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Journal Description

JMIR Public Health & Surveillance (JPHS, Editor-in-chief: Travis Sanchez, Emory University/Rollins School of Public Health) is a PubMed-indexed, peer-reviewed sister journal of the Journal of Medical Internet Research (JMIR), the top cited journal in health informatics, ranked #1 by Clarivate's Journal Impact Factor. JPH is a multidisciplinary journal with a unique focus on the intersection of innovation and technology in public health, and includes topics like health communication, public health informatics, surveillance, participatory epidemiology, infodemiology and infoveillance, digital disease detection, digital public health interventions, mass media/social media campaigns, and emerging population health analysis systems and tools. 

We publish regular articles, reviews, protocols/system descriptions and viewpoint papers on all aspects of public health, with a focus on innovation and technology in public health.

Apart from publishing traditional public health research and viewpoint papers as well as reports from traditional surveillance systems, JPH was one of the first (if not the only) peer-reviewed journal which publishes papers with surveillance or pharmacovigilance data from non-traditional, unstructured big data and text sources such as social media and the Internet (infoveillance, digital disease detection), or reports on novel participatory epidemiology projects, where observations are solicited from the public.  

Among other innovations, JPH is also dedicated to support rapid open data sharing and rapid open access to surveillance and outbreak data. As one of the novel features we plan to publish rapid or even real-time surveillance reports and open data. The methods and description of the surveillance system may be peer-reviewed and published only once in detail, in a  "baseline report" (in a JMIR Res Protoc or a JMIR Public Health & Surveill paper), and authors then have the possibility to publish data and reports in frequent intervals rapidly and with only minimal additional peer-review (we call this article type "Rapid Surveillance Reports"). JMIR Publications may even work with authors/researchers and developers of selected surveillance systems on APIs for semi-automated reports (e.g. weekly reports to be automatically published in JPHS and indexed in PubMed, based on data-feeds from surveillance systems and minmal narratives and abstracts).

Furthermore, duing epidemics and public health emergencies, submissions with critical data will be processed with expedited peer-review to enable publication within days or even in real-time.

We also publish descriptions of open data resources and open source software. Where possible, we can and want to publish or even host the actual software or dataset on the journal website.


Recent Articles:

  • Source: freepik; Copyright: katemangostar; URL:; License: Licensed by JMIR.

    Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports:...


    Background: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods: We present a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% . Conclusions: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.

  • The HIV Care Continuum Dashboard (montage). Source: The Authors / Placeit; Copyright: The Authors; URL:; License: Licensed by JMIR.

    New York City HIV Care Continuum Dashboards: Using Surveillance Data to Improve HIV Care Among People Living With HIV in New York City


    Background: HIV surveillance data can be used to improve patient outcomes. Objective: This study aimed to describe and present findings from the HIV care continuum dashboards (CCDs) initiative, which uses surveillance data to quantify and track outcomes for HIV patients at major clinical institutions in New York City. Methods: HIV surveillance data collected since 2011 were used to provide high-volume New York City clinical facilities with their performance on two key outcomes: linkage to care (LTC), among patients newly diagnosed with HIV and viral load suppression (VLS), among patients in HIV care. Results: The initiative included 21 facilities covering 33.78% (1135/3360) of new HIV diagnoses and 46.34% (28,405/61,298) of patients in HIV care in New York City in 2011 and was extended to a total of 47 sites covering 44.23% (1008/2279) of new diagnoses and 69.59% (43,897/63,083) of New York City patients in care in 2016. Since feedback of outcomes to providers began, aggregate LTC has improved by 1 percentage point and VLS by 16 percentage points. Conclusions: Disseminating information on key facility–level HIV outcomes promotes collaboration between public health and the clinical community to end the HIV epidemic. Similar initiatives can be adopted by other jurisdictions with mature surveillance systems and supportive laws and policies.

  • Children awaiting urine sample collection in Torrock, south of Chad. Source: Image created by the Authors; Copyright: The Authors; URL:; License: Creative Commons Attribution (CC-BY).

