Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria

METHODOLOGY Open Access Imp o ing es ima es o he bu den o se e e acu e malnu i ion and p edic ions o caseload o p og ams ea ing se e e acu e malnu i ion: expe iences om Nige ia Assaye Bul i 1* , And é B iend 2,3 , Nancy M. Dale 2 , A jan De Wag 1 , Fa aja Chiwile 1 , S anley Chi ekwe 4 , Ch is Isokpunwu 5 and Ma k Mya 6 Abs ac Backg ound: The bu den o se e e acu e malnu i ion (SAM) is es ima ed using unadjus ed p e alence es ima es. SAM is an acu e condi ion and many child en wi h SAM will ei he eco e o die wi hin a ew weeks. Es ima ing SAM bu den using unadjus ed p e alence es ima es esul s in signi ican unde es ima ion. This has a nega i e impac on alloca ion o esou ces o he p e en ion and ea men o SAM. A simple me hod o adjus ing p e alence es ima es in ended o imp o e he accu acy o bu den es ima es and caseload p edic ions has been p oposed. This me hod employs an incidence co ec ion ac o . Applica ion o his me hod using he globally ecommended incidence co ec ion ac o has led o p og ams unde es ima ing bu den and caseload in some se ings. Me hods: A me hod o es ima ing a locally app op ia e incidence co ec ion ac o om p e alence, popula ion size, p og am caseload, and p og am co e age was de eloped and es ed using da a om he Nige ian na ional SAM ea men p og am. Resul s: Applying he de eloped me hod esul ed in e o s in caseload p edic ion o abou 10%. This is a conside able imp o emen upon he cu en me hod, which esul ed in a 79.5% unde es ima e. Me hods o imp o ing he p ecision o es ima es a e p oposed. Conclusions: I is possible o conside ably imp o e p edic ions o caseload by applying a simple model o da a ha a e eadily a ailable o p og am manage s. This implies ha mo e accu a e es ima es o bu den may also be made using he same me hods and da a. Keywo ds: Se e e acu e malnu i ion, Bu den, Caseload, P e alence, Incidence, Nige ia Backg ound A child wi h se e e acu e malnu i ion (SAM) has a high isk o nea e m mo ali y [1, 2]. I has been es ima ed ha SAM a ec ed mo e han 16 million child en globally in 2016 [3]. This igu e is based on p e alence es ima es om c oss-sec ional su eys. SAM is an acu e condi ion and many child en wi h SAM will ei he eco e o die wi hin a ew weeks. Es ima ing he numbe o SAM cases p esen in a popula ion o e a gi en pe iod o ime, he “SAM bu den”, using unadjus ed p e alence es ima es is likely, he e o e, o miss many new (inciden ) cases and signi ican ly unde es ima e he SAM bu den [4]. A ecen es ima e o he annual global SAM bu den ha a emp s o accoun o inciden cases sugges s ha 110 million cases pe yea migh be a mo e accu a e es ima e [5]. Poo es ima es o SAM bu den a e a p oblem o p og am manage s a all le els. Unde es ima ion has a nega i e * Co espondence: [email p o ec ed] 1 Uni ed Na ions Child en’s Fund (UNICEF), Abuja, Nige ia Full lis o au ho in o ma ion is a ailable a he end o he a icle © The Au ho (s). 2017 Open Access This a icle is dis ibu ed unde he e ms o he C ea i e Commons A ibu ion 4.0 In e na ional License (h p://c ea i ecommons.o g/licenses/by/4.0/), which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided you gi e app op ia e c edi o he o iginal au ho (s) and he sou ce, p o ide a link o he C ea i e Commons license, and indica e i changes we e made. The C ea i e Commons Public Domain Dedica ion wai e (h p://c ea i ecommons.o g/publicdomain/ze o/1.0/) applies o he da a made a ailable in his a icle, unless o he wise s a ed. Bul i e al. A chi es o Public Heal h (2017) 75:66 DOI 10.1186/s13690-017-0234-4 impac on he p io i iza ion o esou ce alloca ion o he p e en ion and ea men o SAM bo h globally and locally [6]. Bu den is he sum o p e alen