Am. J. Trop. Med. Hyg., 102(4), 2020, pp. 811–820doi:10.4269/ajtmh.19-0600Copyright © 2020 by The American Society of Tropical Medicine and Hygiene

A Robust Estimator of Malaria Incidence from Routine Health Facility Data

Julie Thwing,1 Alioune Camara,2 Baltazar Candrinho,3 Rose Zulliger,1,4 James Colborn,5 John Painter,1,6

and Mateusz M. Plucinski1,6*1MalariaBranch,Divisionof ParasiticDiseases andMalaria, Center forGlobal Health,Centers for DiseaseControl andPrevention, Atlanta,Georgia;

2National Malaria Control Program, Ministry of Health, Conakry, Guinea; 3National Malaria Control Program, Ministry of Health, Maputo,Mozambique; 4U.S. President’s Malaria Initiative, Centers for Disease Control and Prevention, Maputo, Mozambique; 5Clinton Health Access

Initiative, Maputo, Mozambique; 6U.S. President’s Malaria Initiative, Centers for Disease Control and Prevention, Atlanta, Georgia

Abstract. Routine incidentmalaria casedata havebecomeapillar ofmalaria surveillance in sub-SaharanAfrica. Thesedataprovidegranular, timely information to trackmalaria burden.However, incidencedata are sensitive to changes in careseeking rates, rates of testingof suspect cases, and reporting completeness. Basedonaset of assumptions,wederived asimple algebraic formula to convert crude incidence rates to a corrected estimation of incidence, adjusting for biases invariable and suboptimal rates of care seeking, testing of suspect cases, and reporting completeness. We applied thecorrection to routine incidencedata fromGuineaandMozambique, andaggregate data for sub-SaharanAfrican countriesfrom theWorldMalaria Report.We calculated continent-wide needs formalaria tests and treatments, assuming universaltesting but current care seeking rates. Countries in southern and eastern Africa reporting recent increases in malariaincidence generally had lower overall corrected incidence than countries in Central and West Africa. Under current careseeking rates, the unmet need formalaria testswas estimated tobe 160million (M) (interquartile range [IQR]: 139–188) andfor malaria treatments to be 37 M (IQR: 29–51). Maps of corrected incidence were more consistent with maps ofcommunity survey prevalence thanwas crude incidence inGuinea andMozambique. Crudemalaria incidence rates needtobe interpreted in the context of suboptimal testing andcare seeking rates,which vary over space and time. Adjusting forthese factors can provide insight into the spatiotemporal trends of malaria burden.

INTRODUCTION

Since 2000, with the increasing availability of funds andnew tools, many sub-Saharan African countries have dra-matically scaled upmalaria control interventions, resulting inhigh coverage with vector control tools such as insecticide-treated nets, increasing access to high-quality diagnosis atpoint of care with rapid diagnostic tests (RDTs), and highlyeffective treatment with artemisinin-based combinationtherapy (ACT).1 As a result, parasite prevalence (asmeasuredby large-scale, cross-sectional, community-based surveys)has fallen inmany countries, in some cases to levels so low thatcontinuing to follow trends using parasite prevalence is chal-lenging. There is increasing interest in using alternate metricsformeasuring intervention effectiveness and assessingmalariaburden, particularly by tracking the incidence of confirmedmalaria infection, reported by health facilities through routinedata collection systems.Accurately measuring malaria incidence is not straightfor-

ward. It requires adequate health facility (and/or communityhealth worker) infrastructure to enable access to the health-care sector, conditions that foster care seeking behavior,adequate supplies and training to ensure testing of patientswith febrile illness, documentation of patients with positivetests, and accurate reporting into a functional database.Currently, most countries annually report the aggregatenumber of incident cases detected through routine systems tothe World Health Organization (WHO). However, with the ex-ception of a few low-incidence countries where routine dataare used, WHO uses a mathematical model to estimate theincidence for most sub-Saharan African countries.1 TheWHO

approach models incidence based on prevalence data fromhousehold surveys, metadata such as climate data, and ad-ministrative data on intervention coverage. Like other meth-ods using statistical approaches such as time series analysis,thesemethods aredifficult to reproduce, aredependent on theavailability of additional metadata, require advanced statisti-cal skills, and are in general not readily suited for routine usebyendemic country programs.National Malaria Control Programs (NMCPs) increasingly

