Responding to the challenge: Country-led priority-setting for health action

Does greater health spending translate into better service delivery?

With cardiometabolic diseases standing out as a major shared regional priority and chronic disability accounting for a growing share of the total disease burden, a clear set of delivery needs is evident: reliable first-contact care, regular follow-up, rehabilitation, and stronger referral coordination within an integrated health network (27, 31, 32, 33). Comparative evidence indicates that substantial hospital use occurs for conditions that potentially could be managed through effective primary care. A multicountry analysis of public-sector hospital discharge data from Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, and Peru found that hospitalizations for ambulatory care-sensitive conditions (those conditions for which hospitalization may not always be necessary) represented, on average, 17.4% of both discharges and inpatient days across countries between 2015 and 2019. Chronic NCDs accounted for 50.6% of these hospitalizations, pointing to opportunities to strengthen primary care and reduce unnecessary hospital use (34).

These findings are consistent with broader evidence that many health systems in the Region are not yet consistently organized to meet these needs, with the dominant organization of care often better suited to episodic, fragmented, and single-condition responses (31, 32). Recent survey data from 34 countries and territories in Latin America and the Caribbean (35) underscore this pattern:

• Service packages exist but may be incomplete: 85% of countries and territories report having formal, comprehensive service packages, with 90% or more of those with such packages including promotion, prevention, diagnostic testing, and rehabilitation; fewer (about 79%) explicitly include palliative care and self-care.
• Service delivery is only partially integrated: about 71% of countries and territories organize facilities into subnational governance units or health networks, but only 41% of countries and territories report universal population empanelment (assigning people to specific healthcare providers or teams).
• Referral and continuity mechanisms remain weakly institutionalized: while about 82% of countries and territories have standardized referral guidelines, only about 53% report explicit agreements between referring and receiving institutions and only 8% monitor adherence to defined care pathways.

One way to assess this delivery gap is to examine whether countries with similar levels of health spending achieve similar levels of potentially avertable health loss. Figure 27 shows that they do not. Countries with comparable current health expenditure per capita can still differ substantially in the share of health loss that remains potentially avertable under the global benchmark, suggesting that financing levels alone do not determine how effectively systems convert resources into health gains. Furthermore, some countries spend less than others but achieve better performance in terms of health loss. Compare, for example, the countries in the lower left quadrant (Peru, Saint Vincent and the Grenadines, Grenada, and Dominica) that have relatively lower potentially avertable burden despite having lower spending, with the countries in the upper right quadrant (Paraguay, The Bahamas, Mexico, and Trinidad and Tobago) that remain above the regional median in potentially avertable burden despite having comparatively higher spending. These patterns are consistent with an important role for how resources are allocated, priorities are set, and services are organized and delivered. They also suggest that countries may be able to reduce some of the remaining delivery gaps even before substantial new financing becomes available.

A further implication is that the delivery gap is not uniform across countries, even though some priorities, like cardiometabolic conditions, are clearly panregional. In some contexts, the gap is more heavily shaped by maternal and neonatal conditions, infectious disease control, or injuries. In other countries, chronic disability, mental health, substance use, and long-term rehabilitation needs are more prominent. The delivery gap therefore reflects both a shared regional challenge and persistent inequities in health needs, implementation capacity, and service readiness across countries and subregions.

The widening delivery gap is as much an organizational challenge as a resource challenge. Additional resources – including health workers, medicines and diagnostics, and infrastructure – remain important, especially in underfunded settings. For the health workforce, the issue extends beyond the number of personnel available to include their distribution, competencies, team composition, supervision, and ability to deliver integrated PHC across the life course. Furthermore, health systems may fail to generate commensurate health gains when resources remain tied to fragmented financing schemes, historical investment patterns, or provider arrangements that are misaligned to current needs (36, 37). The practical challenge is therefore to link priorities to purchasing, provider incentives, implementation, and routine monitoring so that quality services can be delivered at scale (36, 37). Building on PAHO's framework for integrated health service delivery networks (38), this implies using concrete policy levers in four domains: governance, organization and management, model of care, and financial incentives.