cases a he s a o a pe iod and inciden cases ha a ise du ing ha pe iod. The numbe o p e alen cases in a popula ion a a gi en poin in ime can be es ima ed using a combina ion o a p e alence es ima e om a c oss-sec ional su ey and popula ion da a. This in o ma ion is usually al eady a ailable o p og am manage s. Incidence is mo e com- plica ed and mo e expensi e o es ima e. The ela ionship be ween incidence and p e alence is equen ly desc ibed using a “ba h ub”me apho [7]. In his model he low o wa e in o he ba h ub is analogous o incidence, he le el o he wa e in he ba h ub ep esen s p e alence, and he low o wa e ou o he ba h ub h ough he d ain ep esen s eco e y and mo ali y. Incidence in ela ion o p e a- lence depends, o a la ge ex en , upon he a e age du a ion o illness (see Fig. 1). The simple ela ionship be ween p e alence, inci- dence, and du a ion o illness makes i possible o c ea e a simple ma hema ical model ha allows he es ima ion o bu den using p e alence and popula ion es ima es oge he wi h o he da a (e.g. p og am co e age and p og am caseloads) ha will usually be a ailable o p og am manage s. The Communi y Managemen o Acu e Malnu i ion (CMAM) Fo um has p oposed a simple me hod o es i- ma e SAM bu den and p edic he numbe o cases ha a p og am will ea o e a gi en planning pe iod [8]. The numbe o p e alen cases p esen in a popula ion a he ime o a p e alence su ey is es ima ed as he p oduc o p e alence and popula ion size: Es ima ed numbe o p e alen cases ¼NP whe e: Nis he size o he popula ion o in e es Pis he p e alence o he condi ion o in e es The popula ion bu den (B) consis s o bo h p e alen cases and new (inciden ) cases ha a e expec ed o occu in he p og am a ea o e a gi en planning pe iod: Bu den BðÞ¼Es ima ed numbe o p e alen cases þExpec ed numbe o inciden cases The expec ed numbe o inciden cases can be es i- ma ed using: Expec ed nume o inciden cases ¼NPK whe e Kis a co ec ion ac o [9] calcula ed as: K¼Du a ion o planning pe iod A e age du a ion o a disease episode This allows he popula ion bu den (B) o be es ima ed: B¼Es ima ed numbe o p e alen cases þExpec ed numbe o inciden cases B¼NP þNPK B¼NP 1þKðÞ The popula ion bu den (B) can be used o p edic he numbe o cases ha a p og am will ea o e he planning pe iod (L) using an es ima e o p og am co e age (C): Fig. 1 The “ba h ub”me apho o he ela ionship be ween incidence and p e alence. The a e a which cases lea e he popula ion depends upon he a e age du a ion o illness Bul i e al. A chi es o Public Heal h (2017) 75:66 Page 2 o 8 Expec ed p og am caseload LðÞ ¼Expec ed co e age CðÞPopula ion bu den BðÞ L¼CNP 1þKðÞ All o he e ms in his es ima o a e subjec o unce ain y. Unce ain y ega ding co e age (C) and p e alence (P) is usually quan i iable and is quan i ied by con idence in- e als o c edible in e als on poin es ima es. The p e alence o se e e acu e malnu i ion (SAM) is o en es ima ed wi h poo ela i e p ecision. Fo example, he commonly used S anda dised Moni o ing and Assess- men o Relie and T ansi ions (SMART) p e alence su - eys ypically ha e e ec i e sample sizes (i.e. he sample size a e accoun ing o su ey design e ec s) be ween n= 300 and n= 400 [10]. An e ec i e sample size o n= 400 yields an exac 95% con idence in e al o [0.55%; 3.24%] on a 1.50% poin es ima e o SAM p e a- lence [11]. The ela i e p ecision o his es ima e is: Rela i e p ecision %ðÞ¼ 3:24−0:55 1:50 100 ¼179:3% Co e age is ypically es ima ed wi h a p ecision o abou ± 10% on a 50% es ima e [12]. This is a 40% ela- i e p ecision. Use ul accu acy o popula ion es ima es can be achie ed by co ec ing census da a o accoun o popu- la ion g ow h and mig a ion. I can o en be assumed ha he popula ion is es ima ed wi h li le o no