Stéphane Helleringer

Magnitude of Ebola relative to other causes of death in Liberia, Sierra Leone, and Guinea

Correspondence With more than 20 000 cases reported, the outbreak of Ebola virus disease (EVD) in west Africa is by far the largest in recorded history. Despite the scale of the current outbreak, EVD is often perceived as a “small-scale killer”. 1 By comparison, malaria caused an estimated 854 000 deaths world wide in 2013. 2 However, although limited at the global level, the impact of EVD on mortality could be substantial in countries with intense transmission. We thus aimed to compare EVD with other causes of death in Liberia, Sierra Leone, and Guinea in 2014. We did an uncertainty analysis of EVD mortality (see appendix), based on two parameters: the extent of under- reporting of EVD cases and the case fatality rate (CFR)—ie, the proportion of EVD cases who die. Similar to other analyses of EVD spread, 3 we hypothesised that there were up to 2·5 times more EVD cases than reported. This factor derives from a mathematical model, which compared the reported number of EVD cases to the number of beds in use in Ebola treatment units in August, 2014. 4 We assumed that the CFR varied between 60% and 85%. The lower rate corresponds to CFRs seen among hospitalised EVD patients with known disease outcomes. 5 Lower CFRs have been documented, but only in Ebola treatment units that implement non-standard treatment protocols. 6 The upper rate corresponds to CFRs seen in non-hospitalised EVD patients. 5 We estimated the number of EVD deaths as the product of (1) the reported number of EVD cases, (2) the under-reporting factor and (3) the CFR. Based solely on confirmed and probable EVD cases, the number of EVD deaths in 2014 ranged from 2928 to 10 372 in Liberia, from 4468 to 15 824 in Sierra Leone, and from 1739 to 5548 in Guinea. www.thelancet.com/lancetgh Vol 3 May 2015 We used the most recent (2013) national estimates of non-EVD mortality, 2,7 together with projections of population growth, to calculate the expected number of deaths from non- EVD causes in Liberia, Sierra Leone, and Guinea in 2014. For all com binations of model parameters, we mapped how the estimated number of EVD deaths ranked relative to the expected number of deaths from non-EVD causes. Liberia In Liberia, for virtually all model parameters, EVD deaths exceeded the expected number of deaths due to the leading non-EVD cause of death (fi gure). In Sierra Leone, a broad range of model parameters also indicated that EVD might have killed more people in 2014 than the leading non- EVD cause of death (ie, malaria). In other sets of model parameters, EVD still killed more people than the second Sierra Leone Guinea 10k 15k 5k 10k Ratio of true: reported EVD cases Magnitude of Ebola relative to other causes of death in Liberia, Sierra Leone, and Guinea 5k Malaria See Online for appendix 2·5k LRI 5k LRI Malaria Case fatality rate among EVD cases (%) Ranking of EVD deaths relative to non-EVD causes of death EVD=1st cause of death EVD=3rd cause of death EVD=lower ranking EVD=2nd cause of death Figure: Comparison of Ebola virus disease (EVD) deaths and expected deaths from non-EVD causes in Liberia, Sierra Leone, and Guinea in 2014 LRI=lower respiratory infections. White contours represent estimated number of EVD deaths in 2014 for specifi c combinations of model parameters (where 2·5k = 2500 deaths, etc). Calculations of number of EVD deaths, and comparisons with other causes of death, are described in the appendix. Range of possible case fatality rates (CFRs) is narrower in Guinea than in the other two countries because a higher percentage of EVD deaths was recorded in that country among reported EVD cases. Lower bound of CFR is thus set at level estimated by surveillance data in that country. To aid fi gure interpretation, we illustrate the case of Sierra Leone. In the red area, there are more EVD deaths than we expect deaths from the leading non-EVD cause of death in that country—ie, malaria. Hence, in this region of the parameter space, EVD would be the fi rst cause of death. In the light blue area, there are fewer EVD deaths than deaths from the leading non-EVD cause of death, but there are more EVD deaths than deaths from the second non-EVD cause of death (ie, LRI). Hence EVD would be the second cause of death in that region of the parameter space. In the slightly darker blue area at the bottom of the graph, there are fewer EVD deaths than deaths from LRI, but more than deaths from HIV/ AIDS—ie, the third leading non-EVD cause of death (see appendix). EVD would thus be the third leading cause of death in that region of the parameter space. The boundary between the red and light blue areas (marked “malaria”) represents combinations of model parameters where the number of EVD deaths is equal to the expected number of malaria deaths. The boundary between the light blue and the darker blue areas (marked “LRI”) represents combinations for which the number of EVD deaths is equal to the expected number of LRI deaths. In Guinea, the entire graph is dark blue because the number of EVD deaths is lower than the expected number of deaths from the third non-EVD cause of death in the country for all model parameters. e255

Improving the quality of adult mortality data collected in demographic surveys: validation study of a new siblings' survival questionnaire in Niakhar, Senegal.