track and report the number of confirmed malaria cases, typi-cally calculating the incidence of confirmed cases per 1,000population by dividing the number of reported confirmed casesby the population of each catchment area and multiplying by1,000. With the increase in malaria donor support and sub-sequent scale-up of malaria control interventions, there is oftengreat pressure to demonstrate a decrease in incidence. How-ever, incidence of confirmed cases is an indicator that needs tobe interpreted with care because of several sources of potentialbias. It is highly sensitive to a number of factors, includingsuboptimal care seeking, testing practices, and completenessof reporting, which are not uniform in space and time, but all ofwhich tend to decrease reported incidence. Completeness ofdata, both in terms of proportion of structures reporting and interms of inclusion of all cases diagnosed, plays a major role inthe number of cases reported. As the number of health facilitiesand community health workers reporting increases, the numberofconfirmedcases reported is likely to increase, andasefforts toimprove reporting quality ensure more complete reporting ofpatients seen at individual facilities, this is also likely to increasenumbers of cases reported. As ministries of health make effortsto improve access to and quality of care, and care seeking in-creases, many malaria cases that would have gone undetectedare now diagnosed, treated, and counted. Finally, as RDTs areincreasingly accepted and available and testing patients withfebrile illness at point of care is increasingly normalized, thenumber of patients with suspected malaria who receive testing

*Address correspondence to Mateusz M. Plucinski, Malaria Branch,Division of Parasitic Diseases and Malaria, Center for Global Health,Centers for DiseaseControl and Prevention, 1600Clifton Rd., Atlanta,GA 30329-4018. E-mail: mplucinski@cdc.gov

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and a confirmed diagnosis also increases. Many NMCPs thatpreviously reported decreases in incidence are now seeing re-ported increases.1Manyof thesearealsomakinggreat efforts toimprove health services, including data quality and complete-ness of reporting, access to and quality of care, and increasingtesting of suspected malaria cases, complicating efforts to de-termine whether the observed increases in incidence are real.Amore accurate and less biasedmeasure of incidence is of

crucial importance to NMCPs and their partners. It wouldallow for characterization of short- and long-term trends inmalaria transmission, which in turnwould permit programs totrack the impact of interventions, and to identify areas thatrequire more focused interventions. In addition, a more ac-curate measure of the number of incident malaria cases iscritical for decisions around procurement of malaria com-modities, particularly in the context of striving to reach uni-versal testing and treatment.As routine health facility data improve in quality and avail-

ability in endemic countries, there arises the potential to capi-talize on those data to provide a robust estimator of malariaincidence based on routine data. Here, we introduce a methodfor adjusting routinely reportedmalaria incidence to account forbiases in healthcare seeking and testing rates. We apply themethod to routine data from Guinea and Mozambique as casestudies in the potential utility of the indicator. Finally, we applythe method to aggregate country data on incident cases fromthe WHO World Malaria Report to obtain a more accurate es-timate for the number of incident malaria cases seen by healthsystems and the subsequent needs for RDTs and ACTs.

METHODS

We developed a simple algebraic method to calculate acorrected incidence that could be used by NMCPs and theirpartners, without a background in modeling or statistical soft-ware, to interpret routinely reported surveillance data, adjustingfor testing practices, care seeking behavior, and reportingcompleteness. The overall objective was to develop a robustestimator of incidence that would allow comparison of in-cidence of symptomatic malaria infection between districts orregions and evaluation of trends over time.Derivation of estimator. We developed a framework to es-

timate community incidence of symptomatic (febrile) malariainfection, based on four routinely reported data elements: pop-ulation, numberof all-causeacutecareconsultations, numberofpatients tested for malaria, and number of patients with con-firmedmalaria infection. In agivencommunity over thecourseofa year, a certain number of individuals will develop febrile ill-nesses, and a certain proportion of these febrile illnesses will beassociated with symptomatic malaria infection (Figure 1A). Thisapproach is designed to estimate the approximate number ofthese febrile malaria episodes. By contrast, the health surveil-lance system typically reports population of the catchment area,all-cause acute care consultations, patients tested, andpatientswho test positive (Figure 1B). If case management was ideal (allpeople seeking care with fever and malaria infection received adiagnostic test formalaria), it would be relatively straightforwardto estimate the community incidence of febrile malaria infectionby adjusting for care seeking (Figure 1C).Unfortunately, data from both health facility–based and

community surveys have demonstrated that a substantial

FIGURE 1. Schematic of process of inferring truemalaria burden from routine data, showing the true universe ofmalaria cases (A), the subset thatare reported through data information systems (B), what would be reported under ideal casemanagement practices (C), andwhat is reported underactual case management practices (D).

