What can we learn from previous pandemics and from the response to COVID-19 so far?

In spite of being the most foreshadowed global catastrophe in recent history, the COVID-19 pandemic has managed to catch all of us by surprise. Comparisons with the 7 December 1941 attacks on Pearl Harbor, and with the destruction of the World Trade Center by terrorists on 11 September 2001, are instructive. Following those attacks, and after a careful, investigative study of a broad range of signals and human intelligence, it was possible to reach the conclusion that they were foreseeable but that the pertinent signs had not been recognized, and that a sufficiently recognizable pattern therefore did not emerge in time to allow for mitigating action. This is different than the present case in important respects, but, as we shall see, it is also the same. History repeats itself in a different form

What Counts in Economic Evaluations in Health? Benefit-cost Analysis Compared to Other Forms of Economic Evaluations

Economic evaluations are increasingly popular, both in the field of global health as well as in purely domestic settings. However, the proliferation and use of economic evaluations by members of multiple publics, many of whom are non-economists, creates misunderstandings as well as strategic opportunities. In this extended essay, Lauer and colleagues develop a critical analysis of economic evaluations that is intended to clarify concepts and terms, and thereby to enable a diverse community of users, performers, and commissioners of economic analyses in health to better understand and use such studies. The authors pay particular attention to cost-effectiveness analysis, long the mainstay of economic evaluations in health, and to benefit-cost analysis. The article starts by noting that economic evaluations in health (EEHs) take a number of typical forms, although all involve a comparison of inputs and outcomes, either of which may or may not be market-traded goods. They call a particular choice of inputs and outcomes a ‘table of accounts’. They argue that the notion of a table of accounts provides a useful way to understand the methodological diversity of EEHs, one which subsumes more established but also more restrictive terminology (e.g. the notion of ‘study perspective’). Lauer and colleagues present tables of account for a number of commonly used EEHs. They then discuss at length benefit-cost analysis, a distinctive form of EEH that has recently attracted substantial attention in the form of so-called ‘investment cases’ in healt

Cost effectiveness of strategies to combat chronic obstructive pulmonary disease and asthma in sub-Saharan Africa and South East Asia: mathematical modelling study

Objectives To determine the population level costs, effects, and cost effectiveness of selected, individual based interventions to combat chronic obstructive pulmonary disease (COPD) and asthma in the context of low and middle income countries

Malaria intervention scale-up in Africa : effectiveness predictions for health programme planning tools, based on dynamic transmission modelling

Scale-up of malaria prevention and treatment needs to continue to further important gains made in the past decade, but national strategies and budget allocations are not always evidence-based. Statistical models were developed summarizing dynamically simulated relations between increases in coverage and intervention impact, to inform a malaria module in the Spectrum health programme planning tool.; The dynamic Plasmodium falciparum transmission model OpenMalaria was used to simulate health effects of scale-up of insecticide-treated net (ITN) usage, indoor residual spraying (IRS), management of uncomplicated malaria cases (CM) and seasonal malaria chemoprophylaxis (SMC) over a 10-year horizon, over a range of settings with stable endemic malaria. Generalized linear regression models (GLMs) were used to summarize determinants of impact across a range of sub-Sahara African settings.; Selected (best) GLMs explained 94-97 % of variation in simulated post-intervention parasite infection prevalence, 86-97 % of variation in case incidence (three age groups, three 3-year horizons), and 74-95 % of variation in malaria mortality. For any given effective population coverage, CM and ITNs were predicted to avert most prevalent infections, cases and deaths, with lower impacts for IRS, and impacts of SMC limited to young children reached. Proportional impacts were larger at lower endemicity, and (except for SMC) largest in low-endemic settings with little seasonality. Incremental health impacts for a given coverage increase started to diminish noticeably at above ~40 % coverage, while in high-endemic settings, CM and ITNs acted in synergy by lowering endemicity. Vector control and CM, by reducing endemicity and acquired immunity, entail a partial rebound in malaria mortality among people above 5 years of age from around 5-7 years following scale-up. SMC does not reduce endemicity, but slightly shifts malaria to older ages by reducing immunity in child cohorts reached.; Health improvements following malaria intervention scale-up vary with endemicity, seasonality, age and time. Statistical models can emulate epidemiological dynamics and inform strategic planning and target setting for malaria control