Stéphane Helleringer and colleagues conducted a validation study in Niakhar, Senegal to investigate whether a new approach, sibling survival calendars, improves the quality of adult mortality data collected in demographic surveys. Please see later in the article for the Editors Summary

Geographical disparities in HIV prevalence and care among men who have sex with men in Malawi: results from a multisite cross-sectional survey

BACKGROUNDnEpidemiological assessment of geographical heterogeneity of HIV among men who have sex with men (MSM) is necessary to inform HIV prevention and care strategies in the more generalised HIV epidemics across sub-Saharan Africa, including Malawi. We aimed to measure the HIV prevalence, risks, and access to HIV care among MSM across multiple localities to better inform HIV programming for MSM in Malawi.nnnMETHODSnBetween Aug 1, 2011, and Sept 13, 2014, we recruited MSM into cross-sectional research via respondent-driven sampling (RDS) in seven districts of Malawi. RDS and site weights were used to estimate national HIV prevalence and engagement in care and in multilevel regression models to identify correlates of prevalent HIV infection. The comparative prevalence ratio of HIV among MSM relative to adult men was calculated by use of direct age-stratification.nnnFINDINGSn2453 MSM were enrolled with a population HIV prevalence of 18·2% (95% CI 15·5-21·2), as low as 4·1% (2·2-7·6) in Mzuzu and as high as 24·5% (19·5-30·3) in Mulanje. The comparative HIV prevalence ratio was 2·52 when comparing MSM with the adult male population. Age-stratified HIV prevalence showed early onset of infection with 11·8% (95% CI 7·3-18·4) of MSM aged 18-19 years HIV infected. Factors positively associated with HIV infection included being aged 21-30 years and reporting female or transgender identity. Among HIV infected MSM, less than 1% reported ever being diagnosed with HIV infection (0·9%, 95% CI 0·4-2·5) and initiated antiretroviral treatment (0·2%, 0·2-0·3).nnnINTERPRETATIONnHIV disproportionately affects MSM in Malawi with disparities sustained across the HIV care continuum. These issues are geographically heterogeneous and begin among young MSM, supporting geographically focused and age-specific approaches to confidential HIV testing with linkage to HIV services.nnnFUNDINGnMalawi Department of Nutrition, HIV and AIDS (DNHA), UNDP, UNFPA, UNAIDS, and UNICEF.

Age differences between sexual partners, behavioural and demographic correlates, and HIV infection on Likoma Island, Malawi

Patterns of age differences between sexual partners – “age-mixing” – may partially explain the magnitude of HIV epidemics in Sub-Saharan Africa. However, evidence of age-disparity as a risk factor for HIV remains mixed. We used data from a socio-centric study of sexual behaviour in Malawi to quantify the age-mixing pattern and to find associations between relationship characteristics and age differences for 1,922 participants. Three age difference measures were explored as predictors of prevalent HIV infection. We found that for each year increase in male participant age, the average age difference with their partners increased by 0.26 years, while among women it remained approximately constant around 5 years. Women in the study had larger within-individual variation in partner ages compared to men. Spousal partnerships and never using a condom during sex were associated with larger age differences in relationships of both men and women. Men who were more than five years younger than their partners had 5.39 times higher odds (95% CI: 0.93–31.24) of being HIV-infected than men 0–4 years older. The relationship between HIV-infection and age-asymmetry may be more complex than previously described. The role that women play in HIV transmission should not be under-estimated, particularly in populations with large within-individual variation in partner ages.

