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      The African Field Epidemiology Network-Networking for effective field epidemiology capacity building and service delivery

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          Abstract

          Networks are a catalyst for promoting common goals and objectives of their membership. Public Health networks in Africa are crucial, because of the severe resource limitations that nations face in dealing with priority public health problems. For a long time, networks have existed on the continent and globally, but many of these are disease-specific with a narrow scope. The African Field Epidemiology Network (AFENET) is a public health network established in 2005 as a non-profit networking alliance of Field Epidemiology and Laboratory Training Programs (FELTPs) and Field Epidemiology Training Programs (FETPs) in Africa. AFENET is dedicated to helping ministries of health in Africa build strong, effective and sustainable programs and capacity to improve public health systems by partnering with global public health experts. The Network's goal is to strengthen field epidemiology and public health laboratory capacity to contribute effectively to addressing epidemics and other major public health problems in Africa. AFENET currently networks 12 FELTPs and FETPs in sub-Saharan Africa with operations in 20 countries. AFENET has a unique tripartite working relationship with government technocrats from human health and animal sectors, academicians from partner universities, and development partners, presenting the Network with a distinct vantage point. Through the Network, African nations are making strides in strengthening their health systems. Members are able to: leverage resources to support field epidemiology and public health laboratory training and service delivery notably in the area of outbreak investigation and response as well as disease surveillance; by-pass government bureaucracies that often hinder and frustrate development partners; and consolidate efforts of different partners channelled through the FELTPs by networking graduates through alumni associations and calling on them to offer technical support in various public health capacities as the need arises. AFENET presents a bridging platform between governments and the private sector, allowing for continuity of health interventions at the national and regional level while offering free exit and entry for existing and new partners respectively. AFENET has established itself as a versatile networking model that is highly responsive to members’ needs. Based on the successes recorded in AFENET's first 5 years, we envision that the Network's membership will continue to expand as new training programs are established. The lessons learned will be useful in initiating new programs and building sustainability frameworks for FETPs and FELTPs in Africa. AFENET will continue to play a role in coordinating, advocacy, and building capacity for epidemic disease preparedness and response.

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          Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology

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            Ebola Hemorrhagic Fever Associated with Novel Virus Strain, Uganda, 2007–2008