e o . This may no , howe e , be he case in eme gencies in which he e is conside able and ongoing popula ion mo emen and / o high le els o mo ali y. Caseload (L) is a simple coun o p og am admissions. This da a is collec ed and epo ed on a ou ine basis and can usually be assumed o be measu ed wi h li le o no e o . The e is conside able unce ain y abou he alue o he incidence co ec ion ac o (K). The a e age du a ion o an un ea ed SAM episode ha is cu en ly being used globally is 7.5 mon hs. This is based on da a om wo coho s ud- ies and p o ides an incidence co ec ion ac o (K)o 1.6 o a one-yea planning pe iod [13]. I was assumed ha his alue o Kwould apply in all con ex s. Go e nmen s, Uni ed Na ions agencies, non-go e nmen al o ganiza ions (NGOs), and o he SAM ea men p og am implemen ing pa ne s ha e, in he absence o o he e idence, been using his alue o K o es ima e he bu den and expec ed case- load and o ad oca e o esou ces o ea child en wi h SAM. Repo s om SAM ea men p og ams sugges ha he use o K= 1.6 has led o p og ams unde es ima ing caseload in some Wes A ican se ings. Recen wo k indi- ca es ha a single alue o K o use globally may no be use ul (see Table 1) [6, 14–16]. Da a om he Nige ian Communi y-based Manage- men o Acu e Malnu i ion (CMAM) p og am om 2014 and 2015 a e p esen ed in his a icle. This p o- g am s a ed ope a ions in 2009 and has ea ed be- ween 300 housand and 500 housand SAM cases each yea . Du ing he cou se o implemen a ion i was ecognized ha he use o K=1.6hadled o conside able unde es ima ion o SAM bu den and p og am caseload. Gi en he public heal h and secu - i y si ua ion in Nige ia i is an icipa ed ha he Ni- ge ian CMAM p og am will un o many yea s and accu a e es ima es o expec ed caseloads will be e- qui ed o secu e adequa e con inued unding. This a icle p esen s a me hod o adjus o calib a e he alue o Kusing he popula ion o he p og am a ea, he numbe o p og am admissions, es ima es o p o- g am co e age, and es ima es o he p e alence o SAM in o de o p o ide mo e accu a e es ima es o bu den and expec ed caseload du ing p og am implemen a ion. The me hod is illus a ed using da a om he Nige ian CMAM p og am. The e ised es ima e o Kmay also be use ul o p edic SAM bu den and caseload om p e a- lence su eys in simila se ings. Me hods The caseload es ima ion o mula: L¼CNP 1þKðÞ can be ea anged o ind Kgi en he o he e ms: L¼CNP 1þKðÞ 1þK¼L CNP K¼L CNP −1 A sui able alue o Kcan be ound by subs i u ing known alues o L,C,N, and Pwi h Lbeing he ob- se ed p og am caseload (i.e. he numbe o admissions). The me hod ou lined he e assumes ha bo h popula- ion (N) and caseload (L) a e measu ed wi h li le o no e o al hough he me hod can be easily ex ended o ac- commoda e unce ain y in hese e ms. The p incipal sou ces o unce ain y in his analysis a e, he e o e, p e alence (P) and co e age (C). This can lead o con- side able unce ain y in hei p oduc (PC) used in he es ima o (Addi ional ile 1). An app oxima e 95% con idence in e al o he p od- uc o wo p opo ions (i.e. p e alence (P) and co e age (C) in his applica ion): θ b¼PC is gi en by: Bul i e al. A chi es o Public Heal h (2017) 75:66 Page 3 o 8 θ be1:96SE logθ bÞ  whe e: SE logθ b¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1−P nPPþ1−C nCC  and n P and n C a e he sample sizes used o es ima e p e alence (P) and co e age (C) espec i ely [17]. This o mula is no immedia ely applicable o he so s o da a likely o be a ailable o p og am manage s because he e ec i e sample sizes used o es ima e bo h p e a- lence and co e age (n P and n C ) will di e om epo ed sample sizes due o design e ec s in oduced by he use o complex samples and / o he use o p io in o ma- ion [12,18,19]. P e alence is usually