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Pneumonic plague (PP) is characterized by high infection rate, person-to-person transmission, and rapid progression to severe disease. In 2017, a PP epidemic occurred in 2 Madagascar urban areas, Antananarivo and Toamasina. We used epidemiologic data and Yersinia pestis genomic characterization to determine the sources of this epidemic. Human plague emerged independently from environmental reservoirs in rural endemic foci >20 times during August–November 2017. Confirmed cases from 5 emergences, including 4 PP cases, were documented in urban areas. Epidemiologic and genetic analyses of cases associated with the first emergence event to reach urban areas confirmed that transmission started in August; spread to Antananarivo, Toamasina, and other locations; and persisted in Antananarivo until at least mid-November. Two other Y. pestis lineages may have caused persistent PP transmission chains in Antananarivo. Multiple Y. pestis lineages were independently introduced to urban areas from several rural foci via travel of infected persons during the epidemic.

Abstract Data on population health are vital to evidence-based decision making but are rarely adequately localized or updated in continuous time. They also suffer from low ascertainment rates, particularly in rural areas where barriers to healthcare can cause infrequent touch points with the health system. Here, we demonstrate a novel statistical method to estimate the incidence of endemic diseases at the community level from passive surveillance data collected at primary health centers. The zero-corrected, gravity-model (ZERO-G) estimator explicitly models sampling intensity as a function of health facility characteristics and statistically accounts for extremely low rates of ascertainment. The result is a standardized, real-time estimate of disease incidence at a spatial resolution nearly ten times finer than typically reported by facility-based passive surveillance systems. We assessed the robustness of this method by applying it to a case study of field-collected malaria incidence rates from a rural health district in southeastern Madagascar. The ZERO-G estimator decreased geographic and financial bias in the dataset by over 90% and doubled the agreement rate between spatial patterns in malaria incidence and incidence estimates derived from prevalence surveys. The ZERO-G estimator is a promising method for adjusting passive surveillance data of common, endemic diseases, increasing the availability of continuously updated, high quality surveillance datasets at the community scale.

While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.

During outbreaks, the lack of diagnostic "gold standard" can mask the true burden of infection in the population and hamper the allocation of resources required for control. Here, we present an analytical framework to evaluate and optimize the use of diagnostics when multiple yet imperfect diagnostic tests are available. We apply it to laboratory results of 2,136 samples, analyzed with 3 diagnostic tests (based on up to 7 diagnostic outcomes), collected during the 2017 pneumonic (PP) and bubonic plague (BP) outbreak in Madagascar, which was unprecedented both in the number of notified cases, clinical presentation, and spatial distribution. The extent of these outbreaks has however remained unclear due to nonoptimal assays. Using latent class methods, we estimate that 7% to 15% of notified cases were Yersinia pestis-infected. Overreporting was highest during the peak of the outbreak and lowest in the rural settings endemic to Y. pestis. Molecular biology methods offered the best compromise between sensitivity and specificity. The specificity of the rapid diagnostic test was relatively low (PP: 82%, BP: 85%), particularly for use in contexts with large quantities of misclassified cases. Comparison with data from a subsequent seasonal Y. pestis outbreak in 2018 reveal better test performance (BP: specificity 99%, sensitivity: 91%), indicating that factors related to the response to a large, explosive outbreak may well have affected test performance. We used our framework to optimize the case classification and derive consolidated epidemic trends. Our approach may help reduce uncertainties in other outbreaks where diagnostics are imperfect.

