BMC Medical Informatics and Decision Making Algorithms for optimizing drug therapy

Peter Wanger firstname.lastname@example.com
Department of Clinical Neuroscience
Karolinska Institutet St Erik's Eye Hospital S
112 82StockholmSweden

Lene Martin email@example.com
Department of Clinical Neuroscience
Karolinska Institutet St Erik's Eye Hospital S
112 82StockholmSweden

BMC Medical Informatics and Decision Making Algorithms for optimizing drug therapy
10.1186/1472-6947-4-10BioMed Central Page 1 of 8 (page number not for citation purposes) Research article * Corresponding author
Background: Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet.Methods:One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease-and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface) and JavaScript (program logic).Results: Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs based on one of these three models could be constructed regarding all but one of the studied disorders. The single exception was depression, where reliable relationships between patient characteristics, drug classes and outcome of therapy remain to be defined.Conclusion:Algorithms for optimizing drug therapy can, with presumably rare exceptions, be developed for any disorder, using standard Internet programming methods.

Background

During the last decades the possibility of computer sup-port for optimum management of various disorders has attracted interest [1][2][3]. Regarding drug therapy the approach has usually been limited to one particular disease [4][5][6][7]. Diabetes has been extensively studied and thoroughly analyzed also from a systems perspective [8]. Several attempts have been made to develop a general model for medical guidelines, which could be used also for the development of computer programs [9][10][11][12]. Computerized physician order entry has been shown to reduce the frequency of serious medication errors. Decision support tools such as alerting functions for patient medication allergy are a key part of these applications. However, Abookire and co-workers [13] analyzed trend data obtained over a five-year period that showed decreasing compliance to allergy alert functions within computerized order entry. They concluded that optimal performance requires iterative refinement and that, as systems become increasingly complex, mechanisms to monitor their performance become increasingly critical [13]. In another study large differences were seen for all main types of medication errors: dose errors, frequency errors, route errors, substitution errors, and allergies [14]. In a university hospital setting, the reductions in transcription errors and medication turn-around times supported the view that computerized physician order entry and an electronic medication administration record can provide a good return on investment [15].

In the current study guidelines for 246 diseases were analyzed regarding the content of facts, and relationships between these facts, influencing the selection and dosage of therapeutic drugs. The aim of study was to identify one or more algorithms (descriptions of how to solve a problem in a definite number of steps), which could be used to support optimum drug therapy in any disease.

Methods

The studied material consisted of 110 officially endorsed text documents, published between 1996 and 2004. Forty-four of these documents were published by the Swedish Medical Products Agency [16], 13 by the Swedish Agency for Evaluation of Medical Technologies [17] and 53 (in three booklets) by the Swedish Strategic Programme for The Rational Use of Antimicrobial Agents and Surveillance of Resistance (STRAMA) [18], established in order to prevent bacterial resistance to common antibiotics. The documents contained guidelines for drug therapy in altogether 246 disorders. Information regarding contraindications, dosage etc was available from an existing pharmaceutical website [19] and was retrieved by linking to context-relevant pages.

These documents were analyzed as follows: All statements in the particular document regarding patient-, diseaseand drug-related factors were recorded. In a second step the statements in the texts describing the relationship between these factors were identified. After that the obtained information was rewritten in a format similar to a generic computer language, using if-then statements and logical operators as needed. These rewritten documents were grouped according to similarity in structure, based on the number of if-then statements, the need for branching or calculations in the code and the occurrence of 'leftovers', i.e. information in the text that could not be explicitly expressed using if-then statements and logical operators. In theory this procedure could result in an intractable combinatorial explosion. This was observed only in one situation, regarding side effects of anti-hypertensive medication. Figure 1 shows the number of facts, relevant for the selection of appropriate drug class in the 246 studied diseases. Three algorithms were sufficient for the intended purpose. The simplest model is a list, containing the data, which imply that the patient should or should not receive a particular treatment. This model is adequate in situations where only one pharmacological treatment modality exists, such as influenza, where neuramidase inhibitors are the only drug class approved in Sweden, see figure 2. A 16 page summary of 35 pages of background documentation contained the following data items influencing drug therapy: four risk groups, time from start of symptom, two available neuramidases, one of which was approved also for use in children. Pharmaceutical properties regarding all approved drugs is available online (accessed from the program using 'deep linking'). In the actual program implementation information was added about current epidemiological state regarding influenza by linking to a government authority website, diagnostic criteria and some management aspects (in pop-up windows) (Fig. 2).

Results

In situations, where treatment can by provided using drugs from different pharmacological classes, a more elaborate model is required. An example is heart failure. A 25page summary of 69 pages of background documentation described six physiological states and 11 available drug classes. This knowledge was modelled using an ordered set of it-then statements, see figure 3. The function containing these sets of ordered rules is activated every time the user enters some information on the screen. For an example of user interface, see figure 4. In the actual program implementation information was added about treatment in emergency situations and in different clinical stages of the disease (in pop-up windows) (Fig. 4). The algorithms mainly consist of sets of if-then statements, grouped in a single function, which is activated each time the user enters some information by checking a check box or radio button on the screen [20]. This information is equivalent to a fact list, and the if-then rules mimic a knowledge base. Thus in these aspects the algorithms are similar to conventional expert systems [21], but a separate inference engine was not needed. However, the if-then rules had to be ordered according to the output such that positive information, i.e. recommendations to use a particular drug, always was overwritten when factors were present that implied that this particular drug was contraindicated or less suitable. Such sets of if-then statements were able to manage the identified information in all, but one instance. The exception was depression, where explicit knowledge regarding the relationships between patient characteristics and optimum choice of drug is lacking.

