Meeting global health challenges through operational research and management science
Geoff Royston a
a. Consultant, Burton Leonard, Harrogate, HG3 3SJ, England.
Correspondence to Geoff Royston (e-mail: firstname.lastname@example.org).
(Submitted: 19 January 2011 – Revised version received: 26 April 2011 – Accepted: 23 May 2011 – Published online: 05 July 2011.)
Bulletin of the World Health Organization 2011;89:683-688. doi: 10.2471/BLT.11.086066
The historic international health conference at Alma-Ata in 1978 first identified the need for research on “operation, control and evaluation problems” in primary health care.1 Since then, successive reports from the World Health Organization (WHO) have expressed the need for improvements in organization and delivery and have called for research, both on particular interventions and on entire health systems.2 Penetrating the “fog of delivery” clearly presents a major global health research challenge.
Operational research (also known as operations research) and management science have made major contributions in improving organization and delivery in many fields of human activity. Operational research originated in the military arena with the design of an integrated information and control system for the British air force in World War II, work which was estimated to have doubled the efficacy of its fighter command. Since then its use has spread, often providing big returns on investment. Two recent examples are: the world’s largest logistics company redesigned its overnight delivery network which was estimated to yield savings of more than 270 million United States dollars (US$) and a global automobile manufacturer streamlined its prototype vehicle testing, saving US$ 250 million annually. These examples and more are available at: http://www.scienceofbetter.org.
However, operational research and management science are underused in the health field, certainly in global health. For example, the Global Fund to Fight AIDS, TB and Malaria allows 5–10% of each grant for monitoring, evaluation and operations research. However, recent estimates are that projects only budget an average of 3% for operational research and actually spend considerably less.3
Despite the low priority that global health has given operational research, some valuable work has been and is being done. Some examples include: a 32-country programme on primary-care operations research established in 1981 by the US Agency for International Development;4 a sustained operational research effort over several decades underpinning the development of a global strategy on tuberculosis control;5 and an established body of operational research around HIV/AIDS.6
In global health, operational research has an extremely broad interpretation. The term is used for almost any type of improvement-oriented investigation into a programme’s operations. Where management science generally uses systems modelling and related analytical techniques, operational research in global health does not use these tools sufficiently. Almost two decades ago a review of this field noted this gap and stated that many operational research studies in global health “do not carry the full flavour of operational research”.7With some exceptions, such as for HIV/AIDS where there has been a good deal of operational research modelling work,8,9 that gap clearly remains, particularly for neglected tropical diseases.10 For example, guides on operational research published by WHO and the Global Fund to Fight AIDS, TB and Malaria focus mainly on the use of interviews, questionnaires and observations, with less emphasis on experimentation and no coverage of modelling or other analytical methods.11 Stronger links between the practical and analytical approaches would bridge this gap.
Strengthening the use of management science in global health would also improve communication between operational research workers in global health and development. A recent review of operational research in developing countries provides useful detail.12
Management scientists use a range of analytical tools, from quantitative prediction and optimization techniques to qualitative problem structuring and solution search approaches (Fig. 1).13 The tools listed on the top left side of the figure are predominantly qualitative and are typically used with groups of people in participative sessions while those on the bottom right are predominantly quantitative and are typically used by individual analysts in desk-based work. For example, behavioural simulation involves groups of stakeholders acting out a system change or problem situation to gain insights – in a risk-free environment – into likely consequences of their actions. Discrete event and agent simulation seeks to gain such insights rapidly by using computer modelling to represent key system components (agents), their interactions and resulting changes (discrete events).
Fig. 1. Analytical tools of operational research and management science
Qualitative approaches are most likely to be of use at the early stages of analysis, or when time and/or data are in short supply or when the problems are ill-defined. Several problem-focused qualitative analytical tools, such as root cause analysis or influence diagramming (which could be termed “system mapping” approaches), combine analytical rigour with simplicity and transparency. They are particularly useful where it may be difficult or inappropriate to use more quantitative methods.
