Assessment of the utility of a symptom-based algorithm for identifying febrile patients for malaria diagnostic testing in Senegal

Thwing, Julie; Ba, Fatou; Diaby, Alou; Diédhiou, Younouss; Sylla, A.; Sall, G; Diouf, M. L.; Guèye, Alioune Badara; Gaye, Seynabou; Ndiop, Médoune; Cissé, Moustapha; Ndiaye, Daouda; Bâ, Mady

doi:10.1186/s12936-017-1750-y

Cited by 10 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Malaria occurrence varies between weeks and years is Sussundenga. This pattern was also reported in Chimoio, Mozambique and other regions in the world [23,28,29]. This study identi ed three malaria clusters of malaria occurrence for under ve age categories.…”

Section: Discussionsupporting

confidence: 74%

See 1 more Smart Citation

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Ferrão

Earland

Novela³

et al. 2020

Preprint

View full text Add to dashboard Cite

Malaria is one of the leading causes of morbidity and mortality in Mozambique with the 5th highest prevalence in the world. Sussundenga district in Manica Province has documented high P. falciparum incidence at the local rural health center (RHC). Sussundenga is a rural district located along the Mozambique, Zimbabwe border. P. falciparum transmission in this area is unique as there are differing control policies on each side of the international border. The study objective was to analyze the P. falciparum temporal variation and model its pattern in Sussundenga district, Mozambique. Data from weekly epidemiological bulletins (BES) were collected from 2015 to 2019, which records confirmed P. falciparum cases from health facilities. These data categorize confirmed cases into two age groups: under 5 years and 5-years and older. P. falciparum incidence and temporal variation were calculated. Temporal clusters were identified using dendrograms. A time-series analysis was carried out. For temporal modeling a Box-Jenkins method was used applying an autoregressive integrated moving average (ARIMA). Over the study period, 372,498 cases of P. falciparum were recorded in Sussundenga, 177,957 from under 5 years (47.5 %) and 194,541 (52.2 %) from 5 years and older. There were weekly and yearly variations in incidence overall (p < 0.001). There was a decreasing trend in cases for those under 5 years while there was a slight increase in those 5 and older. For those under 5, week 2 of the year had the highest number of cases (1170 Sd 34 ) while week 35 had the fewest (354 Sd 17.8). For those 5-years and older, cases also peak at week 2 (1295 Sd 245) with week 31 having the fewest cases (341 Sd 193.5). The findings indicate that cases are decreasing in those under 5 years and are increasing slightly in those 5 years and over. The P. falciparum case occurrence presents a weekly temporal pattern peaking during the wet season. Based on the temporal distribution and using ARIMA modelling, more efficient strategies that target this seasonality can be implemented to reduce the overall malaria burden in Sussundenga District, and regionally.

show abstract

Section: Discussionsupporting

confidence: 74%

“…In Chimoio a similar study indicated and ARIMA (2,1,0) (2,1,1) model with the goodness of t of 72.5 % [12]. Similar studies were carried out in Ghana, Afghanistan, India, Ghana, Zambia, South Africa and Senegal and Nigeria using monthly data with comparable results [7,8,9,10,26,29].…”

Section: Discussionsupporting

confidence: 55%

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Ferrão

Earland

Novela³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Malaria testing of all febrile patients is a critical, early step of malaria case-management [35] which if not systematically performed leads to missed malaria diagnosis both in high and low transmission settings [51][52][53][54]. Low malaria testing rates of fevers have been commonly reported as one of the weakest components of outpatient malaria case-management despite the testing readiness at health facilities [10,16,22,26,55,56].…”

Section: Discussionmentioning

confidence: 99%

Trends in health workers’ compliance with outpatient malaria case-management guidelines across malaria epidemiological zones in Kenya, 2010–2016