The delivery gap is also shaped by the social conditions of the people that services are meant to reach. Precarious employment, inadequate housing, poverty, gender norms, and cultural or administrative barriers can limit whether people seek care, attend appointments, adhere to treatment, and remain engaged in continuous services, even when care is formally available. Narrowing the delivery gap therefore requires aligning priorities and service organization with the conditions that determine who can access and sustain effective coverage. When needs are broad, resources are constrained, and delivery capacity is uneven, countries cannot expand every service at once or rely on implicit historical patterns of provision. More explicit choices are needed about which conditions and interventions to prioritize first, which PHC-oriented delivery platforms should provide them, and how services should be organized to improve continuity and effective coverage over time (27, 33).

How do countries in the Americas set health priorities?

Priority-setting in health requires balancing multiple criteria (Box 2). Under finite resources, cost-effectiveness remains a central criterion for deciding which interventions can generate the greatest health gains for the resources used. But efficiency alone is not enough. Priority-setting also needs to account for the scale of the health problem, the expected budget impact and affordability of interventions, and short-term feasibility, including whether health systems have the capacity to implement and sustain services at adequate quality and scale (39, 40). It must also consider equity, financial protection, and the fair distribution of health gains across population groups. At the same time, it must protect past gains and sustain established disease-specific programs – including vaccination and communicable disease elimination commitments under PAHO's Disease Elimination Initiative (Box 3). This requires integrating program-specific evidence on transmission, coverage, surveillance, and resurgence risk alongside burden and equity evidence (41), including disaggregated data on socioeconomic condition, geography, ethnicity, and other inequality dimensions. Such disaggregated data help identify populations with the greatest unmet need, weakest coverage, and highest risk of being missed by routine delivery. Under constrained resources, focusing on these populations can advance both equity and efficiency when improvement potential is greatest among disadvantaged groups while helping define the measures needed to overcome these barriers. Beyond these core criteria, countries may weigh additional considerations such as training requirements, research and development needs, and other strategic system priorities. For that reason, priority-setting is increasingly understood as a multi-criterion, deliberative process in which evidence must be combined with transparent and ethical procedures, stakeholder engagement, and broader judgments about legitimacy and fairness (41, 42).

Across the Americas, more explicit institutional mechanisms for priority-setting have expanded, even if they remain uneven in maturity and influence. Health technology assessment (HTA) is one such multidisciplinary process that systematically evaluates the clinical, economic, organizational, social, and ethical implications of health technologies, such as medicines, diagnostics, medical devices, procedures, and digital health tools. It has become an increasingly important instrument for supporting evidence-informed decisions on what to include in health benefit packages and how to align coverage decisions with value, affordability, and broader health system goals (39, 43).

At the regional level, PAHO has supported this agenda through initiatives such as Red de Evaluación de Tecnologías en Salud de las Américas (RedETSA) [The Health Technology Assessment Network of the Americas], a regional network intended to strengthen the use of HTA in decision-making. Its current platform reports 21 member countries represented by 43 institutions, suggesting broad regional uptake and exchange (44, 45). Complementing this, the IDB's Criteria Network has supported evidence-informed priority-setting by helping countries strengthen decisions on which health technologies to finance with public resources and, more recently, by broadening its focus toward health spending efficiency (46).

HTA uptake has not translated into uniform institutionalization. A regional assessment found that only 13 of 30 countries had established HTA bodies or organizations (47). Among the 46 institutional respondents from those countries, 59% reported that there was no legislation in place to ensure that decision-making was supported by HTA, while 39% reported no mechanisms for monitoring and evaluating HTA recommendations. The same study concluded that links between HTA and decision-making remained weak and that most countries in Central America and the Caribbean were still in the early stages of implementation. As a result, the spread of HTA-related structures marks important progress but not yet a uniform or fully embedded regional model of evidence-informed priority-setting. Strengthening institutional capacity for HTA and explicit priority-setting should therefore be treated as a regional priority: where these capacities are weak or absent, evidence is less consistently translated into affordable health benefit packages and operational service priorities.

Even where evidence of HTA activity and explicit prioritization processes have expanded, the translation of those choices into actionable benefit packages remains inconsistent. Recent guidance emphasizes that packages are only meaningful if they are defined clearly enough to shape entitlements and service delivery and affordable enough to be implemented within available resources (39, 40). In practice, many packages risk becoming aspirational rather than operational. Insufficient resourcing forces providers into implicit rationing, compromises quality, and leaves delivery misaligned with the package's stated goals, especially when package revisions are undertaken without corresponding changes in budget or provider payment methods (40).

