Modeling optimal cervical cancer prevention strategies in Nigeria
 Nadia Demarteau^{1}Email author,
 Imran O MorhasonBello^{2},
 Babatunde Akinwunmi^{2} and
 Isaac F Adewole^{2}
DOI: 10.1186/1471240714365
© Demarteau et al.; licensee BioMed Central Ltd. 2014
Received: 10 October 2013
Accepted: 15 May 2014
Published: 24 May 2014
Abstract
Background
This study aims to assess the most efficient combinations of vaccination and screening coverage for the prevention of cervical cancer (CC) at different levels of expenditure in Nigeria.
Methods
An optimization procedure, using a linear programming approach and requiring the use of two models (an evaluation and an optimization model), was developed. The evaluation model, a Markov model, estimated the annual number of CC cases at steady state in a population of 100,000 women for four alternative strategies: screening only; vaccination only; screening and vaccination; and no prevention. The results of the Markov model for each scenario were used as inputs to the optimization model determining the optimal proportion of the population to receive screening and/or vaccination under different scenarios. The scenarios varied by available budget, maximum screening and vaccination coverage, and overall reachable population.
Results
In the basecase optimization model analyses, with a coverage constraint of 20% for one lifetime screening, 95% for vaccination and a budget constraint of $1 per woman per year to minimize CC incidence, the optimal mix of prevention strategies would result in a reduction of CC incidence of 31% (3dose vaccination available) or 46% (2dose vaccination available) compared with CC incidence prevaccination. With a 3dose vaccination schedule, the optimal combination of the different strategies across the population would be 20% screening alone, 39% vaccination alone and 41% with no prevention, while with a 2dose vaccination schedule the optimal combination would be 71% vaccination alone, and 29% with no prevention. Sensitivity analyses indicated that the results are sensitive to the constraints included in the optimization model as well as the cervical intraepithelial neoplasia (CIN) and CC treatment cost.
Conclusions
The results of the optimization model indicate that, in Nigeria, the most efficient allocation of a limited budget would be to invest in both vaccination and screening with a 3dose vaccination schedule, and in vaccination alone before implementing a screening program with a 2dose vaccination schedule.
Keywords
CC Human papillomavirus vaccination Optimization model Africa NigeriaBackground
The incidence of cervical cancer (CC) in the SubSaharan Africa region, where Nigeria is located, is among the highest in the world. The CC incidence per 100,000 in SubSaharan Africa is 19.1 [1], whereas the world average rate is 15.2 per 100,000. CC death rates are also high in SubSaharan Africa, with rates per 100,000 of 12.8, compared with the world average of 7.8 per 100,000. In SubSaharan countries, CC is either the most common cancer in women or the second most common cancer (after breast cancer) in women [1].
In many developed countries, where national routine screening programs using the Papanicolaou (Pap) smear have been implemented, the CC incidence and mortality have been significantly reduced [2–5]. Early detection and treatment of cervical precancerous lesions is associated with high cure rates, whereas failure to detect precancerous lesion increase the risk to CC development and hence the risk of premature death. In many SubSaharan African countries, there are currently no programs for mass CC screening and even when such programs are set up in family planning clinics they are perceived as cumbersome and underutilized [6, 7].
Vaccination provides an alternative or a supplementary intervention for CC prevention. Infection with human papillomavirus (HPV) has been shown to be a necessary condition for the development of CC [8–10]. Eight HPV genotypes (HPV 16, 18, 45, 31, 33, 52, 58, and 35) account for more than 90% of CC cases, with HPV 16 and 18 accounting for about 70% of cases worldwide [11, 12]. Two HPV vaccines are currently available, an AS04adjuvanted HPV16/18 vaccine and a HPV6/11/16/18 L1 viruslike particle vaccine that covers two nononcogenic HPV types (HPV 6 and 11), as well as the oncogenic types HPV 16 and 18. Both vaccines have an efficacy of approximately 98% against the HPV 16 and 18 genotypes, but have different levels of crossprotection against other oncogenic HPV types [13–15]. The currently approved schedule for the available vaccines requires three doses over a 6month time period for optimum efficacy and is generally given before the onset of sexual activity [16, 17]. However, recent studies have indicated that two doses of vaccine may be sufficient and the 2dose schedule was consequently registered in different countries worldwide including Nigeria [18, 19]. The full long term duration of protection has not been fully determined as yet, but sustained immunogenicity and efficacy have been shown for up to 9.4 years for the HPV 16/18 AS04 adjuvanted vaccine [20]. Also, a conservative estimate from a modeling exercise estimated that the antibody levels would remain well above levels induced by natural infection for at least 50 years [21]. Even though the correlate of protection is unknown, neutralizing antibodies are considered to be the primary mechanism of vaccineinduced protection, hence these results potentially indicate long term protection with the vaccine.
