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It estimates both the health impact and the cost of a programme of interventions designed to reduce adolescent death and disability. The outputs from an epidemiological model are used as inputs to an economic model that estimates the economic and social benefits arising from the interventions. The OHT model (Avenir Health, 2023) is overseen by the UN Inter-Agency Working Group on Costing, which has developed and governed the tool since the first version was launched in 2012.
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It estimates the costs and benefits of interventions to address many of the major causes of the adolescent burden of disease, including SRH, as well as a number of communicable and NCDs. Avenir Health undertook the OHT modelling for this project.
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Making an investment case for interventions that address adolescent health relies on the following: ƒ selecting interventions; ƒ identifying the target population to which the interventions will be delivered; ƒ specifying what proportion of the target population (population in need) will receive the intervention; ƒ calculating the cost associated with delivering the intervention; and ƒ being able to quantify the impact of the intervention on the particular aspect of adolescent health and well-being considered.
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The version of OHT used in this study is Spectrum 6.3 Beta 19, which has some 390 interventions across 12 major health programmes and 70 sub-programmes. It should be noted that the epidemiological models currently available within the tool to undertake cost and health impact modelling do not cover the full set of diseases and risk factors. Future expansions of the modelling framework are planned. 16 Adolescents in a changing world.
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16 Adolescents in a changing world. The case for urgent investment In the current version of the model, from a total of 390 there are 181 interventions for which the model also calculates health outcomes in terms of mortality, morbidity, fertility and other demographic characteristics. For the remaining interventions, the tool calculates costs only (no impact) and therefore these interventions were excluded from the modelling.
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The modelling approach described below relies on comparing the benefits and costs from two different scenarios to address adolescent health over a specific period. These two scenarios (base and scale-up) were developed with an intervention period of 2023–2035. OHT files were set up for each of the 40 countries included in the modelling.
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Ninety-five interventions were chosen to be included in the modelling based on their relevance to adolescents and their importance in addressing the adolescent burden of disease. These are listed in the format used by OHT in Annex 2, Table A2.2, and are similar to those used in previous studies of adolescent health (Sheehan et al., 2017; Sweeny et al., 2019; Rasmussen, Sheehan et al., 2019).
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For each set of interventions chosen in OHT, the target population was also specified (for example, adolescents aged 10 to 19 years). The population in need of treatment was determined as a percentage of the target population and then the baseline and target treatment coverage rates for this population were specified. The cost and impact of interventions are therefore modelled as a result of changing treatment coverage rates.
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For the base scenario, the baseline coverage rates were kept constant over the period to 2035. For the scale-up scenario, target coverage rates in 2035 were set at 1.25 times the rate in 2023, except for some NCD interventions for which the target rates in 2035 were set using global targets. A linear scale up from 2023 to 2035 was modelled. Further details of the modelling methodology are included in Annex 1.
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2.1.3 Cost For the health model, the approach to estimating the costs associated with the two scenarios is similar to that for the reproductive, maternal, newborn and child health (RMNCH) set out in Stenberg et al. (2014) and used for the UNFPA global adolescent health study (Sheehan et al., 2017; Sweeny et al., 2019). The OHT includes default cost assumptions for the resource inputs needed to provide each health service, including drugs and supplies, service delivery inputs, and health workforce time.
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The results from the OHT modelling were aggregated to provide estimates by country and year of the costs of drugs and supplies, the number of inpatient days, the number of outpatient visits and the amount of personnel time in minutes per health intervention and per service delivery platform. The cost of inpatient days was calculated by multiplying the number of days by the WHO CHOICE estimates of the average cost per bed day for each country (WHO, 2011) expressed in US dollars at 2023 values.
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Similarly, the cost of outpatient visits was estimated by multiplying the estimated number of outpatient visits by the WHO CHOICE estimates of the average cost per outpatient visit for each country. These service delivery costs from the WHO database are estimated by country and differentiated by service delivery platform.
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The cost of personnel time was calculated by multiplying the personnel time in years by country by WHO CHOICE estimates of the average country-specific annual salaries for physicians, nurses and other health workers expressed in US dollars at 2023 values (Serje et al., 2018).
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The other components of cost: supply chain, infrastructure and equipment, governance, health information systems, health financing, emergency and reconstruction and additional programmes were calculated as a markup of the combined direct costs of drugs and supplies, inpatient days, outpatient visits and personnel time.
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Results from a study that looked at the investment needed to advance the UHC agenda in 67 countries for the period 2016 to 2030 (Stenberg et al., 2017, Tables S16–S17) indicated that the total investment cost is, on average, 2.2 to 2.5 times the combined direct costs. As some of the costs included within their estimates are not likely to be applicable for the adolescent investment case, we estimate total costs as two times the combined direct costs.
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The effects of varying these multipliers is planned in future work. All costs were expressed in US dollars at 2023 prices and where necessary prices were adjusted from their base year to 2023 using the United States of America GDP implicit price deflator (US BEA, 2023). 17 2.
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17 2. Health investments 2.1.4 Health outcomes The reproductive, sexual and other health outcomes from modelling interventions with the OHT are usually expressed as the number of deaths and amount of morbidity that occurs for each health condition each year for each age group and sex (where this is available). For some areas within the OHT, it is possible to obtain health outcome data by age and sex, in which case the impact on adolescents could be assessed directly.
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In other areas, this data is for the whole of the population being targeted, in which case the adolescent proportion was calculated according to that used for allocating costs based on population proportions. In addition, estimates were made of the numbers of mothers and newborns with serious disabilities, as well as the number of adolescents with serious disabilities arising from each of the NCDs being modelled. Based on the analysis by Stenberg et al.
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Based on the analysis by Stenberg et al. (2014), it was assumed that, for adolescent mothers, the number with a serious disability from obstructed labour was six times that of the number of deaths with half of these being unable to work and half able to work at 50% of the productivity of a healthy adolescent.
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Similarly, the number with a serious disability from other maternal disorders was estimated to be twice the number of deaths from this cause divided equally between those unable to work and those able to work at 50% productivity. A similar approach was used to estimate serious disability associated with the causes of newborn deaths.
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Serious disability from prematurity was assumed to be 1.65 times the number of deaths, from asphyxia 0.9 times, and from congenital abnormalities equal to the number of deaths.. The assumptions about scale-up in the contraceptive prevalence rates for adolescents over the period to 2035 were projected to reduce adolescent fertility rates on average from 79.1 to 60.6 per 100 000 (see Annex 2, Table A2.3) and a reduction in births to mothers aged 15–19 of 21 929 016 (see Annex 2, Table A2.4).
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The model indicates that the increased service coverage would lead to reductions in deaths among adolescent mothers of 144 840, newborns of 1 048 207 and children of 825 874, as well as in stillbirths of 695 835 (see Annex 2, Table A2.5). The model also indicates smaller projected reductions in deaths from alcohol dependence (9551), bipolar disease (169 283), depression (10 097) and epilepsy (2632) (see Annex 2, Table A2.6).
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The model also indicates large projected reductions in the numbers of adolescents with anaemia (22 758 155), particularly for pregnant adolescents (21 231 485) (see Annex 2, Table A2.7), and in the number of stunted children born to adolescent mothers (26 526 759) (see Annex 2, Table A2.8).
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Among adolescent females, the interventions included for mental disorders and other NCDs are estimated to reduce the number of cases of depression (3 067 243), anxiety (2 007 641) and epilepsy (526 765), and, to a lesser extent, alcohol dependence (205 214), asthma (334 336), attention disorders (35 330) and conduct disorders (72 006) (see Annex 2, Table A2.9).
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Among adolescent males, the interventions included for mental disorders and other NCDs would be expected to reduce the number of cases of depression (2 309 561), anxiety (1 629 340) and epilepsy (632 039), and to a lesser extent, alcohol dependence (652 195), asthma (523 295), attention disorders (96 836) and conduct disorders (216 609) (see Annex 2, Table A2.10).
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2.1.5 Economic and social benefits To estimate the ROI for health intervention programmes, it is necessary to express the improved health outcomes in economic terms and compare these with the costs of the programmes. As noted above, the approach builds on previous studies (Sheehan et al., 2017; Sweeny et al., 2019) focused on economic and social benefits and is described below.
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Economic benefits Comparing an intervention scale-up scenario with a base scenario enables the number of deaths and amount of morbidity averted to be calculated and compared to the additional cost. Within our model, we consider that the economic benefits of interventions preventing deaths occur when people who would otherwise die prematurely enter the workforce and produce economic output. A similar benefit occurs for people who would otherwise suffer a serious disability that prevents them from working.
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In this study, the economic modelling of mortality follows the cohort of deaths averted for each of the years from 2023 to 2035. Each cohort is classified by age and sex. As the cohort ages, it is subject to the mortality rates applicable to that age group, sex and year based on estimates from the UN World Population Prospects data (UN, 2022b) for each of the 40 countries.
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The effect of avoided mortality on the labour force is calculated by taking the numbers of deaths avoided by age and gender and applying a corresponding 18 Adolescents in a changing world. The case for urgent investment labour force participation rate for this age, gender and year sourced from the International Labor Organization (ILO) projections of labour force participation rates (ILO, 2023) in each of the 40 countries.
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The contribution that each of these labour force cohorts makes to economic output is calculated by multiplying the number in each age and sex category by a productivity level that varies with age and year. To do this, the average productivity is first calculated by dividing the World Bank estimate of GDP in current US dollars by the labour force for the most recent year for which data are available (2021) (World Bank, 2023b).
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This average productivity then increases at an annual rate determined by the country’s income status. The methodology for this is described in more detail in Sweeny et al. (2019). Based on this analysis, the modelling assumes that annual productivity growth for LICs, LMICs, UMICs and HICs is 2.1%, 2.5%, 1.6% and 0.7%, respectively. Further, it is assumed that the country’s income status changes after 15 years. For LMICs and UMICs, this results in slower productivity growth after 15 years.
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Productivity varies by age so, as a proxy for this, the distribution of hourly wage rates by age for Australia for 2021 (ABS, 2022) is used. Average productivity for each age group is calculated by multiplying average productivity by the ratio of hourly wage rates for the age group to overall hourly wage rates.
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The total GDP generated is calculated by summing the GDP produced by each cohort for each year of the period in which they are in the labour force using ILO projections of labour force participation rates and productivity estimates to calculate the contribution to GDP each year. The contribution to GDP of each cohort of persons who would otherwise suffer from serious disabilities is calculated in a similar way as for mortality, using the same assumptions about participation rates and productivity.
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Social benefits Health has an intrinsic value and, in addition to the market value put on health improvements (here captured by labour productivity), many studies apply a value of a statistical life (VSL) approach. This allows researchers to fully capture the value of the social benefit of health improvement. Following past approaches, we consider social benefits as a subset of the VSL year. Building on the results of Viscusi and Aldy (2003), Jamison et al.
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(2013) estimated the value of a life year as between 1.4 and 4.2 times GDP per capita, averaging 1.6 globally. This is a partial VSL method where GDP per capita is projected for each country. Stenberg et al. (2014) applied an approach in which benefits not captured in labour productivity measures were referred to as “social benefits.” The social benefits were estimated within a VSL envelope that was, on average, assuming the value of a life year as 1.5 times GDP per capita.
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As the calculated economic benefit in the Stenberg et al. (2014) study represented roughly one times GDP per capita, this left a residual value of 0.5 times GDP per capita as the estimated social benefit. In other words, the economic benefits as a share of GDP vary by country but the social benefits are valued at the same rate (0.5 GDP per capita) across all countries.
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Following the same approach, a value of 0.5 GDP per capita is assigned to each healthy life year gained (HLYG) from the interventions to estimate the social benefit of improved health. 2.1.6 ROI in adolescent health The rate of ROI can be expressed in a number of different but related ways. In this study, we report BCRs as they are intuitively easier to understand. The BCR divides the estimate of benefits by the estimate of costs.
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A BCR greater than one means that the benefits of an intervention programme are greater than the costs of the programme. The economic benefits, social benefits and the cost of intervention programmes are reported in Annex 2, Table A2.11, based on the calculations described in the previous sections. It is standard practice when calculating BCRs to express these benefits and costs in NPV terms.
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Given benefits and costs for years in the future are usually regarded as having a lower value than those in the present, a discount rate is applied to these future benefits and costs. Most analyses of long-term projects adopt the standard World Bank discount rate of 3% to calculate NPVs (Weitzman, 2001; United States Office of Management and Budget, 2003; Arrow et al., 2013; Campos et al., 2013). Table 2.1 (and Table A2.11 in Annex 2) shows the benefits, costs and BCRs in terms of US dollars.
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The economic benefits in the 40 countries modelled arising from the increase in the workforce equate to US$ 112 632 million. The social benefits equate to US$ 60 964 million, with an overall benefit of US$ 173 596 million. 19 2. Health investments Overall, the unweighted BCR arising from the investment in adolescent health is 9.6, using the World Bank standard 3% discount rate when considering all benefits.
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This is similar to the average BCR reported for the UNFPA adolescent investment case (Sheehan et al., 2017) for all countries in that study, which was 10.0. These BCRs are significantly higher than one, which is the break-even value when benefits equal costs.
