Hrev_master [page 14] [Healthcare in Low-resource Settings 2015; 3:3260] A tool to guide the process of integrating health system responses to public health problems Tilahun Nigatu Haregu,1 Geoffrey Setswe,2 Jullian Elliott,3 Brian Oldenburg4 1African Population and Health Research Center, Nairobi, Kenya; 2Human Sciences Research Council, Pretoria, South Africa; 3Burnet institute, Melbourne; 4University of Melbourne, Australia Abstract An integrated model of health system responses to public health problems is consid- ered to be the most preferable approach. Accordingly, there are several models that stip- ulate what an integrated architecture should look like. However, tools that can guide the overall process of integration are lacking. This tool is designed to guide the entire process of integration of health system responses to major public health problems. It is developed by taking into account the contexts of health systems of developing countries and the emer- gence of double-burden of chronic diseases in these settings. Chronic diseases – HIV/AIDS and NCDs – represented the evidence base for the development of the model. System level horizontal integration of health system responses were considered in the development of this tool. Introduction The tool presented here is based on the Analysis – Synthesis – Action continuum. It considers integration as a spiral rather than a linear process. The potential users of this tool are health policy makers, health care man- agers and health policy and systems researchers. These users may use this tool out of sequence based on their contexts and needs. As this tool is generic in its nature, users should adapt it to their own health sys- tem context, public health problems, and responses considered for integration. This tool was developed based on an action model of integration presented elsewhere.1-5 It builds upon the best available evidence and it combines theoretical, empirical and practical evidence. It is worth noting that there are sev- eral other models that address the different components of this tool.6-10 This tool presents a unique consolidation of the translation of these models in a form of a guiding tool along with essential new elements. The contents of this tool are conceptually validated and were enriched using inputs from expert consulta- tions. This tool is divided into five major sections: i) analysing the connections between prob- lems; ii) examining similarities between responses; iii) scanning the environment for integration; iv) repackaging evidence for com- munication; v) managing integration. Analysing the connections between the problems Convergence between the prob- lems Understanding population level (epidemio- logical) overlap between the distributions of two public health problems is important to inform overall policy approaches that address the problems. Considering the socio-ecological model, epidemiological overlap between two diseases has three dimensions: population groups (segments of the population based on different factors), geographic settings (differ- ent places within a certain county/region), and time (a point or a period of time of interest).11 To assess overlap between two problems in terms of the population groups, one needs to use a 3x3 table and assign different population groups/segments into the cells. To assess over- lap between two problems in terms of the geo- graphic settings, one needs to use a 3x3 table and assign different geographic settings into the cells. To assess concurrence between the two problems in terms of their magnitude (at a defined population and place) at a point in time, one needs to use a 3x3 tool and assign the magnitude of the problems into the cells. The average/medium magnitude to be used for comparison could be national prevalence (for sub-national considerations) or global preva- lence (for national considerations). When both problem A and problem B have high magni- tude, the need for integrated response is more likely to be higher. This is exemplified in Table 1. To assess epidemiological overlap between two problems in terms of their trend (of mag- nitude) across time (at a defined population and place), one needs to use a 3x3 matrix and assign the trends in the magnitude of the prob- lems into the cells (Table 2). A trend-line would be important to assess the presence of overlap- ping trends. When both problems have an increasing trend, the need for integrated response becomes more likely. The time period for the trend needs to be set based on rele- vance and availability of data. Trends without a defined pattern may be treated in a different way. Correlational analysis could also be used in such cases. Linkage between the problems Information about the inter-relationships between problems is important to inform the content of interventions packages.12 The link- age between two problems takes two forms: Risk and Severity. Risk is when the presence of problem A affects the probability of occurrence of problem B and/or vice versa. Severity is when the presence of problem A affects the severity of problem B and/or vice versa. To assess the linkage between two problems in terms of risk and severity, one needs to com- pare the risk and severity in the sub-popula- tions with that of the general population. Tool