Numerous studies have investigated the costeffectiveness of HPV vaccination or CC screening in many countries in Europe, Africa, and Latin America, and most have concluded that both methods of prevention are costeffective.
Standard costeffectiveness or budgetimpact analyses are however not the best methods to determine which mix of prevention strategies provides the most efficient use of limited resources. Standard costeffectiveness analyses do not typically take into account affordability constraints when estimating the costeffectiveness of different combinations of prevention strategies, and are also limited in their ability to examine the comparative efficiency of many different combinations of prevention interventions. Because neither vaccination nor screening alone can provide 100% protection against CC, an optimal prevention strategy might include a combination of both. Budgetimpact analyses typically estimate only affordability and do not link budget impact to health outcomes.
An alternative approach to economic assessment is optimization modeling applied previously in many different areas such as transport, agriculture, industry, and banking [22], and more recently in the health care sector [23–28]. This approach uses mathematical programming techniques to select the combination of alternative interventions that achieves the best clinical outcome while meeting preselected constraints on the available budget and on the feasibility of different coverage levels for the alternative interventions.
Optimization modeling provides valuable additional information compared with either costeffectiveness or budgetimpact analysis, since it explicitly evaluates multiple available options to select the combination that fulfills all the constraints introduced in the model while obtaining the most efficient result: lowest cost for the best outcome [22–28]. This is especially suitable for assessing public health interventions, where large but limited budgets must be allocated among different intervention options to allow a specific health goal to be reached. Compared with costeffectiveness analysis for decisionmaking, optimization modeling does not require a prespecified costeffectiveness threshold, which is associated with much debate in the literature.
The goal of this analysis was to provide information for Nigeria, as an example of a SubSaharan African country currently investigating a solution to improve CC prevention, about the most efficient combinations of prevention and screening coverage at different levels of expenditure. Nigeria has a population of about 170 million and is also the most populous country in Africa with a high CC burden [29]. Moreover, women in Nigeria typically present at an advanced CC stage; at least 80% present with stage III disease and 10% with stage IV disease based on the Classification of Malignant Tumours (TNM), accounting for the observed high mortality rates [7].
We used an optimization model to identify the combinations of vaccination and screening coverage that would provide the greatest estimated reduction in the annual CC incidence for different levels of expenditure per woman in Nigeria. This information can be used by policymakers in Nigeria and other countries in Africa with similar CC incidence and mortality rates when designing strategies to reduce the CC burden in their country.
Methods
The optimization model used in this study to identify the optimal combination of CC prevention strategies in Nigeria has been applied previously to the United Kingdom (UK) and Brazil to run similar analyses [30]. This evaluation estimates the optimal mix of CC prevention strategies to be implemented annually under specific constrains at steady state to minimize CC incidence. The steady state, in this evaluation, refers to a year over which, following the implementation of the prevention strategy in the entire population, all the benefits as well as the associated costs are captured across the entire population.
The optimization procedure requires the use of two models. The first model embedded within the optimization procedure was a Markov cohort model: the “evaluation” model. It was used to generate estimates of the annual incident CC cases at steady state in a population of 100,000 women for each of four alternative strategies considered in the evaluation: screening only; vaccination only; screening plus vaccination; and no prevention. The number of incident CC cases was chosen as the primary outcome measure because CC prevention represents the ultimate goal of screening or vaccination. The results of the Markov model for each scenario were used as inputs to the optimization model. The optimization model was then used to determine the optimal mix of interventions for maximizing the reduction in CC incidence under different scenarios. Those scenarios varied by available budget and by constraints on the maximum screening and vaccination coverage to be reached and the overall reachable population. Alternative scenarios were considered by varying the screening and vaccination coverage constraints to model different feasible intervention uptakes within a SubSaharan African country such as Nigeria. The purpose of testing different budget scenarios was to reflect the reality of limited health care funding and to demonstrate the incremental reduction in CC cases that would be possible with additional spending. This evaluation is intended to inform decisionmakers about the health and economic impact of different prevention strategies as well as the optimal potential program.
Evaluation model
Markov model inputs
Variable  Input value  Data source 