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Table 2.1 Adolescent health, economic and social benefits, costs ($ million), and BCRs Economic benefit Social benefit Cost BCR economic BCR economic & social BCR economic BCR economic & social Unweighted average Weighted average Low-income 16 975 8 076 5 857 6.9 10.2 2.9 4.3 Lower middle-income 73 061 39 633 11 598 7.7 11.8 6.3 9.7 Upper middle-income 22 596 13 255 14 751 3.2 5.0 1.5 2.4 All countries modelled 112 632 60 964 32 206 6.3 9.6 3.5 5.4 Note: See description of weighted and unweighted average in section 1.3.2.
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Source: Estimates from modeling. 2.1.7 Discussion Successfully achieving the SDGs and the goals of the Global Strategy for Women’s, Children’s and Adolescents’ Health 2016–2030 (WHO, 2018a) will require investment metrics to support informed decision-making on appropriate interventions. This study has demonstrated high rates of ROI targeting key aspects of adolescent health with a BCR of 9.6, with benefits greatly exceeding costs.
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This means that for each dollar invested, there will be US$ 9.6 returned in benefits. The BCRs comfortably exceed one at the 3% discount rate typically used in analysing health projects and for the other discount rates used for a sensitivity analysis. These results are similar to those obtained in the global adolescent investment case (Sheehan et al., 2017; Sweeny et al., 2017).
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They are also close to the results of the more general investment case for RMNCH (Stenberg et al., 2014), which estimated a BCR of 8.7 at the 3% discount rate for a similar range of interventions modelled using OHT. However, the analysis presented here differs in a number of significant ways from that reported in previous studies. In Stenberg et al. (2014), the number of interventions modelled was 50 and restricted to RMNCH.
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For the study for UNFPA on adolescent health (Sheehan et al., 2017), the number of interventions was 66 while the current study focuses on 95 interventions. Experience with previous studies informed more targeted interventions but also pointed to the inclusion of a range of public health interventions addressing risk factors and additional NCDs, such as asthma, psychosis and conduct disorders. The period modelled in the UNFPA study was 2015 to 2030. In this study, the period is 2023 to 2035.
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In this study, the period is 2023 to 2035. The costing methodology has drawn upon updated estimates of resource needs as presented in Stenberg et al. (2017). Studies on stillbirths (ten Hoope-Bender et al., 2016), depression and anxiety (Chisholm et al., 2016) and cardiovascular disease (Bertram et al., 2018) using the OHT have also reported ROIs of similar magnitude.
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The intergenerational benefits of improving the health of adolescents in preparation for parenthood are beginning to be explored in detail, and it is likely that quantifying these benefits will further demonstrate the value of interventions in adolescence. Achieving the strong ROIs suggested by these models will require that programmes are skillfully designed and implemented.
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They must also respond to the needs of adolescents by meeting the requirements of adolescent-friendly health services and ensuring the delivery of interventions to decrease risky behaviours, improving protection and avoiding unwanted pregnancies. Ensuring quality in service delivery is crucial for effective programmes (Duber et al., 2018). 20 Adolescents in a changing world.
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20 Adolescents in a changing world. The case for urgent investment Limitations In general, the interventions included in the modelling had to have both an estimated health impact and cost, and tools available to run the analysis (here, the OHT was used, but other tools could also be applied).
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Consequently, some areas that are prominent in the GBD estimates for adolescents were not included (for example, prevention of drowning and other accidental deaths and self-harm) as there were insufficient associated interventions with known impacts and costs. Although it would increase the ROIs, any health expenditure savings due to the interventions have not been included in the calculation of benefits as there is limited information on health expenditure by disease in many countries.
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This benefit would be expected to be substantially smaller than the economic contribution that was used as the basis of our benefit modelling, but would nonetheless lead to higher estimates of the ROI. 2.2 HPV 2.2.1 HPV vaccination and cervical cancer elimination Cervical cancer is the fourth most common cause of neoplasm mortality among women worldwide, accounting for 341 831 deaths in 2020 (IARC, 2023).
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Persistent infection by selected types of HPV is a necessary step in the pathogenesis of cervical cancer (Walboomers et al., 1999). HPV also contributes to the pathogenesis of several less prevalent cancers, such as cancers of the vagina, vulvae, penis, anus and oropharynx (throat). There are over 170 different types of HPV. More than 40 types are typically transmitted through sexual contact and infect the anogenital region.