    Prevalence of Schistosoma Haematobium Measured by a Mobile Health System in an Unexplored Endemic Region in the Subprefecture of Torrock, Chad


    Background: Schistosoma haematobium is a parasitic digenetic trematode responsible for schistosomiasis (also known as bilharzia). The disease is caused by penetration of the skin by the parasite, spread by intermediate host molluscs in stagnant waters, and can be treated by administration of praziquantel. Schistosomiasis is considered to be an important but neglected tropical disease. Objective: The aim of this pilot study was to investigate the prevalence of schistosomiasis in the subprefecture of Torrock, an endemic area in Chad where no earlier investigation had been conducted and no distribution system for pharmacotherapy has ever existed. Methods: This study examined 1875 children aged 1 to 14 years over a period of 1 year. After centrifugation, urine examination was performed by a direct microscopic investigation for eggs. The investigation was conducted with a mobile health (mHealth) approach, using short message service (SMS) for communication among parents, local health workers, a pharmacist, and a medical doctor. An initial awareness campaign requested parents to have their children examined for schistosomiasis. Urine was then collected at home by the parents following the SMS request. Urine results that proved positive were sent to a medical doctor by SMS, who in turn ordered a pharmacist by SMS to distribute praziquantel to the infected children. Results: Direct microscopic examination of urine found 467 positive cases (24.9% of the total sample). Of all male and female samples, 341 (34%) and 127 (14.4%) samples were positive, respectively. The infection rate was equally distributed over age groups. The newly developed mHealth system had a limited level of participation (8%) from an estimated total of 25,000 children in the target group. Conclusions: The prevalence of schistosomiasis in children in the subprefecture of Torrock is moderately high. Efforts will be required to enhance the awareness of parents and to reach a larger percentage of the population. Systematic governmental measures should be put in place as soon as possible to increase awareness in the area and to diagnose and treat cases of schistosomiasis.

  • Source: Flickr; Copyright: alobos life; URL:; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Factors Associated With Willingness to Use Pre-Exposure Prophylaxis in Brazil, Mexico, and Peru: Web-Based Survey Among Men Who Have Sex With Men


    Background: HIV disproportionally affects key populations including men who have sex with men (MSM). HIV prevalence among MSM varies from 17% in Brazil and Mexico to 13% in Peru, whereas it is below 0.5% for the general population in each country. Pre-exposure prophylaxis (PrEP) with emtricitabine/tenofovir is being implemented in the context of combination HIV prevention. Reports on willingness to use PrEP among MSM have started to emerge over the last few years. Previously reported factors associated with willingness to use PrEP include awareness, higher sexual risk behavior, and previous sexually transmitted infection. Objective: This study aimed to evaluate the factors associated with willingness to use daily oral PrEP among MSM in 3 Latin American, middle-income countries (Brazil, Mexico, and Peru). Methods: This Web-based, cross-sectional survey was advertised in 2 gay social network apps (Grindr and Hornet) used by MSM to find sexual partners and on Facebook during 2 months in 2018. Inclusion criteria were being 18 years or older, cisgender men, and HIV-negative by self-report. Eligible individuals answered questions on demographics, behavior, and PrEP (awareness, willingness to use, barriers, and facilitators). Multivariable logistic regression modeling was performed to assess the factors associated with willingness to use daily oral PrEP in each country. Results: From a total sample of 43,687 individuals, 44.54% of MSM (19,457/43,687) were eligible and completed the Web-based survey—Brazil: 58.42% (11,367/19,457), Mexico: 30.50% (5934/19,457), and Peru: 11.08% (2156/19,457); median age was 28 years (interquartile range: 24-34), and almost half lived in large urban cities. Most participants were recruited on Grindr (69%, 13,349/19,457). Almost 20% (3862/19,352) had never tested for HIV, and condomless receptive anal sex was reported by 40% (7755/19,326) in the previous 6 months. Whereas 67.51% (13,110/19,376) would be eligible for PrEP, only 9.80% (1858/18,959) of participants had high HIV risk perception. PrEP awareness was reported by 64.92% (12,592/19,396); this was lower in Peru (46.60%, 1002/2156). Overall, willingness to use PrEP was reported by 64.23% (12,498/19,457); it was highest in Mexico (70%, 4158/5934) and lowest in Peru (58%, 1241/2156). In multivariate regression models adjusted for age, schooling, and income in each country, willingness to use PrEP was positively associated with PrEP awareness and PrEP facilitators (eg, free PrEP and HIV testing) and negatively associated with behavioral (eg, concerned by daily pill regimen) and belief barriers (eg, sexual partners may expect condomless sex). Conclusions: In this first cross-country, Web-based survey in Latin America, willingness to use PrEP was found to be high and directly related to PrEP awareness. Interventions to increase awareness and PrEP knowledge about safety and efficacy are crucial to increase PrEP demand. This study provides important information to support the implementation of PrEP in Brazil, Mexico, and Peru.