In some diseases the selection and dosage of drugs depend on the degree to which one or more particular factors are present. In such cases a specific program implementation of an established decision model based on fuzzy set the-ory [22] could be used. An example is the adjustment of medication according the patient's description of side effects in the treatment of high blood pressure [6,23], see figure 5. The data in this program were obtained from a study of 1013 treated and 125 untreated hypertensive patients [24]. Thirteen types of complaints were recorded and correlated to the ten available drug classes. The resulting matrix was analysed by four physicians, one professor of endocrinology, with special interest in hypertension, two clinical pharmacologists and one family physician, and modified according to scientific evidence and clinical experience. Clicking on radio buttons enters the type and degree of complaint and the program ranks the available drug classes according to the degree to which they can be assumed not to cause the patient's complaint(s). The algorithm is an implementation of a decision theory, based on fuzzy sets [22]. A verbal description of the implementation would be quite lengthy, but the workings of the algorithm and the code can be seen on the author's website [23].

The pharmaceutical information about all therapeutics drugs, approved in Sweden, is available on the Internet at a site managed by the association of pharmaceutical industries [19]. By linking to this site, the programs provide access to the relevant and continously updated drug information regarding all drugs, registered for use in the current disease. In addition, each program is linked to the appropriate guideline document, so that the user can verify the program's treatment advice.

Discussion

The main findings in the current study are that simple algorithms can support drug therapy and that such algo-rithms can be easily implemented using standard Internet programming techniques. Problems in the implementation of information system designed to support actual care are discussed in a study by Bates et. al. [25]. The authors concluded that 'ten commandments' have to be taken into account. The most important factors for success were speed, anticipation of needs and good fit into the user's workflow. The programs developed in the current study easily meet these criteria, provided that the users have access to the Internet. However, it should be noted that the programs require no communication of patientrelated information over the Internet, other than transmission of the prescription to the pharmacy, if such a system is implemented.

One disorder, depression, was identified for which relevant computer program support could not be constructed. Figure 2 Example of user interface, one drug class. Underlined headings, e.g. Neuramidase inhibitors, are links to pop-up windows

Example of user interface, one drug class

Initially, the program model with the above-mentioned decision algorithm, based on fuzzy sets, was thought to be applicable, since the number of relevant facts is well within manageable range; four major classes of drugs, slightly more than ten diagnostic variables, some ten receptors and seven neurotransmitters. However, the information in the official consensus document [26], did not provide sufficient information, due to the lack of explicit relationships between the patient's characteristics, as described e.g. by diagnostic rating scales, and the pharmacological properties of the various types of antidepressants. Outcome studies regarding depression also give little information about which drug should be recommended for a particular patient. The exception is a recent report by Joyce et al [27], who found a markedly superior response to nortriptyline compared to fluoxetine in men above 40 years of age, and the reverse effect in women, aged 18 -24 years.

Problems in pharmacotherapy related to the simultaneous occurrence of multiple disorders in the same patient are well studied and presented in the pharmacological information as contraindications or recommendations of "caution in patients with disease ...". This information is used as output from the programs in the current study.

The information in the analyzed text documents was not always sufficiently detailed and logically consistent to allow the construction of computer code. Additional information could usually be gathered from other sources, but such procedures may induce errors and should be avoided. Methods are currently available to Example of a set of rules (verbose pseudocode) Figure 3 Example of a set of rules (verbose pseudocode) support logical structure in text documents, e.g. the Guideline Elements Model [8]. This model relies on XML tagging, a standard for content description in documents intended for electronic processing. A formal structure would increase the usefulness of guidelines [24], since it would be possible to present them in the format of fast, easily available and "easy-to-use" programs, as in the recently published PresGuid system [11], which relies on XML tagging of text based guidelines for automatic conversion to computer programs. Experience will tell whether this approach will be more efficient than the simpler strategy of manual extraction of relevant data from the guideline document, combined with JavaScript coding, used in the current study.

The introduction of a new drug for treatment of a particular disease, or the results from new scientific studies may have considerable influence on guidelines. Yet only a few documents were changed during the study period and the lifespan of pharmacotherapeutic guidelines can be assumed to be 2-5 years. However, new information may appear unexpectedly, and a reasonably complete system for pharmacotherapy requires frequent updates. The approach taken in the current study, with the programs stored on a website as scripted HTML pages, facilitates the maintenance and update of the system and ensures that all users always have access to the latest version. An additional way to increase relevance over time is to link from the programs to recently published studies, e.g. by providing automatic searches in the PubMed database [28].

Conclusion

In the current study based on guidelines regarding two hundred and forty-six diseases, three basic program Example of user interface Figure 4 Example of user interface. Underlined headings are links to pop-up windows, check boxes refer to facts influencing selection of drugs. models were found to be sufficient for computerized decision-support in pharmacotherapy. The programs could be developed using HTML and JavaScript, i.e. simple and widespread programming techniques. No specific development tools are needed although standard programming tools such as Macromedia Dreamweaver ® are helpful. The use of Internet implies that only one version of the programs is in use, that upgrading is very conven-ient, and that no interference occurs with existing systems. No confidential patient data has to be communicated and the programs are always available from any Internet connected computer.

In summary, algorithms for optimizing drug therapy can, with presumably rare exceptions, be developed for any disorder using standard Internet programming methods.

Output from a program for adjusting medication for high blood pressure according to the patient's side effects, in this case ankle edema Figure 5 Output from a program for adjusting medication for high blood pressure according to the patient's side effects, in this case ankle edema Publish with Bio Med Central and every scientist can read your work free of charge

Number of facts influencing the selection of therapeutic drugs in the studied disorders (n = 246)Figure 1Number of facts influencing the selection of therapeutic drugs in the studied disorders (n = 246).