Abstract Background Plague, a fatal disease caused by the bacillus, Yersinia pestis, still affects resources-limited countries. Information on antibody response to plague infection in human is scarce. Anti-F1 Ig G are among the known protective antibodies against Y. pestis infection. As a vaccine preventable disease, knowledge on antibody response is valuable for the development of an effective vaccine to reduce infection rate among exposed population in plague-endemic regions. In this study, we aim to describe short and long-term humoral immune responses against Y. pestis in plague-confirmed patients from Madagascar, the most affected country in the world. Methods Bubonic (BP) and pneumonic plague (PP) patients were recruited from plague- endemic foci in the central highlands of Madagascar between 2005 and 2017. For short-term follow-up, 6 suspected patients were enrolled and prospectively investigated for kinetics of the anti-F1 IgG response, whereas the persistence of antibodies was retrospectively studied in 71 confirmed convalescent patients, using an ELISA which was validated for the detection of plague in human blood samples in Madagascar. Results Similarly to previous findings, anti-F1 IgG rose quickly during the first week after disease onset and increased up to day 30. In the long-term study, 56% of confirmed cases remained seropositive, amongst which 60 and 40% could be considered as high- and low-antibody responders, respectively. Antibodies persisted for several years and up to 14.8 years for one individual. Antibody titers decreased over time but there was no correlation between titer and time elapsed between the disease onset and serum sampling. In addition, the seroprevalence rate was not significantly different between gender (P = 0.65) nor age (P = 0.096). Conclusion Our study highlighted that the circulating antibody response to F1 antigen, which is specific to Y. pestis, may be attributable to individual immune responsiveness. The finding that a circulating anti-F1 antibody titer could persist for more than a decade in both BP and PP recovered patients, suggests its probable involvement in patients’ protection. However, complementary studies including analyses of the cellular immune response to Y. pestis are required for the better understanding of long-lasting protection and development of a potential vaccine against plague.

Geographic distance is a critical barrier to healthcare access, particularly for rural communities with poor transportation infrastructure who rely on non-motorized transportation. There is broad consensus on the importance of community health workers (CHWs) to reduce the effects of geographic isolation on healthcare access. Due to a lack of fine-scale spatial data and individual patient records, little is known about the precise effects of CHWs on removing geographic barriers at this level of the healthcare system. Relying on a high-quality, crowd-sourced dataset that includes all paths and buildings in the area, we explored the impact of geographic distance from CHWs on the use of CHW services for children under 5 years in the rural district of Ifanadiana, southeastern Madagascar from 2018-2021. We then used this analysis to determine key features of an optimal geographic design of the CHW system, specifically optimizing a single CHW location or installing additional CHW sites. We found that consultation rates by CHWs decreased with increasing distance patients travel to the CHW by approximately 28.1% per km. The optimization exercise revealed that the majority of CHW sites (50/80) were already in an optimal location or shared an optimal location with a primary health clinic. Relocating the remaining CHW sites based on a geographic optimum was predicted to increase consultation rates by only 7.4%. On the other hand, adding a second CHW site was predicted to increase consultation rates by 31.5%, with a larger effect in more geographically dispersed catchments. Geographic distance remains a barrier at the level of the CHW, but optimizing CHW site location based on geography alone will not result in large gains in consultation rates. Rather, alternative strategies, such as the creation of additional CHW sites or the implementation of proactive care, should be considered.

There are many outstanding questions about how to control the global COVID-19 pandemic. The information void has been especially stark in the World Health Organization Africa Region, which has low per capita reported cases, low testing rates, low access to therapeutic drugs, and has the longest wait for vaccines. As with all disease, the central challenge in responding to COVID-19 is that it requires integrating complex health systems that incorporate prevention, testing, front line health care, and reliable data to inform policies and their implementation within a relevant timeframe. It requires that the population can rely on the health system, and decision-makers can rely on the data. To understand the process and challenges of such an integrated response in an under-resourced rural African setting, we present the COVID-19 strategy in Ifanadiana District, where a partnership between Malagasy Ministry of Public Health (MoPH) and non-governmental organizations integrates prevention, diagnosis, surveillance, and treatment, in the context of a model health system. These efforts touch every level of the health system in the district—community, primary care centers, hospital—including the establishment of the only RT-PCR lab for SARS-CoV-2 testing outside of the capital. Starting in March of 2021, a second wave of COVID-19 occurred in Madagascar, but there remain fewer cases in Ifanadiana than for many other diseases (e.g., malaria). At the Ifanadiana District Hospital, there have been two deaths that are officially attributed to COVID-19. Here, we describe the main components and challenges of this integrated response, the broad epidemiological contours of the epidemic, and how complex data sources can be developed to address many questions of COVID-19 science. Because of data limitations, it still remains unclear how this epidemic will affect rural areas of Madagascar and other developing countries where health system utilization is relatively low and there is limited capacity to diagnose and treat COVID-19 patients. Widespread population based seroprevalence studies are being implemented in Ifanadiana to inform the COVID-19 response strategy as health systems must simultaneously manage perennial and endemic disease threats.