Vaccination  
Vaccine coverage  50% to 95%  Assumption 
Vaccine efficacy 1618  98%  
Proportion of HPV 16/18 related CIN1, CIN2/3 and CC  22%, 41%, 50%  [37] 
Vaccine efficacy 10 other HPV oncogenic types  68.3%  
Proportion of 10 other HPV type related CIN1, CIN2/3 and CC  24%, 35% 32%  [37] 
Age at vaccination  12 years  Assumption 
Vaccine waning  None  Assumption 
Screening  
Once—age  35 years  Assumption 
Twice—ages  35, 40 years  Assumption 
Three times—ages  35, 40, 45 years  Assumption 
Percentage screened  20% to 40%  Assumption 
Cytology sensitivity CIN1  70%  [39] 
Cytology sensitivity CIN2/3  80%  [39] 
Compliance with CIN1 treatment  100%  [39] 
Compliance with CIN2/3 treatment  100%  [39] 
Efficacy of CIN treatment  95%  [39] 
Cost (2011 US dollars)*  
Vaccine cost per dose  2365 NGN ($15)  PAHO price 
Cytology test  3010 NGN ($16)  [40] 
Colposcopy + biopsy  7225 NGN ($46)  [40] 
Annual CIN1 treatment (average resources used per patient: 1.7 consultations, 3.2 procedures, 1.1 medications, 0.5 hospitalizations)  40,672 NGN ($258)  [40] 
Annual CIN2/3 treatment (average resources used per patient: 1.8 consultations, 4.1 procedures, 2.1 medications, 0.9 hospitalizations)  68,200 NGN ($432)  [40] 
Lifetime CC treatment cost (average resources used per patient: 2.4 consultations, 7.1 procedures, 6.1 medications, 1.1 hospitalizations)  227,026 NGN ($1440)  [40] 
Transition probabilities  
Agespecific mortality rate  0.01 to 1  [41] 
Uninfected to HPV  0.0 to 0.14  [34] 
HPV to CIN1  0.05  [42] 
CIN1 to CIN 2/3  0.09  
CIN2/3 to persistent CIN2/3  0.11  
HPV clearance to uninfected  0.29 to 0.55  
CIN1 clearance  0.45  
CIN 2/3 clearance  0.23  
Persistent CIN2/3 to cancer  0.0 to 0.10  Estimated from calibration to CC 
Cancer to cancer cured  0.084  Estimated from 5year survival of 34.4% Nigeria ([1]) 
The validity of the model was assessed by comparing the estimated agedependent CC incidence without any prevention strategy to the CC incidence reported in GLOBOCAN [1]. Calibration to the reported CC incidence was done by adjusting the persistent CIN2/3 to CC transition probability.
Health care services used for treating CIN grade 1, CIN grades 2 and 3, and CC were taken from a retrospective chart review performed at the university college hospital at Ibadan where patients’ charts are archived. The chart review collected the medical resources used (outpatient health care professional visits, outpatient diagnostic procedures, outpatient treatment procedures, medications, and hospitalizations) to treat a patient with CIN1, CIN2/3 or CC. This study received approval from the University of Ibadan/University College Hospital Ethics Committee. For precancerous lesions, resources used over a 1year period from the date of diagnosis were collected; for CC, lifetime resources were collected (from diagnosis until either death or cure). For each condition, 10 patients with the required information at the time of data collection (2010) were identified (using consecutive medical records) and the medical resources used were extracted from their hospital medical records. The associated costs were estimated after assigning unit costs from the hospital record to each of the medical resources used. The costs were adjusted to 2012 values based on the Nigerian consumer price index for the health care sector [40]. The average costs from all patients per condition were used as input to the model (Table 1). Health care services for screening were based on expert opinion, and unit costs were estimated based on the average unit costs for each procedure reported in the hospital records from the university college hospital at Ibadan. The cost of the vaccine program was assumed to be $15 per dose (based on the Pan American Health Organization (PAHO) price).
Vaccine efficacy was estimated as the weighted average vaccine efficacy of 98% for HPV types 16 and 18 and 68.4% for the 10 most frequent nonvaccine HPV types (HPV31/33/35/39/45/51/52/56/58/59) related to CC, based on the clinical trial results of the AS04adjuvanted HPV16/18 vaccine, with weights reflecting the relative frequency of the different HPV types in Nigerian women (Table 1). Matching efficacy was assumed for both the 3dose and the 2dose vaccination schedule.
For each prevention strategy, the Markov model estimated the lifetime costs for prevention and treatment of CIN and CC and the lifetime incident CC cases for a cohort of women. The lifetime outcomes from the Markov model were divided by the total lifeyears lived by the cohort and multiplied by 100,000 to provide an estimate of 1year values at steady state for a population of 100,000 women, assuming that the age distribution for the population remained constant over time. These outcomes were then used as inputs to the optimization model. Because the estimated Markov model results were used to estimate the steadystate, crosssectional, 1year values for the whole population of interest, no discount rate was applied.
Optimization model
We used Solver (Frontline), an Excel addin to solve the optimization model. In the base case, we considered only a screening frequency of once in a lifetime at age 35 years. As a result, only four prevention strategies were included: no prevention; one lifetime screening; vaccination alone; and vaccination plus one lifetime screening. The optimization model was used to estimate the proportion of the population receiving each of the CC prevention strategies that minimized the expected CC incidence, considering a fixed budget and prespecified constraints on screening coverage, vaccine coverage, and overall reachable population. The four different CC prevention strategies were mutually exclusive. In the basecase analyses, the optimization model distributed the population between the four predefined prevention strategies in the objective function under several constraints:

■ x _{ i } is a continuous decision variable representing the proportion of the population in strategy i; these four values for x _{ i } are the optimization variables i (i = 1, …, 4) that denote the four different predefined prevention strategies: no prevention, screening once, vaccination alone, and vaccination plus screening once.

■ s denotes a subset of the i strategies including screening (2 strategies out of 4, 1 with screening alone, 1 with both vaccination and screening).

■ v denotes a subset of the i strategies including vaccination (2 strategies out of 4, 1 with vaccination alone and 1 with both vaccination and screening).

■ x _{ nprev } represents, among the x _{ i }, the proportion of the population receiving no preventive measure.

■ Coverage _{ 1 } and Coverage _{ 2 } denote the upper bounds for coverage for screening and vaccination, respectively; these are selected to represent either readily achievable or ideal values.

■ CC _{ i } represents the number of CC cases at steady state per 100,000 women receiving prevention strategy i as estimated by the evaluation model.

■ b _{ i } is the cost for 100,000 women at steady state receiving strategy i as estimated by the evaluation model.

■ B is the overall CCrelated (prevention and treatment) budget across the population.
Base case analyses
The prevaccination budget was estimated assuming that there is no national screening or vaccination program in Nigeria, the associated expenditure per woman per year being the cost of treatment for those with CC, corresponding to $0.25 per woman per year across the entire female population. In the basecase analyses, the constraint on annual expenditure per woman was increased gradually from $0.25 to $2.0, and the annual incident number of CC cases was estimated for each level of annual expenditure when the optimal combination of prevention strategies is implemented. For the two vaccination schedules 3 and 2 doses were considered. Three scenarios were estimated for the full range of budget constraints: (1) maximum screening coverage of 20% and maximum vaccination coverage of 95%; (2) maximum screening coverage of 40% and maximum vaccination coverage of 95%; and (3) maximum screening coverage of 20% and maximum vaccination coverage of 50%. These ranges were selected based on expected or targeted ranges for screening and vaccination coverage within the Nigerian setting. An additional constraint required a minimum number of people to receive no prevention. This constraint equals the lower of one minus the upperbound coverage constraint for either screening or vaccination and hence is directly linked to the screening and vaccination coverage constraint.
Sensitivity analyses
Univariate sensitivity analyses were conducted to estimate the effects on the incident number of CC cases for two different budget constraints, set at $1 and $2 per woman per year, when changing the costs associated with screening, vaccination, and treatment of CIN and CC. Ranges of plus or minus 20% were used for the screening costs, and ranges of plus or minus one standard deviation from the means were used for the costs of treatment of CIN and CC, using the data from the retrospective chart review. In addition, the impact on CC cases of adding strategies with more frequent screening, two lifetime screenings (at ages 35 and 40 years) or three lifetime screenings (at ages 35, 40 and 45 years), was tested. The sensitivity analysis on treatment costs accounted for setting with higher or lower costs than the one estimated in the retrospective cost evaluation that may not be representative of all settings in Nigeria. An additional scenario included the possibility of implementing an HPV test screening instead of the Pap test. In this scenario the cost of the screening test was set to 50% of the Pap test costs and CIN sensitivity was increased by an absolute value of 10%. Finally, the duration of vaccine efficacy was reduced from lifetime to 25 years, and also the vaccine efficacy was reduced by an absolute value of 20%.
Results
The evaluation model
Costs and clinical outcomes for women under each prevention strategy*
Scenario investigated  Annual cost for 100,000 women (per 1 Woman)^{**}  Annual CC cases for 100,000 women 