            Ebola hemorrhagic fever (EHF) is a severe, often fatal disease of humans and nonhuman primates caused by a single-stranded RNA virus belonging to the Filoviridae family. The virus was first isolated in 1976 after hemorrhagic fever outbreaks in Zaire and Sudan that resulted in >250 deaths ( 1 , 2 ). Before the outbreak described here, 4 distinct species of Ebola virus were known: Zaire ebolavirus, Sudan ebolavirus, Côte d’Ivoire ebolavirus, and Reston ebolavirus. Although the reservoirs for the virus and mechanisms of transmission have not been fully elucidated, a recent study reported that 4% of bats from Gabon were positive for Zaire Ebola virus immunoglobulin (Ig) G ( 3 ). Initial human infection presumably occurs when humans are exposed to infected body fluids of the animal reservoir or intermediate host. Thereafter, person-to-person transmission occurs through direct contact with body fluids of an infected person ( 1 , 2 , 4 ). After an incubation period of 1–21 days ( 1 , 2 , 5 ), an acute febrile illness develops in infected persons; the disease is characterized by the sudden onset of fever, chills, headache, and myalgia, followed later by rash, sore throat, nausea, vomiting, diarrhea, and abdominal pain ( 1 , 2 , 5 ). Approximately half of infected persons manifest hemorrhagic signs, e.g., bleeding from the nasal cavity, passing of blood in the urine, and/or gastrointestinal and vaginal bleeding ( 5 , 6 ). The case-fatality rate (CFR) for Zaire Ebola virus and Sudan Ebola virus (SEBOV) infections varies from 53% to 90% ( 7 ). The first EHF outbreak in Uganda occurred in 2000 and affected Gulu, Masindi, and Mbarara districts, with a total of 425 case-patients and 224 deaths (CFR 53%). The outbreak was caused by the Sudan ebolavirus species ( 8 ). On November 5, 2007, the Uganda Ministry of Health (UMOH) received a report of the deaths of 20 persons in Bundibugyo district, western Uganda. However, UMOH had received initial reports of 2 suspected cases of a febrile diarrheal illness on August 2, 2007. A UMOH team investigated these 2 cases, but the findings were not conclusive because of inadequate in-country laboratory capacity. In the second report, ill persons had an acute febrile hemorrhagic illness, and tests conducted by the US Centers for Disease Control and Prevention (Atlanta, GA, USA) confirmed the illness as EHF on November 29, 2007. Methods Outbreak Site Bundibugyo district is located in western Uganda (Figure 1). Approximately 60% of the district is covered by the Rwenzori Mountains and Semliki National Park and Game Reserve, which have a broad range of wildlife, including primates. The district has a population of 253,493 and a population density of 108 persons/km2 ( 9 ), 1 hospital, and 26 health centers. The main economic activities are cocoa farming, fishing, and tourism. Hunting of wild animals is common among settlements near the national park. Figure 1 Major towns in Uganda. Districts and major towns share the same names. Green shading, national parks; red lines, main roads; blue shading, perennial lakes. Epidemiologic Activities During November 29, 2007–February 20, 2008, village health teams conducted active searches in the communities through daily door-to-door visits in their respective villages while investigation teams of local and international experts reviewed case notes at health facilities and verified suspected cases reported by the village health teams. Cases were categorized as suspected EHF, probable EHF, and confirmed EHF (Table 1).The UMOH developed working case definitions and established an enhanced surveillance system for identifying suspected case-patients in the affected district and in contiguous districts. Table 1 Case definitions for epidemiologic investigation of EHF outbreak, Bundibugyo district, Uganda, 2007–2008* Classification Definition Suspected case Sudden onset of fever and at least 4 of the following symptoms in a resident of or visitor to the affected subcounties in Bundibugyo district: vomiting, diarrhea, abdominal pain, conjunctivitis, skin rash, unexplained bleeding from any body part, muscle pain, intense fatigue, difficulty swallowing, difficulty breathing, hiccups, or headache since August 1, 2007, OR sudden onset of fever in any person who had had contact with a person with suspected, probable, or confirmed EHF, OR sudden death in a person in the community without any other explanation. Probable case Suspected EHF in any person (dead or alive) with at least 3 of the following symptoms; vomiting, diarrhea, or unexplained bleeding from any site, conjunctivitis, or skin rash; AND with an epidemiologic link to a person with probable or confirmed EHF, OR either no specimen collected for laboratory testing or a negative laboratory result in a specimen collected 0–3 days after onset of symptoms in a person with suspected EHF. Confirmed case Laboratory confirmation of infection by isolation of virus from any body fluid or tissue, OR detection of viral antigen in any body fluid or tissue by antigen-detection ELISA, reverse-transcription–PCR, or