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Competing interestsNone declared.Authors' contributionsPW conceived and designed the study, collected data, supervised data analysis, did most of the JavaScript and some of the HTML programming and contributed to the writing of the paper.LM did most of the HTML and some of the JavaScript programming and contributed to the data analysis and the writing of the paper.Both authors read and approved the final manuscript.
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Cost analysis of large-scale implementation of the 'Helping Babies Breathe' newborn resuscitation-training program in Tanzania

Sumona Chaudhury
Lauren Arlington
Shelby Brenan
Allan Kaijunga Kairuki
Amunga Robson Meda
Kahabi G Isangula
Victor Mponzi
Dunstan Bishanga
Erica Thomas
Georgina Msemo
Mary Azayo
Alice Molinier
Brett D Nelson
Cost analysis of large-scale implementation of the 'Helping Babies Breathe' newborn resuscitation-training program in Tanzania
10.1186/s12913-016-1924-2R E S E A R C H A R T I C L E Open Access
Background: Helping Babies Breathe (HBB) has become the gold standard globally for training birth-attendants in neonatal resuscitation in low-resource settings in efforts to reduce early newborn asphyxia and mortality. The purpose of this study was to do a first-ever activity-based cost-analysis of at-scale HBB program implementation and initial follow-up in a large region of Tanzania and evaluate costs of national scale-up as one component of a multi-method external evaluation of the implementation of HBB at scale in Tanzania. Methods: We used activity-based costing to examine budget expense data during the two-month implementation and follow-up of HBB in one of the target regions. Activity-cost centers included administrative, initial training (including resuscitation equipment), and follow-up training expenses. Sensitivity analysis was utilized to project cost scenarios incurred to achieve countrywide expansion of the program across all mainland regions of Tanzania and to model costs of program maintenance over one and five years following initiation. Results: Total costs for the Mbeya Region were $202,240, with the highest proportion due to initial training and equipment (45.2%), followed by central program administration (37.2%), and follow-up visits (17.6%). Within Mbeya, 49 training sessions were undertaken, involving the training of 1,341 health providers from 336 health facilities in eight districts. To similarly expand the HBB program across the 25 regions of mainland Tanzania, the total economic cost is projected to be around $4,000,000 (around $600 per facility). Following sensitivity analyses, the estimated total for all Tanzania initial rollout lies between $2,934,793 to $4,309,595. In order to maintain the program nationally under the current model, it is estimated it would cost $2,019,115 for a further one year and $5,640,794 for a further five years of ongoing program support. Conclusion: HBB implementation is a relatively low-cost intervention with potential for high impact on perinatal mortality in resource-poor settings. It is shown here that nationwide expansion of this program across the range of health provision levels and regions of Tanzania would be feasible. This study provides policymakers and investors with the relevant cost-estimation for national rollout of this potentially neonatal life-saving intervention.

Background

An estimated 2.9 million neonatal lives (from birth through day 28) are still being lost each year globally, with the persisting highest risks of death occurring in African countries and within the first 24 h of life [1]. Addressing global standards of care for neonates to reduce these deaths is of increasing importance in accelerating progress toward the fulfillment of global child mortality reduction targets [2]. Neonatal deaths are due in part to a lack of trained birth attendants with basic requisite skills for newborn resuscitation [2]. 'Helping Babies Breathe' (HBB) is an evidence-based curriculum devised to meet the training needs of large groups of birth attendants to become skilled in the essentials of neonatal resuscitation, with a focus on achieving adequate ventilation of apneic newborns within the first minute of lifethe so-called "Golden Minute" [3].

The American Academy of Pediatrics developed HBB, in partnership with USAID, Save the Children, and UNICEF, amongst others [4]. As HBB becomes the gold standard of care for minimum newborn resuscitation training of all birth attendants globally, both government and non-governmental entities are increasingly focusing on costs and impacts of implementation. Although some studies have investigated the cost-effectiveness of Essential Newborn Care (ENC) and integrated newborn care packages incorporating resuscitation to reduce newborn mortality, very few attempts have been made to date to quantify the costs and effects of newborn resuscitation at scale in resource-poor settings where the potential costeffectiveness may be invaluable [5][6][7][8][9][10][11][12][13]. Although ENC expansion alone in resource-poor settings has been investigated, with Manayasan et al. reporting a 41% reduction in neonatal mortality (RR 0.59 95% CI 0.48-0.77), further investigation of asphyxia-related deaths and those potentially preventable through neonatal resuscitation capacitybuilding warrant further investigation [9].

Although large-scale formal cost-effectiveness analyses have been planned to address expansion of such HBB programs in resource-poor settings [13], our study is the first to fully describe the costs of implementing the HBB program at a regional and national scale. Prior to this study, cost analysis of HBB introduction had been limited to a missionary hospital in Tanzania, in which HBB was found to be a highly cost-effective intervention [6]. By accounting for costs at a regional and national scale in Tanzania, we attempt to account for the diverse scope of service provision and potential for achieving economies of scale for governments considering expansion in comparable resource-limited settings as called for by Msemo et al. [7].

In September 2009, the Tanzanian Ministry of Health and Social Welfare (MOHSW) launched a national HBB training program. Since 2012, through the funding support of the Children's Investment Fund Foundation and implementation partner Jhpiego, HBB has been implemented at scale in 16 regions throughout Tanzania in a phased region-by-region rollout among a targeted 14,000 facility-based providers. Initial one-day HBB training sessions were held centrally within several districts in each target region with providers from all levels of the health care system. Four to six weeks posttraining, follow-up visits were conducted at all of the facilities for program monitoring, equipment assessment, and follow-up appraisal of the trainees' skills through objective structured clinical examinations (OSCEs) [14]. Additional subsequent follow-up and supportive supervision visits were also provided longitudinally. The Tanzania MOHSW has plans in progress to integrate HBB expansion with ENC, to achieve economies of scale and demonstrate global standards of newborn care.

