scholarly journals Malaria Morbidities Following Universal Coverage Campaign for Long-Lasting Insecticidal Nets: A Case Study in Ukerewe District, Northwestern Tanzania

2020 ◽  
Vol Volume 11 ◽  
pp. 53-60
Author(s):  
Anthony Kapesa ◽  
Namanya Basinda ◽  
Elias C. Nyanza ◽  
Joshua Monge ◽  
Sospatro E Ngallaba ◽  
...  
2016 ◽  
Vol 4 (2) ◽  
pp. 251-263 ◽  
Author(s):  
Shabbir Lalji ◽  
Jeremiah M Ngondi ◽  
Narjis G Thawer ◽  
Autman Tembo ◽  
Renata Mandike ◽  
...  

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Jorge A. H. Arroz ◽  
Baltazar Candrinho ◽  
Chandana Mendis ◽  
Melanie Lopez ◽  
Maria do Rosário O. Martins

Abstract Objective The aim is to compare the cost-effectiveness of two long-lasting insecticidal nets (LLINs) delivery models (standard vs. new) in universal coverage (UC) campaigns in rural Mozambique. Results The total financial cost of delivering LLINs was US$ 231,237.30 and US$ 174,790.14 in the intervention (302,648 LLINs were delivered) and control districts (219,613 LLINs were delivered), respectively. The average cost-effectiveness ratio (ACER) per LLIN delivered and ACER per household (HH) achieving UC was lower in the intervention districts. The incremental cost-effectiveness ratio (ICER) per LLIN and ICER per HH reaching UC were US$ 0.68 and US$ 2.24, respectively. Both incremental net benefit (for delivered LLIN and for HHs reaching UC) were positive (intervention deemed cost-effective). Overall, the newer delivery model was the more cost-effective intervention. However, the long-term sustainability of either delivery models is far from guaranteed in Mozambique’s current economic context.


2020 ◽  
Author(s):  
Erica A Wetzler ◽  
Jorge A.H. Arroz ◽  
Chulwoo Park ◽  
Marta Chande ◽  
Figueiredo Mussambala ◽  
...  

Abstract Background Malaria was the leading cause of post-neonatal deaths in Mozambique in 2017. The use of long-lasting insecticidal nets (LLINs) is recognized as one of the most effective ways to reduce malaria morbidity and mortality, especially in children. In 2015, Mozambique committed to the expansion of LLIN coverage nationwide, culminating in the first countrywide campaign in 2017, reaching 95% of registered households. Between 2012 and 2019, more than 34 million LLINs were distributed. No previous analyses have estimated changes in mortality attributable to the scale-up of LLINs, accounting for provincial differences in mortality rates and coverage of health interventions. Methods From 2012 to 2020, the population-based model NetCALC was used to predict provincial household LLIN coverage based upon the number of LLINs distributed annually. NetCALC also projected how many LLINs are needed to maintain universal coverage in 10 provinces from 2021 to 2025. Based upon the annual provincial coverage of LLINs, the Lives Saved Tool (LiST), a multi-cause mathematical model, estimated under-5 lives saved, and reductions in under-5 mortality attributable to LLIN expansion in 10 provinces of Mozambique between 2012 and 2020, and projected lives saved from 2021 to 2025 if universal coverage of LLINs is sustained. Results Results from the LiST models estimate that 64,470 child deaths were averted between 2012 and 2019. If currently planned quantities of LLINs are distributed in 2020, and universal coverage is maintained from 2021 to 2025, an additional 68,695 child deaths could be averted. From 2011 to 2020, the percent reduction in all-cause child mortality was 19.2%, from 114.5 per 1,000 to 93.2 per 1000 in the LLIN distribution model compared to 9.5% in the baseline model. If universal coverage continues through 2025, this reduction will be sustained. Conclusions LiST and NetCALC used together are useful in estimating lives saved and mortality in countries such as Mozambique where vital registration data to measure changes in mortality are not consistently available. Universal coverage of LLINs can save a substantial number of child lives and reduce child mortality in Mozambique but will require resource mobilization. Without continued investment, thousands of avoidable child deaths will occur.


