Dataset Information

Understanding the dynamics of the Seguro Popular de Salud policy implementation in Mexico from a complex adaptive systems perspective.

ABSTRACT: In 2003, Mexico's Seguro Popular de Salud (SPS), was launched as an innovative financial mechanism implemented to channel new funds to provide health insurance to 50 million Mexicans and to reduce systemic financial inequities. The objective of this article is to understand the complexity and dynamics that contributed to the adaptation of the policy in the implementation stage, how these changes occurred, and why, from a complex and adaptive systems perspective.A complex adaptive systems (CAS) framework was used to carry out a secondary analysis of data obtained from four SPS's implementation evaluations. We first identified key actors, their roles, incentives and power, and their responses to the policy and guidelines. We then developed a causal loop diagram to disentangle the feedback dynamics associated with the modifications of the policy implementation which we then analyzed using a CAS perspective.Implementation variations were identified in seven core design features during the first 10 years of implementation period, and in each case, the SPS's central coordination introduced modifications in response to the reactions of the different actors. We identified several CAS phenomena associated with these changes including phase transitions, network emergence, resistance to change, history dependence, and feedback loops.Our findings generate valuable lessons to policy implementation processes, especially those involving a monetary component, where the emergence of coping mechanisms and other CAS phenomena inevitably lead to modifications of policies and their interpretation by those who implement them. These include the difficulty of implementing strategies that aim to pool funds through solidarity among beneficiaries where the rich support the poor when there are no incentives for the rich to do so. Also, how resistance to change and history dependence can pose significant challenges to implementing changes, where the local actors use their significant power to oppose or modify these changes.

SUBMITTER: Nigenda G

PROVIDER: S-EPMC4866010 | biostudies-other | 2016 May

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundIn practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model.MethodologyIn a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions.ResultsIn this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM.ConclusionsThis research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.

Project description:Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of ? (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p?<?0.05, tstat?>?tcritical) for ?, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p?<?0.1 and tstat?<?tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat?>?tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.

Project description:Health system reforms across Africa, Asia and Latin America in recent decades demonstrate the value of health policy and systems research (HPSR) in moving towards the goals of universal health coverage in different circumstances and by various means. The role of evidence in policy making is widely accepted; less well understood is the influence of the concrete conditions under which HPSR is carried out within the national context and which often determine policy outcomes. We investigated the varied experiences of HPSR in Mexico, Cambodia and Ghana (each selected purposively as a strong example reflecting important lessons under varying conditions) to illustrate the ways in which HPSR is used to influence health policy. We reviewed the academic and grey literature and policy documents, constructed three country case studies and interviewed two leading experts from each of Mexico and Cambodia and three from Ghana (using semi-structured interviews, anonymized to ensure objectivity). For the design of the study, design of the semi-structured topic guide and the analysis of results, we used a modified version of the context-based analytical framework developed by Dobrow et al. (Evidence-based health policy: context and utilisation. Social Science & Medicine 2004;58:207-17). The results demonstrate that HPSR plays a varied but essential role in effective health policy making and that the use, implementation and outcomes of research and research-based evidence occurs inevitably within a national context that is characterized by political circumstances, the infrastructure and capacity for research and the longer-term experience with HPSR processes. This analysis of national experiences demonstrates that embedding HPSR in the policy process is both possible and productive under varying economic and political circumstances. Supporting research structures with social development legislation, establishing relationships based on trust between researchers and policy makers and building a strong domestic capacity for health systems research all demonstrate means by which the value of HPSR can be materialized in strengthening health systems.

Project description:Context in implementation science includes not only characteristics of a setting in which an intervention will be delivered, but also social systems (e.g., interrelationships). Context is dynamic and interacts with both, the intervention and its implementation. Therefore, contextual analysis is recognized as an indispensable part of implementation science methodology: it provides the foundation for successful and sustainable implementation projects. Yet, driven by the prevailing post-positivist understanding of context, contextual analysis typically focuses on individual characteristics of context i.e., contextual dynamics and interactions go unnoticed. Conducting contextual analysis from a constructivist perspective promotes a multilayered approach, building a more comprehensive understanding of context, and thus facilitating successful implementation. In this article, we highlight the limitations of prevailing perspectives on context and approaches to contextual analysis. We then describe how contextual analysis can be enriched by working from a constructivist perspective. We finish with a discussion of the methodological and practical implications the proposed changes would entail. Emerging literature attempts to address both the concept of context and methods for contextual analysis. Various theories, models and frameworks consider context, however, many of these are reductionistic and do not acknowledge the dynamic nature of context or interactions within it. To complement recent conceptualizations of context, we suggest consider the following five constructivist concepts: 1) social space; 2) social place; 3) agency; 4) sensation; and 5) embodiment. We demonstrate the value of these concepts using COVID-19 vaccination uptake as an example and integrate the concepts in the Context and Implementation of Complex Interventions (CICI) framework-an implementation science framework that pays ample attention to context. To study context from a constructivist perspective, we also suggest additional considerations in view of methodologies for data collection and analysis, e.g., rapid ethnographic methods. A constructivist perspective contributes to a stronger conceptualization of contextual analysis. Considering the five constructivist concepts helps to overcome contextual analysis' current shortcomings, while revealing complex dynamics that usually go unnoticed. Thus, more comprehensive understanding of context can be developed to inform subsequent phases of an implementation project, thereby maximizing an intervention's uptake and sustainability.

Dataset Information

Understanding the dynamics of the Seguro Popular de Salud policy implementation in Mexico from a complex adaptive systems perspective.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets