Table 2.

Summary of main differences between the proposed and current natural history models of HPV infection and cervical carcinogenesis.^a

Model feature	Existing models	Proposed modeling framework
Health states	Normal cervix HPV infection Immune^b CIN1, CIN2, CIN3 Cervical cancer Pros: Histologic diagnoses correspond to existing clinical action thresholds (i.e., treating CIN2+). Evidence supports reduced likelihood of type-specific re-infection (immunity). Cons: Histologic diagnoses are not reproducible and may be inconsistent across studies. CIN1-2-3 transitions do not reflect the causal pathway and lead to non-identifiability issues in transitions. Data are unavailable to specify the probability, duration, and potential waning of natural immunity, leading to non-identifiability issues.	Normal cervix HPV infection Precancer Cervical cancer Pros: Health states are parsimonious and align with the causal pathway of cervical carcinogenesis. The precancer state is rigorously defined to allow for more accurate evaluations of novel biomarkers. The definition of precancer can be further refined as more biomarker data become available. Cons: Transitions require intensive statistical analysis of prospective data sources with reference diagnoses of precancer.
Corollary transitions	HPV acquisition HPV clearance (with or without immunity^b) HPV progression/regression to/from CIN1, CIN2, CIN3 Invasion Pros: Transitions can be derived from the literature and clinical data. Cons: Latency and reactivation are ignored.	HPV appearance HPV disappearance HPV progression Invasion Pros: Transitions explicitly consider the possibility of latency and reactivation. Cons: Fewer levers exist to fit empirical data.
Variables that modify transitions	HPV acquisition: Age, HPV type, history of prior type-specific infection^b HPV clearance: Age, HPV type HPV progression/regression: Age, HPV type Invasion: Age, HPV type Stratified models for: WLHIV Pros: Models can be adapted to fit most populations through the calibration process. Cons: The age of a woman is an imperfect proxy for the age of an infection. Markov models may face challenges incorporating time-in-state transitions across health states. HPV clearance does not vary by HPV type, but progression does. Models calibrated to settings in Sub-Saharan Africa may not reflect potential differences in HPV-related transitions in populations with reduced cell-mediated Immunity.	HPV appearance: Age, HPV type HPV disappearance: time since infection HPV progression: HPV type, time since infection Invasion: HPV type, time since infection/duration of precancer Stratified models for: Lower HPV prevalence setting Higher HPV prevalence setting WLHIV Pros: Model transitions consider time-in-state, discerning the differential impact of new and old infections in older women. Models will reflect differences in HPV clearance and progression between populations, if these exist. Cons: Models require prospective longitudinal data from Sub-Saharan African settings. Microsimulation models may be required to incorporate transitions that vary by time-in-state; model development and data requirements are intense.
Direct estimation of transition Probabilities ^c	Varies by model Pros: Depends on data source. Cons: Depends on data source.	HPV appearance (when data available) HPV disappearance HPV progression (Lower HPV prevalence setting) Data sources: Guanacaste Natural History Study, Costa Rica Vaccine Trial and Long-Term Follow-Up Study, ALTS (Lower HPV Prevalence Model) ACCME Cohort, Project Itoju (Higher HPV Prevalence Model) Pros: Models will consider potentially different transitions in Sub-Saharan African populations. Cons: Data sources in Sub-Saharan Africa are very limited.
Uncertain parameters requiring calibration or model-fitting Techniques ^c	Estimation of most transitions requires some use of model-fitting techniques Pros: Models can be adapted to fit a wide range of populations. Cons: Over-reliance on calibration techniques leads to non-identifiability Issues that Interfere with model transparency and comparative modeling efforts.	HPV appearance (when data unavailable) HPV progression (Higher HPV prevalence settings) Invasion Pros: Fewer transitions open to calibration can lead to identifiable models that better reflect the natural history of disease. Models are more parsimonious and transparent. Cons: Data requirements are intensive.

Model feature

Existing models

Proposed modeling framework

Health states

Normal cervix
HPV infection
Immune^b
CIN1, CIN2, CIN3
Cervical cancer
Pros:

Histologic diagnoses correspond to existing clinical action thresholds (i.e., treating CIN2+).
Evidence supports reduced likelihood of type-specific re-infection (immunity).