es ima ed using su eys employing complex sample designs. The p e alence es ima es used in his epo we e made by combining esul s om se e al c oss-sec ional household su eys ha used a wo-s age clus e sample design ep esen a i e a he s a e le el ol- lowing he SMART me hodology [10, 20, 21]. Co e age o CMAM p og ams is o en es ima ed using spa ially s a i ied samples [12, 22–24]. Semi-Quan i a i e E alua ion o Access and Co e age (SQUEAC) co e age assessmen s use a Bayesian be a-binomial conjuga e analysis in which he conjuga e p io con ains in o - ma ion ha con ibu es “pseudo-obse a ions” o he analysis [12, 19]. The e ec i e sample size associa ed wi h he es ima e o a p opo ion can be calcula ed om he epo ed poin es ima e (p) and i s associa ed uppe and lowe 95% con idence limi s (UCL and LCL). Va iance (VAR) is calcula ed as: VAR ¼UCL−LCL 21:96  2 The e ec i e sample size (n e ec i e ) is calcula ed as: ne ec i e ¼p1−pðÞ VAR ounded o he nea es whole numbe . This calcula ion is pe o med o ind bo h n P and n C be o e calcula ing SE logθ b . We used he app oach ou lined abo e o ind a sui able alue o K o he Nige ian CMAM p o- g am. Da a ela ing o p og am admissions (i.e. case- load) in 2014 and 2015 we e aken om ou ine p og am moni o ing epo s. Popula ion es ima es we e made using da a om he 2006 Nige ian Census co ec ed o popula ion g ow h and mig a ion [25]. P e alence es ima es o SAM we e a ailable o 2014 and 2015 [20, 21]. An es ima e o p og am co e age was a ailable om a wide-a ea Simpli ied Lo -quali y- assu ance E alua ion o Access and Co e age (SLEAC) su ey comple ed in ea ly 2014 [12, 26, 27]. The epo ed co e age om his su ey was used o Table 1 Values o incidence co ec ion ac o s (K) ound in ecen s udies a Coun y Yea (s) K b SAM case de ini ion(s) c W/H Re e ence c Da a Sou ce(s) Me hod Sou ce Nige 2010–2013 4.30–9.50 W/H < −3 z-sco es o MUAC <115 mm o bi- la e al pi ing edema WGS Su eillance sys em (weekly) Rou ine p og am da a (weekly) Rou ine p og am da a (mon hly) Simple ma hema ical models Deconinck e al., 2016 [14] Nige 2006–2007 5.37–11.78 W/H < −3 z-sco es o MUAC <115 mm o bi- la e al pi ing edema WGS Communi y coho (mon hly) Compa men al model o es ima e mean du a ion o SAM episodes Isanaka e al., 2011 [6] Mali 2010–2013 2.10–2.50 W/H < −3 z-sco es o MUAC <115 mm o bi- la e al pi ing edema WGS Communi y coho (qua e ly) Su eys (occasional) Simple ma hema ical models Isanaka e al., 2016 [15] Nige 2010–2011 5.00–8.10 W/H < −3 z-sco es o bila e al pi ing edema WGS Communi y coho (mon hly) Su eys (mon hly) Simple ma hema ical models Bu kina Faso 2009–2010 7.30–17.00 MUAC <110 mm (p e alence) MUAC <120 mm (incidence) WGS Su eys (annual) Rou ine p og am da a (mon hly) Simple ma hema ical models Va ious d 2005–2009 11.21 W/H < 70% o median NCHS Su eys Caseloads o 5 mon hs a e su ey Linea eg ession Dale e al., 2017 [16] a A ange o me hods and da a sou ces we e used (su eillance sys ems, wo kload e u ns, coho s udies, epea ed c oss-sec ional su eys, compa men al models, and eg ession o obse ed caseload agains p e alence) we e used o es ima e he incidence co ec ion ac o s (K). Re e o he o iginal a icles o de ails b A ange o alues (i.e. om di e en me hods, da a sou ces, se ings, and case-de ini ions o se e e acu e malnu i ion) is gi en when a ailable c SAM = se e e acu e malnu i ion, W/H = weigh - o -heigh , MUAC = mid-uppe -a m ci cum e ence, WGS = Wo ld Heal h O ganiza ion child g ow h s anda ds, NCHS = Na ional Cen e o Heal h S a is ics child g ow h e e ence d 24 da ase s (su eys and p og am admissions) om DRC (8), Bu undi (2), Somalia (2), Sudan (7), Myanma (2), and Nige (3). The incidence co ec ion ac o (K) gi en in he able is o pooled da