presented in Table 3 summarizes the risk and severity of a problem in the sub-populations, along with a three-point scale, as compared to that of the general population. When data are available, it would be preferable to use quanti- tative measures of risk and severity to demon- strate actual levels. The greater the risk and severity of the problems in the sub-populations (as compared to the general population), the higher is the need for integrated response. Co-occurrence of the problems Evidence about the magnitude of co-occur- rence of two problems in an individual is use- ful to inform planning and resource allocation.13 Co-occurrence of two diseases can be expressed in two forms: Co-morbidity (when there is an index disease) and Multimorbidity (when there is no index dis- ease).14 To explore the magnitude of co-occur- rence of two problems, one needs to compare the prevalence of each problem among those having the other with that of the general popu- lation (for comorbidity); and the prevalence of both diseases in the population to prevalence that would otherwise occur by chance. Tool described in Table 4 summarizes these meas- Healthcare in Low-resource Settings 2015; volume 3: 3260 Correspondence: Tilahun Nigatu Haregu, African Population and Health Research Center, Manga Close, Off Kirawa Road, 10787-00100 Nairobi, Kenya. Tel: +254.20.400.1000 - Fax: + 254.20.400.1101. E-mail: tilahunigatu@gmail.com Key words: Healthcare system; Public health problems; Integration. Received for publication: 15 March 2014. Revision received: 22 July 2014. Accepted for publication: 22 July 2014. This work is licensed under a Creative Commons Attribution 3.0 License (by-nc 3.0). ©Copyrigh T.N. Haregu et al., 2015 Licensee PAGEPress, Italy Healthcare in Low-resource Settings 2015; 3:3260 doi:10.4081/hls.2015.3260 Non co mmerc ial us e o nly [Healthcare in Low-resource Settings 2015; 3:3260] [page 15] ures. In situations where actual prevalence values are available, they can be used for the compar- ison. The greater the prevalence of comorbidi- ty and multimorbidity, the higher the need for integrated response. Examining similarities between responses Define response A health system response to a public health problem contains several components at differ- ent levels. At upstream (Macro) level are strategic functions including policy making, leadership and governance. At mid-stream (Meso) level are management functions like planning, coordination, resource mobilization etc. At down-stream (Micro) level are opera- tional functions such as service provision, data collection etc. Within each of the elements of the response, several functions and structures are involved.15 An effort of integration may involve all or some of these functions/structures. Some processes may require a stronger integration than others. One possible method to establish this is by analysing the similarities between parallel processes (e.g. Treatment of A and Treatment of B). This is based on the assump- tion that a higher level of similarity predicts a stronger need for integration. Analysis of sim- ilarities between the responses to problem A and Problem B starts with defining the func- tions of interest that constitutes a response. Depending on the intended focus and type of integration, identify and describe the elements of the response that could be the possible can- didates for integration. The scale of the details of these functions would vary based on the level of the health system. An example of list of core functions and their description is present- ed in Table 5. Identify comparators Once the response functions, the possible candidates for integration, are defined, the next step will be to assess the similarities between the parallel functions. Assessment of similarity between two functions requires comparators – parameters that are used to compare two functions. To identify parame- ters/attributes of the functions that could be used to compare two processes in order to identify similarities and differences, a list of possible parameters is given in Table 6. Rate degree of similarity The degree of (relational) similarity is the extent to which a pair of parallel response functions (e.g. prevention of A and prevention of B) shares common parameters/attributes. Short Communciation Table 1. A 3X3 matrix for convergence between the problems. Magnitude of problem B High Average Low Magnitude of Problem A High Medium/average Low Cut-off points that differentiate between high, medium and low (in task 1 and 2) are relative and highly dependent on local contexts. Thus, these are left to the users of this tool. Groups/settings assigned to high-high will be the most likely focus of integration. Cluster analysis could be used if actual values are available. Table 2. A 3X3 matrix for relating time-trends of two problems. Time-trend of problem B Increasing Stabilized Decreasing Time-trend of Problem A Increasing Stabilized Decreasing Table 3. Matrix for rating linkage between two problems. Greater Similar Lower Risk of problem B among A+ as compared to general population Risk of problem A among B+ as compared to general population Severity of problem B among A+ compared to general population Severity of problem A among B+ compared to general population Table 4. Matrix of