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Several of these are high-risk HPV types that can contribute to the development of cancer. HPV types 16 and 18 are associated with 70% of cervical cancers, 80% of anal cancers, 60% of vaginal cancers and 40% of vulvar cancers. Several types of HPV, particularly type 16, have been found to be also associated with oropharyngeal cancer, which affects the middle part of the throat and includes the base of the tongue, the tonsils, the soft palate and the walls of the pharynx.
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The International Agency for Research on Cancer (IARC) has reviewed the evidence on the role of HPV in different cancers. Their meta-analysis of type-specific HPV DNA prevalence in cervical cancer indicated that HPV types 16 and 18 are responsible for 54.4% and 15.9% of cervical cancer, respectively, with HPV types 31, 33, 45, 52 and 58 being responsible for a further 17.3%. HPV types 6 and 11 are responsible for 0.65% (IARC, 2012).
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There are currently three main types of HPV vaccine available: ƒ Cervarix (GSK), protecting against HPV types 16 and 18; ƒ Gardasil 4 (Merck), protecting against HPV types 16, 18, 11 and 6; and ƒ Gardasil 9 (Merck), protecting against HPV types 16, 18, 11, 6, 31, 33, 45, 52 and 58. Using the evidence from IARC, this means that Cervarix, Gardasil 4 and Gardasil 9 can prevent 70.3%, 70.9% and 88.3% of cervical cancers, respectively.
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Current HPV vaccines are prophylactic, not therapeutic; they need to be administered prior to a HPV infection. The vaccines were initially recommended for girls before they become at risk of HPV infection so before they become sexually active. In 2020, WHO released its global strategy towards the elimination of cervical cancer as a public health problem (WHO, 2020b).
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This strategy has the following targets to be achieved by 2030: ƒ 90% of girls fully vaccinated with the HPV vaccine by 15 years of age; ƒ 70% of women screened with a high-precision test at 35 and 45 years of age; and ƒ 90% of women identified with precancerous lesions and cervical disease receiving treatment and care. These targets are often referred to as the 90–70–90 strategy. It is supported by the International Papillomavirus Society (Garland et al., 2018).
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Based on analysis by Bertram and Gauvreau (cited in WHO 2020b), the strategy claimed that: 21 2. Health investments Investing in the interventions to meet the 90–70–90 targets offers immense economic and societal benefits. An estimated US$ 3.20 will be returned to the economy for every US$ 1 invested by 2050, owing to increases in women’s workforce participation with this figure rising to US$ 26.00 when societal benefits are incorporated.
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Victoria University has undertaken a number of projects estimating the ROI from vaccination against HPV and screening and treatment for cervical cancer (e.g., Sheehan et al., 2017; Rasmussen, Sheehan et al., 2019). A study on the investment case for HPV vaccination in Viet Nam for UNFPA was undertaken with the Daffodil Centre at the University of Sydney, a joint venture with the Cancer Council New South Wales (Sweeny, Nguyen et al., 2023).
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2.2.2 Epidemiological modelling Studies of the feasibility and cost-effectiveness of widespread HPV vaccination programmes have relied on epidemiological models to predict the alleviation of the burden of cancer. This is based on the impact of the vaccine on the known precursors of HPV-attributable cancers, such as HPV infection and precancerous cervical lesions.
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Once the efficacy of the HPV vaccine had been established, a number of studies were undertaken on the effectiveness and cost-effectiveness of widespread vaccination programmes. These studies are reviewed in Sweeny, Nguyen et al. (2023).
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(2023). In a recent study across 78 LICs and MICs, the Daffodil Centre, in conjunction with colleagues from the WHO Cervical Cancer Elimination Modelling Consortium, assessed the impact of achieving the 90–70–90 triple intervention targets on cervical cancer mortality and deaths averted over the next century (Canfell et al., 2020).
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They found that in the next 10 years, a one-third reduction in the rate of premature mortality from cervical cancer in LICs and MICs is possible, and over the next century, successful implementation of the WHO elimination strategy would reduce cervical cancer mortality by almost 99% and save more than 62 million women’s lives.
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To undertake these analyses, the Daffodil Centre has developed the Policy1-Cervix model, which is a dynamic model of HPV transmission, HPV vaccination, cervical precancer, cancer survival, screening, diagnosis and treatment. The model has been validated extensively and used for a number of screening and vaccination evaluations across a range of countries. It is described in detail in Simms et al. (2019), Canfell et al. (2020) and Sweeny, Nguyen et al. (2023).
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(2020) and Sweeny, Nguyen et al. (2023). For the purposes of this report, the Policy1-Cervix epidemiological and economic modelling compares a base scenario with a scale-up scenario. The base scenario is a continuation of the status quo with low levels of vaccination, screening and treatment. The scale-up scenario assumes girls-only vaccination at nine years old with a catch-up for girls aged 10–14 years and twice-lifetime HPV testing at age 35 and 45 years with cancer treatment scale-up.