a assuming co e age (C) o 38% ( om Roge s e al., 2015). Conside able a ia ion in Kbe ween se ings was obse ed Bul i e al. A chi es o Public Heal h (2017) 75:66 Page 4 o 8 bo h 2014 and 2015 since i was he only co e age da a a ailable. Da a we e en e ed and analyzed using Mic oso Excel. This so wa e was used because i is likely o be a ailable and amilia o CMAM p og am manage s. A Mic oso Excel sp eadshee ha pe - o ms he equi ed calcula ionsisp o idedasonline suppo ing ma e ial. All calcula ions we e checked using he RLanguage and En i onmen o S a is ical Compu ing e sion 3.3.3 [28]. The me hod used o calcula e he 95% con idence limi s o he p oduc o p e alence and co e age (PC)isap- p oxima e. A less app oxima e 95% con idence in e al (i.e. an in e al ha con ains he ue alue close o 95% o he ime) may be calcula ed using a boo s ap es ima o [29, 30]. Es ima es o he incidence co ec ion ac o (K) we e made using a boo s ap es ima o o he p oduc o p e alence and co e age (PC). A pe cen ile boo s ap es i- ma o wi h one million eplica es o p e alence and co e - age d awn om app op ia e binomial dis ibu ions was used [29]. Da a we e analyzed using he RLanguage and En i onmen o S a is ical Compu ing e sion 3.3.3 [28]. Resul s Table 2 shows he obse ed and expec ed (i.e. calcu- la ed using K=1.6)caseloadsand he e isedinci- dence co ec ion ac o s (K) o 2014 and 2015 oge he wi h he da a on which he calcula ions we e based. Use o K= 1.6 o p edic caseload had esul ed in g oss unde es ima es in bo h yea s. The esul ing e ised es ima es o Kwe e K= 14.39 (95% CI = 6.64; 30.02) and K= 11.66 (95% CI = 5.94; 22.10) o 2014 and 2015 espec i ely. These es ima es we e pooled gi ing K= 13.02 (95% CI = 6.80; 19.25). The inal wo ows o Table 2 show he expec ed caseloads o 2014 and 2015 using he pooled es ima e o Kand di e ence be ween he obse ed and expec ed case- loads. Table 3 compa es es ima es o he incidence co ec ion ac o (K) calcula ed using he app oxima e me hod and he boo s ap es ima o . Discussion The app oach ou lined in his documen can p o ide use ul es ima es o locally app op ia e incidence co ec ion ac- o s. Applying he alue o Kes ima ed o 2014 o he popula ion, p e alence, and co e age da a o 2015 yields a p edic ed caseload o 484,766 cases. This is a 21.6% o e - es ima e o he obse ed caseload o 2015. Some o his e o may ha e been due o lowe han speci ied co e age du ing he implemen a ion phase o addi ional CMAM p og amming ini ia ed in ea ly 2015 as pa o he ongoing eme gency esponse in No he n Nige ia. This deg ee o e o in caseload p edic ion is a conside able imp o emen Table 2 Incidence co ec ion ac o s o no he n Nige ia CMAM p og am 2014 and 2015 and he da a used o calcula e hem Yea 2014 2015 Da a sou ces Popula ion N3,550,827 4,281,700 Nige ia census 2006 co ec ed o popula ion g ow h and mig a ion. Popula ion is o child en aged be ween 6 and 59 mon hs in dis ic s in which CMAM se ices we e deli e ed. P e alence a P1.60% (0.50%; 2.71%) 2.01% (0.82%; 3.19%) Pooled p e alence om s a e le el SMART su eys P og am Co e age b C36.6% (32.3%; 40.9%) 36.6% (32.3%; 40.9%) Wide-a ea SLEAC su ey Obse ed caseload L320,047 398,676 Rou ine p og am moni o ing da a Expec ed caseload (using K= 1.6) E K= 1.6 =CNP(1 + K) 54,063 81,897 C,N, and Pas abo e (C and P exp essed as p opo ions). Calcula ions a e based on K= 1.6 Di e ence (obse ed − expec ed) L−E K= 1.6 265,984 316,779 Calcula ed as he di e ence be ween obse ed caseload (L) and expec ed caseload (E). P e alence × Co e age PC 0.59% (0.29%; 1.18%) 0.74% (0.40%; 1.34%) Calcula ed (see ex ) Incidence co ec ion ac o c K¼L PCN −1 14.39 (6.64; 30.02) 11.66 (5.94; 22.10) Calcula ed (see ex ) Expec ed caseload (using pooled adjus ed