  • The PrEP4Love website (montage). Source: The Authors / Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    #PrEP4Love: An Evaluation of a Sex-Positive HIV Prevention Campaign


    Background: Pre-exposure prophylaxis (PrEP) is an effective but underutilized method for preventing HIV transmission in communities vulnerable to HIV. Public health campaigns aimed at increasing PrEP awareness and access have less evaluation data. Objective: The aim of this study was to evaluate Chicago’s PrEP campaign, PrEP4Love (P4L), a campaign that uses health equity and sex-positivity approaches for information dissemination. Methods: P4L launched in February 2016 and remains an active campaign to date. The analysis period for this paper was from the launch date in February 2016 through May 15, 2016. Our analysis reviews the Web-based reach of the campaign through views on social media platforms (Facebook and Instagram), smart ads, or ads served to individuals across a variety of Web platforms based on their demographics and browsing history, and P4L website clicks. Results: In total, 40,913,560 unique views were generated across various social media platforms. A total of 24,548 users clicked on P4L ads and 32,223,987 views were received from smart ads. The 3 most clicked on ads were STD Signs & Symptoms—More Information on STD Symptoms, HIV & AIDS Prevention, and HIV Prevention Medication. An additional 6,970,127 views were gained through Facebook and another 1,719,446 views through Instagram. There was an average of 182 clicks per day on the P4L website. Conclusions: This is the first study investigating public responses to a health equity and sex-positive social marketing campaign for PrEP. Overall, the campaign reached millions of individuals. More studies of PrEP social marketing are needed to evaluate the relationship of targeted public health campaigns on stigma and to guide future PrEP promotion strategies.

  • Source: Flickr; Copyright: Guille Locke; URL:; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: Multistate Analysis


    Background: Overuse and misuse of prescription opioids have become significant public health burdens in the United States. About 11.5 million people are estimated to have misused prescription opioids for nonmedical purposes in 2016. This has led to a significant number of drug overdose deaths in the United States. Previous studies have examined spatiotemporal clusters of opioid misuse, but they have been restricted to circular shaped regions. Objective: The goal of this study was to identify spatiotemporal hot spots of opioid users and opioid prescription claims using Medicare data. Methods: We examined spatiotemporal clusters with significantly higher number of beneficiaries and rate of prescriptions for opioids using Medicare payment data from the Centers for Medicare & Medicaid Services. We used network scan statistics to detect significant clusters with arbitrary shapes, the Kulldorff scan statistic to examine the significant clusters for each year (2013, 2014, and 2015) and an expectation-based version to examine the significant clusters relative to past years. Regression analysis was used to characterize the demographics of the counties that are a part of any significant cluster, and data mining techniques were used to discover the specialties of the anomalous providers. Results: We examined anomalous spatial clusters with respect to opioid prescription claims and beneficiary counts and found some common patterns across states: the counties in the most anomalous clusters were fairly stable in 2014 and 2015, but they have shrunk from 2013. In Virginia, a higher percentage of African Americans in a county lower the odds of the county being anomalous in terms of opioid beneficiary counts to about 0.96 in 2015. For opioid prescription claim counts, the odds were 0.92. This pattern was consistent across the 3 states and across the 3 years. A higher number of people in the county with access to Medicaid increased the odds of the county being in the anomalous cluster to 1.16 in both types of counts in Virginia. A higher number of people with access to direct purchase of insurance plans decreased the odds of a county being in an anomalous cluster to 0.85. The expectation-based scan statistic, which captures change over time, revealed different clusters than the Kulldorff statistic. Providers with an unusually high number of opioid beneficiaries and opioid claims include specialties such as physician’s assistant, nurse practitioner, and family practice. Conclusions: Our analysis of the Medicare claims data provides characteristics of the counties and provider specialties that have higher odds of being anomalous. The empirical analysis identifies highly refined spatial hot spots that are likely to encounter prescription opioid misuse and overdose. The methodology is generic and can be applied to monitor providers and their prescription behaviors in regions that are at a high risk of abuse.

  • Source: Freepik; Copyright:; URL:; License: Licensed by JMIR.

    Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study


    Background: Although dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources. Objective: This study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts. Methods: We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation. Results: Our results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods. Conclusions: The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets.

  • Source: iStock by Getty Images; Copyright: Jikaboom; URL:; License: Licensed by the authors.

    “Where There’s Smoke, There’s Fire”: A Content Analysis of Print and Web-Based News Media Reporting of the Philip Morris–Funded Foundation for a...