812 THWING AND OTHERS

proportion of those seeking care for febrile illness does notreceive a diagnostic test. Of patients with febrile illness, somewill have malaria infection and some will not. Among thosewithmalaria infection, somewill be tested (andmost likely testpositive) and others will not be tested. Among febrile patientswithout malaria infection, some will be tested (and most willtest negative) and some will not (Figure 1D). For the purposesof this analysis, we assume that those without febrile illnessare not tested.From the routine surveillance data, the number of patients

who were tested and received a positive test result (A), thenumber of patients who were tested and received a negativetest result (B), and the number of patients not tested (C) arereadily reported or calculated and are available. Among thosenot tested are those with febrile illness and malaria infection(A*), thosewith febrile illness andwithoutmalaria infection (B*),and those without febrile illness (D). Whereas A, B, and C areknown, A*, B*, and D are unknown. These unknown variablescan be imputed following two assumptions related to testingpractices (Box 1).To translate the results reported by the surveillance system

to reflect community incidence (and thereby account for thosenot reflected in the data, whether through lack of care seekingor through care seeking, i.e., not being counted and reported),two further assumptions are needed: the incidence of non-malaria fevers in the population and how the test positivity rateamong those who seek care for fever compares with the testpositivity rate among those who do not seek care or are oth-erwise not reflected in the data (Box 1).To solve for the unknowns and understand how to translate

this incidence to community febrile malaria infection in-cidence, we need to make a number of assumptions, as de-tailed previously. Although the values for these assumptionshave been drawn from the literature, and are described in thefollowing paragraphs, if country-specific values for these as-sumptions become available, it would be appropriate for acountry to use those values in its calculations.For parameterizing assumption 1,we relied on high-quality

health facility surveys in which all patients are screened for

febrile illness and those with febrile illness are tested formalaria (N. Bayoh et al., unpublished data).2–14 β in thisscenario is calculated as follows: (number of patients whotest negative)/(number of patients who test negative +number of patients with nonfebrile illness). Although thenumber of published health facility surveys in which this wascarried out is limited, a literature search found that β wasapproximately 0.73 (n = 14, range: 0.56–0.94, interquartilerange [IQR]: 0.65–0.84) for children aged < 5 years and 0.57(n = 10, range: 0.42–0.75, IQR: 0.49–0.61) for patients aged³ 5 years2 (Table 1; Supplemental Figure S1). Alternately, ifsuch a health facility survey exists for a given country,βcouldbe calculated and used for corrected incidence for thatcountry.For assumption 2, data to calculate α, the ratio of the test

positivity rate among patientswhomhealthcareworkers donot test to the test positivity rate among those whomhealthcare workers test, are available from the health fa-cility surveys in which exit interviews are conducted, testresults among those tested by the healthcare provider arerecorded, and tests are offered to patients identified ashaving a febrile illness but not tested. Supplemental TableS1 provides values of α calculated from health facilitysurveys in three countries,13–15 although country-specificvalues could be used if there has been a recent health fa-cility survey in which malaria testing is offered to all pa-tients with febrile illness at an exit interview. In mostsettings, healthcare providers do not test all patients withfebrile illness but factor in clinical judgment to decidewhom to test. If clinical judgment was perfect (no patientswith malaria infection untested), α would be zero. Thehigher the α, the higher the test positivity rate among pa-tients with febrile illness not tested compared to the testpositivity rate among patients with febrile illness tested. Ifα = 100%, the test positivity rate among patients with fe-brile illness not tested would be equal to the test positivityrate among patients with febrile illness tested.Few published data are available to estimate γ, the es-

timated annual incidence per person of nonmalaria fevers

BOX 1Description of model variables, parameters, and assumptions

Variable/parameter Definition Source Assumptions

A Patients who tested positive Routine data Assumption 1 The proportion of nonmalariaconsults that are for febrileillness (β) is constant

A* Patients with febrile illness and malariainfection, but not tested

Estimated using Equation (2)

B Patients who tested negative Routine dataB* Patients with febrile illness and nomalaria

infection, but not testedEstimated using Equation (1)

C Patients not tested Routine data Assumption 2 The ratio of the test positivity rateamong febrile individuals nottested for malaria is proportionalto the test positivity rate amongfebrile individuals tested formalaria (at a constant ratio α)

D Patients without febrile illness Estimated using Equation (3)α Ratio of test positivity rate amongpatients

with febrile illness not tested to thosetested Ap

Ap+Bp=A

A+B

Estimated from health facilitysurveys with systematic feverscreening and re-testing

β Proportion of fever among nonmalariaconsults B+Bp=B+Bp +D

Estimated from health facilitysurveys with systematic feverscreening and re-testing

Assumption 3 There is a constant mean number ofnonmalaria fevers per person peryear (γ)

γ Mean number of nonmalaria fevers perperson per year

Estimated from population-basedlongitudinal cohorts

Assumption 4 The test positivity rate in those withfebrile malaria infection who donot seek care is proportional tothe test positivity rate in thosewho seek care