The main gap in many settings is therefore twofold. First, available evidence is not consistently translated into explicit priority-setting decisions, including decisions about what to include in health benefit packages. Second, even where priorities are made explicit, they are not always translated into the financing, purchasing, and delivery arrangements needed to deliver prioritized interventions with quality at scale (39, 40). A recurring implementation failure is that health benefit packages are specified without the purchasing and supply conditions required to deliver them, or they are altered based on shifting political interests or demands from special interest groups. Priority-setting should include explicit assumptions about the availability of essential products, infrastructure, and human resources, procurement and purchasing arrangements, budget impact, and delivery readiness.

Regional experience also shows that explicit priority-setting does not follow a single model. Countries have adopted at least five broad approaches:

• Guaranteed-condition models, as in Chile's Garantías Explícitas en Salud/Acceso Universal con Garantías Explícitas [Explicit Health Guarantees/Universal Access with Explicit Guarantees] plan that specifies a defined set of conditions with enforceable guarantees on access, quality, timeliness, and financial protection, layered onto broader public and private insurance coverage;
• System-wide statutory or insurance-based benefit plans, ranging from Colombia's broad exclusions-based coverage and Peru's positive-list minimum benefits floor to Uruguay's comprehensive universal package embedded within its national integrated health system;
• Layered arrangements combining essential services with protection for selected high-cost conditions, illustrated historically by Mexico's Catálogo Universal de Servicios de Salud [Universal Catalogue of Health Services] and Fondo de Protección contra Gastos Catastróficos [Fund for Protection against Catastrophic Expenditures], which differentiated benefits according to level of care, complexity, and financial risk;
• Targeted positive-list packages linked to defined populations and implementation arrangements, as in Argentina's Plan Nacer/Programa Sumar (a package focused initially on maternal and child health services) that combined gradual package expansion with enrollment- and performance-linked financing, and Honduras's Paquete Básico de Salud [Basic Health Package] that defined a feasible primary-care package for poor rural populations through decentralized contracting; and
• Universal rights models without a single finite package, as in Brazil's Sistema Único de Saúde [Unified Health System], where broad entitlements are operationalized through institutional priority-setting instruments such as HTA-informed protocols.

These models address different policy objectives. Some strengthen enforceability, while others clarify entitlements. Some protect against high-cost shocks, while others gain implementation traction through narrower packages and clearer financing, contracting, and monitoring arrangements. Broad universal guarantees, in turn, require strong institutional priority-setting mechanisms, such as HTA and budget impact analysis, as well as financing and delivery arrangements to translate legal rights into effective coverage. For more details about each of these approaches, including trade-offs and implementation lessons, see Appendix V.

Taken together, the country experiences reviewed here suggest that explicit priority-setting is most credible and operationally useful when it makes difficult choices more transparent, links those choices to financing and purchasing arrangements, and defines benefits in ways that can realistically be delivered. Across these approaches, a common tension persists between universal approaches and targeted approaches, although they should not be viewed as mutually exclusive. Progressive universalism recognizes multiple pathways toward UHC that prioritize the poor, including universal public financing for priority interventions or broader packages with targeted subsidies or exemptions for poorer populations (48). The broader and more ambitious the coverage promise, the greater the risk that it will exceed implementation capacity and push rationing (restricting access to health services) back into implicit and inequitable forms (49, 50, 51, 52, ⁠53). Conversely, narrower or more targeted packages may be less comprehensive, but they can offer greater operational traction when linked to payment rules, monitoring systems, and clearly defined delivery responsibilities (49, 50, ⁠51). Across these experiences, health benefit packages appear most useful when they are accompanied by investments in first-level care, integrated networks, outreach, prevention, and the public health functions needed to support continuity of care; without these conditions, formal entitlements may remain difficult to deliver consistently and equitably at scale.