Cons:

Histologic diagnoses are not reproducible and may be inconsistent across studies.
CIN1-2-3 transitions do not reflect the causal pathway and lead to non-identifiability issues in transitions.
Data are unavailable to specify the probability, duration, and potential waning of natural immunity, leading to non-identifiability issues.

Normal cervix
HPV infection
Precancer
Cervical cancer
Pros:

Health states are parsimonious and align with the causal pathway of cervical carcinogenesis.
The precancer state is rigorously defined to allow for more accurate evaluations of novel biomarkers.
The definition of precancer can be further refined as more biomarker data become available.

Cons:

Transitions require intensive statistical analysis of prospective data sources with reference diagnoses of precancer.

Corollary transitions

HPV acquisition
HPV clearance (with or without immunity^b)
HPV progression/regression to/from CIN1, CIN2, CIN3
Invasion
Pros:

Transitions can be derived from the literature and clinical data.

Cons:

Latency and reactivation are ignored.

HPV appearance
HPV disappearance
HPV progression
Invasion
Pros:

Transitions explicitly consider the possibility of latency and reactivation.

Cons:

Fewer levers exist to fit empirical data.

Variables that modify transitions

HPV acquisition: Age, HPV type, history of prior type-specific infection^b
HPV clearance: Age, HPV type
HPV progression/regression: Age, HPV type
Invasion: Age, HPV type

Stratified models for:
WLHIV
Pros:

Models can be adapted to fit most populations through the calibration process.

Cons:

The age of a woman is an imperfect proxy for the age of an infection.
Markov models may face challenges incorporating time-in-state transitions across health states.
HPV clearance does not vary by HPV type, but progression does.
Models calibrated to settings in Sub-Saharan Africa may not reflect potential differences in HPV-related transitions in populations with reduced cell-mediated Immunity.

HPV appearance: Age, HPV type
HPV disappearance: time since infection
HPV progression: HPV type, time since infection
Invasion: HPV type, time since infection/duration of precancer

Stratified models for:
Lower HPV prevalence setting
Higher HPV prevalence setting
WLHIV
Pros:

Model transitions consider time-in-state, discerning the differential impact of new and old infections in older women.
Models will reflect differences in HPV clearance and progression between populations, if these exist.

Cons:

Models require prospective longitudinal data from Sub-Saharan African settings.
Microsimulation models may be required to incorporate transitions that vary by time-in-state; model development and data requirements are intense.

Direct estimation of transition Probabilities ^c

Varies by model
Pros:

Depends on data source.

Cons:

Depends on data source.

HPV appearance (when data available)
HPV disappearance
HPV progression (Lower HPV prevalence setting)

Data sources:
Guanacaste Natural History Study, Costa Rica Vaccine Trial and Long-Term Follow-Up Study, ALTS (Lower HPV Prevalence Model)
ACCME Cohort, Project Itoju (Higher HPV Prevalence Model)
Pros:

Models will consider potentially different transitions in Sub-Saharan African populations.

Cons:

Data sources in Sub-Saharan Africa are very limited.

Uncertain parameters requiring calibration or model-fitting Techniques ^c

Estimation of most transitions requires some use of model-fitting techniques
Pros:

Models can be adapted to fit a wide range of populations.

Cons:

Over-reliance on calibration techniques leads to non-identifiability Issues that Interfere with model transparency and comparative modeling efforts.

HPV appearance (when data unavailable)
HPV progression (Higher HPV prevalence settings)
Invasion
Pros:

Fewer transitions open to calibration can lead to identifiable models that better reflect the natural history of disease.
Models are more parsimonious and transparent.

Cons:

Data requirements are intensive.

HPV: human papillomavirus; CIN: cervical intraepithelial neoplasia (grade 1, 2, or 3); WLHIV: women living with HIV.

“Immune” refers to a reduced risk of HPV type-specific re-infection. Existing models typically include either a separate immune health state or a reduced likelihood of repeat acquisition with the same type.

Data on HPV transition risks for WLHIV, as a function of immune and antiretroviral therapy status, are very limited. The HIV Model deserves thorough consideration that is beyond the scope of this paper.