λ Malaria-attributable fraction among test-positive patients

Estimated by comparing antigenconcentration in febrile andafebrile patients

MALARIA INCIDENCE FROM ROUTINE DATA 813

per year (assumption 3). In Dielmo and Ndiop, Senegal, apopulation-based longitudinal cohort was followed weeklywith fever screening and malaria testing since 1990,allowing reporting of annual incidence of malarial andnonmalarial fevers.16 Of note, the investigators definedmalarial fevers as those with parasite density above theestimated pyrogenic threshold, which was calculated an-nually; thus, some fevers with low-densitymalaria infectionwere categorized as nonmalarial fever. Whereas the annualincidence of symptomatic malaria infection per persondecreased from 2.76 in 1990 to 0.05 in 2012 among chil-dren, and from 0.39 in 1990 to 0.05 in 2012 among adults,nonmalaria fever incidence was relatively more stable. Al-though nonmalaria fever incidence per person fluctuated(up to 3.67 among children and up to 2.83 among adults)with other infectious disease trends, it remained consis-tently above 2.0 for children and 1.0 for adults. To ensurethat periods of high incidence of nonmalarial febrile illnessdo not artificially elevate the estimates, we used γ = 2 forchildren younger than 5 years and γ = 1 for those aged 5years and older.To parameterize assumption 4, we used the results of a

meta-analysis of Malaria Indicator Surveys and De-mographic and Health Surveys from 2003 to 2015 to esti-mate the test positivity rate among patients who do not seekcare compared with the test positivity rate among patientswho seek care.17 During these household surveys, care-givers of children younger than 6–59 months were askedwhether the child had fever in the last 2 weeks and whethercare was sought with a trained provider. These childrenwere also all tested for malaria as part of the survey. Foreach survey, RDT positivity among children for whom carewas not sought was plotted against RDT positivity amongchildren for whom care was sought. The ratio of the testpositivity rate among children with reported febrile illnessfor whom care was not sought to the test positivity amongchildren with reported febrile illness for whom care wassought was equal to or slightly greater than 1. Data were notavailable for those aged 5 years and older. For the purposesof this analysis, we assumed that the test positivity rate ofindividuals with febrile illness who are not represented in thereported surveillance data (whether through not seekingcare or through data incompleteness) is equal to the testpositivity rate of those represented in the surveillance data.The first two equations (Equations (1) and (2)) were solved to

provide estimates for B*, A*, and D:

β¼ BþBp

BþBp þDBp ¼

C�A1�ββ

1� ββ

þ αA

AþB

.�1�α

AAþB

�þ 1

, (1)

Ap

Ap þBp¼α

AAþB

Ap ¼α

AAþB�

1� αA

AþB

� Bp, (2)

Ap þBp þD¼C D¼C�Ap �Bp: (3)

To adjust for febrile illnesseswithmalaria infection that are notcaptured in the surveillance data, we scale by the incidenceper person of nonmalarial fever (as this is assumed to be in-dependent of malaria transmission intensity).To adjust for incident parasitemia/antigenemia in febrile

patients, the number of febrile illness of nonmalaria etiology isa sum of patients without malaria infection and a certain pro-portion of febrile test-positive patients ([1−λ] [A + A*]), where λis the malaria-attributable fraction among test-positive pa-tients. We modeled λ to be 1 when the test positivity rate was0, decreasing linearly to a minimum ðλminÞ of 0.75 (range:0.5–0.95) when the test positivity rate reached 1.18

λ¼ 1þ AþAp

AþAp þBþBpðλmin � 1Þ: (4)

The number of episodes of febrile malaria illnesses amongpatients seeking care imputed from the surveillance data isA+A*. Assuming the test positivity rate among those reflectedin the data is similar to those not reflected in the data (as-sumption 4), the incidence of febrile illness with malaria in-fection at the community level can be estimated bymultiplyingthe incidence of malaria per person as calculated from thesurveillance data by the correction factor:

AþAp

population� γBþBpþð1�λÞðAþApÞ

pop

¼γAþAp

BþBpþð1�λÞðAþApÞ:

(5)

Incidenceper 1,000 can be calculated bymultiplying by 1,000.For all results presented, we report malaria incidence

counting all malaria feverswithmalaria infection, regardless ofthe attribution of fever etiology.Case study 1: Estimating RDT and ACT needs for sub-

SaharanAfrica.Weabstracted the followingdataelements fromroutine health information data submitted by malaria-endemic

TABLE 1Estimates for parameters for correction of routine health facility data to account for under-testing, derived fromempirical distributions of parametersfrom health facility surveys with systematic fever screening and retesting

Median (Interquartile range)

Age < 5 years Age ³ 5 years All ages

Proportion of fever among nonmalariaconsults (β)

0.73 (0.65–0.84) 0.57 (0.49–0.61) 0.59 (0.54–0.66)

Ratio of malaria test positivity in untestedvs. tested febrile patients (α)

– – 0.48 (0.42–0.55)

