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BackgroundNeglected tropical diseases (NTDs) are generally assumed to be concentrated in poor populations, but evidence on this remains scattered. We describe within-country socioeconomic inequalities in nine NTDs listed in the London Declaration for intensified control and/or elimination: lymphatic filariasis (LF), onchocerciasis, schistosomiasis, soil-transmitted helminthiasis (STH), trachoma, Chagas’ disease, human African trypanosomiasis (HAT), leprosy, and visceral leishmaniasis (VL).MethodologyWe conducted a systematic literature review, including publications between 2004–2013 found in Embase, Medline (OvidSP), Cochrane Central, Web of Science, Popline, Lilacs, and Scielo. We included publications in international peer-reviewed journals on studies concerning the top 20 countries in terms of the burden of the NTD under study.Principal findingsWe identified 5,516 publications, of which 93 met the inclusion criteria. Of these, 59 papers reported substantial and statistically significant socioeconomic inequalities in NTD distribution, with higher odds of infection or disease among poor and less-educated people compared with better-off groups. The findings were mixed in 23 studies, and 11 studies showed no substantial or statistically significant inequality. Most information was available for STH, VL, schistosomiasis, and, to a lesser extent, for trachoma. For the other NTDs, evidence on their socioeconomic distribution was scarce.The magnitude of inequality varied, but often, the odds of infection or disease were twice as high among socioeconomically disadvantaged groups compared with better-off strata. Inequalities often took the form of a gradient, with higher odds of infection or disease each step down the socioeconomic hierarchy. Notwithstanding these inequalities, the prevalence of some NTDs was sometimes also high among better-off groups in some highly endemic areas.ConclusionsWhile recent evidence on socioeconomic inequalities is scarce for most individual NTDs, for some, there is considerable evidence of substantially higher odds of infection or disease among socioeconomically disadvantaged groups. NTD control activities as proposed in the London Declaration, when set up in a way that they reach the most in need, will benefit the poorest populations in poor countries.

Background It has been argued that as smoking prevalence declines in countries, the smokers that remain include higher proportions of those who are unwilling or unable to quit (a process known as ‘hardening’). ‘Smokeless tobacco and e-cigarettes have been promoted as a strategy to deal with such smokers. If hardening is occurring, there would be a positive association between smoking prevalence and quitting, with less quitting at lower prevalence. There would also be a neutral or negative association between prevalence and the number of cigarettes smoked. Methods We examined US state-level associations using the Tobacco Use Supplement (1992/1993–2010/ 2011) and Eurobarometer surveys for 31 European countries (2006–2009–2012) using regressions of quit attempts, quit ratios, and number of cigarettes smoked on smoking prevalence over time. Results For each 1% drop in smoking prevalence, quit attempts increase by 0.55%±.07 (p<0.001) in the USA and remain stable in Europe (p=0.53), US quit ratios increase by 1.13%±0.06 (p<0.001), and consumption drops by 0.32 cig/day±0.02 (p<0.001) in the USA and 0.22 cig/day±0.05 (p<0.001) in Europe. These associations remain stable over time (p>0.24), with significantly lower consumption at any given prevalence level as time passed in the USA (−0.15 (cig/day)/year ±0.06, p<0.05). Conclusions Consistent with prior research using different data and methods, these population-level results reject the hypothesis of hardening as smoking prevalence drops, instead supporting softening of the smoking population as prevalence declines.

BackgroundCurrently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies.Methods and ResultsDYNAMO-HIA quantifies the impact of user-specified risk-factor changes on multiple diseases and in turn on overall population health, comparing one reference scenario with one or more intervention scenarios. The Markov-based modeling approach allows for explicit risk-factor states and simulation of a real-life population. A built-in parameter estimation module ensures that only standard population-level epidemiological evidence is required, i.e. data on incidence, prevalence, relative risks, and mortality. DYNAMO-HIA provides a rich output of summary measures – e.g. life expectancy and disease-free life expectancy – and detailed data – e.g. prevalences and mortality/survival rates – by age, sex, and risk-factor status over time. DYNAMO-HIA is controlled via a graphical user interface and is publicly available from the internet, ensuring general accessibility. We illustrate the use of DYNAMO-HIA with two example applications: a policy causing an overall increase in alcohol consumption and quantifying the disease-burden of smoking.ConclusionBy combining modest data needs with general accessibility and user friendliness within the causal framework of HIA, DYNAMO-HIA is a potential standard tool for health impact assessment based on epidemiologic evidence.