The experiences of Colombia and Mexico provide instructive contrasts. Mexico's past experience shows the value of targeted prioritization and clearer entitlements while also illustrating the risks that arise when benefit design, purchasing arrangements, and system structure remain only partially aligned. Under those conditions, segmentation and implementation instability can persist despite formal package specification. Colombia highlights the opposite tension: movement toward broader and more implicit coverage can advance formal equity, but it also increases pressure for transparent exclusions, durable priority-setting institutions, and stronger fiscal discipline if coverage promises are to remain credible over time.

The central lesson is that effective package design must be matched by institutional, financing, and delivery arrangements capable of sustaining implementation at scale (49, 50, 51, ⁠52). These findings lead directly to the next policy question: how can high-priority interventions be grouped into coherent service bundles and brought to scale through PHC-oriented delivery platforms that are better aligned with the Region's changing burden profile?

How can priority interventions be organized for delivery at scale?

In the Region of the Americas, implementing prioritized health services and public health actions requires organizing interventions through integrated service delivery networks centered on strong primary care and supported by essential public health functions, multisectoral action, and community engagement and empowerment.

In many settings, health benefit packages remain more effective as instruments of prioritization than as instruments of delivery: they clarify intent but are only partially linked to the institutional, fiscal, and operational conditions required for routine implementation. This challenge arises because a benefit package defines what services to provide but not how to organize, roll out, and sustain them.

A more useful intermediate step is to translate priorities into implementable delivery platforms and associated service bundles – that is, groups of interventions that address related conditions through shared delivery platforms, a competent and equitably distributed health workforce, essential medicines and supplies, and overlapping management functions. This bundle-based approach is valuable because it links epidemiological concentration to operational design and can improve technical efficiency by reducing duplication and enabling shared use of delivery platforms, inputs, and management functions. Together, explicit priority-setting and benefit-package design support allocative efficiency by directing available resources toward higher-value priorities. Service-bundle organization and delivery reform support technical efficiency by reducing duplication and making better use of shared inputs and delivery platforms. Service bundles help countries move beyond disease-by-disease expansion toward a smaller set of scalable delivery units that can be costed, phased, monitored, and adapted as implementation capacity improves (54). In practice, the bundle-based approach requires defining (33, 48, 55):

• Which interventions belong together;
• Through which delivery platforms they should be provided;
• What health workforce capacities, medicines, diagnostics, supplies, referral arrangements, and information systems they require;
• What bottlenecks are most likely to limit scale-up.

In this way, bundles become the operational bridge between PHC-oriented priority-setting and implementation (Figure 28): they are small enough to be costed and managed, yet broad enough to capture the shared delivery functions that single-disease approaches often miss. Pooled procurement mechanisms such as PAHO's Regional Revolving Funds illustrate how standardized product baskets, quality assurance, and predictable purchasing cycles can reduce costs and support continuity of access to essential medicines, vaccines, and health supplies.

The Region's disease burden should not be approached as isolated disease clusters. Many conditions share common upstream risk structures, require continuity of care over time, and depend on overlapping delivery functions, including outreach, first-contact care, medicine continuity, referral, and follow-up. Table 3 illustrates how the main health needs and conditions identified in Chapter 2 can be organized into priority service bundles and linked to delivery arrangements within PHC-oriented integrated health service delivery networks (54). It is not intended as an alternative classification of integrated network domains, but as an operational bridge between burden-based priority-setting and service delivery.

Taken together, the priority service bundles outlined in Table 3 and the El Salvador HEARTS experience described in Box 4 suggest that the Region's delivery response should be organized around a limited number of shared service delivery platforms embedded within integrated networks of care, rather than an ever-expanding list of stand-alone interventions. For many of the priority areas identified in Chapter 2, PHC and essential public health functions emerge as the main organizing platform (33, 37, 55). This is true for the cardiometabolic bundle as well as for maternal and child health and nutrition, mental health and substance use, infectious disease resilience, and much of long-term disability support. Secondary and tertiary care remain indispensable, but mainly as referral-linked complements for complications, acute events, specialized treatment, and rehabilitation rather than as the primary organizing logic of the system (33, 48, 55). In that sense, the bundles in Table 3 do not simply regroup conditions; they highlight a delivery model in which integration at the first level of care, supported by essential public health functions and effective referral pathways, becomes central to translating priority-setting into effective coverage and access to quality care.