incidence co ec ion ac o ) E K= 13.02 =CNP(1 + K) 291,527 441,612 C,N, and Pas abo e (C and P exp essed as p opo ions). Calcula ions a e based on K= 13.02 (see ex ). Di e ence (obse ed − expec ed) L−E K= 13.02 28,520 −42,936 Calcula ed as he di e ence be ween obse ed caseload (L) and expec ed caseload (E K = 13.02 ). a P e alence is o MUAC <115 mm o bila e al pi ing edema. This case-de ini ion accoun s o c. 98% o all p og am admissions based on an analysis o ou ine p og am moni o ing da a om wo s a es o no he n Nige ia (n= 102,245 admissions om Janua y 2010 o Decembe 2013). P e alence es ima es o he s a es in which he p og am was ope a ing a e epo ed. This was calcula ed as he popula ion weigh ed a e age o SMART su ey esul s om indi idual s a es b Co e age e e s o poin co e age (i.e. he p opo ion o cu en SAM cases ound by he su ey ha we e en olled in he CMAM p og am). Resul s om a wide-a ea SLEAC su ey om Feb ua y 2014 a e used o bo h yea s [Banda e al., 2014] c The o mula o he es ima o o Kis ea anged o e lec he ac ha PC was calcula ed p io o use Bul i e al. A chi es o Public Heal h (2017) 75:66 Page 5 o 8 upon he 79.5% unde es ima e expe ienced when using K= 1.6. Applying he pooled es ima e o K(i.e. K= 13.02) o he popula ion, p e alence, and co e age da a yields p e- dic ed caseloads o 291,257 cases and 441,612 cases o 2014 and 2015 espec i ely. These a e a 9.0% unde es ima e and 10.8% o e es ima e o he ue cases o 2014 and 2015. E o a e likely o dec ease o e ime as mo e annual es ima es o Kbecome a ailable. No all e o s ha e he same consequences. Fo example, o e es ima ion may ha e posi i e consequences i p og am co e age is limi ed by s op-s a unding and supply b eaks caused by unde es i- ma ion o bu den and / o p edic ed caseload. Unde es i- ma ion may lead o an unde - esou ced p og am in which p og am ac i i ies essen ial o achie ing and main aining co e age (e.g. communi y mobiliza ion, communi y sensi iza ion, and communi y-based case- inding ac i i ies) a e neglec ed in o de o main ain co e clinical ac i i ies. Unde es ima ion, in some cases, may lead o supply b eaks necessi a ing he empo a y closu e o p og ams. Con idence in e als o he boo s ap es ima es o he incidence co ec ion ac o s a e wide han when he ap- p oxima e me hod is used. Es ima es made using he ap- p oxima e me hod a e likely o be spu iously p ecise. The use o app oxima e me hods o calcula e con idence in e als is, howe e , a widely accep ed p ac ice o many public heal h applica ions. The app oxima e me hod has he ad an age o being easy o implemen using so wa e, such as Mic oso Excel, ha is a ailable and amilia o CMAM p og am manage s. Es ima es o he incidence co ec ion ac o (K) lack p ecision e en when he app oxima e me hod is used. Fo example, he 95% con idence in e al o he 2015 es ima e o he incidence co ec ion ac o (K) using he app oxima e me hod anges be ween K= 5.94 and K= 22.10. This ansla es in o a 95% con idence in e al o he caseload p edic ion o be ween abou 218 hou- sand and 728 housand. This deg ee o imp ecision may limi he u ili y o he me hod as a planning ool. The p incipal sou ces o imp ecision a e in es ima es o p e alence and co e age. Imp o ing he p ecision o es ima es o p e alence and / o co e age will imp o e he p ecision wi h which he incidence co ec ion ac o (K) is es ima ed. SAM p e alence is usually es ima ed wi h poo ela i e p ecision. Rela i e p ecision o he p e alence es ima es a e 138% o he 2014 SAM p e alence es ima e and 118% o he 2015 p e alence es ima e. The lack o p e- cision in p e alence es ima es is due, in pa , o he use o sample designs ha educe he e ec i e sample sizes o su eys. I is likely ha p ecision could be imp o ed using, o example, s