Some of the more quantitative or computational approaches can be useful, even with limited data, as their key outputs are often determined more by the structure and logic of a situation than precise values of parameters. One such approach is “system dynamics” modelling, a more aggregate modelling approach than discrete event or agent simulation and one that pays particular attention to feedback effects. A noteworthy example of this is the system dynamics epidemiological model used in the global campaign to eradicate polio. The model incorporated a feedback loop that represented how observed impacts of interventions would influence subsequent interventions. This informed a debate on the best approach by showing that eradication was a more effective and less costly long-term strategy than control as the latter approach would not prevent regular major flare-ups of the disease.14
These tools are not mutually exclusive. For example, workers on pandemic influenza have used tools including: brainstorming-based SWOT (strengths, weaknesses, opportunities and threats) analysis,15 behavioural simulation exercises, 16 scenario analysis,17 system dynamics modelling18 and various combinations of simulation and mathematical modelling.19
Nor should modelling be divorced from experiment; they can make a powerful combination with the strengths of one compensating for the limitations of the other. For example, given some basic experimental data, modelling can rapidly explore the effect of varying testing intervals in screening programmes rather than waiting years for field tests on each option.
Note that the word “system” appears twice at the very centre of Fig. 1. While some aspects of health-care delivery are essentially logistical, and so amenable to the use of traditional analytic methods, many health issues are complex and require systemic analysis,20 as is being increasingly recognized. For instance in a recent WHO report,21 which stated “systems thinking has a huge and untapped potential, first in deciphering the complexity of an entire health system, and then in applying this understanding to design and evaluate interventions”. Systems thinking has several important differences from the more usual “linear” perspective, some of which are summarized in Table 1.
Global health challenges
We now look at some key generic challenges that arise in global health (Fig. 2) and consider how operational research and management science can contribute to each.
Fig. 2. Global health challenges for operational research and management science
Operational research methods are useful for the systematic identification of problems and the search for potential solutions. Structured approaches to identifying options, such as the strategic choice approach23 or systematic creativity approaches such as the Russian-invented methodology TRIZ (translated as “theory of inventive problem solving”),24 have great potential for use in low-resource settings.25 New approaches are important for global health because strategies and programmes must be designed to deal with both current and future challenges – from the global spread of disease to the impact of climate change. This can sometimes take us beyond traditional forecasting methodologies to the use of scenario analysis and other futures thinking methods. There has been a fair amount of such work in the health field and its methods have been adopted in high-profile initiatives such as by the Government Office for Science in the United Kingdom of Great Britain and Northern Ireland. However, little appears to have been done on global health with the exception of scenario analyses on pandemic influenza17 and on AIDS in Africa up to the year 2025.26
With an expected increase in extreme events linked to climate change, disaster planning and humanitarian logistics are becoming even more important.27 Decision support tools specifically designed for wide-scale emergency situations should play an increasing role.28 Developing experience in this sort of logistics analysis will have application in global health both in and beyond emergency situations.
Choosing appropriate interventions is clearly a crucial step. Effectiveness, safety, cost and equity should all be considered and researchers will be familiar with standard text-book methods for assessing these. But, in reality, assessments are rarely straightforward. It is often more effective and efficient to combine interventions, or to treat more than one disease at a time. For example, the integration of treatments for different diseases into cost-saving combined “packages”. Finding the best combinations and delivery methods is a major research exercise in its own right. Modelling different intervention strategies before roll-out is now ubiquitous in many industries but is less common in health care.29 Combining this approach with the necessary field and pilot studies should speed up and focus efforts. Modelling work has been done on ways to reduce maternal mortality30 and in cervical cancer screening in low-resource settings.31
Introducing new interventions
Innovation is a key issue in global health. For instance telemedicine is increasingly being used in high-income countries, and beginning to be applied in middle- and low-income countries, enabled by the rapid global spread of mobile phone networks. However, many such applications in developing countries are not evaluated so there is a clear role for operational research to assess them and to improve their design and delivery.32
Mobile phones also allow much easier collection and collation of data for operational and related research. For example, an analysis of mobile phone data in Zanzibar (in the United Republic of Tanzania) showed movement patterns to regions with high levels of malaria. Combining this information with mathematical models of disease transmission suggested ways to improve malaria control.33
The contribution that operational research and management science can make to design and delivery is not restricted to high technology. Oral rehydration therapy is a “low-tech–low-cost–high-impact” innovation in which operational research was used to explore ways it could be administered at low cost using readily available ingredients by lay people, with an escalation pathway to treatment by health-care professionals when necessary.34
Small-scale projects generally need considerable modifications to work on a larger scale. For example, we need to understand better what happens when whole countries are treated with drugs or vaccines. Such upscaling includes a need to forecast future demand, to decide the location and size of facilities and to set staffing levels. Classic operational research techniques such as simulation modelling can be used in locating services, managing the pharmaceutical supply chain and developing the health care workforce. One paper35 presents models for efficient and equitable location of community health facilities in rural areas while another36 describes the development of a simulation model of task allocation to reduce pressures on physicians at HIV clinics in Rwanda.