The purpose of this study was to conduct a cost analysis as one component of a multi-method external evaluation of the implementation of an HBB program at scale in Tanzania. This cost analysis aimed to determine the full costs of initial program implementation in one generally representative region of Tanzania. Any differences in this region with other regions of Tanzania were accounted for in additional scale-up and sensitivity analyses to model what the projected costs would be for program implementation throughout national mainland Tanzania. Further sensitivity analyses are also presented for appraisal of potential maintenance costs over a oneand five-year period, according to the costs of conducting repeated refresher trainings using the current model. As HBB is further expanded across Tanzania and across the globe, these cost data will help to inform stakeholders on the human and financial resources needed to accelerate reductions in neonatal mortality and establish global standards of newborn care [5][6][7][8][9][10][11][12]15].

Methods

Aim

The primary objective of this study was to do a first-ever activity-based cost-analysis of at-scale HBB program implementation and initial follow-up in a large region of Tanzania and evaluate costs of national scale-up as one component of a multi-method external evaluation of the implementation of HBB at scale in Tanzania.

Study design

The activity-based cost-analysis study utilized real-time cost data collection during a two-month period of program administration in a cross-sectional design in a selected region of Tanzania. Sensitivity analyses were utilized to estimate national costs of scale-up.

Study setting

The study was purposively conducted in Mbeya Region, which is located in the southwest mainland of Tanzania, is largely rural, and is surrounded by highlands. The regional capital is Mbeya City. Mbeya Region is one of Tanzania's 25 mainland regions, covering an area of 62,420 km 2 with a population of 2,707,410 according to 2012 census findings [16]. Within the Mbeya Region, there are a total of 407 health facilities (386 of which are operational), the majority being dispensaries, followed by health centers, and hospitals [17]. The Mbeya Region was purposely selected for regional-level program cost analysis as is thought to be highly representative of the overall variation of urban and rural providers within the mainland Tanzanian healthcare provider landscape. Scale-up analysis is conducted to extrapolate findings to the other regions where HBB is being implemented. Scale-up and sensitivity analyses consider the effects of variation in region characteristics, including geographical area and population density to capture potential cost differences between regions upon national scale up.

Data collection and cost analysis

The cost data related to training implementation and facility-level follow-up were collected between February and March 2014. Cost-analysis of the program used a micro-costing, bottom-up approach combining activitybased costing (ABC), using real-time budget expense data [18][19][20][21]. Activity-based costing is a preferred method in the context of program expansion [21,22]. Expenditure data were collected from the central, Dar es Salaam-based, Jhpiego program office to determine expenses attributable to the HBB program in Mbeya. Cost data for this study were collected using a series of questionnaires. Staff at the implementing organization headquarters filled out standardized structured forms to obtain real-time cost data on office costs, personnel costs, initial training sessions, refresher trainings, and monitoring visits. Itemization of all individual input activities performed as part of the HBB program (e.g., the initial training sessions, equipment distribution, monitoring visits) was completed using these standardized data collection instruments.

Three categories of costs were considered in this study: (1) program-specific costs, (2) personnel costs, and (3) capital costs. The activity cost centers within these cost categories were itemized as: (1) initial training session and equipment, (2) facility-based follow-up visits 4-6 weeks post-training, and (3) central administration of the program (Tables 1 and 2). Sensitivity analyses account for geographical and economic sources of variation in cost and for costs of maintenance of the program according to repetition of the follow-up visits that would be required over a one-year period to sustain the program and according to the refresher trainings and project continued administration and equipment costs over a five-year period (Tables 3 and 4).

Personnel costs

Central administration personnel costs were limited to staff of the implementing partner, Jhpiego. These are partially recurring costs and refer to cost-activity center A: central administration. Personnel employed by Jhpiego in Tanzania are involved in many different programs aside from HBB, therefore, their expenses were adjusted to reflect the proportion of personnel time attributable to HBB in Mbeya Region during the two- month implementation period. Personnel included program leadership, implementation staff, monitoring staff, and administrative support. Further roles of central administration staff were itemized in the cost analyses, sub-divided into audit and finance, communications, human resources, operations, procurement, program staff, and transport staff (Tables 1 and 2), with a breakdown of exact costs and their respective proportions for the Mbeya initiation presented ( Table 2). Further administration costs were attributable to office space rent, office utilities and supplies, and staff benefits. When considering the maintenance of the program per refresher training conducted, a proportion of these costs would be requisite. It is estimated here that approximately 20% of the initiation central administration costs would be required per refresher training (Table 4). Program implementation administration costs were again attributable to implementation partner Jhpiego staff with additional assistance of regional and district-level MOHSW leadership. These are partially recurring costs referring to cost-activity centers B and C: initial-training and follow-up training. Roles of program implementation staff are again itemized, sub-divided into per diems for trainers, trainees, implementing partner staff, and ministry of health staff (Tables 1 and 2), with a breakdown of exact cost and their respective proportions for the Mbeya Region initiation presented ( Table 2). Cost-activity center C represents a fully recurring cost and provides the basis for the program maintenance-cost analysis, as ongoing program support is based on conducting repeated followup trainings in the form of refresher trainings with a proportion of cost-activity centers A and B costs to account for the repeated central administration and resuscitation equipment costs required (Table 4).