a i ied sample designs and la ge sample sizes. This would, howe e , equi e conside able changes o cu en p ac ice. Lack o p ecision is also due o he way ha su ey da a a e analyzed. Replacing he classic es ima o : P e alence ¼Numbe o SAM cases ound in he su ey sample Su ey sample size wi h a PROBIT es ima o has been demons a ed o e- duce he hal -wid h o 95% CIs by abou 60% wi h only small losses o accu acy [31–33]. Sligh ly La ge gains in p ecision ha e been demons a ed using a Bayesian- PROBIT es ima o [19,34]. The ad an age o da a ana- ly ic app oaches o imp o ing p ecision a e ha hey can be applied o da a collec ed wi h cu en ly used su - ey me hods including his o ical da a a li le ex a cos . The p ecision o he co e age es ima e was no an issue in he wo k epo ed he e because a la ge s a i ied sample was used o es ima e co e age wi h good ela i e p ecision (i.e. 23.5%). P ecision o co e age es ima es may, howe e , be a p oblem o smalle p og ams. We in es iga ed his issue using da a om 227 SQUEAC co e age assessmen s o dis ic -le el NGO-deli e ed CMAM p og ams pe o med be ween Janua y 2010 and July 2015 and p o ided o us by he Co e age Moni o - ing Ne wo k. The median ela i e p ecision o co e age es ima es be ween 40% and 60% was 42.6% (IQR = 38.4%; 48.5%). This is an expec ed esul as SQUEAC co e age assessmen s a e usually designed o es ima e co e age wi h his le el o p ecision [12]. The poo e ela i e p ecision o SAM p e alence es i- ma es means ha e o s o imp o e he p ecision o hese es ima es a e likely o yield g ea e imp o emen s in he p ecision wi h which he incidence co ec ion ac o (K)is es ima ed han may be achie ed by e o s o imp o e he p ecision o co e age es ima es. This is illus a ed in Table 4 using he da a om 2015. I is impo an o no e ha imp o emen in he p ecision o p e alence es ima es can be achie ed wi h e y li le inc ease in cos s bu ha imp o emen s in he p ecision o co e age es ima es would en ail conside able inc eases in cos s. Limi a ions A key limi a ion o he wo k epo ed he e is ha co e - age da a was no cu en , pa icula ly o 2015. A limi a ion o he me hod desc ibed he e is ha bu den and caseload may be in luenced by mig a ion in o and ou o he p og am a ea. Rapid and subs an ial changes in he popula ion o he p og am a ea a e likely o a ec Table 3 Es ima es o he incidence co ec ion ac o made using wo me hods o calcula e he p oduc o p e alence and co e age Yea K (app oxima e) K (boo s ap) 2014 14.39 (6.64; 30.02) 14.72 (7.73; 40.44) 2105 11.66 (5.94; 22.10) 11.91 (6.17; 27.08) Pooled 13.02 (6.80; 19.25) 13.32 (6.10; 20.53) Bul i e al. A chi es o Public Heal h (2017) 75:66 Page 6 o 8 popula ion size (N), p e alence (P), and p og am co e age (C). Mig a ion may, he e o e, esul in g ossly inaccu a e p edic ions o bu den (B) and caseload (L) ha a ebased on es ima es o popula ion size (N), p e alence (P), and p og am co e age (C). Moni o ing popula ion mo emen s and adjus ing bu den and caseload p edic ions may help o add ess his p oblem. Adjus men may also equi e ha addi ional p e alence and co e age su eys be unde aken. In he case o he Nige ian CMAM p og am he e ha e been epo s o SAM cases en e ing Nige ia om Nige and being admi ed o CMAM si es in dis ic s ha bo de Nige . The e ec o his on he wo k epo ed he e is likely o be small since da a o he whole coun y we e used. I is impo an o no e ha his may ha e la ge e ec s on bu - den (B) and caseload (L) p edic ions o (e.g.) small NGO- deli e ed p og ams ope a ing in bo de dis ic s. The assump ion ha caseload (L)ismeasu edwi h li le o no e o may also be a limi a ion. In he case o he Nige ian CMAM p og am he e ha e been e- po s om 3 o he 114 dis ic s in which he p o- g am is ope a ing