Background
The Global Burden of Disease study marked a turning point in global health measurement. By bringing together mortality, morbidity, and risk factor attribution within a common empirical architecture, it made possible a systematic comparison of health loss across populations and over time [
1]. Yet, as global health policy has moved from measuring disease burden toward universal health coverage and sustainable development, the evaluative question and space have also changed [
2,
3]. It is no longer sufficient to ask only how much health was lost. It is also necessary to ask whether individuals had the conditions required to avoid that loss.
Yet the GBD measures only one dimension of health: the outcome of disease and premature death. DALYs capture the outcome dimension of health capability: years cut short by premature death and years lived with illness or disability. They do not, however, measure whether people had the resources, institutional arrangements, health system settings and social freedoms required to realise health in the first place. Some of these conditions feature in the GBD architecture only as risk factors to which burden is retrospectively attributed. The conditions determining whether health capability can be realised are not counted as a distinct component of the capability gap and, in effect, are invisible to the metric.
This distinction is not merely technical; it has implications for health equity, measuring health gaps, and service delivery. Sen’s capability approach holds that human development should be evaluated not only by achieved outcomes but also by the real freedoms individuals have to lead the lives they value [
4]. Applied to health, this means that disease burden and health capability deficits are not the same. A person who dies young due to inadequate access to healthcare and a person who dies at the same age due to an unavoidable genetic condition may produce identical DALYs, yet they represent different societal capability gaps with divergent policy implications.
The paper makes three contributions: it distinguishes realised health loss from health capability deprivation; it identifies Health Resource Constraints as a distinct measurement domain rather than only as retrospective risk factors; and it sketches how a GBD+ framework could extend burden measurement while preserving the life-course accounting logic of the GBD.
A Conceptual Sketch of the Health Capability Gap
The Health Capability Gap (HCG) is conceived as the aggregate life-course shortfall between full health capability and realised health capability, measured through health outcomes. This is comparable to the concept of DALYs in the GBD framework: DALYs are a health-loss measure that captures only achieved outcomes, whereas HCG is a capability-gap measure that captures both health loss and resource gaps. HCG comprises three components that contribute to this shortfall: Health Resource Constraints (HRC), Complete Health Capability Exclusion (CHE), and Partial Health Capability Loss (PHE).
CHE captures the complete loss of life-span capability due to premature mortality (YLLK), while PHE captures partial loss of capability due to illness, disability, or functional limitation (YLDK). Corresponding to YLL in the GBD, (YLLK) reinterprets the same quantity as involuntary exclusion from the life course—the total elimination of the capacity to exercise health-related freedoms—rather than as a demographic shortfall. Corresponding to YLD in GBD, (YLDK) reinterprets non-fatal health loss as a partial capability constraint: a person nominally alive but unable to realise health capability in full.
To these outcome measures, the HCG framework adds an explicit resource-related component, HRC, measured by years of health capability lost due to the structural shortfall in the conditions required to realise health capability (YLS
K). These include access to healthcare, food security, adequate housing, safe water and sanitation, and protection from adverse climate and environmental conditions. Climate change degrades these enabling conditions through extreme heat, flooding, drought, displacement, and climate-sensitive shifts in disease ecology [
6], even before a disease or death is recorded, thereby constituting a present structural deficit independent of any recorded outcome. Together, the three components (YLL
K, YLD
K, and YLS
K) define HCG, and extend the GBD from an outcome-only account of health loss to a GBD+ account of structurally constrained health freedom. To put it metaphorically,
if DALYs are the visible tip of health loss already recorded, HRC is the submerged mass of structural deficits that have yet to surface as disease—and the HCG is the entire iceberg.
Measurement Approach: Years Lost Due to Structural Constraints (YLSK)
The measurement of YLLK and YLDK follows the GBD architecture, with the only difference being their interpretation as measures of aspects of the health capability gap. On the other hand, years of health capability lost due to structural constraints (YLSK) can be estimated by identifying deprivation states that reduce the real freedom to achieve health, such as the absence of essential healthcare, severe food insecurity, unsafe sanitation, inadequate housing, or climate vulnerability.
Three points need to be emphasised. First, HRCs, measured through YLSK, are not the same as GBD risk factor attribution. Risk factors explain what proportion of past DALYs is attributable to upstream exposures. HRCs are prospective capability deficits: present shortfalls that may exist before any recorded disease event. A child without access to primary healthcare or a community exposed to recurrent flooding faces such a deficit before a single DALY is counted. Second, these states are not sequelae in the strict GBD sense, because they are not downstream consequences of disease or injury. They are structural capability-deprivation states. Third, not every exposure is automatically treated as a capability constraint. Behavioural risks require careful treatment because they raise questions about voluntariness, constrained preference formation, addiction, and commercial influence. However, they can be operationalised analogously to sequelae: each state can be assigned a disutility weight, overlapping states can be combined using a multiplicative function, and the result can be accumulated over person-years to estimate healthy capability-years lost.
Each deprivation state can be assigned a Health Resource Constraint disutility weight between 0 and 1, representing the average reduction in health capability associated with that deficit, independent of any pre-existing disease. These weights are conceptually analogous to disability weights in the GBD, which quantify the severity of non-fatal health states for calculating years lived with disability [
7]. The difference is that disability weights capture health loss from disease or injury sequelae, whereas health resource constraint disutility weights capture loss of health capability arising from structural deprivation.
When individuals experience multiple deficits, combined disutility weights can be calculated using the multiplicative approach already used in the GBD for multiple health conditions. Multiplying age-specific person-years exposed to structural deprivation by the average combined disutility weight yields the HRC component, expressed as years of health capability lost due to structural constraint.