HBB program-specific costs

These included costs incurred exclusively in the implementation of the HBB program in the Mbeya Region.  These are partially recurring costs and refer to costactivity centers B and C: initial training and follow-up training (with cost-activity center C: follow-up training, representing a fully recurring cost as above). These include all expenses attributable to HBB training sessions and facility-based follow-up visits, specifically, costs related to training equipment, rental of a training venue, food, transportation, and accommodations. Programspecific costs were differentiated from personnel and capital-costs, both of which may be shared with Jhpiego programs other than HBB and, therefore, represented a proportion of their central office costs. The significance of program-specific costs to decision-makers lies in that they must be regularly renewed as are accounted for in  Based on annual repeat refresher trainings to sustain skills thereafter the maintenance analysis presented below (e.g., in the form of refresher trainings and equipment replacement). Equipment replacement was factored into the analysis of maintenance costs, assuming up to 20% of equipment would require replacing per year following initiation (Table 4).

Capital costs

Capital costs were itemized within cost-activity center A: central administration. This included vehicles, office infrastructure, computers, office furniture, and other assets required for the functioning of Jhpiego's central office in Dar es Salaam and proportionally attributable to the two-month implementation of HBB in Mbeya.

Equipment costs

Equipment costs were itemized within cost-center B: initial training, as in general these costs are not anticipated to be recurring, but a proportion of replacement costs are accounted for in program maintenance cost projections. Resuscitation equipment included Laerdal NeoNatalie mannequins and multiple sets of reusable bag-mask and suction devices, according to the size of the facility. Training materials (e.g., HBB learner's manuals, HBB wall poster, large HBB flipchart, etc.) were also provided.

Sensitivity analyses

Finally, sensitivity analyses were conducted to demonstrate how potential variations in variables across settings might impact overall costs ( Table 3). Sources of variation in program-specific and administrative costs are anticipated according to differences in regional economic variation, distance between the region and central administration, population coverage, and implementation by local ministry of health or an international non-governmental organization. Equipment costs are anticipated to vary across settings, through economies of scale, and may reduce over time as advances are made in production.

Maintenance costs

Additional sensitivity analyses were undertaken to estimate those costs that would be incurred for sustaining the training through repeated refresher trainings and the attendant administration and equipment costs over a oneand five-year period (Table 4). A projected 20% of the program initiation costs was estimated as effort needed for central administration to deliver refresher training. All central administration costs were based upon the costs needed of the non-governmental implementation partner to conduct work. All central administration costs and maintenance administration costs are, therefore, proportionally attributable to the duration of activity needed by a central support mechanism. A projected 20% of initial equipment costs is used to estimate the annual cost of replacing equipment in maintaining the program, assuming a loss of function in some of the materials over time.

Results

Cost analysis

The total cost for implementation of the HBB training program in the Mbeya Region over a two-month period in 2014 was $202,240. This included total initial training costs of $91,425, total follow-up visit costs of $35,659, and total central administration costs of $75,156. In total, 49 training sessions were undertaken, involving the training of 1,341 health providers from 336 health facilities in Mbeya Region, such that the cost of delivering HBB training at the regional level was $4,128 per training session, costing $151 per trainee and $602 per health facility.

Coverage of the HBB training program

A total of 336 of 407 (82.6%) health facilities in the Mbeya Region participated in the trainings, with an average of four providers from each facility, ranging from one to nine, depending on the level of facility. During the facility-based follow-up visits, the Jhpiego program assessed a total of 1,001 health providers from 322 of these 336 trained health facilities (95.8%). Ten of the 11 (90.9%) district councils were also visited.

Cost distribution

Of the overall total program costs for implementing HBB in Mbeya Region, the highest proportion of costs was spent on initial training costs (45.2%), followed by central administration costs (37.2%), and lastly follow-up visit costs (17.6%) for program initiation.

Scale-up costs

All scale-up estimates were based on the best-available national data for scaling to the 25 Tanzanian mainland regions. The five additional regions of Zanzibar are under the leadership of a separate ministry of health and, as island regions, were considered to have unique costing issues and, therefore, are not included in the analyses. However, the estimates presented here rely on assumptions, which are explored further in the sensitivity analyses and discussed as limitations to this study below.

Per health facility

According to national data, there are a total of 7,537 health facilities (6,640 of which are operational) within the Tanzanian mainland. 7 Using per-facility costs calculated in this study, scale-up to 6,226 (82.6% coverage, as with Mbeya Region) of these facilities would cost an estimated $3,747,429.

Per training session

Forty-nine trainings were required to reach 336 health facilities. Coverage of 82.6% of the total national number of health facilities would require an estimated 908 training sessions, costing a projected $3,747,579.

Per trainee

Assuming roughly four healthcare providers trained in HBB per health facility, and assuming 82.6% coverage, the cost of scaling-up training to reach 24,904 providers would be $3,755,772.

Calculations of national costs based on per-facility, per-training, and per-trainee costs in Mbeya Region were fairly consistent. The overall range in these various per-item estimates suggests a minimum potential cost of $3,747,429 and a maximum potential cost of $3,755,772 to cover approximately 82.6% of mainland Tanzania. The effects of possible sources of variation are explored in brief in the following sensitivity analyses.

Sensitivity analyses

Extension in duration of initial training would incur the greatest potential impact on overall national roll out costs. Within regional cost of initial training (cost-center B; Tables 1 and 2), 57.3% of the costs were calculated to be recurring (personnel, venue, food and housing; Table 2). Hence, costs for extended duration of initial training would cost to the national rollout an estimated addition of $970,470 per additional day of initial training (Table 3). Variation in program costs and personnel costs between regionsfor example, as a result of attainable population coverage and distance between the region and the national capitalmay additionally incur significant impacts on national scale-up ( Table 3). The majority of overall personnel costs, including central administration and program-specific personnel, were attributable to program implementation staff, with a further 28.9% to administrative support, 2.0% to leadership, 1.7% to monitoring and evaluation staff, and the remaining 10.0% to fringe benefits.