Number of febrile illnesses ofnonmalaria etiology calculatedfrom surveillance data

B + B* + (1−λ) (A + A*)

Incidence per person of nonmalarialfebrile illness who sought care andwere reported in surveillance data

(B + B* + [1−λ] [A + A*])/population

Adjustment factor, based onincidence of nonmalarial fever

γðB+Bp=populationÞ

814 THWING AND OTHERS

sub-Saharan African countries to the 2016 World MalariaReport: population at risk, total number of all-cause consul-tations, number of patients tested for malaria, and confirmedmalaria cases. We then applied Equations (1)–(3) to estimatethe expected number of fever cases and true malaria casesseeking care. To estimate the full community-level burden, weusedEquation 5 to calculate the expectednumber of fever andtrue incident malaria fevers, assuming universal access andhealthcare seeking.We calculated the median and IQR for the α and β param-

eters from our literature search for studies for which the pa-rameters could be estimated (Table 1). Because the WorldMalaria Report data are not stratified by age, we used the all-agesestimates.Weusedaconservative rate of 1.5 nonmalariafevers per year (range: 1–2).Wecompared the relative ranking ofmalaria-endemic sub-

Saharan African countries when considering the reported(crude) malaria incidence and the corrected malaria in-cidence. We calculated the expected number of malariatests and treatments needed to attain universal testing offever cases and treatment of all confirmed cases under twoscenarios: 1) current rates of care seeking (using total patientencounters reported by the countries, Equations (1)–(3) and2) universal access and complete care seeking, representingthe scenario in which all community cases of fever are testedand treated if positive for malaria (additionally applyingEquation (5)). We estimated the needs using the medianvalues of α and β. We calculated lower and upper bounds onthe estimates by applying the first and third quartiles of theempirical distributions of these parameters (Table 1). Bysubtracting the number of tested and confirmed cases thatwere reported from the estimated needs for tests and treat-ments, we estimated country-level gaps formalaria tests andtreatments.Case study 2: Analyzing spatiotemporal trends in

GuineaandMozambique.Weexported data from theGuineamalaria-specific Routine Malaria Information System and theMozambique integrated Health Management InformationSystem on the number of all-cause consults, patients testedfor malaria, and confirmed malaria cases. The time period in-cluded in the analysis was 2012–2017 for Guinea and2017–2018 for Mozambique. Data from Mozambique werestratified for children aged < 5 years and children and adultsaged ³ 5 years, whereas data from Guinea were aggregatedacross all ages.We applied Equations (1)–(5) to estimate the corrected an-

nualized malaria incidence by month by district/province. Weused themedian forα andβ for all ages forGuinea (Table 1) butusedcountry-specificestimates forα (0.443 in <5 years, 0.603in ³ 5 years) and β (0.783 in < 5 years, 0.544 in ³ 5 years) forMozambique because of the availability of a recent healthfacility surveywith systematic fever screening and retesting ofpatients during exit interviews.13

Because the parameters and assumptions are independentof seasonality, assuming relatively stable rates of nonmalarialfever over the course of a year, the results were not adjustedfor high versus low transmission period.We plotted the crude and corrected incidence for the most

recent year by district/province for each country. For illustra-tive examples, we plotted the monthly crude and correctedincidence over time for Labe Region in Guinea and ZambeziaProvince in Mozambique. Both represent zones that in recent

years have received intensive malaria case management in-terventions, including supervision and training of healthcareworkers and expansions of the community healthcare workerprogram.

RESULTS

Case study 1. The median country-level corrected in-cidence for sub-Saharan Africa was 901 cases/1,000 pop-ulation, ranging from 16/1,000 (Comoros) to 2,107/1,000(Burkina Faso) (Figure 2). This was several fold higher thanthe reported crude incidence, which ranged from 2/1,000(Comoros) to 462/1,000 (Burundi), with a median of 79/1,000.Whereas the crude incidence was generally highest in

countries along the southern and western branches of the RiftValley (Mozambique, Malawi, Zambia, Burundi, and Rwanda),with a second cluster of high incidence in West Africa, thecorrected incidence map showed different patterns. Thecorrected incidence estimates showed very high incidence incentral Africa, as well as throughout West Africa. The RiftValley countries that ranked highly in the crude reported in-cidence generally fell in the corrected incidence rankings(Supplemental Figure S2), and these countries typically had alower ratio of corrected tocrude incidence, suggestinga largerproportion of true cases being detected and reported(Supplemental Figure S3).We estimated that of the 471million (M) reported outpatient

