Conceptualizing Care Continua: Lessons from HIV, Hepatitis C Virus, Tuberculosis and Implications for the Development of Improved Care and Prevention Continua

Abstract

BackgroundTo examine the application of continuum models to tuberculosis, HIV, and other conditions; to theorize the concept of continua; and to learn lessons that could inform the development of improved care and prevention continua as public health metrics.MethodsAn analytic review of literature drawn from several fields of health care.ResultsThe continuum construct is now part of public health evaluation systems for HIV, and is increasingly used in public health and the medical literature. Issues with the comparability and optimal design of care continuum models have been raised, and their methodologic and theoretic underpinnings and scope of focus have been under-addressed. Review of relevant publications suggests that a key limitation of current models is their lack of measures reflecting incidence and mortality. Issues relating to continua data being longitudinal or cross-sectional, definition of numerators and denominators for each step, data sources, measures of timeliness of step completion, theoretic models to facilitate inferences of causes of care continuum gaps, how measures of prevention efforts, reinfection/relapses, and interactions of continua for co-occurring comorbidities should be reflected, and how analyses of differences in retention over time, across geographic regions, and in response to interventions should be conducted are critical to the development of sound care and prevention continuum models.ConclusionLessons learned from the application of continuum models to HIV and other conditions suggest that the application of well-formulated constructs of care and prevention continua, that depict, in well defined, standardized steps, incidence and mortality, along with degrees of and time to screening, engagement in care and prevention, treatment and treatment outcomes, including relapse or reinfection, may be vital tools in evaluating intervention and program outcomes, and in improving population health and population health metrics for a wide range conditions.

Research priorities to inform “Treat All” policy implementation for people living with HIV in sub‐Saharan Africa: a consensus statement from the International epidemiology Databases to Evaluate AIDS (IeDEA)

“…Shortening this period maximizes individual clinical benefit and reduces the risk of onward transmission, ultimately reducing both new infections and HIV‐related morbidity and mortality. Although the 90‐90‐90 targets and HIV care continua delineate important milestones, current metrics do not reflect the timeliness with which these key outcomes are achieved . In addition, in some settings, routine HIV viral load monitoring is infrequent, and pre‐ART CD4 count monitoring is declining in frequency with Treat All implementation , which limits opportunities to evaluate individual and public health impacts of HIV programming.…”

Cascade of care for people living with HIV infection in Southern Brazil: results from a public health network

“…If the denominator is kept constant throughout sequential steps, the overall impact of cumulative losses is visually more apparent. If the numerator in the first step is taken as the denominator of the subsequent step, each step is essentially represented as a separate event and the impact of cumulative losses through sequential steps is visually de-emphasized 26 . Some studies represent these two approaches together, probably because they provide better information about the healthcare process, especially for viral load Cad.…”

Universal coverage but unmet need: National and regional estimates of attrition across the diabetes care continuum in Thailand

“…One promising approach to addressing this challenge is the use of care cascades to identify points of loss, or gaps, in the chronic disease care continuum across screening, diagnosis, treatment, and control. Care cascades were originally used to model loss to follow up in HIV/AIDS care and have subsequently been applied to a range of other chronic conditions, including hypertension and diabetes [10,11]. Prior studies using data across multiple LMIC, for example, revealed large gaps between screening and diagnosis, with 80% unmet need in diabetes care [12][13][14].…”