Slovenska priporočila za obravnavo bolnikov z kronično odpovedjo prebavil so povzeta so po smernicah Evropskega združenja za klinično prehrano in metabolizem (angl. ESPEN; European Society for Clinical Nutrition and Metabolism) in prilagojena slovenskim izkušnjah zdravljenja odpovedi prebavil. Pretežni del bolnikov z odpovedjo prebavil predstavljajo bolniki s sindromom kratkega črevesa (SKČ), zato je poudarek priporočil na diagnostiki, obravnavi in zdravljenju bolnikov s SKČ. Namen priporočil je vzpostavitev bolj učinkovite prepoznave in obravnave bolnikov z odpovedjo ter vzpostavitvi temeljnih strokovnih izhodišč za zdravljenje teh bolnikov v Sloveniji. Dejavnost zdravljenja kronične odpovedi prebavil z parenteralno prehrano na domu pri odrasli pacientih je bila v Sloveniji formalno vzpostavljena leta 2008, v 13 letih smo zdravili več kot 230 pacientov, v začetku leta 2021 je v 2 centrih (OI Ljubljana, UKC Ljubljana) v zdravljenje vključeno 95 odraslih bolnikov. Pred 6 leti Smo paciente v programu zdravljenja odpovedi prebavil v okviru OI LjubljanA vključili v evropski register premljanja bolnikov na Parenteralni prehrani na domu (PPD), v okviru ESPEN.

COVID-19 has wreaked havoc globally with particular concerns for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers from other infectious diseases: ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with different implications for the final epidemic burden: (1) low case detection, (2) differences in epidemiology (e.g. low R 0), and (3) policy interventions. The low number of cases have led some SSA governments to relaxing these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the incidence of COVID-19 cases as of July 2020 can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. We then re-evaluate these findings in the context of the COVID-19 epidemic in Madagascar through August 2020. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of a growing health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar for the coming year, hence the importance of clinical trials and continually improving access to healthcare.

Health care is most effective when a patient’s basic primary care needs are met as close to home as possible, with advanced care accessible when needed. In Ifanadiana District, Madagascar, a collaboration between the Ministry of Public Health (MoPH) and PIVOT, a non-governmental organization (NGO), fosters Networks of Care (NOC) to support high-quality, patient-centered care. The district’s health system has three levels of care: community, health center, district hospital; a regional hospital is available for tertiary care services. We explore the MoPH/PIVOT collaboration through a case study which focuses on noteworthy elements of the collaboration across the four NOC domains: (I) agreement and enabling environment, (II) operational standards, (III) quality, efficiency, and responsibility, (IV) learning and adaptation. Under Domain I, we describe formal agreements between the MoPH and PIVOT and the process for engaging communities in creating effective NOC. Domain II discusses patient referral across levels of the health system and improvements to facility readiness and service availability. Under Domain III the collaboration prioritizes communication and supervision to support clinical quality, and social support for patients. Domain IV focuses on evaluation, research, and the use of data to modify programs to better meet community needs. The case study, organized by the domains of the NOC framework, demonstrates that a collaboration between the MoPH and an NGO can create effective NOC in a remote district with limited accessibility and advance the country’s agenda to achieve universal health coverage.