consults, 329 M (70%) were for patients with fever, repre-senting the true need for malaria tests under current rates ofcare seeking (Table 2, Supplemental Figure S4). Countriesreported testing only 170 M patients, thus representing a gapof 160 M (IQR: 139–188) malaria tests across sub-SaharanAfrica. We estimated that 126 M (IQR: 117–139) of these testswere for febrile patients with malaria (Table 2, SupplementalFigure S5). Against a total of 89 M reported cases, this left a37M (IQR: 29–51) gap inmalaria treatments under current careseeking rates. When considering the true, community-levelincidence of fever and malaria cases, we estimated a totalneed for 2.283 billion (IQR: 1.4–3.1) malaria tests and 871 M(IQR: 515–1,339) malaria treatments per year across thecontinent.Case study 2. In Guinea, the health district-level crude in-

cidence showed a different pattern than the corrected in-cidence (Figure 3A and B). The crude incidence was generallylower in the holoendemic southeast of the country, which ishighly forested and has the highest prevalence as measuredduring the most recent household survey19 (Figure 3C),compared with the Sahelian north of the country, whereprevalence is substantially lower. By contrast, the correctedincidence showed a pattern more consistent with the preva-lence data, with the lowest incidence in the north of thecountry. In Guinea’s Labe region, the crude incidence wasreported to be consistently increasing since 2014, at a rate of11% per year (Figure 4A). After correction, the incidenceshowed an overall annual decrease of −7.1% per year.In Mozambique, the crude incidence was highest in the

southern provinces (excluding Maputo) (Figure 3D). By con-trast, the corrected incidencewashighest in thenorth,with theexception of Inhambane Province (Figure 3D), more closelymatching data from the most recent household survey20

(Figure 3E).

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In Zambezia Province located in central Mozambique, thecrude and corrected incidence showed different trends overthe last 2 years. Whereas the crude incidence increased at anannual rate of 16% over that time period, the corrected in-cidence showed a −1.8% decrease (Figure 4B). The patternheld separately for children < 5 years and older children andadults (Supplemental Figure S6).

DISCUSSION

Our analysis of routinely reported health data show largeand variable discrepancies between crude (reported) in-cidence and our corrected incidence measure, which corre-sponds more closely with other metrics of malaria burden(e.g., prevalence). These discrepancies reinforce the notion

that crude incidence data should be interpreted in the contextof several key biases. Our method for addressing two impor-tant biases—uneven rates of access/care seeking and under-testing of fever cases—can provide NMCPs with a simplemethod for capitalizing on routine health system data formaking timely, granular, evidence-based decisions, whileminimizing the biases inherent in routine data.Thismethodcomesat a timewhen themalaria community is

moving away from periodic surveys toward real-time analysisof routine data. As the prevalence of malaria decreases, ren-dering parasite prevalence less useful as a metric of malariaburden, the incidence ofmalaria has been increasingly used tounderstand trends in burden, monitor effectiveness of inter-ventions, and target interventions. During the last decade, asministries of health have moved to increase access to health

TABLE 2Estimates for malaria tests and treatments under universal testing of fever policy after correction for under-testing and incomplete reporting asestimated from routine health information data submitted by sub-Saharan African countries to the World Malaria Report, 2016

Current care seeking rates Universal access scenario

Reported (M) True need (M), median (IQR) Gap (M), median (IQR) True need (M), median (IQR) Gap (M), median (IQR)

All-cause consults 471 – – – –

Malaria tests 170 329 (308–358) 160 (139–188) 2,173 (1,383–3,075) 2,002 (1,212–2,905)Malaria treatments 89 126 (117–139) 37 (29–51) 871 (515–1,339) 782 (426–1,251)IQR = interquartile range; M = million.

FIGURE 2. Crude (A) and corrected (B) malaria incidence rates, as reported and estimated from routine health information data submitted bycountries to the World Malaria Report, 2016. (C andD) show the lower and upper bounds of the corrected estimates. Note the difference in scalesbetween crude and corrected maps.

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care and malaria control programs have scaled up the use ofRDTs at point of care, reported incidence has been increasing,often in zones in which malaria control activities have mostintensified, leading to consternation among malaria controlprograms.Although there have beenmanymodel-based approaches

to understanding incidence of malaria, the approach re-ported here relies on an accessible, simple algebraic formulaapplied to routine health information system data, based onseveral simple assumptions to account for differences intesting and in care seeking or data reporting. This approachallows NMCP managers without a background in modelingor training to easily calculate a corrected incidence that ac-counts for difference in data completeness, care seeking,and testing, and allows them to better understand trends inmalaria incidence.The estimates presented here for corrected incidence

define any febrile patient with a positive malaria test as amalaria case. A certain proportion of these will have a coin-fection with another infectious agent, and for some of these,malaria might not be the etiologic agent of the current febrileillness. Accounting for themalaria attributable fractionwouldlikely result in estimates of malaria-attributable fever in-cidence more in line with the prevalence-to-incidencemodelresults typically reported by WHO. Nevertheless, pro-grammatically, inferring etiology of a fever is of secondaryimportance, and the more important indicator to follow for