2015 ◽  
Vol 6 (10) ◽  
pp. 849 ◽  
Author(s):  
Jackson Songa ◽  
Dorcus Wandera ◽  
Rose Ayugi ◽  
Ambrose Muthaura
Keyword(s):  

2019 ◽  
Vol 4 (Suppl 3) ◽  
pp. A20.1-A20
Author(s):  
Desire Habonimana ◽  
Gabriel Ndayisaba ◽  
Gideon Nimako

BackgroundThe use of long-lasting insecticidal nets (LLINs) for malaria prevention is a cost-effective intervention. WHO recommends universal coverage and use of LLINs. In lower- and middle-income countries, LLINs are provided free of charge but are either not used or misused. Our study sought to improve LLIN use in Kayange community of north-western Burundi by using a model for improvement (MFI).MethodsA one-group, pre/post-test study was conducted. LLIN weekly use was assessed for four weeks before intervention and for another four weeks after intervention. The study was conducted in 96 households. The intervention consisted of testing four different weekly small change actions by using the MFI.ResultsOf the 96 households, 83 households (87%) owned at least one LLIN. However, only 40 households (42%) owned at least one LLIN for every two people. After intervention, the number of LLINs used increased from 32 to 75 per cent (134% increase) and the number of persons (general population) sleeping under LLIN from 35 to 73 per cent (108% increase). The number of children under 5 years old sleeping under LLIN increased from 31 to 76 per cent (145% increase) and the number of pregnant women who slept under LLIN from 43 to 73 per cent (69% increase). Also, the averages of the number of nights in each week that the general population slept under LLIN increased from 2.13 to 5.11 (140% increase), children under 5 years old slept under LLIN from 1.68 to 4.78 (184% increase) and pregnant women slept under LLIN from 1.56 to 4.47 (186% increase).ConclusionOur intervention led to significant increase in all outcome indicators. This increase is the result of a combination of an enabling context and an effective implementation of an evidence-based quality improvement intervention. Small tests of change at the community level have the potential for achieving improved outcomes.


2012 ◽  
Vol 12 (S1) ◽  
Author(s):  
Hong Teck Chua ◽  
Julius Chee Ho Cheah
Keyword(s):  

2021 ◽  
Vol 13 (16) ◽  
pp. 3330
Author(s):  
Mingshan Duan ◽  
Jiangjiang Xia ◽  
Zhongwei Yan ◽  
Lei Han ◽  
Lejian Zhang ◽  
...  

Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance data of space-based satellites with universal coverage can be converted into a proxy field of RR. In this study, a convolutional neural network-based data-driven model is developed to convert the radiance data (infrared bands 07, 09, 13, 16, and 16–13) of Himawari-8 into the radar combined reflectivity factor (CREF). A weighted loss function is designed to solve the data imbalance problem due to the sparse convective pixels in the sample. The developed model demonstrates an overall reconstruction capability and performs well in terms of classification scores with 35 dBZ as the threshold. A five-channel input is more efficient in reconstructing the CREF than the commonly used one-channel input. In a case study of a convective event over North China in the summer using the test dataset, U-Net reproduces the location, shape and strength of the convective storm well. The present RR reconstruction technology based on deep learning and Himawari-8 radiance data is shown to be an efficient tool for producing high-resolution RR products, which are especially needed for regions without or with poor radar coverage.


2012 ◽  
Vol 206 (10) ◽  
pp. 1622-1629 ◽  
Author(s):  
Nicolas Moiroux ◽  
Marinely B. Gomez ◽  
Cédric Pennetier ◽  
Emmanuel Elanga ◽  
Armel Djènontin ◽  
...  

2012 ◽  
Vol 11 (1) ◽  
Author(s):  
Philippa A West ◽  
Natacha Protopopoff ◽  
Mark W Rowland ◽  
Matthew J Kirby ◽  
Richard M Oxborough ◽  
...  

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