Integrating into health systems
Successes in global health programmes often result from synergistic interactions between individual, community and national actors rather than from any single “magic bullet”. We need a greater focus on how interventions should be used in a complex behavioural environment, to better capture the dynamics of social networks and to understand how complex systems can adapt positively to change. This is a task where operational research and management science tools can be useful, as demonstrated by systems analysis of programmes for cervical cancer prevention37 or agent simulation modelling of spread of HIV in villages.38
Health systems are of course embedded in wider systems. Modelling can be a powerful tool in considering the sustainability of health interventions in the context of the wider environment39 and in designing systems that affect health, such as waste management.40
One of the greatest challenges for global health is the measurement and evaluation of performance of projects and programmes (Fig. 2). Recent evaluation has shown that the results of the United Nations Children’s Fund (UNICEF) accelerated child survival and development programme fell far short of original claims,41 a study that prompted an accompanying editorial in the Lancet that stated: “Evaluation must now become the top priority in global health”.
Standard control trial approaches to evaluation are sometimes feasible and appropriate but often a more flexible systems-oriented approach is required, together with modelling (for example, for assessing screening programmes42 or to help assess the effectiveness of preventive interventions).43 Decision tree modelling can give rapid insights into the operational effectiveness and cost-effectiveness of procedures44 and programmes45 and a variety of operational research and management science tools have assisted evaluative work on broader global health issues.46
Ingredients for success
A first requirement for success is the availability of appropriate research and analysis skills and resources, which is clearly a challenge particularly in developing countries. This can be ameliorated by training people within the health service who can lead and champion operational research, such as the programme launched by the International Union Against Tuberculosis and Lung Disease.47 Further steps might include extending the scope of the international consortium to strengthen health research capacity in Africa to include a full range of operational research and management science activities.
A second requirement is the effective deployment of capacity in operational research and management science. While there is an important role for people with these skills in developed countries to contribute to global health endeavours, more work is needed at the local level. Two recent articles48,49 discussed factors for and barriers against successful operational research in low-income countries. While they share some features of operational research in health with countries such as the United Kingdom of Great Britain and Northern Ireland,50 some features are specific to low-income settings. Success factors that are particularly important in such settings include involving appropriate people, using accessible methods and ensuring effective communication of results (Box 1).
Box 1. Success factors for operational research in global health
Attention to these factors should help reduce implementation problems. Developing stronger links with those active in practical quality improvement work in health care, such as the Institute for Health care Improvement,51 could also assist.
Achieving major improvements in global health will require some fundamental changes,52 including advances in research and analysis of organization and delivery of health care. Operational research and management science approaches can inform a range of important design and delivery issues, but need to pay more attention to success factors and to draw on a broader range of analytical methods, with more interchange with wider operational research work. With greater capability in this field, operational research and management science can play a significant role in global health.
The author thanks Dan Colley, Nigel Crisp, Don Enarson, Richard Feachem, Andrew Green, Anthony Harries, David Lalloo, Eva Lee, Barnett Parker, Jonathan Rosenhead, Clive Smee, Colin Thunhurst, Martin Utley and Rony Zachariah.
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