programs is the corrected incidence of malaria-positivefevers. Because all malaria-positive fevers should be treatedto clear parasitemia, for the purposes of commodity quantifi-cation, programs should rely on the incidence of malaria-positive fevers. Moreover, because population-level inferenceof fever etiology is inherently noisy and imprecise, incidence ofmalaria-positive fever cases is a more appropriate indicator forprograms to track.There are inherent limitations and uncertainties tied to any

approach attempting to estimate community incidence fromroutine data. First, this approach assumes that the key dataelements—number of all-cause acute care consultations,number of patients tested formalaria, and number of patientswith confirmed malaria infection—are reported in an un-biased manner. Any systematic misclassification of these,for example, systematic underreporting of negative RDT re-sults or reporting of negative RDTs as positive, will impactthe validity of the model. Moreover, the model was param-eterized based on data available from a limited number ofstudies and from a limited number of countries. As such, theprecision, accuracy, and generalizability of the parameterestimates used here may be limited. More health facilitysurveys with rigorous fever screening from different settingswill add to the evidence baseandwill improve the precision ofthe methodology, as well as allow a proper assessment of itsuncertainties. Therefore, the incidence estimates presentedhere should be interpreted in the context of the uncertainties

FIGURE 3. Crude (A andD) and corrected (B and E) malaria incidence rates, as reported and estimated from routine health information data fromGuinea and Mozambique. (C and F) show the parasite prevalence in children younger than 5 years for the most recent household survey in eachcountry.19,20 Note the difference in axis scales between the crude and corrected incidence and difference in aggregation level for Guinea, whereincidence data are by health district and prevalence data are by region.

MALARIA INCIDENCE FROM ROUTINE DATA 817

of the key parameters, and the results of relative compari-sons (across time and space) are likely more robust than theabsolute estimates. Validation of the approach, for example,with incidence directly calculated from demographic andhealth surveillance cohort data and testing parameters cal-culated from health facility surveys, is recommended tocalibrate the model assumptions.Strengths of the approach outlined here include its sim-

plicity and transparency, as well as its flexibility in parametersbeing tailored to specific countries using available country-specific data. The second step of the adjustment (translatinghealth facility incidence to community incidence) is agnosticas to the reason for under-reporting, whether it is through lowaccess to health care or incomplete recording and reporting.As a result, longitudinal data with temporal differences inreporting and testing can still be analyzed for trends in in-cidence. Nevertheless, the validity of our approach is tied tothe validity of the assumptions. Although there is substantialevidence to suggest a high background rate of fever among

outpatients (assumption 1) and a similar rate of test positivityregardless of care seeking, at least for children aged < 5 years(assumption 4), more research is needed to expand the evi-dence base around expected rates of positivity among un-tested fever patients (assumption 2) and the annual rate ofnonmalaria fevers in the community (assumption 3).Our results underscore the importance of accounting for

biases in crude incidence. Faced with limited resources,NMCPs often have to choose high-priority areas for certaininterventions. This is particularly relevant in the context ofindoor residual spraying, where NMCPs often have the re-sources to spray only a small percentage of the country’sstructures and choosing the appropriate areas to havethe maximum impact on transmission is an important pro-grammatic decision. Here, our case studies fromGuinea andMozambique show that administrative areas that report lowcrude incidence and ostensibly are not good candidates fortargeted interventions might in actuality have higher in-cidence than areas that have higher reported incidence

FIGURE 4. Longitudinal trends in crude (black) and corrected (red) malaria incidence in Labe Region, Guinea, (A) and Zambezia Province,Mozambique (B). Note the difference in axis scales between the crude and corrected incidence. This figure appears in color at www.ajtmh.org.

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because of higher access to care and higher testing rates.Risk stratification in these countries changes substantiallywhen considering the corrected incidence rather than thecrude incidence, with the corrected incidence figures moreclosely matching prevalence patterns from householdsurveys.In addition to facilitating more accurate interpretation of

incidence data, this approach (particularly the first threeequations, using current levels of care seeking) may help im-prove the accuracy of RDT and ACT needs quantification.When RDT procurement is based on the historical use, if lessthan 50% of patients with febrile illness have historicallybenefitted from an RDT, needs will be underestimated, andcare providers will be unable to test all patients with febrileillness. ProcuringRDTs andACTsbased on the assumption ofuniversal testing of patients seeking care for febrile illness willnecessitate a substantial increase in ACT and RDT pro-curement across the continent. Current gaps in RDT and ACTneeds, even under current care seeking rates, are substantial,with estimates in the 37 M ACT and 160 M RDT range.When applied to national-level data reported to the WHO,