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Vaccination was assumed to scale up to 90% coverage. Cervical screening involves HPV testing at ages 35 and 45 years with scale-up to 45% coverage by 2023, 70% by 2030, and 90% by 2045. In addition, it is assumed that 50% of women who are diagnosed with invasive cervical cancer will receive appropriate surgery, radiotherapy and chemotherapy by 2023, which will increase to 90% by 2030. These assumptions align with the goals of the WHO global strategy towards the elimination of cervical cancer.
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We have assumed that full efficacy was achieved with two doses of a 9-valent vaccine for recipients younger than 15 years and with three doses for older recipients. 2.2.3 Economic model The health and cost outcomes can be used as inputs to an economic model that estimates the ROI from each scenario. This approach has been used in a number of studies (Stenberg et al., 2014; Chisholm et al., 2016; Bertram et al., 2018; Sheehan et al., 2017; Sweeny et al., 2019).
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In a study for the UNFPA on the ROI for adolescent health, a simplified model was used to calculate the ROI for an HPV vaccination programme for 75 LICs and MICs (Sheehan et al., 2017). A similar approach was used in a study for UNICEF on an adolescent investment case for Burundi (Rasmussen, Sheehan et al., 2019). The economic benefits and social benefits were calculated as described in section 2.1.5. 22 Adolescents in a changing world.
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22 Adolescents in a changing world. The case for urgent investment In order to compare the economic benefits and costs associated with the intervention programme, both were expressed in terms of NPV using the standard World Bank discount rate of 3%. A common investment metric is the BCR and this is calculated by dividing the economic and social benefits by the cost, both in NPV terms. 2.2.4 Results The Daffodil Centre modelled the impact of the base and scale-up scenarios over the course of 100 years.
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In order to make the health and economic outcomes comparable to other studies in this report, we estimated outcomes over the period 2023 to 2035 by adjusting the modeling results by 13/100. Table 2.2 (and Table A2.12 in Annex 2) reports the results of the modelling for HPV. The scale-up scenario will avert 8 123 880 deaths from cervical cancer across the 78 countries compared to the base case scenario, with most of these in LICs (2 575 618) and, especially, LMICs (5 136 439).
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Table 2.2 ROI from scale-up of HPV vaccination, screening and treatment, BCRs at 3% discount rate Status Deaths averted Economic benefit, $M Social benefit, $M Cost $M BCR economic BCR economic plus social BCR economic BCR economic plus social Unweighted average Weighted average Low- income 2 575 618 12 344 6 297 1 616 10.5 16.0 7.6 11.5 Lower middle- income 5 136 439 47 278 31,185 4 036 14.6 23.4 11.7 19.4 Upper middle-income 411 823 6 217 5 019 427 6.2 11.5 14.6 26.3 All countries 8 123 880 65 839 42 500 6 079 12.7 20.2 10.8 17.8 Note: See the description of weighted and unweighted averages in section 1.3.2.
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Source: Estimates from modeling. For a total cost of US$ 6 079 million, there will be economic and social benefits of US$ 65 839 million and US$ 42 500 million, respectively. Dividing the total economic benefits by the total cost will result in a weighted average BCR of 10.8 and 17.8 if social benefits are included. Averaging across the BCRs of the 78 countries gives unweighted BCRs of 12.7 and 20.2.
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There are significant differences among countries reflecting differences in HPV and cervical cancer death rates, productivity, income status and other characteristics. The analysis presented here differs significantly from that included in the UNFPA study (Sheehan et al., 2017). This study includes the three aspects of the WHO cervical cancer elimination strategy—vaccination, screening and treatment—while the UNFPA study only included vaccination.
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The assumptions about the base and scale-up scenarios also differ in assumptions about the number of vaccine doses and their prices. Importantly, this analysis uses a proven large-scale epidemiological model to estimate the health and cost impacts of the scenarios. The two studies also have different intervention periods. 2.3 TB This section estimates the ROI from achieving the WHO End TB Strategy for adolescents in 50 countries, which account for 95.7% of adolescent TB deaths and 71.3% of cases.
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A number of countries are eligible for funding for the prevention of TB from the Global Fund to Fight AIDS, Tuberculosis and Malaria. This fund enables strategic investment to accelerate the elimination of human immunodeficiency virus HIV/acquired immunodeficiency syndrome (AIDS), TB and malaria, and build resilient and sustainable systems for health.