No prevention  $26,201 ($0.26)  17.45 
One lifetime screening  $75,418 ($0.75)  12.15 
Vaccination (3 doses)  $191,415 ($1.91)  6.01 
Vaccination (3 doses) and one lifetime screening  $238,580 ($2.39)  4.25 
Two lifetime screenings (sensitivity analysis only)  $115,082 ($1.15)  9.63 
Three lifetime screenings (sensitivity analysis only)  $145,316 ($1.45)  7.85 
Vaccination (3 doses) and two lifetime screenings (sensitivity analysis only)  $275,519 ($2.76)  3.39 
Vaccination (3 doses) and three lifetime screenings (sensitivity analysis only)  $303,324 ($3.03)  2.74 
Vaccination (2 doses)  $130,603 ($1.31)  6.01 
Vaccination (2 doses) and one lifetime screening  $177,775 ($1.78)  4.25 
Vaccination (2 doses) and two lifetime screenings (sensitivity analysis only)  $214,717 ($2.15)  3.39 
Vaccination (2 doses) and three lifetime screenings (sensitivity analysis only)  $242,523 ($2.43)  2.74 
Optimization model: base case
With a budget constraint of $2.00 per woman per year, the maximum prevention coverage could be reached and the optimal mix of prevention strategies to minimize CC incidence would be (with both a 2 and a 3dose vaccination schedule), 75% with vaccination alone, 20% with vaccination and one lifetime screening, and 5% with no prevention strategy. This would result in a CC reduction of 64% with both a 3dose and a 2dose vaccination schedule. The resulting expenditure would be $1.93 (3dose vaccination schedule) and $1.35 (2dose vaccination schedule) per woman per year.
With a 3dose vaccination schedule (Figures 2, 4 and 6) the optimal mix of prevention strategies includes screening alone for the lowest budget constraint. With a 2dose vaccination schedule (Figures 3, 5 and 7), the lowest budget constraints do not include screening but include vaccination alone. Screening is only part of the optimal mix of prevention strategies for budget constraints of at least $1.50 per woman per year with a vaccination coverage constraint of 95% or at least $1.00 per woman per year with a vaccination coverage constraint of 50%.
Sensitivity analysis
Annual CC cases and change from the prevaccination situation under the optimal budget allocation
Sensitivity analyses scenario  Screening and vaccine coverage constraints  

20% screening, 95% vaccination  40% screening, 95% vaccination  20% screening, 50% vaccination  
Base case (3 doses)  
Base case—mean treatment costs and one lifetime screening  12.0 (31%)  11.6 (34%)  12.1 (31%) 
Prevention cost minus 20% and CIN treatment costs minus 1 SD  11.6 (34%)  10.8 (38%)  12.3 (29%) 
Prevention costs plus 20% and CIN treatment costs plus 1 SD  12.3 (29%)  12.3 (29%)  12.1 (31%) 
CC treatment costs minus 1 SD  10.7 (38%)  10.4 (40%)  11.7 (33%) 
CC treatment costs plus 1 SD  13.5 (23%)  13.1 (25%)  13.5 (23%) 
Allow scenarios with two lifetime screenings  12.0 (31%)  11.6 (34%)  12.1 (30%) 
Allow scenarios with two or three lifetime screenings  12.0 (31%)  11.6 (34%)  12.1 (31%) 
Screening using HPV test  11.6 (34%)  10.8 (38%)  12.0 (31%) 
Vaccine (3 doses) duration of protection = 25 years, vaccine efficacy reduced by 20%  12.8 (27%)  12.3 (30%)  13.1 (25%) 
Alternative scenario (2 doses) ^{ * }  
Base case mean treatment costs and one lifetime screening  9.4 (46%)  9.4 (46%)  11.0 (37%) 
Prevention cost minus 20% and CIN treatment costs minus 1 SD  8.8 (50%)  8.4 (52%)  11.0 (37%) 
Prevention costs plus 20% and CIN treatment costs plus 1 SD  9.4 (46%)  9.4 (46%)  11.1 (36%) 
CC treatment costs minus 1 SD  7.9 (55%)  7.9 (55%)  11.0 (37%) 
CC treatment costs plus 1 SD  11.4 (35%)  11.4 (35%)  11.6 (33%) 
Allow scenarios with two lifetime screenings  9.4 (46%)  9.4 (46%)  11.0 (37%) 
Allow scenarios with two or three lifetime screenings  9.4 (46%)  9.4 (46%)  11.0 (37%) 
Screening using HPV test  8.8 (49%)  8.5 (51%)  11.3 (35%) 
Vaccine (2 doses) duration of protection = 25 years, vaccine efficacy reduced by 20%  10.8 (38%)  10.6 (39%)  12.3 (29%) 
Annual CC cases and change from the prevaccination situation under the optimal budget allocation
Sensitivity analyses scenarios  Screening and vaccine coverage constraints  