classifying levels of co-occurrence of two problems. Greater Similar Lower Prevalence of A among B+ as compared to prevalence of A (PA) Prevalence of B among A+ as compared to prevalence of B (PB) Prevalence of AB in general population as compared to (PA*PB) Table 5. List of major functions that constitute response to health problems. Categories Functions Description of the functions Policy Leadership High level political commitment Policy advising Providing inputs for policy making Policy making Formulation/approval of policies Governance Overseeing policy implementation processes Program Prevention Measures taken to prevent disease Treatment Services provided to control/treat disease Care and support Services provided to improve quality of life System strengthening Interventions that improve system capacity Management Planning Strategic and annual planning Implementation Overseeing implementation of programs Resource mobilization Securing resources needed for programs Multisectoral coordination Coordination of multiple actors/sectors Strategic information Patient monitoring Monitoring the progress of patients Disease monitoring Monitoring of disease/epidemic patterns Program M&E Monitoring and evaluation of programs Dissemination Communication of findings of M&E Non co mmerc ial us e o nly [page 16] [Healthcare in Low-resource Settings 2015; 3:3260] The most appropriate and applicable set of parameters should be used for the rating. The rating scale may vary from dichotomous scale to a higher point Likert scales. Using a select- ed set of parameters, one should rate the degree/strength of similarities between a pair of parallel functions. A sample template for rat- ing the similarity between program related functions of problem A and Problem B is given in Table 7. Determine importance of similari- ties In addition to the degree of similarity, the relative importance of similarity is also essen- tial. The importance of the similarities between a pair of parallel functions can be viewed from four major perspectives: policy – the strategic importance of the similarity for policy purpose; managerial – the importance of the similarity for decision making; economic – the importance of the similarity in efficient use of resources; and practical – the impor- tance of the similarity for program implemen- tation. To determine the relative importance of the similarities between a pair of parallel func- tions by considering the policy, management, economical, and practice perspectives one should follow Table 8. Scanning the environment for integration After establishing the need for integration (section I) and identifying candidate func- tions/structures for integration (section II), the third phase is assessing whether the envi- ronment is enabling/conducive for integration. This is conducted using environmental scan- ning. In principle, three components of the environment need to be considered: Internal (Staffs, Managers, Organizational set up), Task-related (patients, competitors i.e. other actors, partners, donors, pressure groups), and External (Political, Economic, Socio-cultural and Technological factors). From the perspec- tive of integration, the following themes are important. Motivation for integration Interest among managers and staffs (of Unit A and Unit B) to integrate the relevant func- tions/structures and operate in an integrated approach. To assess whether policy makers, managers and staffs of unit A and unit B are interested to integrate the respective func- tions and thereby operate in an integrated approach one should follow Table 9. Capacity for integration Capacity to integrate (for managers) and capacity to operate in an integrated approach Short Communication Table 6. List of potential parameters that may be used to assess similarity. Parameters Descriptions Operational characteristics Nature and technical complexity Timing of the functions Time and frequency (when and how often) Actors/performers The skills/expertise/speciality required Methods/tools Models and approaches used Targets/users The characteristics of the customers/users Results/outputs The attributes of the end products Input requirements Monetary and non-monetary requirements Levels in the system Levels of health system where the functions happen Lines of accountability Command and communication chains Monitoring modalities Monitoring requirements (formats, schedules, etc.) Priority and interests Accorded priorities and vested interests Table 7. Matrix for rating degree of similarity of parallel functions. Pairs of parallel functions Degree of similarity (these are examples only, add more to this list) Low Medium High Prevention (of A and B) Treatment (of A and B) Care & support (of A and B) Health system strengthening (of A and B) Table 8. Matrix for rating relative importance of similarity between parallel functions. Similarity between Relative importance (these are examples only) Low Medium High Prevention (of A and B) Treatment (of A and B) Care and support (of A and B) Health system strengthening (of A and B) At the end of this section, an initial short-list of possible candidates (for integration) of response functions would be reached. Though higher degrees of similarity and higher relative importance of the similarity could be the mainstay