Formally, the Health Capability Gap can be expressed as HCG = YLLᴷ + YLDᴷ + YLSᴷ. The first two components correspond to DALYs, reinterpreted as complete and partial losses of health capability. The third component, YLSᴷ, captures years of health capability lost due to structural constraints, adjusted to avoid double-counting losses already reflected in morbidity or mortality. Thus, HCG extends DALYs by accounting for residual structural capability loss that has not yet manifested as a recorded disease event and that outcome-only metrics do not capture.
A complementary summary metric, Capability-Adjusted Life Expectancy (CALE), captures the expected years of life lived in full health capability under current structural and epidemiological conditions. The measure is related to poverty-free and poverty-adjusted life expectancy, which recognises that survival alone is incomplete when years are lived in poverty [
8,
9]. The difference is that those measures adjust survival for poverty, whereas CALE/HCG extends the GBD architecture by treating the underlying manifestations (such as severe food insecurity, unsafe sanitation, inadequate housing) as structural constraints among others and by adding health capability loss to the DALY outcome components. A formal representation of the accounting framework and the relationship between CALE and HALE is provided in the Supplementary Appendix.
Implications
Adopting the Health Capability Gap as a GBD+ framework would have four implications. First, it would directly reveal the structural origins of health inequality—including climate vulnerability—that are invisible to outcome metrics. Existing metrics show that populations differ in health outcomes, but do not distinguish outcomes arising from inadequate structural conditions from those arising from less avoidable causes. Settings with high HRC relative to DALYs are those in which the GBD is most likely to severely underestimate health capability deprivation, because the structural component may not yet have produced a disease event. Integrating HRC directly into the measurement of the health capability gap makes it visible and quantifiable, enabling investment in the structural conditions that produce health rather than solely in treating their absence.
Second, it supports the diagnosis of the intervention mix required and sharpens accountability within the health system. The HCG decomposes deprivation into HRC, PHE, and CHE. A system achieving low DALYs through treatment in a structurally deprived setting is performing differently from one that maintains the structural conditions preventing disease. This directs resources toward the dominant source of capability loss: structural investment, where HRC dominates, and clinical scale-up, where CHE or PHE dominate.
Third, it aligns measurement with normative commitments. The Sustainable Development Goals, Universal Health Coverage, and WHO’s constitutional framing of health as a right are articulated in capability or rights-based language, yet monitored largely through outcome-based indicators [
2,
3,
9]. The HCG operationalises what these frameworks actually protect: the conditions under which health capability can be freely realised.
Fourth, it would reposition climate change as a structural determinant of capability. The GBD attributes disease burden to climate-related risks, but it does not count structural losses that precede disease events, such as disrupted food systems, displacement, or loss of secure habitation.
Limitations and Future Directions
The proposed approach is not without limitations. First, there is a risk of double counting. The proposed measurement strategy affects the health capability gap through two pathways: directly through deprivation ((YLSK)) and indirectly through disease outcomes already captured in DALYs ((YLLK + YLDK)). HRC must measure the structural shortfall, net of outcome effects, already in DALYs; simply summing (YLSK) and DALYs would count the same deprivation twice. Hence, HRC must be operationalised, with particular attention to addressing double-counting. This could be done by borrowing the exposure-counterfactual logic of GBD comparative risk assessment, but by treating structural deprivation as an ex ante loss of capability rather than merely as a source of attributable DALYs. Implementation could use mediation analysis, multistate life-table methods, and causal approaches such as sequential g-estimation or inverse probability weighting to separate direct capability loss from disease pathways already counted as YLLK and YLDK.
Second, the multiplicative combination function is a theoretical assumption—deficits may interact synergistically or antagonistically—and sensitivity analyses using alternative combination rules should accompany estimates until empirical data are available. Third, incorporating behavioural risks into measurement requires normative judgement, as not all choices are made under equal conditions. Fourth, Health Resource Constraint disutility weights require validation; GBD disability weights capture average valuations of health states, not necessarily capability losses arising from structural deprivation across institutional contexts. Fifth, as illustrated here, the HCG represents a population-level aggregate and requires distributional extension by income, geography, gender, age, disability, and other axes of inequality.
A further consideration for future research concerns the distributional sensitivity of the aggregate HCG. Because the metric sums all shortfalls from full health capability, a large number of mild capability deficits in a well-resourced population could, in principle, outweigh a smaller number of severe deficits in a highly deprived population. Whether this is judged appropriate depends on the normative framework adopted. Where a sufficientarian [
10] standard is preferred—holding that justice requires ensuring everyone reaches a minimally adequate threshold of health capability—future work could develop a distribution-sensitive variant that restricts the HCG to person-years lived or lost below a defined sufficiency threshold, or that weights deficits by their depth below that threshold. This would strengthen the metric’s ability to track severe capability poverty and align it more closely with rights-based monitoring.
Conclusions
The GBD measures what happened to health. The HCG provides a structured way to measure not only realised health loss but also the structural shortfall in the conditions required to achieve health. Explicitly integrating health resource constraints into the measurement of health capability yields a GBD+ metric that complements DALYs wherever structural deprivation exists, and even more so when climate and environmental conditions erode the enabling conditions for health capability. The GBD has built an unparalleled infrastructure for counting what is lost; the HCG extends it into the space that matters equally: the health capability that structural conditions foreclosed before any disease event registered. Grounded in the capability approach, the sufficientarian threshold account, and a disutility-weight framework mirroring the GBD’s own measurement logic, the HCG offers a foundation for global health monitoring that counts not only burden but also freedom.