20% screening, 95% vaccination  40% screening, 95% vaccination  20% screening, 50% vaccination  
Base case (3 doses)  
Base case—mean treatment costs and one lifetime screening  6.2 (64%)  6.0 (66%)  11.4 (35%) 
Prevention cost plus 20% and CIN treatment costs plus 1 SD  6.2 (64%)  5.9 (66%)  11.4 (35%) 
Prevention costs minus 20% and CIN treatment costs minus 1 SD  6.2 (64%)  6.1 (65%)  11.4 (35%) 
CC treatment costs plus 1 SD  6.2 (64%)  5.9 (66%)  11.4 (35%) 
CC treatment costs minus 1 SD  6.2 (64%)  6.3 (64%)  11.4 (35%) 
Allow scenarios with two lifetime screenings  6.1 (65%)  6.0 (66%)  11.2 (36%) 
Allow scenarios with two or three lifetime screenings  5.9 (66%)  6.0 (66%)  11.1 (37%) 
Screening using HPV test  6.2 (65%)  5.8 (67%)  11.3 (35%) 
Vaccine duration of protection = 25 years, vaccine efficacy reduced by 20%  7.3 (58%)  7.1 (59%)  12.0 (31%) 
Alternative scenario (2 doses) ^{ * }  
Base case—mean treatment costs and one lifetime screening  6.2 (64%)  5.9 (66%)  11.4 (35%) 
Prevention cost plus 20% and CIN treatment costs plus 1 SD  6.2 (64%)  5.9 (66%)  11.4 (35%) 
Prevention costs minus 20% and CIN treatment costs minus 1 SD  6.2 (64%)  5.9 (66%)  11.4 (35%) 
CC treatment costs plus 1 SD  6.2 (64%)  5.9 (66%)  11.4 (35%) 
CC treatment costs minus 1 SD  6.2 (64%)  5.9 (66%)  11.4 (35%) 
Allow scenarios with two lifetime screenings  6.1 (65%)  5.5 (68%)  11.2 (36%) 
Allow scenarios with two or three lifetime screenings  5.9 (66%)  5.3 (70%)  11.1 (37%) 
Screening using HPV test  6.2 (65%)  5.8 (67%)  11.3 (35%) 
Vaccine duration of protection = 25 years, vaccine efficacy reduced by 20%  8.1 (54%)  7.7 (56%)  12.3 (29%) 
With a $2 per woman budget constraint, the maximum vaccination and screening coverage is reached under the optimal mix of strategies for all sensitivity analyses performed, and hence the maximum CC reduction is reached with both the 3dose and the 2dose vaccination schedule scenarios and under all sensitivity analyses.
Discussion
We have developed a model that would identify the optimal mix of CC prevention strategies (screening and or vaccination) to minimize the number of CC cases for different scenarios defined by constraints on budget, maximum screening and vaccination coverage, and overall reachable population. Under the base case, three scenarios were considered for either a 3dose or a 2dose potential vaccination schedule to capture multiple alternatives regarding the available budget and feasibility of implementation in Nigeria.
Main findings
The results of the Markov evaluation models indicated that the number of CC cases expected from a 100% coverage of each prevention strategy was lowest for vaccination (6.01 per 100,000 women per year) compared with one lifetime screening (12.15 per 100,000 women per year), two lifetime screenings (9.63 per 100,000 women per year), three lifetime screenings (7.85 per 100,000 women per year), or no prevention (17.45 per 100,000 women per year).
In the basecase optimization model analyses, with upperbound coverage constraints of 20% for screening and of 95% for vaccination and a budget constraint at $1 per woman, the optimal mix of prevention strategies would result in a 31% CC reduction compared with today’s CC incidence with a 3dose vaccination schedule, and in a 46% CC reduction with a 2dose vaccination schedule. With a 3dose vaccination schedule, the optimal combination would be 20% with screening alone, 39% with vaccination alone and 41% without any prevention, while with a 2dose vaccination schedule the optimal combination would be 0% screened, 71% vaccinated, and 29% without any prevention. Under the lower budget constraints, the optimal strategy with a 3dose vaccination schedule would always be a combination of screening alone, vaccination alone and no prevention, while with a 2dose vaccination schedule the optimal combination would only include vaccination and no prevention by screening. Using an increment in budget constraint of $0.25 per women per year going from $0.25 to $2.00, any budget constraint equal to or higher than $2.00 per woman with a 3dose vaccination schedule, or $1.50 per woman with a 2dose vaccination schedule, would result in the optimal strategy with a maximum CC reduction using a strategy consisting of 75% with vaccination alone, 20% with vaccination and screening and 5% without prevention. The associated CC reduction would be 64%. These strategies would be specifically associated with a budget of $1.93 per woman with a 3dose vaccination schedule and $1.35 per woman with a 2dose vaccination schedule.
The results were similar for different screening and vaccination coverages, except that in the case of an upperbound vaccination coverage of 50%, the percentage reduction in CC cases compared with the prevaccination situation had a maximum value of 35% at an expenditure of $1.18 per woman with a 3dose vaccination schedule and $0.88 per woman with a 2dose vaccination schedule. Sensitivity analyses indicated that the results were sensitive to the CIN and CC treatment cost. Higher treatment costs resulted in lower achievable coverage with vaccination and screening within the $1 budget constraint, and hence a lower reduction in the number of CC cases. With a 2dose vaccination schedule, all sensitivity analyses tended to first maximize the vaccination coverage. With a 3dose vaccination schedule, most sensitivity analyses (except a high CIN treatment cost) tended to maximize both vaccination and screening and in all cases resulted in a lower reduction in CC than the reduction obtained with a 2dose vaccination schedule.
The results from the optimization model indicate that when introducing a prevention program for CC, in a country like Nigeria where none is currently in place, with a 2dose vaccination schedule it would be optimal to invest in vaccination alone to the extent feasible, followed by vaccination plus one lifetime screening. With a 3dose vaccination schedule, a combination of both vaccination and screening would be optimal. This result depends therefore on the relative costs of vaccination and screening as well as the relative efficacy between the two interventions. The optimization would favor a strategy with a low costs and a high efficacy (sensitivity for the screening).
A similar model was recently used to estimate the optimal mix of CC screening and HPV vaccination in the UK and Brazil [30]. These two countries already had a screening program in place, with coverage levels of 65% in the UK and 50% in Brazil, each with a screening frequency of 3 years applied to all screened women. The optimization model indicated that keeping the existing screening program while extending the screening interval to 6 years and adding vaccination with 80% coverage would result in a reduction in CC cases of 41% in the UK and 54% in Brazil, with no change in expenditure per woman compared with the prevaccination situation. This is very different from the situation observed in Nigeria, where no nationwide screening programs are currently in place and one lifetime screening is standard for the few women who are screened. As a result, reducing CC in SubSaharan Africa will require that additional resources be spent per woman on either screening or HPV vaccination programs.