of the selection, this will also depend on judgement by the responsible body. Table 9. Matrix for rating levels of motivation towards and capacity for integration. Levels of motivation Low Medium High Policy makers Managers Practitioners Levels of capacity Managerial capacity Technical capacity Institutional capacity Table 10. Matrix for rating levels of acceptability of integration by end users. End users Levels of acceptability Low Medium High Service users/customers (e.g. patients) Funding agencies (donors) Governing bodies (including government) Non co mmerc ial us e o nly [Healthcare in Low-resource Settings 2015; 3:3260] [page 17] (for staffs and institution/infrastructure). To assess the capacity (managerial, technical and institutional) to integrate the functions and operate in an integrated approach one should refer to Table 9. Acceptability of integration The extent to which the integrated approach is acceptable to the end users (patients, donors, governments) of the processes or the arrangements. To assess whether an integrate approach is acceptable to end users of the functions one should follow Table 10. Influences on integration The effects (reactions) of important stake- holders and their activities on integration process. Influences may be negative, neutral or positive. To assess the possible reactions of other important stakeholders towards the inte- grated approach one should refer to Table 11. Implications of integration The possible effects (impacts) of the inte- gration on important stakeholders and their business. This may also be positive, neutral or negative. Assessing how the integration of the functions/structures might affect other impor- tant stakeholders is described in Table 11. Repackaging evidence for inte- gration All the preceding sections of this tool were designed for generating important evidence about the need for integration, identifying the appropriate candidate functions/units for inte- gration and assessing the conduciveness of health system environment for integration. The evidence generated needs to be repack- aged in a form that can better inform decisions related to integration. A matrix of four major elements of evidence communication should include: purpose, audience, content/message, method. The audience (Who) Integration may mean different things for different people. Policy makers, managers, healthcare providers, patients, and researchers have different views about inte- gration. Repackaging evidence of integration needs to take into account these views and interests. The task of this section is to clearly state the target audience, their views, and their interests in relation to integration. The purpose (Why) Repackaging of integration related evidence should be targeted towards achieving a clearly defined purpose. The purpose is usually instru- mental – for practical applications. In some instances, however, it may be symbolic – to confirm decisions, policies and practices. The task of this section is to clearly state the pur- pose(s) of the communication of evidence about integration. The content (What) What needs to be included in the communi- cation package depends on the purpose and the audience of the communication. The task of this section is to prepare the content of com- munication product – the knowledge/evidence that is going to be communicated. The method (How) The method of communication may be selected based on knowledge about the inter- ests of the audience. It may be in the form of printed materials, electronic materials, audio- visuals, conference presentations, etc. The task of this section is to decide on the method of communication and appropriate communi- cation product. Managing integration Once the evidence about integration is effectively communicated, responsible bodies are expected to make decision about the inte- gration. The translation of that decision in to action should be systematic, with steps involv- ing planning, implementation, Monitoring and Evaluation. Short Communication Table 11. Matrix for classifying anticipated reactions of stakeholders and impacts of inte- gration on them. Important stakeholders Anticipated reactions Negative Neutral Positive Stakeholder 1 Stakeholder 2 (add rows for more stakeholders) Anticipated impacts Stakeholder 1 Stakeholder 2 (add rows for more stakeholders) Table 13. Major constructs for evaluation of integration. Indicators for Before integration After integration Change Level of integration Systems’ performance Cost performance units Objectives of integration Goals of health system Table 12. The ten levels of integration. Levels Communication Consultation Coherence Consensus Coordination Cooperation Collaboration Co-location Coalition Combination of integration Baseline level Target level Non co mmerc ial us e o nly [page 18] [Healthcare in Low-resource Settings 2015; 3:3260] Planning integration Integration should be a well-planned process. Integration planning needs to consid- er the parts and the parties that are going to be integrated. Depending on its extent, integra- tion planning may address a range of tasks: i) select the foci of integration (units/functions that are going to be integrated), which may include functions/structures relevant to policy, institutional arrangement, management, pro- gram, and