Effective implementation and sustained scale-up of priority service bundles require attention to multiple health system components, including building and maintaining health workforce capacity; infrastructure and facility capability; outreach programs; medicines and health technologies; diagnostics and supply chains; clinical protocols; referral and counter-referral systems; emergency and rehabilitation capacity; information systems; and performance monitoring (33, 37, 48, 55). Scale-up, therefore, is likely to be incremental, with a core set of publicly financed, widely accessible services implemented with adequate quality and continuity before being expanded more broadly (37, 39, 48). Health workforce readiness may be especially limiting in lower-capacity settings: geographical maldistribution, outmigration, and gaps in competency, team composition, and task-sharing capacity can constrain integrated PHC delivery independently of financing or commodity availability (27, 29). Managing cardiometabolic risk, delivering maternal and neonatal care with continuity, or providing mental health support through task-sharing all require specific cadre configurations and competencies that are not uniformly available across the Region (29, 30, 56).

For health authorities, the bundle-based approach provides a practical framework for managing scale-up of priority interventions under real-world constraints. Monitoring is central to this process: without routine tracking of coverage, quality, outcomes, equity, and expenditure, benefit packages and service bundles can easily drift toward overpromising, underdelivery, and weak course correction (33, 39, 40). The next section illustrates this logic through a focused modeling exercise for a cardiometabolic package, showing how one priority bundle can be specified in cost and delivery terms and linked to potential health gains.

What can an illustrative model for a cardiometabolic priority service bundle show about scale-up costs and projected health gains?

The purpose of the following modeling exercise is not to define a universal package for all countries, but to illustrate how a priority area can be specified in operational terms and linked to projected costs, delivery requirements, and expected health gains under an illustrative scale-up scenario.

The modeling exercise focuses on a cardiometabolic service bundle because cardiometabolic conditions emerged from Chapter 2 as one of the clearest shared regional priorities, with high current burden and substantial remaining improvement potential. The modeled package also aligns with the risk-attribution findings in Chapter 2: cardiovascular diseases and diabetes are linked to shared modifiable risks, including high systolic blood pressure, high LDL cholesterol, high fasting plasma glucose, high body mass index, and dietary risks.

Using the preloaded NCD module from the WHO OneHealth Tool (57), 10 cardiometabolic interventions, aligned with WHO-recommended cost-effective interventions including several NCD Best Buys, were modeled:

1. Screening for cardiovascular disease/diabetes risk;
2. Follow-up care for those at low absolute risk (10–⁠20%) of cardiovascular disease/diabetes;
3. Treatment for very high cholesterol among those with low absolute risk of cardiovascular disease/diabetes;
4. Treatment for high blood pressure among those with low absolute risk of cardiovascular disease/diabetes;
5. Treatment for those with 20–⁠30% absolute risk of cardiovascular disease/diabetes;
6. Treatment for those with greater than 30% absolute risk of cardiovascular disease/diabetes;
7. Treatment of new cases of acute myocardial infarction with aspirin;
8. Care for cases with established ischemic heart disease;
9. Care for cases with established cerebrovascular disease and post-stroke;
10. Standard glycemic control.

The model was run for 2025–⁠2030 using the same 10 interventions and coverage scale-up assumptions across six countries to support cross-country comparability. The six countries represented the SHIx clusters: Haiti (Cluster 1), Guyana (Cluster 2), Paraguay (Cluster 3), Costa Rica (Cluster 4), Uruguay (Cluster 5), and Canada (Cluster 6). Baseline 2025 and target 2030 coverage values were entered to create an illustrative scale-up pathway for the interventions. (See Appendix VI for full methodological details and considerations.)

The modeled package translates the cardiometabolic service bundle into four operational components: (​1) cardiovascular disease screening and risk stratification; (​2) ongoing cardiometabolic risk management; (​3) acute cardiovascular care; and (​4) post-event care (Table 4). Each component is linked to a delivery platform, a scale-up bottleneck, and an expected pathway for health gains. The service bundle should be understood as centered in PHC but with strong referral links to hospital care.