Childhood Illness Prevalence and Health Seeking Behavior Patterns in Rural Tanzania

IntroductionThis paper identifies factors influencing differences in the prevalence of diarrhea, fever and acute respiratory infection (ARI), and health seeking behavior among caregivers of children under age five in rural Tanzania.MethodsUsing cross-sectional survey data collected in Kilombero, Ulanga, and Rufiji districts, the analysis included 1,643 caregivers who lived with 2,077 children under five years old. Logistic multivariate and multinomial regressions were used to analyze factors related to disease prevalence and to health seeking behavior.ResultsOne quarter of the children had experienced fever in the past two weeks, 12.0xa0% had diarrhea and 6.7xa0% experienced ARI. Children two years of age and older were less likely to experience morbidity than children under one year [ORfeveru2009=u20090.77, 95xa0% CI 0.61-0.96; ORdiarrheau2009=u20090.26, 95xa0% CI 0.18-0.37; ORARIu2009=u20090.60 95xa0% CI 0.41-0.89]. Children aged two and older were more likely than children under one to receive no care or to receive care at home, rather than to receive care at a facility [RRRdiarrheau2009=u20093.47, 95xa0% CI 1.19-10.17 for “No care”]. Children living with an educated caregiver were less likely to receive no care or home care rather than care at a facility as compared to those who lived with an uneducated caregiver [RRRdiarrheau2009=u20090.28, 95xa0% CI 1.10-0.79 for “No care”]. Children living in the wealthiest households were less likely to receive no care or home care for fever as compared to those who lived poorest households. Children living more than 1xa0km from health facility were more likely to receive no care or to receive home care for diarrhea rather than care at a facility as compared to those living less than 1xa0km from a facility [RRRdiarrheau2009=u20093.50, 95xa0% CI 1.13-10.82 for “No care”]. Finally, caregivers who lived with more than one child under age five were more likely to provide no care or home care rather than to seek treatment at a facility as compared to those living with only one child under five.ConclusionsOur results suggest that child age, caregiver education attainment, and household wealth and location may be associated with childhood illness and care seeking behavior patterns. Interventions should be explored that target children and caregivers according to these factors, thereby better addressing barriers and optimizing health outcomes especially for children at risk of dying before the age of five.

Connecting the dots : network data and models in HIV epidemiology

Effective HIV prevention requires knowledge of the structure and dynamics of the social networks across which infections are transmitted. These networks most commonly comprise chains of sexual relationships, but in some populations, sharing of contaminated needles is also an important, or even the main mechanism that connects people in the network. Whereas network data have long been collected during survey interviews, new data sources have become increasingly common in recent years, because of advances in molecular biology and the use of partner notification services in HIV prevention and treatment programmes. We review current and emerging methods for collecting HIV-related network data, as well as modelling frameworks commonly used to infer network parameters and map potential HIV transmission pathways within the network. We discuss the relative strengths and weaknesses of existing methods and models, and we propose a research agenda for advancing network analysis in HIV epidemiology. We make the case for a combination approach that integrates multiple data sources into a coherent statistical framework.

Assessing the direct effects of the Ebola outbreak on life expectancy in Liberia, Sierra Leone and Guinea

Background: An EVD outbreak may reduce life expectancy directly (due to high mortality among EVD cases) and indirectly (e.g., due to lower utilization of healthcare and subsequent increases in non-EVD mortality). In this paper, we investigated the direct effects of EVD on life expectancy in Liberia, Sierra Leone and Guinea (LSLG thereafter). Methods: We used data on EVD cases and deaths published in situation reports by the World Health Organization (WHO), as well as data on the age of EVD cases reported from patient datasets. We used data on non-EVD mortality from the most recent life tables published prior to the EVD outbreak. We then formulated three scenarios based on hypotheses about a) the extent of under-reporting of EVD cases and b) the EVD case fatality ratio. For each scenario, we re-estimated the number of EVD deaths in LSLG and we applied standard life table techniques to calculate life expectancy. Results: In Liberia, possible reductions in life expectancy resulting from EVD deaths ranged from 1.63 year (low EVD scenario) to 5.56 years (high EVD scenario), whereas in Sierra Leone, possible life expectancy declines ranged from 1.38 to 5.10 years. In Guinea, the direct effects of EVD on life expectancy were more limited (<1.20 year). Conclusions: Our high EVD scenario suggests that, due to EVD deaths, life expectancy may have declined in Liberia and Sierra Leone to levels these two countries had not experienced since 2001-2003, i.e., approximately the end of their civil wars. The total effects of EVD on life expectancy may however be larger due to possible concomitant increases in non-EVD mortality during the outbreak.