Economic variation, including alterations in interest rates and the value of the currency (estimated within the range of −5% to +5% of current), may incur changes to all elements of the program costs, such that overall costs of national rollout may vary from between $3,560,051 and $3,934,793. The extent to which expansion of HBB training is achieved, in terms of the number of regional facilities supplied with trained birth attendants and the consequent coverage of the population with HBB services, influences all elements of program costs. Variation of this coverage from 65% to 95% of the total population may lead to a variation in the total cost of national rollout of the range of $2,948,702 to $4,309,595. Changes in the distance between a region and central administration, as well as the population density of the region, may impact costs of transportation, program-specific personnel as they may need to travel for more days, as well as the costs of the venue, food, and housing. Variation in regional distance from central administration may incur variation in all Tanzania program rollout costs in the range of $3,582,678 to $3,912,166. Variation in equipment costs ranging from −20% to +20% depending on, for example, economy of scale, would be expected to incur impacts on program supply costs, such that the total Tanzania program cost may vary from between $3,605,951 and $3,888,893.

Maintenance costs

Analyses are presented for costs of repeated refresher trainings over a one-year and five-year duration following HBB program initiation. Costs of repeated refresher training are assumed to be consistent with follow-up training costs during the initiation. Over this period, it is anticipated that the in-country Ministry of Health would assume responsibility for the program to achieve economies of scale resulting in a reduced regular investment of 20% of the initial central administration costs for each refresher-training. Twenty percent of initial equipment costs are anticipated for replacements for each year following initiation. It is, thus, estimated that it would cost around $173 per facility for a single refresher training session ($1,079,821 for all Tanzania) and hence $2,019,115 to sustain the program nationally for one year based on twice yearly refresher visits in the first year, and $5,640,794 to sustain the program nationally for five years based on annual refresher trainings thereafter.

Discussion

HBB is considered one of the leading interventions for improving health outcomes in low-and middle-income countries [23]. As HBB is further expanded across the globe, these at-scale cost data will be an essential tool providing stakeholders with critical information on the human and financial resources needed to deliver reductions in perinatal mortality. Our calculations project a cost to implementing an HBB training program at $4,128 at the regional level, serving around seven health facilities at a cost approximating $602 per health facility. We estimate each re-training to cost $1,211 for seven health facilities ($173 per facility). Voissus et al. found in a single hospital site in Tanzania that initial training cost $2,084 and re-training cost $1,515 [6]. Our cost data capture cost-influential factors involved in rolling out an HBB program at a regional level, we expect these data to be a reasonable estimate of regional costs of scale-up in regions comparable to Mbeya Region. Our national estimate of $4,000,000 is further examined in sensitivity analyses to consider the effects of variations in costinfluential variables across the differing regions of Tanzania, giving bounds to this estimate of $2,934,793 to $4,309,595. Our cost estimates are based on a oneday initial training period. Further estimation of the impact of extended duration to the initial training is considered to have the greatest overall impact on potential costs, at an excess of just under $1,000,000 per day. Additional cost, therefore, is a consideration in decisions regarding the optimal duration of the initial training period in resource-limited settings.

Resuscitation training of birth attendants within integrated newborn care packages along with other evidencebased measures to save lives have resulted in significant reductions in neonatal mortality [5][6][7][8][9][10][11][12]. Carlo et al. did not find a reduction in mortality following introduction of newborn resuscitation training, however, the resuscitation training was conducted after ENC training that included elements of resuscitation training, diluting any potential effects [12]. Bang et al. reported significant reductions in neonatal case-fatality due to severe asphyxia (by 45%, from 39 to 20% (p < 0.07)) and asphyxia-related neonatal mortality (by 65%, from 11 to 4% (p < 0.02)) in a study of community health workers in India trained in both newborn care and resuscitation skills [24]. The FIRST BREATH trial estimated reductions to be as high as a 30-40% [12]. Similarly, Sabin et al. estimated a 45% reduction from a combined program (RR 0.55 95% CI 0.33-0.90) [5]. Countries implementing HBB and newborn care programs are anticipated to benefit from the systems approach to training, such that expansion of HBB may be conveniently undertaken in conjunction with ENC and other interventions for full potential neonatal mortality impacts and cost-savings to be realized [25].

This study in Tanzania represents a first-ever costanalysis of implementing the HBB training program at scale. Regional costs were calculated to project cost estimates for national rollout of the program. Data were captured in a real-time effort to expand HBB across Mbeya Region. This is the first report of a regional cost-analysis in a low-income country setting. Activity-based costing methodology, used in combination with the bottom-up costing and ingredients approach gives an accurate measure of the costs involved at the regional level, as well as a valid basis from which to estimate national initiation and maintenance costs for the HBB program. Costs for the HBB program were divided into three activity-cost centersinitial training and equipment, facility-based follow-up visits, and central administrationand into three cost categoriesprogram-specific costs, personnel costs, and capital costs. Of these, initial training costs were found to be the highest, and capital costs were the lowest. Within the training costs, personnel costs were the highest, followed by the cost of HBB training-related equipment.

