MALARIA VECTOR CONTROL HISTORY AND CHALLENGES IN ETHIOPIA: MINI REVIEW

In Ethiopia, malaria has been an ancient and historical vector-borne disease. Over 75% of the land surface is malarious, and around 60% of the population is malaria at risk with varying intensity. In 1966, concerted eradication efforts began with the motivation being to eradicate malaria from Ethiopia by 1980. And indoor residual spray (IRS) with Dichloro Diphenyl Trichloroethane (DDT) and trained local staff on vector control methods was the main focus. However, malaria prevalence increased, and the major epidemic occurs every 5-8 years. It manifests a significant problem to the economic and social development of the country. The government directed a malaria control action plan under the objectives of Roll Back Malaria (RBM), which guided prevention and control activities. Between 2005 to 2018, Ethiopia distributed around 100 million long-lasting insecticide nets (LLINs), and 93.7% of the at-risk population were protected up to 2018 by the IRS. The malaria prevalence rate in 2011 was 1.3 whereas, in 2015, it decreased to 0.5. Ethiopia plans to achieve nationwide malaria elimination by 2030. Malaria is still public health threaten disease & accounts for 30% of the overall disability-adjusted life years lost in Ethiopia. And vector control interventions effectiveness has a serious obstacle due to resistance development to all available insecticide and the flexibility of Anopheles mosquito species behavior. Focuses on the history of malaria eradication and control and the controversial issues in malaria elimination. Requires thoughtful consideration of all risks, benefits, and challenges

Knowledge and perception towards net care and repair practice in Ethiopia.

BACKGROUND: Long-lasting insecticidal nets (LLINs) are a key malaria control intervention. Although LLINs are presumed to be effective for 3 years under field or programmatic conditions, net care and repair approaches by users influence the physical and chemical durability. Understanding how knowledge, perception and practices influence net care and repair practices could guide the development of targeted behavioural change communication interventions related to net care and repair in Ethiopia and elsewhere. METHODS: This population-based, household survey was conducted in four regions of Ethiopia [Amhara, Oromia, Tigray, Southern Nations Nationalities Peoples Region (SNNPR)] in June 2015. A total of 1839 households were selected using multi-stage sampling procedures. The household respondents were the heads of households. A questionnaire was administered and the data were captured electronically. STATA software version 12 was used to analyse the data. Survey commands were used to account for the multi-stage sampling approach. Household descriptive statistics related to characteristics and levels of knowledge and perception on net care and repair are presented. Ordinal logistic regression was used to identify factors associated with net care and repair perceptions. RESULTS: Less than a quarter of the respondents (22.3%: 95% CI 20.4-24.3%) reported adequate knowledge of net care and repair; 24.6% (95% CI 22.7-26.5%) of the respondents reported receiving information on net care and repair in the previous 6 months. Thirty-five per cent of the respondents (35.1%: 95% CI 32.9-37.4%) reported positive perceptions towards net care and repair. Respondents with adequate knowledge on net care and repair (AOR 1.58: 95% CI 1.2-2.02), and those who discussed net care and repair with their family (AOR 1.47: 95% CI 1.14-1.89) had higher odds of having positive perceptions towards net care and repair. CONCLUSIONS: The low level of reported knowledge on net care and repair, as well as the low level of reported positive perception towards net repair need to be addressed. Targeted behavioural change communication campaigns could be used to target specific groups; increased net care and repair would lead to longer lasting nets

Reemergence of yellow fever in Ethiopia after 50 years, 2013: epidemiological and entomological investigations

Abstract Background Yellow Fever (YF) is a viral hemorrhagic disease transmitted by aedes mosquito species. Approximately, 200,000 cases and 30,000 deaths occur worldwide every year. In Ethiopia, the last outbreak was reported in 1966 with 2200 cases and 450 deaths. A number of cases with deaths from unknown febrile illness reported from South Ari district starting from November 2012. This investigation was conducted to identify the causative agent, source of the outbreak and recommend appropriate interventions. Methods Medical records were reviewed and Patients and clinicians involved in managing the case were interviewed. Descriptive data analysis was done by time, person and place. Serum samples were collected for serological analysis it was done using Enzyme-linked Immunosorbent Assay for initial screening and confirmatory tests were done using Plaque Reduction and Neutralization Test. Breteau and container indices were used for the entomological investigation to determine the risk of epidemic. Results A total of 141 Suspected YF cases with 43 deaths (CFR = 30.5%) were reported from November 2012 to October 2013 from South Omo Zone. All age groups were affected (mean 27.5, Range 1–75 Years). Of the total cases, 85.1% cases had jaundice and 56.7% cases had fever. Seven of the 21 samples were IgM positive for YF virus. Aedes bromeliae and Aedes aegypti were identified as responsible vectors of YF in affected area. The Breteau indices of Arkisha and Aykamer Kebeles were 44.4% and 33.3%, whereas the container indices were 12.9% and 22.2%, respectively. Conclusion The investigation revealed that YF outbreak was reemerged after 50 years in Ethiopia. Vaccination should be given for the affected and neighboring districts and Case based surveillance should be initiated to detect every case

Fixed effects from the mixed-effects logistic regression for hookworm infection.

<p>This model used data from the 413 students between the ages of 5 and 18 years, and with complete data for the included variables. Of these 413 students, 71 (17%) were positive for hookworm. The variance (in the logit scale) of the random intercept for school in this model was 1.17.</p

Fixed effects from the mixed-effects logistic regression for with stunting.

<p>This model used data from the 3,686 students between the ages of 5 and 18 years, and with complete data for the included variables. Of these 3,686 students, 1,019 (28%) were stunted. The variance (in the logit scale) of the random intercept for school in this model was 1.16.</p

Fixed effects from the mixed-effects logistic regression for anemia.

<p>This model used data from the 3,672 students between the ages of 5 and 18 years, and with complete data for the included variables. Of these 3,672 students, 850 (23%) were anemic. The variance (in the logit scale) of the random intercept for school in this model was 0.487.</p

Fixed effects from the mixed-effects logistic regression for wasting.

<p>This model used data from the 3,686 students between the ages of 5 and 18 years, and with complete data for the included variables. Of these 3,686 students, 531 (14%) were stunted. The variance (in the logit scale) of the random intercept for school in this model was 0.381.</p

The school and woreda locations within SNNPR (A), and the position of SNNPR within Ethiopia (B).

<p>This figure was made using administrative boundaries from the GADM database of Global Administrative Areas [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005948#pntd.0005948.ref020" target="_blank">20</a>].</p

The distributions of children’s anemia, stunting, and wasting classifications according to their hookworm infection intensities.

<p>Bar heights represent the percentages of each anemia, stunting, and wasting severity, in children with each hookworm infection intensity class. The number of children with each hookworm infection intensity-anemia/stunting/wasting class combination is given by <i>n</i>. Light, moderate, and heavy hookworm infections were defined as 1–1,999, 2,000–3,999, and 4,000 EPG or more, respectively [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005948#pntd.0005948.ref022" target="_blank">22</a>]. Mild stunting and wasting were defined as zHFA and zBMI values between -2 and -3, respectively, and severe stunting and wasting values below -3. Anemia classes were calculated using blood hemoglobin concentrations, taking into account age, gender, and elevation [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005948#pntd.0005948.ref023" target="_blank">23</a>].</p