o bene icia ies being egis e ed a mo e han one CMAM si e wi h he assumed in en ion o ecei ing addi ional ood and d ugs. New CMAM si es we e opened in hese dis ic s and some o he double egis a ion may ha e been due o in- o mal ans e s be ween si es. An in o mal ans e would ha e been epo ed as a new admission a he des ina ion si e and, some weeks la e , as a de aul ing pa ien a he o igina ing si e. The e ec o his would ha e been o inc ease epo ed caseload (L). I seems likely ha double egis a ion will ha e had only a small e ec on caseload (L)usedin hewo k epo ed he e. This would ha e caused only a small inc ease in he es ima es o K epo ed he e. The co e na u e o some double egis a ions does mean ha he magni ude o any inc ease will always be di - icul o quan i y. Conclusion The wo k epo ed he e shows ha i is possible o consid- e ably imp o e p edic ions o CMAM caseload by applying a simple ma hema ical model o da a ha a e eadily a ail- able o p og am manage s. This implies ha mo e accu a e p edic ions o bu den may also be made using he same me hods and da a. The p ecision o es ima es o caseload and bu den may be imp o ed by using PROBIT o Bayesian-PROBIT es ima o s o SAM p e alence. The implica ion o his s udy, and o simila epo s based on a a ie y o app oaches (see Table 1), is ha he cu en es ima es o SAM bu den a e likely o be g oss unde es ima es. Applying he pooled incidence co ec ion ac o ound in his s udy o he 16 million es ima e made using p e alence da a yields an es ima ed global SAM bu den o 208 (95% CI = 109; 308) million cases annually. I seems unlikely, howe e , ha he inci- dence co ec ion ac o es ima ed o he Nige ian CMAM p og am will be globally applicable. Local es i- ma es o Kwill be needed o make local p edic ion o bu den and caseload. These local es ima es o Kcould be applied o local es ima es o p e alence and popula- ion wi h he esul s summed in o de o es ima e global SAM bu den. Gi en he public heal h impo ance o ha ing eliable es ima es o bu den and caseload and he unce ain ies o his app oach based on p og am da a, a con i ma ion o es ima es o Kusing di ec es ima es o incidence om con inuous moni o ing o open coho s and su eillance sys ems in simila se ings may be wa an ed. Compa ison wi h o he indi ec me hods may also p o e use ul. Addi ional ile Addi ional ile 1: Caseload me hod. (XLSX 36 kb) Acknowledgemen s The au ho s wish o hank Fede al Minis y o Heal h o Nige ia o p o iding p og am da a o he analysis. Funding No unding was ob ained o his s udy. A ailabili y o da a and ma e ials Da a will be a ailable upon eques om he co esponding au ho s. Au ho s’con ibu ions AB, ABR, MM concei ed he s udy and d a ed he manusc ip . All au ho s ead and app o ed he inal manusc ip . Table 4 E ec o imp o ed p ecision o SAM p e alence es ima es and co e age es ima es o he p ecision o he es ima e o he incidence co ec ion ac o (K) using 2015 da a om he Nige ian CMAM p og am Incidence co ec ion ac o (K) Scena io Poin es ima e 95% LCL 95% UCL Rela i e p ecision a No change 11.66 5.94 22.10 139.59% Reduce hal -wid h o 95% CI o p e alence by 60% b 11.66 7.72 17.37 82.76% Reduce hal -wid h o 95% CI o co e age by 60% c 11.66 6.00 21.88 136.19% a Rela i e p ecision is calcula ed as 95%UCL−95%LCL Poin Es ima e 100. Smalle alues indica e be e p ecision b This le el o imp o emen is achie able using a PROBIT es ima o wi h exis ing su ey designs and su ey da a c This le el o imp o emen could only be achie ed by a conside able inc ease in su ey sample sizes Bul i e al. A chi es o Public Heal h (2017) 75:66 Page 7 o 8 E hics app o al and consen o pa icipa e No applicable. Consen o publica ion No applicable. 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