information; ii) formulate the goals/objective of the integration; iii) deter- mine baseline (the existing) and the target (the desired level) of integration for each foci of integration (Table 12); iv) identify strate- gies/mechanisms to be used to achieve objec- tive of the integration; v) estimate the cost/resources required for implementing the strategies; vi) weigh the benefits and risks that might be associated with the integration. Once this is done, one should define the key elements of integration plan and prepare the plan. Implementing integration This step is about the application of the integration plan in to action. It involves opera- tionalization of integration plan in to imple- mentation plan and carrying out activities as per the implementation plan. The implementa- tion of integration plan, therefore, involves: i) operationalization (i.e. deciding who will do what and when); ii) implementation (i.e. translating the implementation plan in to action); iii) coordination (i.e. synchronizing activities and actors); iv) supervision (i.e. supervising and taking corrective action); v) monitoring (i.e. measuring progress and com- paring against the plan). Evaluating integration As any other performance improvement ini- tiative, integration should be evaluated (Table 13). The key constructs that are usually impor- tant in the evaluation of integration are: con- figuration (whose objective is to describe the alignment of the processes before and after integration and explain the differences in the integration architecture); synergy [aimed at measuring performance of the integrated architecture (after integration) and compare it with the sum of performance of the units (before integration)]; efficiency (which calcu- lates the unit cost per performance units before and after the integration and describe the differences); effectiveness [whose aim is to determine the level of achievement of the stated objectives of the integration (as stated in the integration plan)]; impact (aimed at determining the difference between the level of achievements of the objectives of the health system before and after the integration). Conclusions The proposed generic tool is developed based on the existing evidence relevant to the integration of responses to major public health problems. It has laid out the basic processes and sub-processes that need to be undertaken in the process of integrating system level responses in a systematic manner. It provides guidance for a comprehensive, evidence-based and step-wise approach to integration. As it includes the generation, synthesis, and utiliza- tion of evidence in its steps, it can suit situa- tions where evidence relevant to integration is yet to be generated. However, this tool has undergone only conceptual and content valida- tion. Further studies are needed to evaluate how the tool can be best streamlined into vari- ous health systems. References 1. Suter E, Oelke ND, Adair CE, Armitage GD. Ten key principles for successful health systems integration. Healthcare Q 2009;13:16-23. 2. Shigayeva A, Atun R, McKee M, Coker R. Health systems, communicable diseases and integration. Health Policy Plann 2010;25(Suppl.1):4-20. 3. Miranda JJ, Kinra S, Casas JP, et al. Non- communicable diseases in low- and mid- dle-income countries: context, determi- nants and health policy. Trop Med Int Health 2008;13:1225-34. 4. Armitage GD, Suter E, Oelke ND, Adair CE. Health systems integration: state of the evidence. Int J Integr Care 2009;9:e82. 5. Haregu TN, Setswe G, Elliott J, Oldenburg B. Developing an action model for integra- tion of health system response to HIV/AIDS and noncommunicable diseases (NCDs) in developing countries. Glob J Health Sci 2013;6:9-22. 6. Budetti PP, Shortell SM, Waters TM, et al. Physician and health system integration. Health Affair 2002;21:203-10. 7. Russell E, Johnson B, Larsen H, et al. Health systems in context: a systematic review of the integration of the social determinants of health within health sys- tems frameworks. Rev Panam Salud Publ 2013;34:461-7. 8. Jackson SF, Birn AE, Fawcett SB, et al. Synergy for health equity: integrating health promotion and social determinants of health approaches in and beyond the Americas. Rev Panam Salud Publ 2013;34:473-80. 9. Evans JM. Health systems integration: competing or shared mental models? Int J of Integr Care 2014;14:e028. 10. Tsasis P, Evans JM, Forrest D, Jones RK. Outcome mapping for health system inte- gration. J Multidisc Healthc 2013;6:99-107. 11. Sword W. A socio-ecological approach to understanding barriers to prenatal care for women of low income. J Adv Nurs 1999;29:1170-7. 12. Govindasamy D, Kranzer K, van Schaik N, et al. Linkage to HIV, TB and non-commu- nicable disease care from a mobile testing unit in Cape Town, South Africa. PloS One 2013;8:e80017. 13. Shwartz M, Iezzoni LI, Moskowitz MA, et al. The importance of comorbidities in explaining differences in patient costs. Med Care 1996;34:767-82. 14. Valderas JM, Starfield B, Sibbald B, et al. Defining comorbidity: implications for understanding health and health services. Ann Fam Med 2009;7:357-63. 15. Murray CJ, Frenk J. A framework for assessing the performance of health sys- tems. B World Health Organ 2000;78:717- 31. Short Communication Non co mmerc ial us e o nly