Normalizing the political economy of improving health.

Problem: Political economy factors are important in determining the adoption and implementation of health policies. Yet these factors are often overlooked in the development of policies that have the potential to influence health. Approach: Political economy analysis provides a way to take into consideration political and social realities, whether at the community, subnational, national, regional or global levels. We aim to demonstrate the value of political economy analysis and to promote its wider use in technical programmes of work. Local setting: We provide examples from across a range of World Health Organization areas of work, including participatory governance, health financing, health taxes, malaria prevention and control, capacity-building and direct country support. Relevant changes: Existing examples of how political economy analysis can be incorporated into technical support demonstrate the variability of this analytical approach, as well as its potential to support policy progress. Applying political economy analysis within the specified programmes of work has enabled more contextually relevant technical support to enhance the likelihood of advancing countries' health-related objectives. Lessons learnt: Embedding political economy into technical work has many benefits, including: enhancing voice and participation in health policies; supporting the adoption and implementation feasibility of technically sound policies; and building capacity to incorporate and understand political factors that influence health-related priorities.

Enablers and inhibitors of the implementation of the Casalud Model, a Mexican innovative healthcare model for non-communicable disease prevention and control.

BACKGROUND: The Mexican healthcare system is under increasing strain due to the rising prevalence of non-communicable diseases (especially type 2 diabetes), mounting costs, and a reactive curative approach focused on treating existing diseases and their complications rather than preventing them. Casalud is a comprehensive primary healthcare model that enables proactive prevention and disease management throughout the continuum of care, using innovative technologies and a patient-centred approach. METHODS: Data were collected over a 2-year period in eight primary health clinics (PHCs) in two states in central Mexico to identify and assess enablers and inhibitors of the implementation process of Casalud. We used mixed quantitative and qualitative data collection tools: surveys, in-depth interviews, and participant and non-participant observations. Transcripts and field notes were analyzed and coded using Framework Analysis, focusing on defining and describing enablers and inhibitors of the implementation process. RESULTS: We identified seven recurring topics in the analyzed textual data. Four topics were categorized as enablers: political support for the Casalud model, alignment with current healthcare trends, ongoing technical improvements (to ease adoption and support), and capacity building. Three topics were categorized as inhibitors: administrative practices, health clinic human resources, and the lack of a shared vision of the model. CONCLUSIONS: Enablers are located at PHCs and across all levels of government, and include political support for, and the technological validity of, the model. The main inhibitor is the persistence of obsolete administrative practices at both state and PHC levels, which puts the administrative feasibility of the model's implementation in jeopardy. Constructing a shared vision around the model could facilitate the implementation of Casalud as well as circumvent administrative inhibitors. In order to overcome PHC-level barriers, it is crucial to have an efficient and straightforward adaptation and updating process for technological tools. One of the key lessons learned from the implementation of the Casalud model is that a degree of uncertainty must be tolerated when quickly scaling up a healthcare intervention. Similar patient-centred technology-based models must remain open to change and be able to quickly adapt to changing circumstances.