Understanding the Adolescent Gap in HIV Testing Among Clients of Antenatal Care Services in West and Central African Countries

New HIV infections among children have declined significantly more slowly in West and central African countries (WCA) than in eastern and southern African countries between 2009 and 2015. Since adolescent fertility is particularly high in WCA countries, frequent mother-to-child transmission (MTCT) of HIV may in part be due to low coverage of HIV testing among adolescents during antenatal care (ANC). We investigated this adolescent gap in HIV testing using survey data from the demographic and health surveys and multiple indicators cluster surveys collected in 21 WCA countries since 2009. We found significant adolescent gaps in HIV testing in 12 out of 21 WCA countries, with the largest gap observed in Nigeria. In countries with a significant adolescent gap in HIV testing, we used Fairlie decompositions to assess what proportion of these gaps were explained by age-related differences in the distribution of (a) marital status, (b) socioeconomic status (SES), (c) MTCT-related knowledge, and (d) patterns of ANC utilization. Differences in SES and MTCT-related knowledge were the most consistent determinants of adolescent gaps in HIV testing during ANC. Differences in ANC utilization (e.g., fewer and possibly delayed ANC visits) also contributed to the adolescent gap in 8 out of 12 countries. Interventions that improve knowledge of MTCT risks, and/or promote the sustained use of ANC services, could help engage HIV-infected adolescents who become pregnant in PMTCT services. Targeting these interventions at the most disadvantaged households will be crucial in further reducing HIV infections among children.

The effect of mass vaccination campaigns against polio on the utilization of routine immunization services: A regression discontinuity design

BACKGROUNDnIn most low and middle-income countries (LMIC), vaccines are primarily distributed by routine immunization services (RI) at health facilities. Additional opportunities for vaccination are also provided through mass vaccination campaigns, conducted periodically as part of disease-specific initiatives. It is unclear whether these campaigns are detrimental to RI services, or wether they may stimulate the utilization of RI.nnnMETHODSnUnobserved confounders and reverse causality have limited existing evaluations of the effects of mass vaccination campaigns on RI services. We explored the use of a regression discontinuity design (RDD) to measure these effects more precisely. This is a quasi-experimental method, which exploits random variations in birth dates to identify the causal effects of vaccination campaigns. We applied RDD to survey data on a nationwide vaccination campaign against Polio conducted in Bangladesh.nnnRESULTSnWe compared systematically the children born immediately before vs. after the vaccination campaign. These two groups had similar background characteristics, but differed by their exposure to the vaccination campaign. Contrary to previous studies, exposure to the campaign had positive effects on RI utilization. Children exposed to the campaign received between 0.296 and 0.469 additional doses of DPT vaccine by age 4months than unexposed children.nnnCONCLUSIONSnRDD constitutes a promising tool to assess the effects of mass vaccination campaigns on RI services. It could be tested in additional settings, using larger and more precise datasets. It could also be extended to measure the effects of other disease-specific interventions on the functioning of health systems, in particular those that occur at a discrete point in time and/or include age-related eligibility criteria.

Beyond Risk Compensation: Clusters of Antiretroviral Treatment (ART) Users in Sexual Networks Can Modify the Impact of ART on HIV Incidence

Introduction Concerns about risk compensation—increased risk behaviours in response to a perception of reduced HIV transmission risk—after the initiation of ART have largely been dispelled in empirical studies, but other changes in sexual networking patterns may still modify the effects of ART on HIV incidence. Methods We developed an exploratory mathematical model of HIV transmission that incorporates the possibility of ART clusters, i.e. subsets of the sexual network in which the density of ART patients is much higher than in the rest of the network. Such clusters may emerge as a result of ART homophily—a tendency for ART patients to preferentially form and maintain relationships with other ART patients. We assessed whether ART clusters may affect the impact of ART on HIV incidence, and how the influence of this effect-modifying variable depends on contextual variables such as HIV prevalence, HIV serosorting, coverage of HIV testing and ART, and adherence to ART. Results ART homophily can modify the impact of ART on HIV incidence in both directions. In concentrated epidemics and generalized epidemics with moderate HIV prevalence (≈ 10%), ART clusters can enhance the impact of ART on HIV incidence, especially when adherence to ART is poor. In hyperendemic settings (≈ 35% HIV prevalence), ART clusters can reduce the impact of ART on HIV incidence when adherence to ART is high but few people living with HIV (PLWH) have been diagnosed. In all contexts, the effects of ART clusters on HIV epidemic dynamics are distinct from those of HIV serosorting. Conclusions Depending on the programmatic and epidemiological context, ART clusters may enhance or reduce the impact of ART on HIV incidence, in contrast to serosorting, which always leads to a lower impact of ART on HIV incidence. ART homophily and the emergence of ART clusters should be measured empirically and incorporated into more refined models used to plan and evaluate ART programmes.