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The Global Fund’s eligibility policy is designed to ensure that available resources are allocated to and invested in countries and regions with the highest disease burden, the least economic capacity and where key and vulnerable populations are disproportionately affected by the three diseases. Eligibility is determined by a country’s income classification and disease burden. All LICs and LMICs are eligible regardless of disease burden. 23 2.
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23 2. Health investments UMICs must have at least a high burden of disease to be eligible for financing. Table A2.13 in Annex 2 lists those UMICs eligible for TB prevention funds. 2.3.1 Cost per death averted The Lancet Commission on TB (Reid et al., 2019) reported on a number of studies that had estimated the cost per TB death averted since the introduction of the WHO End TB Strategy in 2014. This strategy aims to end the TB epidemic by 2030 (WHO, 2015, 2019). Reid et al.
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Reid et al. (2019) quoted costs per TB death averted of between US$ 5000 and US$ 6000 from Jamison et al. (2013), US$ 7000 from a study by the Stop TB partnership (2015), US$ 8000 to US$ 16 000 in the WHO Global Tuberculosis report (WHO, 2018b) and a range of other values (US$ 700 to US$ 5000) from studies done by the Copenhagen Consensus.
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Included in the supplementary material to the Lancet Commission report (Reid et al., 2019) are two figures showing cost of deaths averted for 30 high burden countries for both drug-susceptible TB and multidrug-resistant TB (MDR-TB). The costs per death averted for drug-susceptible TB for countries such as the Democratic Republic of the Congo, Kenya, Mozambique, the United Republic of Tanzania and Zambia are in the range of US$ 1000 to US$ 1500 at 2017 prices.
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For MDR-TB, the values for United Republic of Tanzania and the Congo are around US$ 15 000, but are significantly higher for Mozambique, Democratic Republic of the Congo and Kenya. The most recent edition of the Global Plan to End TB by the Stop TB Partnership (2022) includes modelling by the Copenhagen Consensus of the cost and impact of the Global Plan to End TB, 2023–2030.
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The Copenhagen Consensus found that implementing the Global Plan would reduce death rates from TB by the amounts shown in Table A2.14 in Annex 2. To convert these death rate estimates into numbers of deaths averted we have multiplied the death rates by population projections for the years 2023 to 2030, taken from the latest UN World Population Prospects (UN, 2022b). These numbers are shown in Table A2.15 in Annex 2. The resulting estimates of deaths averted are shown in Table A2.16 in Annex 2.
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Table A2.17 in Annex 2 shows the estimates by the Stop TB Partnership of the cost of achieving these reductions in TB deaths, expressed in constant US dollars at 2020 prices. These costs include treatment, prevention, screening and other programme costs. Dividing the relevant cost, as shown in Table A2.17 in Annex 2, by the corresponding deaths averted, as shown in Table A2.16 in Annex 2, gives the cost per death averted, shown in Table A2.18 in Annex 2.
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Table 2.3 shows the average cost per death averted across the period 2023 to 2030 in 2020 US dollars. To express these in current 2023 US dollars, the figures were multiplied by the increase in the United States of America GDP deflator from 2020 to 2023 (9.8%).
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Table 2.3 TB in adolescents, cost per death averted, average 2023 to 2030, US$ Average 2023–2030 At 2023 prices Low-income countries 2 986 3 279 Lower middle-income countries 2 665 2 926 Upper middle-income countries 11 656 12 798 High-income countries 26 467 29 061 Eligible upper middle-income countries 4 160 4 568 Source: Estimates from modeling.
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2.3.2 Deaths averted For each of the 50 countries and territories, we obtained the TB death rates for those aged between 10–14 and 15–19 by sex for each of the years 1990 to 2019. Using the average annual rate of change in TB deaths for the five-year period to 2019, we projected the death rates from TB for the two age brackets by sex to 2035. 24 Adolescents in a changing world.
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24 Adolescents in a changing world. The case for urgent investment For a base scenario, we used the most recent population projections for females and males for the two age groups from the latest UN World Population Prospects (UN, 2022b) for the years 2020 to 2035, and estimated the number of deaths from TB based on these projected death rates. Death rates were projected under an alternative scale-up scenario, assuming a linear decline to death rates in 2035 that were 10% of those in 2023.