Table 5 presents the main modeled costs, modeled cardiovascular health gains, and public financing context indicators for the scale-up scenario. The total package cost represents the cumulative modeled intervention and program cost of delivering the cardiometabolic service bundle over 2025–⁠2030 under the baseline cost scenario, in which program costs are held constant at 2025 assumptions. The cumulative additional scale-up cost represents the sum of annual incremental costs over 2026–⁠2030 relative to the 2025 baseline. The package-to-impact ratios use this cumulative additional scale-up cost as the numerator and the modeled cardiovascular health gains as the denominator. These ratios are presented only as illustrative summary measures and should not be interpreted as formal incremental cost-effectiveness ratios. Furthermore, this exercise should be interpreted as a planning and deliberation aid, not as a definitive country investment case or a direct estimate of closure of the GBD-based potentially avertable DALY gap. Results depend on the selected intervention package, standardized coverage scale-up assumptions, available OneHealth Tool inputs, the 2025–⁠2030 modeling horizon, and the baseline cost scenario. Actual affordability, feasibility, and impact would require country-specific assessment of fiscal space, workforce availability, medicines and diagnostics, facility readiness, referral capacity, equity priorities, and implementation constraints.

The modeling findings show four key points:

Scaling up a cardiometabolic service bundle can generate meaningful reductions in cardiovascular deaths and gains in healthy life years across different country contexts. Among the five Latin American and Caribbean country examples, modeled cardiovascular gains over 2026–⁠2030 ranged from 357 to 4045 healthy life years gained and from 316 to 3386 deaths averted. Canada showed much larger absolute gains over the same time period – 52 025 healthy life years gained and 56 564 deaths averted – mainly reflecting its larger population and modeled eligible population.

The same type of service bundle may have very different budgetary demands across countries. Excluding Canada, total package costs over 2025–⁠2030 ranged from USD 29.9 million in Guyana to USD 270.7 million in Haiti. With Canada included, the upper bound rises to USD 1.93 billion. The much higher cost for Canada partly reflects the scale of the modeled population: Canada's 2023 population was about 3.4 times Haiti's and nearly 50 times Guyana's. Absolute costs and health gains should therefore be interpreted in relation to population size, baseline needs, modeled target populations, and OneHealth Tool cost structures. The cost per healthy life year gained and death averted varied across countries – from about USD 3000 per healthy life year gained in Canada to about USD 7000 in Haiti – reflecting differences in population size, baseline needs, and other factors. These package-to-impact ratios should not be interpreted as a formal cost-effectiveness ranking or as evidence that the package is intrinsically more efficient in higher-income settings. Rather, these illustrate how modeled costs and modeled health gains vary across country contexts, and why priority-setting needs to examine not only high-burden conditions but also the scale-up effort required, the expected health gains, and the country's fiscal space.

The additional cost of scale-up is considerably smaller than the total package cost because it captures only the incremental cost of expanding coverage above the 2025 baseline. This cumulative additional scale-up cost ranged from USD 2.4 million in Guyana to USD 154.3 million in Canada.

The fiscal context differs sharply across countries. Using a simple five-year comparator based on 2023 domestic general government health expenditure (GGHE-D) held constant across the scale-up period, the cumulative additional scale-up cost represented less than 0.1% of five-year GGHE-D in Canada, Costa Rica, and Uruguay; less than 0.2% in Guyana and Paraguay; and 9.8% in Haiti. These results do not imply automatic affordability thresholds, but they show that similar service-bundle choices carry very different fiscal implications depending on the public financing environment, population size, and economic context.

In summary, the modeling exercise illustrates how countries can move from identifying potentially avertable burden to planning operational scale-up. The OneHealth Tool does not directly estimate how much of the GBD-based avertable DALY gap would be reduced, because its outputs are reported as package costs, healthy life years gained, and deaths averted rather than DALYs averted. Its value is instead translational by making assumptions and trade-offs visible. It shows how an identified area of potentially avertable burden can be operationalized as a defined service bundle with explicit assumptions on coverage expansion, target populations, delivery platforms, system bottlenecks, costs, and expected health gains.

Who benefits when countries prioritize service delivery packages?

The modeling exercise demonstrated the potential aggregate gains from translating burden evidence into an explicit, costed, and deliverable package of priority services. It did not, however, estimate how those gains would be distributed across socioeconomic groups, geographical areas, or households with different levels of financial vulnerability. This distinction matters: a package that is cost-effective on average may still leave behind populations with the greatest unmet need unless expansion is deliberately designed to reach them.