even using conservative assumptions, corrected incidencewas often an order of magnitude or more higher than re-ported incidence. However, this was not uniform. Countriesin which malaria control programs have rapidly scaled-upaccess to malaria case management or countries nearingelimination had corrected malaria incidence closer to re-ported incidence. Similarly, countries that have shown os-tensible increases in malaria incidence in recent years(particularly along the western and southern branches of theRift Valley), triggering worries of true increases in malariatransmission, generally also had a smaller ratio of correctedincidence to crude incidence, suggesting that the trends arelikely due at least in part to increasing healthcare access andbetter testing. Our estimates of the community burden ofmalaria suggest that Central and West Africa represent thehighest burden areas of the continent. Similarly, when ap-plied to longitudinal data from Mozambique and Guinea,regions that had increases in crude incidence in the contextof scale-up of case management interventions had de-creases in corrected incidence.There is strong evidence of heterogeneity in terms of how

well countries across the continent are capturing incidentmalaria cases, reflected in the large variance in the correctionfactor (Supplemental Figure S3). Although all countries arereporting fewer cases than estimated through our model, themagnitude of the variation in this under-detection and under-reporting points to substantial differences in the perfor-mance of malaria control programs related to care seeking,testing, and reporting completeness. In general, more ma-ture malaria control programs would be expected to detectand report a larger proportion of their true malaria burden,and the observed variability across the continent likely re-flects different stages of effectiveness of the malaria controlprograms.

CONCLUSION

Reporting of and decision-making based on malaria in-cidence is becoming increasingly important in this era ofmalaria control. Applying a simple algebraic formula, usingseveral simple assumptions, to routinely reported incidence

data accounts for inherent biases and renders it more usefulfor intervention targeting and monitoring progress, as well asestimating needs for RDTs and ACTs. Moreover, this simpletool can be used by individuals without a background inmodeling or access to sophisticated software. Although notan exact measure, these corrections allow a closer un-derstanding of the real magnitude and distribution of malariaburden and their trends over time.To develop more robust and country-specific values for the

assumptions, health facility surveys for malaria should in-corporate screening of all patients for fever and offer tests tothose who did not receive testing from health facility pro-viders. As malaria burden decreases and children aged < 5years are no longer at highest risk for parasite carriage,community-based surveys should also assess care seekingand parasite carriage among those aged 5 years and older.Finally, longitudinal, all-cause fever surveillance would helptranslate incidence measured at the health facility level to thecommunity. Although the values used for these assumptionsneed to be confirmed and refined, the conservative estimatesused here demonstrate that community-level incidence ofmalaria infection with fever is an order of magnitude higherthanwhat is reported through health facilities. However, manycountries are making much progress in improving access todiagnosis and treatment, and increases in reported incidencemust be interpreted in the light of increased care seeking,testing, and reporting.

Received August 14, 2019. Accepted for publication November 11,2019.

Published online December 12, 2019.

Note: Supplemental table and figures appear at www.ajtmh.org.

Acknowledgments: R. Z., J. P., and M. M. P. were supported by theU.S. President’s Malaria Initiative.

Disclaimer: The findings and conclusions in this report are those of theauthors and do not necessarily represent the official position of theCDC.

Disclosure: Excel tools for applying the robust estimator are freelyavailable at github.com/MateuszPlucinski/Corrected-Incidence.

Authors’ addresses: Julie Thwing, Malaria Branch, Division of Para-sitic Diseases and Malaria, Center for Global Health, Centers for Dis-ease Control and Prevention, Atlanta, GA, E-mail: fez3@cdc.gov.AliouneCamara, NationalMalariaControl Program,Ministry ofHealth,Conakry, Guinea, E-mail: aliounec@gmail.com. Baltazar Candrinho,National Malaria Control Program, Ministry of Health, Maputo,Mozambique, E-mail: candrinhobaltazar@gmail.com. Rose Zulliger,Malaria Branch, Division of Parasitic Diseases andMalaria, Center forGlobal Health, Centers for Disease Control and Prevention, Atlanta,GA, and U.S. President’s Malaria Initiative, Centers for Disease Con-trol and Prevention, Maputo, Mozambique, E-mail: ymr0@cdc.gov.James Colborn, Clinton Health Access Initiative, Maputo, Mozambi-que, E-mail: jcolborn.ic@clintonhealthaccess.org. John Painter andMateusz M. Plucinski, Malaria Branch, Division of Parasitic DiseasesandMalaria,Center forGlobal Health,Centers for DiseaseControl andPrevention, Atlanta, GA, and U.S. President’s Malaria Initiative, Cen-ters for Disease Control and Prevention, Atlanta, GA, E-mails: bzp3@cdc.gov and mplucinski@cdc.gov.

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