Limitations

Costs within this study are estimated under the assumptions of a model of implementation largely administrated by non-governmental organizations and, hence, reflect higher organizational personnel and administration costs. Although there is little available data on the comparative costs of governmental versus non-governmental expenditure in such programs, national ministries of health would be in a position to achieve significant cost savings than are achievable working with a non-governmental implementation partner, to reduce the costs of program expansion as compared to what is presented here. Additional economies of scale could be achieved on larger bulk-purchase of equipment. In the current analysis, the average cost of equipment was $350 per facility in order to provide facilities with multiple sets of training materials, including a Laerdal NeoNatalie mannequin, HBB learner's manuals, wall posters, and multiple sets of HBB newborn resuscitation equipment (e.g. reusable bag-mask device, reusable suction device, etc.) according to the size of the regional facility [26]. Furthermore, integration of HBB within a package of ENC and other essential neonatal services would likely further reduce overall central administration costs, whilst increasing health gains and consolidating a systems based approach to neonatal healthcare [25]. Empirical data on the extent of costsavings achievable through government administrated, integrated delivery of care at high volume, such that economies of scale could be fully realized, within resourcelimited settings are warranted. Such sources of potential cost-savings on overall national HBB program rollout and sustainability costs were not immediately estimable within the limits of the current study.

For this analysis it was necessary to extrapolate real time cost data from Mbeya Region in order to make estimates for scale-up to all of the other mainland regions. While sensitivity analyses served to apply this regional information to projected national implementation, these projections necessarily rely on assumptions that the cost of program implementation in Mbeya Region is representative of other regions within Tanzania and at different times. Efforts to account for major sources of potential variation in costs regionally and over time are presented in the sensitivity analyses. The findings of the sensitivity analysis, suggest that the national estimate for program initiation we project of around $4,000,000 is potentially robust to several sources of cross-regional differences in cost-influential variables. We consider the influence of economic variations such as interest rate and currency valuation changes, population coverage achievable across regions according to geographical area and population density, distance of the region from the site of central administration, as well as potential variations in the costs of equipment. However, our choice of cost-influential variables warrant further investigation, as there is uncertainty regarding the true effects of variation across regions upon national expansion of the program.

Regional expansion presented here achieved 82.6% coverage of facilities and 74.6% coverage of providers. This represents a highly satisfactory proportion of coverage for an at-scale intervention in a real-world lowincome country setting, considering the variation in health-services anticipated across regions and our choice to represent all facilities (total versus operational). For instance, in typical lower-level facilities such as dispensaries, there may be only 1-2 skilled birth attendants on staff such that releasing staff to attend a centralized training at the district hospital can be difficult in conjunction with continued delivery of clinical care. Additionally, HBB-trained providers can be rotated to new departments, and new staff are hired. These result in a dilution of the training coverage.

This study did not attempt to account for program planning and start-up costs, such as the training of the MOHSW master trainers who conducted the regional training sessions. MOHSW leadership contributions were also not included. Furthermore, scale-up analysis is based upon the number of national health facilities information from the Tanzanian MOHSW. We chose to use total facilities, as opposed to operational facilities, to provide a conservative estimate for national rollout.

In the absence of a formal cost-effectiveness analysis at scale, and randomized control trial data to demonstrate the intervention effect size in this context, it is challenging to reliably compare the costs and impacts to similar neonatal care programs. Additionally, there are no reliable baseline measurements of mortality concurrent with these cost measurements. Data are needed on mortality indicators associated with HBB implementation at scale, such as number of lives saved or number of resuscitations conducted, without which it is not possible to estimate the cost per neonatal death averted or disability-adjusted life year gained.

Sustainability

Maintenance costs arising from sustaining the HBB program following initial rollout have been previously estimated to be significantly lower than initial rollout costsapproximately one-third the costs of implementation per year [6]. The consideration of maintenance costs here is essential to ensure further uptake and retention of skills over time, through activities such as refresher trainings. The activity-based costing methodology allows for efficent calculations of valid maintenance costs, however, some assumptions are necessary for considering the proportion of administration and equipment replacement costing. These, unfortunately, are untestable assumptions but are in keeping with expectations established from prior studies [5][6][7][8][9][10][11][12].

Our cost data demonstrate the financial feasibility of HBB regional and national expansion and maintenance over one and five years in low-income country settings. For contextualization of the magnitude of the costs, we consider that the latest national health budget in Tanzania was approximately $800 million [27]. Therefore, countrywide implantation of a $4 million HBB program would account for roughly one half percent of the country's annual health system budget. Given an approximate projected $2 million cost for one-year program maintenance and $6 million cost for five-year program maintenance, the countrywide expansion and sustenance of such newborn standard of care practices is highly cost-feasible.

Further efforts to quantify the costs and impacts of integrated packages of neonatal care involving essential care are called for. Although estimation of these is beyond the scope of this work, this cost analysis does provide a useful framework for policymakers to estimate the potential costs invovled in expanding programs of newborn care, based on similar models of training as HBB is designed to be easily ammended with additional training elements and is intended to provide a platform for training that enhances a systems-approach to delivery of care [3-13, 24, 25].

Conclusion

National rollout of the HBB program in Tanzania is financially feasible. Stakeholders can use the current study as a guide for costing out the expansion of this potentially life-saving neonatal resuscitation program in other resource-limited settings. Formal cost-effectiveness analyses are warranted to assess potential cost-savings per neonatal death averted.

Abbreviations ABC: Activity-based costing; ENC: Essential Newborn Care; HBB: Helping Babies Breathe; MOHSW: Ministry of Health and Social Work; OSCE: Objective structured clinical examination; USAID: United States Agency for International Development; UNICEF: United Nations Children's Fund

Table 1
1Cost-activities of national HBB training program implementation and follow-upPersonnel and capital costs
Comments

Cost-activity center A: Central administration

Leadership
Implementation
Monitoring
Administration
Office support

International hires, in-country leadership
Inclusive of audit, finance, communications, human resources, operations,
procurement, program staff, and transport costs
Office space, supplies