    Background: In September 2017, the Foundation for a Smoke-Free World (FSFW), a not-for-profit organization with a core purpose “to accelerate global efforts to reduce deaths and harm from smoking” was launched. However, the legitimacy of the FSFW’s vision has been questioned by experts in tobacco control because of the organization’s only funding partner, Philip Morris International (PMI). Objective: This study aimed to examine the response to the FSFW in Web-based and print news media to understand how the FSFW and its funding partner, PMI, were framed. Methods: News articles published within a 6-month period after the FSFW was announced were downloaded via Google News and Factiva and coded for topic, framing argument, slant, mention of tobacco control policies, and direct quotes or position statements. Results: A total of 124 news articles were analyzed. The news coverage of the FSFW was framed by 6 key arguments. Over half of the news articles presented a framing argument in opposition to the FSFW (64/124, 51.6%). A further 20.2% (25/124) of articles framed the FSFW positively and 28.2% of articles (35/124) presented a neutral debate with no primary slant. The FSFW was presented as not credible because of the funding link to PMI in 29.0% (36/124) of articles and as a tactic to mislead and undermine effective tobacco control measures in 11.3% of articles (14/124). However, 12.9% of articles (16/124) argued that the FSFW or PMI is part of the solution to reducing the impact of tobacco use. Evidence-based tobacco control policies were mentioned positively in 66.9% (83/124) of news articles and 9.6% (12/124) of articles presented tobacco control policies negatively. Conclusions: The Web-based and print news media reporting of the formation of the FSFW and its mission and vision has primarily been framed by doubt, skepticism, and disapproval.

  • Source: Pexels; Copyright: Krisztina Papp; URL:; License: Licensed by JMIR.

    Programmatic Mapping: Providing Evidence for High Impact HIV Prevention Programs for Female Sex Workers


    Programmatic mapping (PM) is a rapid and efficient mechanism to develop size estimates of key populations including female sex workers (FSWs) and geolocate them at physical locations in a systematic and scientific manner. At the macro level, this information forms the basis for allocating program resources, setting performance targets, and assess coverage. At a micro level, PM data provide specific information on hot spots, estimates of FSWs at those spots, and hot spot typology and days and times of operation, all of which provides targeted service delivery strategies. This information can provide a reliable platform to plan HIV prevention and treatment services to considerable scale and intensity. Above all, the entire PM process requires deep involvement of FSWs, which increases community ownership of the data and can lead to an increased uptake of services. Despite a few limitations, the approach is versatile and can be used in varied country contexts to generate important information about sex work and its dynamics. In this paper, we describe experiences and lessons learned from using evidence generated from PM of FSWs in multiple countries to develop HIV prevention programs at scale.

  • Source: Flickr; Copyright: Alexander Northey; URL:; License: Creative Commons Attribution + Noncommercial (CC-BY-NC).

    Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic


    Background: To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues. Objective: The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed. Methods: Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category. Results: A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category. Conclusions: Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research.

  • Source: Freepik; Copyright: Freepik; URL:; License: Licensed by JMIR.

    Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection


    Background: Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs. Objective: The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature. Methods: We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses. Results: Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal. Conclusions: Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance.

  • Source: Pexels; Copyright: Negative Space; URL:; License: Licensed by JMIR.

    Google Trends in Infodemiology and Infoveillance: Methodology Framework


    Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.

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  • EMPHNET: A resource for improving public health in the Eastern Mediterranean Region

    Date Submitted: Jun 10, 2019

    Open Peer Review Period: Jun 19, 2019 - Jul 3, 2019

    The Eastern Mediterranean Region (EMR) countries face many challenges in improving population health and in progressing towards the Sustainable Development Goals (SDGs). This paper aim to describe the...

    The Eastern Mediterranean Region (EMR) countries face many challenges in improving population health and in progressing towards the Sustainable Development Goals (SDGs). This paper aim to describe the approach, tools and achievements of the Eastern Mediterranean Public Health Network (EMPHNET) towards strengthening health systems in the EMR and progressing toward the sustainable development targets. EMPHNET is a non-profit organization that has worked to support EMR countries in strengthening their public health systems since its establishment in 2009. EMPHNET invests in building workforce capacity in applied epidemiology by supporting Field Epidemiology Training Programs (FETPs) in more than ten countries in the EMR, while ensuring country ownership of these programs. EMPHNET established the Global Health Development (GHD) as an asset to maximize support towards positive change by seizing opportunities for backing up countries in influencing SDG progress. As an implementing arm to EMPHNET, GHD aligns its strategies with national policies and directions. GHD/ EMPHNET work at the regional, national and sub-national levels and tailor solutions to local context. Over the past years, EMPHNET succeeded in partnering with over 13 countries and provided technical assistance to leverage country efforts and maximize resource use. EMPHNET’s Center of Excellence for Applied Epidemiology focuses on building capacity in population health and applied epidemiology. EMPHNET supports countries in delivering effective public health programs by building capacity and conducting research to prevent and control emerging and reemerging diseases, vaccine-preventable diseases, and non-communicable diseases. The commitment to the region together with the increased trust and assertion from the countries helped GHD/ EMPHNET build a strong portfolio made possible with interconnected effort that continues to nurture and foster merit for better health to people living in the EMR.