Amboko

Stepniewska

Macharia

et al. 2020

Malar J

View full text Add to dashboard Cite

Background Health workers' compliance with outpatient malaria case-management guidelines has been improving, specifically regarding the universal testing of suspected cases and the use of artemisinin-based combination therapy (ACT) only for positive results (i.e., ‘test and treat’). Whether the improvements in compliance with ‘test and treat’ guidelines are consistent across different malaria endemicity areas has not been examined. Methods Data from 11 national, cross-sectional, outpatient malaria case-management surveys undertaken in Kenya from 2010 to 2016 were analysed. Four primary indicators (i.e., ‘test and treat’) and eight secondary indicators of artemether-lumefantrine (AL) dosing, dispensing, and counselling were measured. Mixed logistic regression models were used to analyse the annual trends in compliance with the indicators across the different malaria endemicity areas (i.e., from highest to lowest risk being lake endemic, coast endemic, highland epidemic, semi-arid seasonal transmission, and low risk). Results Compliance with all four ‘test and treat’ indicators significantly increased in the area with the highest malaria risk (i.e., lake endemic) as follows: testing of febrile patients (OR = 1.71 annually; 95% CI = 1.51–1.93), AL treatment for test-positive patients (OR = 1.56; 95% CI = 1.26–1.92), no anti-malarial for test-negative patients (OR = 2.04; 95% CI = 1.65–2.54), and composite ‘test and treat’ compliance (OR = 1.80; 95% CI = 1.61–2.01). In the low risk areas, only compliance with test-negative results significantly increased (OR = 2.27; 95% CI = 1.61–3.19) while testing of febrile patients showed declining trends (OR = 0.89; 95% CI = 0.79–1.01). Administration of the first AL dose at the facility significantly increased in the areas of lake endemic (OR = 2.33; 95% CI = 1.76–3.10), coast endemic (OR = 5.02; 95% CI = 2.77–9.09) and semi-arid seasonal transmission (OR = 1.44; 95% CI = 1.02–2.04). In areas of the lowest risk of transmission and highland epidemic zone, none of the AL dosing, dispensing, and counselling tasks significantly changed over time. Conclusions There is variability in health workers' compliance with outpatient malaria case-management guidelines across different malaria-risk areas in Kenya. Major improvements in areas of the highest risk have not been seen in low-risk areas. Interventions to improve practices should be targeted geographically.

show abstract

“…An evaluation conducted in 2013-2014 found that this approach missed an unacceptably high proportion of malaria cases, particularly among children younger than 5 years. 26 As a result, in June 2014, the NMCP issued guidance to test all febrile children younger than 5 years throughout the year, expanding to include all febrile patients older than 5 years during rainy season in 2015, and all febrile patients regardless of age or transmission season in mid-2017. Although the crude testing proportion appears to have plateaued since 2015 indicating no increase in testing, the steady increase in the corrected testing proportion suggests that, in fact, testing practices have continued to improve.…”

Section: Discussionmentioning

confidence: 99%

How Far Are We from Reaching Universal Malaria Testing of All Fever Cases?

Pluciński

Guilavogui²,

Camara³

et al. 2018

The American Journal of Tropical Medicine and Hygiene

Self Cite

View full text Add to dashboard Cite

Universal malaria diagnostic testing of all fever cases is the first step in correct malaria case management. However, monitoring adherence to universal testing is complicated by unreliable recording and reporting of the true number of fever cases. We searched the literature to obtain gold-standard estimates for the proportion of patients attending outpatient clinics in sub-Saharan Africa with malarial and non-malarial febrile illness. To correct for differences in malaria transmission, we calculated the proportion of patients with fever after excluding confirmed malaria cases. Next, we analyzed routine data from Guinea and Senegal to calculate the proportion of outpatients tested after exclusion of confirmed malaria cases from the numerator and denominator. From 12 health facility surveys in sub-Saharan Africa with gold-standard fever screening, the median proportion of febrile illness among outpatients after exclusion of confirmed malaria fevers was 57% (range: 46-80%). Analysis of routine data after exclusion of confirmed malaria cases demonstrated much lower testing proportions of 23% (Guinea) and 13% (Senegal). There was substantial spatial and temporal heterogeneity in this testing proportion, and testing in Senegal was correlated with malaria season. Given the evidence from gold-standard surveys that at least 50% of non-malaria consultations in sub-Saharan Africa are for febrile illness, it appears that a substantial proportion of patients with fever are not tested for malaria in health facilities when considering routine data. Tracking the proportion of patients tested for malaria after exclusion of the confirmed malaria cases could allow programs to make inferences about malaria testing practices using routine data.

show abstract

Assessment of the utility of a symptom-based algorithm for identifying febrile patients for malaria diagnostic testing in Senegal

Cited by 10 publications

References 15 publications

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Trends in health workers’ compliance with outpatient malaria case-management guidelines across malaria epidemiological zones in Kenya, 2010–2016

How Far Are We from Reaching Universal Malaria Testing of All Fever Cases?

Contact Info

Product

Resources

About