Equity must therefore be assessed in two linked dimensions:

• The social conditions that shape risk, care-seeking, and continuity of care, including income, employment, housing, and social exclusion;
• The access barriers that determine whether priority packages reach the populations intended to benefit from them.

For cardiometabolic conditions, equity gaps are especially important because prevention and control depend on continuous access to screening, diagnostics, medicines, follow-up, and referral. Stein et al. (58) illustrate through the hypertension care cascade that improving diagnosis and treatment generates greater cardiovascular benefits among poorer populations who have larger baseline gaps in access to care. In Brazil, Ecuador, and Saint Vincent and the Grenadines, closing diagnosis gaps between wealth quintiles was estimated to avert, on average, 7 cardiovascular disease cases per 1000 people in the lowest wealth quintile, compared with 3 per 1000 in the highest. A critical determinant of this effective coverage is health workforce readiness: shortages, geographical maldistribution, migration, and gaps in team composition or competencies can limit whether priority services are available to the populations intended to benefit from them, even when the package is formally defined and financed (29).

In addition to equity considerations, priority packages need to be assessed beyond aggregate health gains for financial protection reasons. In 2023, OOP spending accounted for an average of 31% of current health expenditure across countries in the Region, ranging from approximately 11% in the United States to 57% in Guatemala (59) (Figure 29). Differences also appear across SHIx clusters: average OOP spending was highest in Cluster 1 (42%) and lowest in Cluster 6 (13%), while Clusters 2–⁠5 ranged from 25% to 30%. If the services required to treat and manage hypertension or diabetes – such as repeat visits, medicines, and laboratory monitoring – are not included in a clearly financed benefit package, households may face recurring OOP payments even when the intervention itself is highly cost-effective. Household spending on medicines can itself be an important source of financial hardship or impoverishing expenditure, particularly for people requiring continuous treatment for hypertension, diabetes, and cardiovascular diseases (60).

Financial hardship from health spending is also unequally distributed across income groups. About one in five people in Latin America and the Caribbean experience financial hardship from health spending, according to WHO estimates, including 16% of the population who experience impoverishing health spending (14).

A clear pattern emerges: financial hardship from health spending – whereby OOP spending exceeds 40% of household discretionary budget – is much higher among poorer households than richer households (Figure 30).

These patterns strengthen the case for explicit and adequately financed priority packages. When priority-setting remains implicit, rationing does not disappear; it shifts to less transparent and often less equitable mechanisms. Explicit package design can reduce this mismatch by defining which services are guaranteed, costing and financing them, and linking them to delivery responsibilities.

Modeling exercises like the one conducted in this chapter provide a foundation for identifying potential aggregate gains. Health authorities can then assess whether those gains can be made larger and fairer by focusing on populations with the greatest unmet need and by financing the components of care most likely to expose households to OOP spending. Frameworks such as PROGRESS-Plus can help ensure systematic consideration of relevant equity dimensions (61), while distributional and extended cost-effectiveness analysis can estimate how expected health gains and financial protection benefits vary across population groups (62, 63) (Box 5).

...

In conclusion, the evidence reviewed in this section suggests that explicit, adequately financed, and equity-aware priority-setting can improve both health outcomes and financial risk protection. Selecting cost-effective interventions is necessary but not sufficient. Coverage must reach populations with the greatest unmet need, and essential packages must be financed in ways that reduce reliance on OOP spending. Equity and efficiency need not conflict when countries prioritize high-burden, high-avertability conditions for which disadvantaged groups face the largest gaps in risk management and care. Realizing these gains, however, requires translating priorities into enforceable delivery and financing arrangements. These arrangements include benefit design, strategic purchasing, medicines lists, monitoring, accountability, and institutional learning.
 

Moving this agenda forward also requires a proportionate research and development agenda. Implementation science should examine how prioritized interventions and PHC-oriented service bundles can be adapted to national and subnational contexts, financed, delivered, scaled, monitored, and sustained. Clinical and technological innovation also remains important for high-burden conditions for which currently available interventions offer limited improvement potential.
 

Chapter 4 examines the stewardship and governance instruments that make this translation and learning possible.