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The number of deaths from TB based on these alternative projected death rates was calculated in the same way. The difference between the numbers of deaths in the base case and scale-up scenarios is the number of TB deaths averted. This is reported in Table A2.19 in Annex 2 for males and females aged 10–14 and 15–19 years. For the 50 countries, the model estimates that there will be 54 229 deaths averted for adolescent females and 62 334 for males.
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The cost of reducing the burden of TB among adolescents is estimated by multiplying the number of deaths averted (Table A2.19) by the cost per death averted (Table A2.18), depending on the country’s/entity’s World Bank income status in 2023. This simplified approach differs from that used in the other health modelling, which uses direct estimates of treatment and other costs. 2.3.3 Economic and social benefits The economic and social benefits were calculated as described in section 2.1.5.
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2.3.4 ROI on TB The ROI measured by BCRs was calculated as described in section 2.1.6. The economic benefits, social benefits and the cost of intervention programmes using the main health model are reported in Table A2.20 in Annex 2, based on the calculations described in the previous sections and expressed in NPV terms.
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As noted previously, because benefits and costs for years in the future are usually regarded as having a lower value than those in the present, a discount rate of 3% per year is applied to these future benefits and costs. Table 2.4 (and Table A2.20 in Annex 2) shows the benefits, costs and BCRs in terms of US dollars. The total cost in NPV terms from 2023 to 2035 for the scale-up compared to the base scenario is US$ 286 million.
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The economic benefits from the increase in the workforce are US$ 10 852 million and the social benefit is US$ 4473 million, with an overall benefit of US$ 15 325 million. The unweighted and weighted ROI measured as BCRs is 39.4 and 37.9, respectively, based on economic benefits and 54.4 and 53.6, respectively, for economic and social benefits combined.
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Table 2.4 TB in adolescents, summary of economic and social benefits, costs and BCRs, 3% discount rate Economic benefit, $M Social benefit, $M Cost, $M BCR economic BCR economic and social BCR economic BCR economic and social Unweighted average Weighted average Low-income 1 861 638 106 17.6 23.6 18.1 24.6 Lower middle-income 7 725 3 287 157 49.1 70.0 52.9 72.8 Upper middle-income 1 266 548 23 55.4 79.3 67.4 95.9 Total 10 852 4473 286 39.4 54.4 37.9 53.6 Note: See the description of weighted and unweighted averages in section 1.3.2.
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Source: Estimates from modeling. 25 2. Health investments 2.4 Myopia The prevalence of myopia in children and adolescents is increasing rapidly in many parts of the world (Holden et al., 2016). Grzybowski et al. (2020) estimated that 60% of school children (6–19 years) in Asia and 40% of children in Europe had myopia. The prevalence of high myopia, which can cause pathologic myopia, among young adults is much higher in East Asia than in Western countries (Matsumura et al., 2019).
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Myopia is not simply a health problem but has wider implications for well-being. Adolescents with vision impairment reported statistically significant lower quality of life, psychosocial functioning and school functioning scores (Wong et al., 2009). A randomized controlled trial (RCT) among 20 000 children in 250 schools in Western China by Ma et al.
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(2014) reported that mathematics test scores at the end of a school year had improved significantly among the 1153 children who had failed visual acuity screening and were offered free spectacles. The FHF and Victoria University have developed an eye health model that provides the basis for evaluating the investment in programmes that scale-up interventions.
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These reduce the burden of vision impairment across 19 countries and territories where the FHF works and the unmet burden is high (Sweeny, Muirhead and Hennessy, 2023). These are Afghanistan, Bangladesh, Burundi, Cambodia, China, Eritrea, Ethiopia, Indonesia, Kenya, Lao People’s Democratic Republic, Myanmar, Nepal, Pakistan, occupied Palestinian territory, including east Jerusalem, Papua New Guinea, Philippines, Rwanda, Timor-Leste and Viet Nam.
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The model has been developed for the two leading causes of blindness and vision impairment, namely cataracts and refractive error (myopia and presbyopia). The model estimates the benefits arising from treating these eye conditions in terms of improvements in both health outcomes and economic benefits. It then compares these to the costs of achieving these benefits.
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The modelling reported here is for interventions to address myopia in adolescents, not cataracts and presbyopia, which are conditions mostly associated with later life. Much of the evidence used in developing this model, including the assumptions used in the modelling, has been derived from a detailed review undertaken as part of the Lancet Global Health Commission on Global Eye Health (Burton et al., 2020), its supplementary material and related publications.
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The eye health model is designed to calculate the impact of an intervention programme on the number of adolescents with myopia. The target population for treatment in a particular year is defined as those with myopia while the coverage rate is the number of people treated as a proportion of the target population.
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