The current evaluation estimated that an optimal allocation of a CC prevention budget of $1 per woman per year could lead to an important reduction in CC burden by 31% to 46%, depending on the scenario chosen. In Nigeria, three tiers of Government (Federal, State and Local) have responsibility for health and decide about budget allocation. The current evaluation suggests that a combination of 2dose vaccination with or without one lifetime screening is expected to lead to an important reduction in CC.
Nationwide deployment of HPV vaccines to reduce the CC burden in Africa is more realistic than implementing a screening program, given the current failure of the national screening program due to logistic and funding challenges. Most African countries have efficient and very well accepted national childhood immunization programs, as in Nigeria. HPV vaccination could be integrated into these programs, provided some operational challenges linked to the adaptation of the program to a different target population (adolescent for the HPV vaccination) are addressed. The cost of implementation would also be drastically reduced compared to that expected from the implementation of a screening program. However, in each country the age at which the vaccination is implemented should be adapted based on their national average age of girls’ sexual debut.
Limitations of the optimization analysis
The optimization model used for this analysis has its limitations. First, the model outcome is obtained when an optimal mix of different prevention strategies is applied in the population and a steadystate incidence of CC is achieved across the population. Many years will elapse before reaching that state. The results therefore represent an optimal “ideal” situation that provides a direction to be followed but not a result that will occur immediately, and nor does it address specific implementation issues. Vaccination will affect the girls who will become the women of the future. Today’s women would not benefit from vaccination, but could directly benefit from a single lifetime screening. The vaccinated girls would not need any screening in the years post vaccination. This cost saving could be used to implement a screening program benefiting older women. This would accelerate the overall CC reduction. No discounting was applied in this analysis, as the model presents a steadystate evaluation over a 1year period. Given the time lag needed to reach steady state, discounting could be considered, but the means of implementation and especially the value of including a discount rate in such an optimization exercise is debatable. A discounting applied to the input of the model down to a today value would not impact the optimization results while ways to implement the optimization on a population overtime still need to be investigated.
Second, a static Markov model was used to evaluate the cost and outcomes of each potential strategy at steady state across a population over a 1year period. As the optimization model takes results from the evaluation model, the robustness of the optimization results therefore also depends upon the validity of the evaluation model. The Markov model used for this analysis did not account for dynamic effects of vaccination on infection, such as herd protection. It also assumed stability in the population demographic structure over time. However, using a dynamic model as the evaluation model instead of the static model would be more difficult to adapt to countries with limited data. It would also lead to nonlinearity between coverage and the outcomes, and thus would require the use of a nonlinear programming optimization model.
Third, the models presented here are deterministic: costs and effects in all combinations are known. It would be possible to characterize the allocation problem under conditions of uncertainty by reformulating the model as a stochastic analysis. However, existing approaches for such stochastic analyses are limited [49]. This is an area for future research, although the principal outcome of evaluating uncertainty is to indicate the need and the potential value of searching for more detailed and specific information [49].
Fourth, the results depend on the strategies initially investigated using the evaluation model. Therefore, including a vaccine with a different profile or a screening program with different characteristics (e.g., HPV testing instead of cytologybased screening) may lead to other optimal scenarios, as presented in the sensitivity analysis conducted in the current evaluation. Furthermore, the analysis does not take into account the possibility that the introduction of vaccination may affect the sensitivity of the screening test [50, 51]. It also does not account for the cost associated with the implementation of a screening or vaccination program in Nigeria. Finally, our model assumes that the interventions evaluated are completely divisible in terms of intervals between screening tests and coverage. It ignores possible fixed setup and implementation costs associated with interventions, which could constrain their divisibility. The fixed costs could be large for screening coverage, such as setting up the necessary infrastructure to collect and analyze samples, or implementing health education programs to increase awareness and overcome psychological and cultural barriers to uptake. Conversely, they may be relatively low for vaccine implementation in situations where other vaccination programs in the relevant age group are already established. In addition, the incremental implementation cost by coverage is unlikely to be linear. Disregarding such indivisible or nonlinear costs could be important in practice. Given the importance of the relative costs of vaccination and screening to the selection of the optimal program, the inclusion of these costs could have a large impact on the results.
Conclusions
The optimization model presented here provides information needed to inform health policy decisions on the optimal allocation of limited resources to prevent CC most effectively. The results indicate that increased spending will be needed in Nigeria and in other countries in SubSaharan Africa that currently lack a CC prevention program, in order to achieve adequate prevention of CC cases. With a 3dose vaccination schedule, the most efficient allocation of a limited budget would be to invest in both vaccination and screening, while with a 2dose vaccination schedule investment should first be directed to vaccination before implementing a screening program.
Note
All costs are in USD ($ in the text).
Abbreviations
 CC:

Cervical cancer
 CIN:

Cervical intraepithelial neoplasia
 CIN1:

Cervical intraepithelial neoplasia, grade 1
 CIN2/3:

Cervical intraepithelial neoplasia, grades 2 and 3
 HPV:

Human papillomavirus
 NGN:

Nigerian naira
 NS:

No solution found
 PAHO:

Pan American Health Organization
 Pap:

Papanicolaou
 PV:

Prevaccination schedule
 SD:

Standard deviation
 TNM:

Classification of Malignant Tumours
 UK:

United Kingdom
 VE:

Vaccine efficacy
 WHO:

World Health Organization
 2LT:

Two lifetime screenings
 3LT:

Three lifetime screenings.
Declarations
Acknowledgements
The authors thank Maud Boyer and Sarah Fico (both Business & Decision Life Sciences) for publication coordination, Josephine Mauskopf (RTI Health Solutions) for writing services and Carole Nadine (Fleetwith Ltd) who provided language editing, on behalf of GlaxoSmithKline Biologicals SA, Rixensart, Belgium.
This study, including preparation of the manuscript, was funded by GlaxoSmithKline Biologicals SA, which was involved in: design and conduct of the study; data collection, analysis and interpretation; manuscript preparation, review and decision to submit for publication.
Authors’ Affiliations
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