Program-specific costs

Cost-activity center B: Initial training

Training of birth attendants
Distribution of HBB equipment

Ministry of Health and implementation partner costs
Trainer and trainee per diems
Laerdal NeoNatalie mannequin, and multiple sets of HBB newborn resuscitation
equipment (e.g., reusable bag-mask device, reusable suction device, etc.) [26]
Training materials (e.g., HBB learner's manuals, HBB wall poster, HBB flipchart, etc.)
Printed material
Administration (inclusive of venue costs, associated accommodation, transportation)

Ensuring competence (OSCEs)

Cost-activity center C: Follow-up visits

Sustaining training
Ensuring skill retention (OSCEs)
Verifying presence of HBB equipment

Ministry of Health and implementation partner costs
Provider and trainer per diems
Printed material
Administration (inclusive of associated accommodation, transportation)

Table 2
2Mbeya Region HBB training program activity-based costsActivity cost centers
Cost in USD
(Percent)

Cost-center A: Central administration

Personnel

Leadership

International hires
446
(0.2)

In-country leadership
2,201
(1.1)

Implementation staff
16,174
(8.0)

Monitoring team
2,281
(1.1)

Administrative staff

Audit and finance
14,961
(7.4)

Communications
3,157
(1.6)

Human resources
5,409
(2.7)

Operations department
7,156
(3.5)

Procurement department
3,693
(1.8)

Program staff
2,358
(1.2)

Transport department
2,115
(1.0)

Benefits
13,408
(6.6)

Office space and supplies

Office space rent
747
(0.4)

Utilities
150
(0.1)

Other contractual costs a
598
(0.3)

Office supplies b
302
(0.1)

Total
75,156
(37.2)

Cost-center B: Initial HBB training

Personnel

Per diem for trainers
9,694
(4.8)

Per diem for trainees
32,066
(15.9)

Per diem for implementing partner
1,725
(0.9)

Per diem for ministry of health staff
203
(0.1)

Equipment [26]

Mannequins (70 USD each)
24,104
(11.9)

Bag-mask devices (15 USD each)
10,407
(5.1)

Penguin suckers (3 USD each)
2,848
(1.4)

Learner workbooks
13
(0.0)

Training forms (registration, OSCE)
505
(0.2)

Other (communication, stationary)
44
(0.0)

Venue
1,569
(0.8)

Food
5,648
(2.8)

Transportation
1,134
(0.6)

Housing
1,465
(0.7)

Total
91,425
(45.2)

Cost-center C: Follow-up training

Personnel

Per diem for trainers
13,857
(6.9)

Table 2
2Mbeya Region HBB training program activity-based costs (Continued)Per diem for providers
369
(0.2)

Per diem for implementing partner
2,744
(1.4)

Per diem for ministry of health staff
450
(0.2)

Supplies (photocopying)
253
(0.1)

Transportation
14,111
(7.0)

Housing
3,875
(1.9)

Total
35,659
(17.6)

Total Costs for Mbeya Region
202,240
(100.0)

a

Other contractual costs include delivery services, waste removal, contract
cleaning, etc

b

Office supplies include computer software, printing and photocopying,
furniture, etc

Table 3
3Sensitivity analysis: variation in cost per facility and for all Tanzania rollout given variance in selected cost-influential variablesProgram-specific: distance
Program-specific: equipment
Central administration
Per facility
All Tanzania

Mbeya Region
$88,908
$38,174
$75,156
$602

National estimates a
1,647,444
707,355
1,392,623
602
3,747,422

Initial training duration
-
-
-
-

+ 1 day
-
-
-
758
4,717,826

+ 2 days
-
-
-
914
5,688,230

Economic variation:

-5%
1,565,072
671,987
1,322,992
572
3,560,051

-3%
1,598,021
686,134
1,350,844
584
3,634,999

+ 3%
1,696,867
728,576
1,434,402
620
3,859,845

+ 5%
1,729,816
742,723
1,462,254
632
3,934,793

Population coverage:

65%
1,296,310
556,590
1,095,801
602
2,948,702

75%
1,495,824
642,255
1,264,455
602
3,402,534

90%
1,794,830
770,637
1,517,212
602
4,082,680

95%
1,894,587
813,470
1,601,539
602
4,309,595

Distance from central administration:

-10%
1,482,700
707,355
1,392,623
575
3,582,678

-5%
1,565,072
707,355
1,392,623
589
3,665,050

+ 5%
1,729,816
707,355
1,392,623
615
3,829,794

+ 10%
1,812,188
707,355
1,392,623
628
3,912,166

Equipment costs:

-20%
1,647,444
565,884
1,392,623
579
3,605,951

-10%
1,647,444
636,620
1,392,623
591
3,676,687

+ 10%
1,647,444
778,091
1,392,623
613
3,818,158

+ 20%
1,647,444
848,826
1,392,623
625
3,888,893

a

Based on a 1-day initial training duration

Table 4
4Maintenance cost-analysis (USD)Mbeya Region

AcknowledgementsWe would like to thank our funders for their support of this study, our implementing partners Jhpiego, and the Ministry of Health and Social Welfare of Tanzania for their leadership and partnership in improving neonatal health services.FundingThe Children's Investment Fund Foundation, London, United Kingdom Availability of data and materials All study data have been made available within the tables of the manuscript.Consent to publishNot applicable.Ethics approval and consent to participateAuthors' contributions SC designed the study and undertook the analysis. SC drafted, reviewed and revised the manuscript. LA revised the regional analysis to incorporate all available data and supported the overall analysis and revisions. SB assisted in study design, conducted preliminary analyses and drafted the manuscript. AKK, ARM, KGI, VM, DB, ET, GM and MA designed the data collection and oversaw and conducted the data collection procedures within nongovernmental and ministry of health partner organizations. AM provided support to overall funding and administration of the study from within our funding partner organization. BN oversaw all aspects of the study, criticallyCompeting interestsThe authors declare not to have any financial and non-financial competing interests.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
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