immunohistochemistry, OR demonstration of serum Ebola virus–specific IgG antibodies by ELISA, with or without IgM, in any person with suspected or probable EHF. Contact A person who had slept in the same household and/or had direct physical contact with a person (dead or alive) with suspected, probable, or confirmed EHF and/or had been exposed to an infected person or to an infected person’s secretions, excretions, tissues, or linens within 3 weeks after that person’s onset of illness. *EHF, Ebola hemorrhagic fever; Ig, immunoglobulin. Investigative Activities Investigation teams actively searched for case-patients in health facilities and communities and retrospectively reviewed hospital records. Increased awareness of the disease through public education campaigns and the media facilitated reporting of suspected cases to health authorities. Village health teams assisted in case identification and reports and contact follow-up at the community level. Clinical and epidemiologic data were systematically collected from persons with suspected, probable, and confirmed cases. Proxies (usually parents, spouses, or adult siblings) were interviewed for information about case-patients who had died before an interview could be conducted. Persons with suspected cases identified in the community were transported by a mobile ambulatory team to designated isolation facilities. A triage desk was established in the outpatient departments of each health facility in the district to screen for suspected cases and notify the surveillance system. Clinical specimens, including blood, were collected for laboratory testing from all persons with suspected EHF. All contacts were entered into, and follow-up schedules were drawn by using, the Field Information Management System database ( 10 ). Laboratory Methods Five milliliters of blood was collected from all persons with suspected EHF at least 4 days after symptom onset when possible. Specimens initially were transported to the Uganda Virus Research Institute (UVRI) and then transferred to US Centers for Disease Control and Prevention for laboratory analysis. However, on December 4, a laboratory was set up at UVRI, and subsequent specimens were tested there, as described by Towner et al. ( 11 ). Virus isolation was not attempted at UVRI. Data Analysis We used Epi Info software version 3.4 ( 12 ) to create a database into which information from individual case investigation forms was entered and updated daily. The age and sex population structure and projections for Bundibugyo district were obtained from the population and housing census 2002 data ( 9 ), and attack rates were computed by using the district population projections. Current geographic maps were obtained from the World Health Organization Health Mapper Mapping Software, version 4.2 ( 13 ). We tabulated risk factors by case status and calculated odds ratios (ORs) using as the reference group persons with suspected EHF who had negative test results. To control for confounding and to test for effect modification, we entered variables with p values 54% of cases occurring in Kasitu subcounty (Table 2). Table 2 Geographic distribution of persons with Ebola hemorrhagic fever, Bundibugyo district, Uganda, 2007–2008 Subcounty Population No. cases No. deaths Case-fatality rate, % Attack rate* Kasitu 33,968 63 18 29 185 Bundibungyo town council 17,590 25 8 32 142 Bubukwanga 23,398 17 7 41 73 Busaru 40,547 8 3 38 20 Harugali 29,162 1 1 100 3 Karugutu 19,384 1 1 100 5 Bubandi 22,063 1 1 100 5 Other subcounties 81,879 0 0 0 0 Total 267,991 116 39 34 43 *Per 100,000 population. The overall attack rate in the district was 43 cases/100,000 population. The highest rate occurred in Kasitu subcounty, followed by the rate in Bundibugyo town council (within Bundibugyo district (Table 2). By sex, the attack rate for men was higher than that for women (64 vs. 47/100,000 population). By age group, the attack rate was higher for persons 41–50 years of age than for persons 51–60 years of age (146 vs. 122/100,000 population). Analysis of Possible Risk Factors All case-patients were investigated for exposures within 3 weeks before development of symptoms. To better determine risk factors for the disease, we conducted a bivariate analysis using the 76 non–case-patients as the reference group to separately assess persons with probable and confirmed EHF (Table 3). Before the institution of strict isolation policies, visitors had direct contact with patients through shaking of hands, hugging, or contact with potentially infected surfaces. Patients with confirmed EHF (OR 8.71, 95% confidence interval [CI] 3.03–26.30) and patients with probable and confirmed cases combined (OR 2.56, 95% CI 1.35–4.85) were significantly more likely to have visited sick persons or to have visited the hospital 3 weeks before becoming sick. Consultation with a traditional healer within 3 weeks before illness onset was not significantly associated with having EHF (OR 0.16, 95% CI 0.01–1.15) (Table 3). Table 3 Bivariate analysis of risk factors for Ebola viral hemorrhagic fever, Bundibugyo district, Uganda, 2007–2008* Potential risk factor Probable case, n = 74 Confirmed case, n = 42 Probable/confirmed case, n = 116 Noncase, 
