), and lastly assessment processes (weight 1). The resultant effect size (24.0%) was not significantly different from the unweighted estimate (25.01%) and fell within the range used in the sensitivity analysis. The effectiveness measure also fails to capture potentially important additional improvements resulting from intervention, for example improved organization of care, better local resource mobilization [7], more rational use of antibiotics [30], and positive effects on staff morale [15]. Conversely the measure does not capture potential declines in quality in other areas that are not the focus of intervention. Methodological research is thus needed to optimize effect measures for complex interventions targeting improvements in hospital care for multiple diseases. While acknowledging these limitations our findings suggest an additional cost of US$0.79 per child admitted to achieve a one percentage point improvement in this summary quality measure. The probabilistic sensitivity analysis reveals that hotel costs and intervention effectiveness contributed to more than 80% of the variation in this ICER. This finding has two implications: (1) The sensitivity analysis underlines the effects of poor data on hotel costs and methodological deficiencies in computing a summary measure of quality improvement; (2) Hotel costs (being key drivers of treatment costs) and intervention effectiveness have a significant impact on the cost effectiveness of this quality of care intervention.

Translating improvements in process measures into improved health status outcomes is problematic and the cluster randomized trial was not designed to measure effects on inpatient child mortality [31]. However, to provide some means to consider the potential value of the intervention in terms of incremental cost per DALY averted and life years gained, we conducted a simple “what-if” analysis. We considered a reasonably conservative range of relative reductions in baseline mortality of between 1% and 10%, absolute reductions of mortality of 0.7% to 0.07% from a baseline of 7%. This range of mortality reductions is considered conservative when compared to findings suggesting improved case management for common childhood diseases in primary care may result in mortality reduction of 13% in Tanzania [32]. Also, evaluations of quality improvement and safety programmes in developed countries have reported mortality reductions of between 5% and 51% [33][37]. The implication is that the cost effectiveness of the interventions is likely to be more favorable than we have suggested. The findings suggest that the incremental cost per DALY averted from scaling up the intervention would vary from US$39.8 to US$398.3. These ICERs compare favorably with other key public health interventions to reduce child mortality considered to be cost effective such as provision of insecticide treated bed nets (US$34.60–US$154.8 per DALY averted) [38] or oral rehydration therapy (ORT) (US$172.2–US$3352.0 per DALY averted) [39]. The intervention is likely most cost effective when hospital mortality is high (Figure 1) and baseline quality of care poor. Such analyses raise the question of whether quality improvement should be targeted at high mortality hospitals. While such simple modeling approaches suggest improving the quality of rural hospital care may be highly cost effective, demonstrating such small reductions in mortality in a randomized controlled trial would be extremely difficult. For example, demonstrating an absolute, inpatient mortality reduction of 0.5% from a baseline of 7% in a typical cluster randomized trial, using conventional values for statistical significance and power, would likely require all 121 Kenyan hospitals to be enrolled, randomized, and evaluated over 1 y [40]. The costs of such a trial would be prohibitively high and would amount to scaling up anyway.

Often the feasibility of scaling up is determined by likely costs. For the multifaceted intervention employed these were estimated to be US$3,559,328 if conducted by non-government personnel and 27% less if by government personnel. This amount can be compared with average annual projected expenditures by the Kenyan government on all care for children under five of US$572 million [41] and on other specific health projects such as the distribution of insecticide treated nets (US$8 million) and prevention of mother to child transmission of HIV (US$6 million) [42]. While the costs of scale-up might be small (approximately 0.6% of the child health budget), scaling up the ETAT+ intervention nationally would either require the child health budget to be increased or that allocation to other areas be reduced by an equivalent amount.

Cost-effectiveness and affordability data are, however, not the only factors that should inform such allocative decisions. Other important considerations may include, equity, likely collateral benefits or adverse effects, and, of course, context and the politics of the day. Unfortunately methods to support and make transparent such complex prioritization decisions remain poorly developed. Advantages of scaling up such an integrated package of interventions encompassed in the ETAT+ strategy include potentially important externalities related to more general health system strengthening and introduction of a culture of improvement [7],[27]. For those in other settings reviewing these results, it should be clear that the greatest apparent cost effectiveness is likely to occur in settings providing, reliably, a minimum set of basic resources but where quality of care, in terms of process, is poor and mortality high. Findings from this work are therefore likely to be generalizable to low-income countries with similar public hospital characteristics, child burden of disease, and comparable quality of delivery of pediatric care as found in hospitals at baseline in Kenya.

Our work adds to a very small body of literature on economic evaluation of quality of care interventions [43]. The few that are available from low-income settings, including evaluations of Integrated Management of Childhood Illness (IMCI), adopted a similar approach [32],[44],[45]. A major limitation of these approaches is that they fail to fully elucidate the value of the intervention to the patients, health workers, and organizations, and by extension to decision makers. Further, because effect measures are unique to the interventions it is not possible to compare results directly with other cost-effectiveness or cost-utility studies. To provide a more explicit framework for comparison requires modeling the link between process measures of improved quality and health outcomes, which would be complex and likely based largely on expert opinion. As an alternative we used an approach that explored potentially plausible overall effects on hospital mortality. This can clearly be challenged but is mainly used for illustration. In the absence of data on mortality effects perhaps a method of valuing quality improvement interventions that encompasses more than health outcomes might be more appropriate and could reflect utility by, for example, eliciting society's preferences.

#### Conclusion

This analysis has shown that the improvement in quality of care attributed to the ETAT+ strategy (7) is associated with additional costs that are affordable to low-income countries like Kenya. The intervention may be relatively cost effective compared with standard care if the improvements observed are associated with reasonably conservative reductions in inpatient child mortality. The absolute costs for scaling up are comparable to or lower than costs of other, major child health interventions. As increasing focus is being given to strengthening health systems there would therefore appear to be a reasonably strong case for scaling up this intervention that improves service provision in rural hospitals for the major causes of child mortality in Kenya. This work also highlights the need for methodological developments in the economic analysis of complex, system-level interventions. These results are likely to be most usefully generalized to low-income countries beyond Kenya with similar facilities, burden of child mortality, and comparable or worse quality of pediatric care in hospitals.

### Supporting Information

Figure S1.

Two-stage analysis plan for intervention effectiveness based on Hayes and Moulton [49].

doi:10.1371/journal.pmed.1001238.s001

(TIF)

Table S1.

Treatment costs per admission.

doi:10.1371/journal.pmed.1001238.s002

(DOC)

Table S2.

Admission treatment costs per diagnosis.

doi:10.1371/journal.pmed.1001238.s003

(DOC)

### Acknowledgments

The authors are grateful to the staff of all the hospitals included in the study and colleagues from the Ministry of Public Health and Sanitation, the Ministry of Medical Services, and the KEMRI/Wellcome Trust Programme for their assistance in the conduct of this study. This work is published with the permission of the Director of KEMRI.

### Author Contributions

Conceived and designed the experiments: EWB PA SC ME. Performed the experiments: EWB. Analyzed the data: EWB PA SC ME. Wrote the first draft of the manuscript: EWB. Contributed to the writing of the manuscript: EWB PA SC ME. ICMJE criteria for authorship read and met: EB PA SC ME. Agree with manuscript results and conclusions: EB PA SC ME.

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