Deaths and years of life lost due to suboptimal breast-feeding among children in the developing world: a global ecological risk assessment

Abstract Objective We estimate attributable fractions, deaths and years of life lost among infants and children ≤2 years of age due to suboptimal breast-feeding in developing countries. Design We compare actual practices to a minimum exposure pattern consisting of exclusive breast-feeding for infants ≤6 months of age and continued breast-feeding for older infants and children ≤2 years of age. For infants, we consider deaths due to diarrhoeal disease and lower respiratory tract infections, and deaths due to all causes are considered in the second year of life. Outcome measures are attributable fractions, deaths, years of life lost and offsetting deaths potentially caused by mother-to-child transmission of HIV through breast-feeding. Setting Developing countries. Subjects Infants and children ≤2 years of age. Results Attributable fractions for deaths due to diarrhoeal disease and lower respiratory tract infections are 55% and 53%, respectively, for the first six months of infancy, 20% and 18% for the second six months, and are 20% for all-cause deaths in the second year of life. Globally, as many as 1.45 million lives (117 million years of life) are lost due to suboptimal breast-feeding in developing countries. Offsetting deaths caused by mother-to-child transmission of HIV through breast-feeding could be as high as 242 000 (18.8 million years of life lost) if relevant World Health Organization recommendations are not followed. Conclusions The size of the gap between current practice and recommendations is striking when one considers breast-feeding involves no out-of-pocket costs, that there exists universal consensus on best practices, and that implementing current international recommendations could potentially save 1.45 million children's lives each yea

Cost-effective interventions for breast cancer, cervical cancer, and colorectal cancer : new results from WHO-CHOICE

Background: Following the adoption of the Global Action Plan for the Prevention and Control of NCDs 2013-2020, an update to the Appendix 3 of the action plan was requested by Member States in 2016, endorsed by the Seventieth World Health Assembly in May 2017 and provides a list of recommended NCD interventions. The main contribution of this paper is to present results of analyses identifying how decision makers can achieve maximum health gain using the cancer interventions listed in the Appendix 3. We also present methods used to calculate new WHO-CHOICE cost-effectiveness results for breast cancer, cervical cancer, and colorectal cancer in Southeast Asia and eastern sub-Saharan Africa. Methods: We used "Generalized Cost-Effectiveness Analysis" for our analysis which uses a hypothetical null reference case, where the impacts of all current interventions are removed, in order to identify the optimal package of interventions. All health system costs, regardless of payer, were included. Health outcomes are reported as the gain in healthy life years due to a specific intervention scenario and were estimated using a deterministic state-transition cohort simulation (Markov model). Results: Vaccination against human papillomavirus (two doses) for 9-13-year-old girls (in eastern sub-Saharan Africa) and HPV vaccination combined with prevention of cervical cancer by screening of women aged 30-49 years through visual inspection with acetic acid linked with timely treatment of pre-cancerous lesions (in Southeast Asia) were found to be the most cost effective interventions. For breast cancer, in both regions the treatment of breast cancer, stages I and II, with surgery ± systemic therapy, at 95% coverage, was found to be the most cost-effective intervention. For colorectal cancer, treatment of colorectal cancer, stages I and II, with surgery ± chemotherapy and radiotherapy, at 95% coverage, was found to be the most cost-effective intervention. Conclusion: The results demonstrate that cancer prevention and control interventions are cost-effective and can be implemented through a step-wise approach to achieve maximum health benefits. As the global community moves toward universal health coverage, this analysis can support decision makers in identifying a core package of cancer services, ensuring treatment and palliative care for all

Cost effectiveness of strategies to combat breast, cervical, and colorectal cancer in sub-Saharan Africa and South East Asia: mathematical modelling study

Objective To determine the costs and health effects of interventions to combat breast, cervical, and colorectal cancers in order to guide resource allocation decisions in developing countries

Adjusting for inflation and currency changes within health economic studies

Objectives: Within health economic studies, it is often necessary to adjust costs obtained from different time periods for inflation. Nevertheless, many studies do not report the methods used for this in sufficient detail. In this article, we outline the principal methods used to adjust for inflation, with a focus on studies relating to healthcare interventions in low- and middle-income countries. We also discuss issues relating to converting local currencies to international dollars and US$and adjusting cost data collected from other countries or previous studies. Methods: We outlined the 3 main methods used to adjust for inflation for studies in these settings: exchanging the local currency to US$ or international dollars and then inflating using US inflation rates (method 1); inflating the local currency using local inflation rates and then exchanging to US$or international dollars (method 2); splitting the costs into tradable and nontradable resources and using method 1 on the tradable resources and method 2 on the nontradable resources (method 3). Results: In a hypothetical example of adjusting a cost of US$100 incurred in Vietnam from 2006 to 2016 prices, the adjusted cost from the 3 methods were US$116.84, US$172.09, and US$161.04, respectively. Conclusions: The different methods for adjusting for inflation can yield substantially different results. We make recommendations regarding the most appropriate method for various scenarios. Moving forward, it is vital that studies report the methodology they use to adjust for inflation more transparently

PopMod: a longitudinal population model with two interacting disease states

This article provides a description of the population model PopMod, which is designed to simulate the health and mortality experience of an arbitrary population subjected to two interacting disease conditions as well as all other "background" causes of death and disability. Among population models with a longitudinal dimension, PopMod is unique in modelling two interacting disease conditions; among the life-table family of population models, PopMod is unique in not assuming statistical independence of the diseases of interest, as well as in modelling age and time independently. Like other multi-state models, however, PopMod takes account of "competing risk" among diseases and causes of death. PopMod represents a new level of complexity among both generic population models and the family of multi-state life tables. While one of its intended uses is to describe the time evolution of population health for standard demographic purposes (e.g. estimates of healthy life expectancy), another prominent aim is to provide a standard measure of effectiveness for intervention and cost-effectiveness analysis. PopMod, and a set of related standard approaches to disease modelling and cost-effectiveness analysis, will facilitate disease modelling and cost-effectiveness analysis in diverse settings and help make results more comparable

Analysis of mammography screening schedules under varying resource constraints for planning breast cancer control programs in low- and middle-income countries : a mathematical study

Background. Low-and-middle-income countries (LMICs) have higher mortality-to-incidence ratio for breast cancer compared to high-income countries (HICs) because of late-stage diagnosis. Mammography screening is recommended for early diagnosis, however, the infrastructure capacity in LMICs are far below that needed for adopting current screening guidelines. Current guidelines are extrapolations from HICs, as limited data had restricted model development specific to LMICs, and thus, economic analysis of screening schedules specific to infrastructure capacities are unavailable. Methods. We applied a new Markov process method for developing cancer progression models and a Markov decision process model to identify optimal screening schedules under a varying number of lifetime screenings per person, a proxy for infrastructure capacity. We modeled Peru, a middle-income country, as a case study and the United States, an HIC, for validation. Results. Implementing 2, 5, 10, and 15 lifetime screens would require about 55, 135, 280, and 405 mammography machines, respectively, and would save 31, 62, 95, and 112 life-years per 1000 women, respectively. Current guidelines recommend 15 lifetime screens, but Peru has only 55 mammography machines nationally. With this capacity, the best strategy is 2 lifetime screenings at age 50 and 56 years. As infrastructure is scaled up to accommodate 5 and 10 lifetime screens, screening between the ages of 44-61 and 41-64 years, respectively, would have the best impact. Our results for the United States are consistent with other models and current guidelines. Limitations. The scope of our model is limited to analysis of national-level guidelines. We did not model heterogeneity across the country. Conclusions. Country-specific optimal screening schedules under varying infrastructure capacities can systematically guide development of cancer control programs and planning of health investments