n = 76 (ref.) Hospitalized/visited hospital, no. (%) 38 (51.40) 36 (85.70) 74 (63.79) 31 (40.80) OR (95% CI) 1.5 (0.8–3.1) 8.7 (3.0–26.3) 2.6 (1.4–4.9) 1 p value 0.2 2× more often than during the SEBOV outbreak in Gulu in 2000 ( 5 ). The Bundibugyo ebolavirus outbreak (CFR 34%) caused a lower proportion of deaths than did the SEBOV outbreak in Gulu (CFR 53%) ( 5 ). Similarly, the CFR for the Bundibugyo outbreak was lower than that reported from other outbreaks outside Uganda ( 1 , 2 ), which indicates either that the new virus strain may be less virulent or that improved interventions led to more timely case identification and better case management. During the Bundibugyo outbreak, vital functions were sustained by supportive treatment, including administering antipyretics, monitoring fluid balance, and giving antibacterial or antimalarial drugs for concurrent bacterial or protozoal infections. Oral rehydration and oral administration of antibacterial drugs were encouraged for all patients, provided they were conscious and not vomiting; otherwise, fluids and antibiotics were administered intravenously. To streamline case detection, village health teams and ambulance teams were trained early to conduct active case search and referral. Strict isolation measures included the establishment of triage in all health facilities, designation of isolation wards, training of healthcare workers in adherence to standard precautions, barrier nursing, supervised burial, and ambulance services. During the Bundibugyo outbreak, case-patients were more likely than non–case-patients to have participated in funeral rituals. The practice exposes contacts to infectious body fluids that have been associated with acquiring EHF ( 4 ). Fourteen health workers were infected during the Bundibugyo outbreak before strict isolation procedures were initiated. During the SEBOV outbreak in Gulu, 64% of health workers were infected after isolation wards were established ( 14 ). The outbreak response had 2 challenges and at least 1 limitation. Investigations at the local and national levels were conducted in a timely manner, but the lack of capacity for laboratory confirmation delayed initial outbreak confirmation and therefore the initiation of an appropriate response. Because hunting in the national parks is illegal, attempts to link the Bundibugyo outbreak to wildlife were futile because none of the families investigated admitted to participating in hunting. The use of hospitalized patients as a comparison group was economical, but their illnesses could have been related to risk factors for Bundibugyo ebolavirus infection, hence rendering those risk factors undetectable. We recommend that an index of suspicion for Ebola viruses (and Marburg virus) be maintained for clusters of cases with fever of sudden onset, intense fatigue, abdominal upsets, and evidence of person-to-person transmission. In concert with timely initiation of active case searching, use of ambulance and burial teams, and strict adherence to patient isolation practices, an index of suspicion should ensure mitigation of the identified risk factors.
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              Taking Stock of Networks and Organizations: A Multilevel Perspective

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                Author and article information

                Journal
                Pan Afr Med J
                Pan Afr Med J
                PAMJ
                The Pan African Medical Journal
                The African Field Epidemiology Network
                1937-8688
                14 December 2011
                2011
                : 10
                : Suppl 1
                : 3
                Affiliations
                [1 ]African Field Epidemiology Network, Kampala, Uganda
                [2 ]Zimbabwe Field Epidemiology Training Program, Department of Community Medicine, Harare, Zimbabwe
                [3 ]Center for Global Health, Centers for Disease Control and Prevention, Atlanta, USA
                Author notes
                [& ]Corresponding author: Gitta Nakacubo Sheba, African Field Epidemiology Network, Kampala, Uganda
                Article
                PAMJ-SUPP-10-1-03
                3266672
                22359691
                6c3d024c-48c9-4f1d-aac9-642e5a15c7a1
                © Sheba Nakacubo Gitta et al.

                The Pan African Medical Journal - ISSN 1937-8688. This is an Open Access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 December 2011
                : 07 December 2011
                Categories
                Research

                Medicine
                field epidemiology,network,african field epidemiology network,public health workforce
                Medicine
                field epidemiology, network, african field epidemiology network, public health workforce

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