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A novel approach for measuring the burden of uncomplicated Plasmodium falciparum malaria: application to data from Zambia.

Crowell V, Yukich JO, Briët OJ, Ross A, Smith TA - PLoS ONE (2013)

Bottom Line: The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management.Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa.The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.

ABSTRACT
Measurement of malaria burden is fraught with complexity, due to the natural history of the disease, delays in seeking treatment or failure of case management. Attempts to establish an appropriate case definition for a malaria episode has often resulted in ambiguities and challenges because of poor information about treatment seeking, patterns of infection, recurrence of fever and asymptomatic infection. While the primary reason for treating malaria is to reduce disease burden, the effects of treatment are generally ignored in estimates of the burden of malaria morbidity, which are usually presented in terms of numbers of clinical cases or episodes, with the main data sources being reports from health facilities and parasite prevalence surveys. The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management. Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa. This paper proposes a novel approach for estimating burden of disease based on the point prevalence of malaria attributable disease, or equivalently, the days with malaria fever in unit time. The technique makes use of data available from standard community surveys, analyses of fever patterns in malaria therapy patients, and data on recall bias. Application of this approach to data from Zambia for 2009-2010 gave an estimate of 2.6 (95% credible interval: 1.5-3.7) malaria attributable fever days per child-year. The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally. To obtain valid estimates of the overall malaria burden using these methods, there remains a need for surveys to include the whole range of ages of hosts in the population and for data on seasonality patterns in confirmed case series.

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Related in: MedlinePlus

Analysis of impact of simulated treatment on morbidity data.The plots were constructed by simulating effective treatments applied to malaria therapy data (see Methods) with fourteen-day morbidity reference periods, a: daily probability that a patient receives treatment that day, given that s/he has a fever that day. The simulations take into account under-reporting, as estimated from the Asembo data, and assume an equal probability of commencing treatment on each day of illness. The arrow corresponds to the estimate made from the Zambian MIS data. b: mean days with illness per report of fever; continuous line: days with fever including fever that was not reported (adjusting for under-reporting estimated from the Asembo data); dashed line: days with fever during intervals for which fever was reported (ignoring recall bias).
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pone-0057297-g005: Analysis of impact of simulated treatment on morbidity data.The plots were constructed by simulating effective treatments applied to malaria therapy data (see Methods) with fourteen-day morbidity reference periods, a: daily probability that a patient receives treatment that day, given that s/he has a fever that day. The simulations take into account under-reporting, as estimated from the Asembo data, and assume an equal probability of commencing treatment on each day of illness. The arrow corresponds to the estimate made from the Zambian MIS data. b: mean days with illness per report of fever; continuous line: days with fever including fever that was not reported (adjusting for under-reporting estimated from the Asembo data); dashed line: days with fever during intervals for which fever was reported (ignoring recall bias).

Mentions: Analyses of malaria therapy patients, with simulation of surveys and treatments (Figure 5), were used to estimate the numbers of days with illness associated with each recall of fever, conditional on the proportion of these (in the MIS) reporting treatment, . For a series of different daily probabilities of treatment, , simulated treatments were assigned stochastically to each day of fever in the malaria therapy dataset. The simulated treatments were assumed to be completely effective, so that in the simulations, the patient had no more fever once treatment was initiated. (In contrast, the true treatments administered to the malaria therapy patients, predominantly at sub-therapeutic doses, were ignored in this analysis). Simulated surveys were implemented in which the data for fourteen-day periods were summarized assuming a level of recall bias determined from the Asembo data.


A novel approach for measuring the burden of uncomplicated Plasmodium falciparum malaria: application to data from Zambia.

Crowell V, Yukich JO, Briët OJ, Ross A, Smith TA - PLoS ONE (2013)

Analysis of impact of simulated treatment on morbidity data.The plots were constructed by simulating effective treatments applied to malaria therapy data (see Methods) with fourteen-day morbidity reference periods, a: daily probability that a patient receives treatment that day, given that s/he has a fever that day. The simulations take into account under-reporting, as estimated from the Asembo data, and assume an equal probability of commencing treatment on each day of illness. The arrow corresponds to the estimate made from the Zambian MIS data. b: mean days with illness per report of fever; continuous line: days with fever including fever that was not reported (adjusting for under-reporting estimated from the Asembo data); dashed line: days with fever during intervals for which fever was reported (ignoring recall bias).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585385&req=5

pone-0057297-g005: Analysis of impact of simulated treatment on morbidity data.The plots were constructed by simulating effective treatments applied to malaria therapy data (see Methods) with fourteen-day morbidity reference periods, a: daily probability that a patient receives treatment that day, given that s/he has a fever that day. The simulations take into account under-reporting, as estimated from the Asembo data, and assume an equal probability of commencing treatment on each day of illness. The arrow corresponds to the estimate made from the Zambian MIS data. b: mean days with illness per report of fever; continuous line: days with fever including fever that was not reported (adjusting for under-reporting estimated from the Asembo data); dashed line: days with fever during intervals for which fever was reported (ignoring recall bias).
Mentions: Analyses of malaria therapy patients, with simulation of surveys and treatments (Figure 5), were used to estimate the numbers of days with illness associated with each recall of fever, conditional on the proportion of these (in the MIS) reporting treatment, . For a series of different daily probabilities of treatment, , simulated treatments were assigned stochastically to each day of fever in the malaria therapy dataset. The simulated treatments were assumed to be completely effective, so that in the simulations, the patient had no more fever once treatment was initiated. (In contrast, the true treatments administered to the malaria therapy patients, predominantly at sub-therapeutic doses, were ignored in this analysis). Simulated surveys were implemented in which the data for fourteen-day periods were summarized assuming a level of recall bias determined from the Asembo data.

Bottom Line: The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management.Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa.The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.

ABSTRACT
Measurement of malaria burden is fraught with complexity, due to the natural history of the disease, delays in seeking treatment or failure of case management. Attempts to establish an appropriate case definition for a malaria episode has often resulted in ambiguities and challenges because of poor information about treatment seeking, patterns of infection, recurrence of fever and asymptomatic infection. While the primary reason for treating malaria is to reduce disease burden, the effects of treatment are generally ignored in estimates of the burden of malaria morbidity, which are usually presented in terms of numbers of clinical cases or episodes, with the main data sources being reports from health facilities and parasite prevalence surveys. The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management. Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa. This paper proposes a novel approach for estimating burden of disease based on the point prevalence of malaria attributable disease, or equivalently, the days with malaria fever in unit time. The technique makes use of data available from standard community surveys, analyses of fever patterns in malaria therapy patients, and data on recall bias. Application of this approach to data from Zambia for 2009-2010 gave an estimate of 2.6 (95% credible interval: 1.5-3.7) malaria attributable fever days per child-year. The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally. To obtain valid estimates of the overall malaria burden using these methods, there remains a need for surveys to include the whole range of ages of hosts in the population and for data on seasonality patterns in confirmed case series.

Show MeSH
Related in: MedlinePlus