Progressive risk assessment
Our health status is analog, not digital. We do not transition overnight from being at-risk of hypertension, to being a confirmed hypertensive. To accurately assess our health, we need a model that measures risk progressively. The evidence used to build a risk model can be split into two dimensions—its source, and its nature.
### Source of evidence
Arranged by proximity to patient:
* Patient (i.e. self-reported)
* Family member, friend, at-home caregiver
* Community health worker
* Nursing, technical and paramedical staff
* Physician
Patient-reported experiences of illness are a crucial but oft-missed dimension of population health. Healthcare episodes start with the experience of being unwell—long before they are labeled “diseases” or “conditions” by healthcare providers.
### Nature of evidence
* Experienced: patient’s subjective experience
* Observed: about the individual, or their environment
* Measured: quantifiable, verifiable parameters
Source & nature together determine the strength of evidence.
For instance, in April 2021, “feeling feverish” had me worried that I had COVID-19. However, the evidence lacked strength.
I took an RT-PCR test the next day, performed by a technician in an accredited lab. This movement from `patient + experienced` to `technical staff + measured` greatly increased the strength of evidence.
Finally, the strength of evidence—how confident are we that there is Coronavirus nucleic acid in my pharynx—combined with utility of the evidence—how useful is a single positive RT-PCR test in determining COVID-19—generates a risk estimate. In this example, a [positive test](#user-content-fn-1)[^1] equalled near-certainty.
### Risk stratification
To make risk assessments actionable, they have to be stratified. Each stratum provides a threshold to trigger action.
Here, we propose four strata—at-risk, suspected, provisionally diagnosed, and definitively diagnosis—based on a clinical understanding of a disease’s progress.
Figure 2: from risk assessment to risk stratification
### Capability mapping
Across diseases and conditions, we **map four risk strata to five** **tiers of care — at-home, community, primary, secondary, and tertiary**. These tiers provide a structure to which both assessments and interventions are mapped, based on how capable the health system is.
Consider a health system where homes have ready access to glucometers, BP monitors, and pregnancy test kits, but RT-PCR and sputum tests are limited to secondary care facilities. The assessments, or diagnoses, enabled by this system can be represented by the following capability map:
Figure 3: A diagnoses capability map
This map is a collection of nodes—each node represents the intersection of a tier of care, and a disease or condition. At each node, we use available skills and tools; collectively, capabilities. Using these capabilities, we update our risk model, and re-stratify the risk faced by the patient. Similar maps can be developed for interventions.
A capability map evolves as processes mature, technologies develop, and care providers acquire new skills. Accurate, inexpensive rapid-antigen tests are moving definitive diagnosis of COVID-19 from labs to homes, similar to how self-use test strips moved pregnancy detection from labs to homes. In the domain of interventions, community-based vitals monitoring & drug dispensing are replacing a large portion of facility-based management of hypertension and diabetes.
These maps enable us to identify nodes that need new, innovative ways of delivering care to achieve high public health impact. Thus, they tell us where we are, and help us see where to go.
### Group-based intervention
Interventions are the last element of our conceptual framework. Effective intervention design requires targeting, which necessitates grouping.
Within a single risk strata, a population can be divided into groups based on the need for, and the access to, healthcare services. Groups are based both on a population’s characteristics, and how the health system is organized.
While designing groups, consider the following dimensions, or axes:
1. Location: proximity to public health facilities, access to transportation, environmental conditions near home or work.
2. Socioeconomic status: measured through parameters such as housing, income, access to clean water \[[1](https://doi.org/10.1080/16549716.2018.1438840)], and education.
3. Risk velocity: change in risk over time, as a supplement to risk strata.
4. Secondary risk factors & comorbidities: At the minimum, relationships where risk of one disease increases risk of another. Example: HIV/AIDS is a 20–40x risk-amplifier for TB \[[2](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3685687)].
Thus, working-age urbanites form a different group than rural pensioners, even if they have the same risk of hypertension.
[^1]: In case you are wondering, I did test positive for COVID.
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