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\begin{fmbox}
\dochead{Research}

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\title{Impact of population based indoor residual spraying with and without
mass drug administration with dihydroartemisinin-piperaquine on malaria
prevalence in a high transmission setting: a controlled trial in northeastern
Uganda}

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\author[
   addressref={aff1},
   corref={aff1},                    % id of corresponding address, if any
   email={dorothy@pilgrimafrica.org}
]{\inits{DE}\fnm{Dorothy C.} \snm{Echodu}}
\author[
   addressref={aff2},
   email={yadoke@yahoo.com}
]{\inits{AY}\fnm{Adoke} \snm{Yeka}}
\author[
   addressref={aff3},
   email={thomas@pilgrimafrica.org}
]{\inits{TE}\fnm{Thomas} \snm{Eganyu}}
\author[
   addressref={aff3},
   email={wycliff.odude@pilgrimafrica.org}
]{\inits{WO}\fnm{Wycliff} \snm{Odude}}
\author[
   addressref={aff3},
   email={fred.bukenya@pilgrimafrica.org}
]{\inits{FB}\fnm{Fred} \snm{Bukenya}}
\author[
   addressref={aff4},
   email={b.amoah@lancaster.ac.uk}
]{\inits{BA}\fnm{Benjamin} \snm{Amoah}}
\author[
   addressref={aff3},
   email={humphrey@pilgrimafrica.org}
]{\inits{HW}\fnm{Humphrey} \snm{Wanzira}}
\author[
   addressref={aff5},
   email={KATHRYN.COLBORN@cuanschutz.edu}
]{\inits{KC}\fnm{Kathryn} \snm{Colborn}}
\author[
   addressref={aff1,aff6},               % id's of addresses, e.g. {aff1,aff2}
   %noteref={n1},                        % id's of article notes, if any
   email={richardelliott@boisestate.edu}   % email address
]{\inits{RCE}\fnm{Richard C.} \snm{Elliott}}
\author[
   addressref={aff1},
   email={suzanne@pilgrimafrica.org}
]{\inits{SP}\fnm{Suzanne E.} \snm{Powell}}
\author[
   addressref={aff3},
   email={kilamam@gmail.com}
]{\inits{MK}\fnm{Maxwell} \snm{Kilama}}
\author[
   addressref={aff2},
   email={mulebeker@gmail.com}
]{\inits{RM}\fnm{Ronald} \snm{Mulebeke}}
\author[
   addressref={aff7},
   email={jnankabirwa@yahoo.co.uk}
]{\inits{JN}\fnm{Joaniter} \snm{Nankabirwa}}
\author[
   addressref={aff4},
   email={b.amoah@lancaster.ac.uk}
]{\inits{EG}\fnm{Emanuele} \snm{Giorgi}}
\author[
   addressref={aff1,aff8},
   email={mellisa@pilgrimafrica.org}
]{\inits{MR}\fnm{Mellisa} \snm{Roskosky}}
\author[
   addressref={aff3},
   email={osborn.omoding@pilgrimafrica.org}
]{\inits{OO}\fnm{Osborn} \snm{Omoding}}
\author[
   addressref={aff7},
   email={sgonahasa1983@gmail.com}
]{\inits{SG}\fnm{Samuel} \snm{Gonahasa}}
\author[
   addressref={aff9},
   email={opigojimmy@gmail.com}
]{\inits{JO}\fnm{Jimmy} \snm{Opigo}}



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\address[id=aff1]{%
  \orgname{Pilgrim Africa},
  \street{8001 14th Avenue NE, Suite A},
  \postcode{98115}
  \city{Seattle, WA},
  \cny{USA}
}

\address[id=aff2]{%
  \orgname{Makerere University College of Health Sciences, School of Public
Health}
  \street{P.O. Box 7072},
  \city{Kampala} 
  \cny{Uganda}
}

\address[id=aff3]{%
  \orgname{Pilgrim Africa},
  \street{Plot 8 Engwau Road, PO Box 577},
  \city{Soroti}
  \cny{Uganda}
}

\address[id=aff4]{%
  \orgname{Lancaster University}
  \street{Furness College, B73}
  \city{Lancaster, United Kingdom}
  \postcode{LA1 4YG}
  \cny{UK}
}

\address[id=aff5]{%
  \orgname{University of Colorado Anschutz Medical Campus}
  \street{12631 East 17th Avenue}
  \city{Aurora, CO}
  \postcode{80045}
  \cny{USA}
}

\address[id=aff6]{%                           % unique id
  \orgname{Micron School of Materials Science and Engineering, Boise State University}, % university, etc
  \street{Engineering Building, Suite 338},                     %
  %\postcode{}                                % post or zip code
  \city{Boise, ID},                              % city
  \postcode{83725}
  \cny{USA}                                    % country
}


\address[id=aff7]{%
  \orgname{Infectious Diseases Research Collaboration}
  %\\street{Ministry of Health Uganda}
  \city{Kampala, Uganda}
  \cny{UG}
}

\address[id=aff8]{%
  \orgname{Johns Hopkins Bloomberg School of Public Health}
  %\\street{Ministry of Health Uganda}
  \city{Baltimore, MD}
  \cny{USA}
}
\address[id=aff9]{%
  \orgname{National Malaria Control Division}
  \street{Ministry of Health Uganda}
  \city{Kampala, Uganda}
  \cny{UG}
}



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\begin{abstractbox}

\begin{abstract} % abstract
%NB Malaria Journal abstract 350 wd limit
\parttitle{Background}

Declines in malaria burden in Uganda have slowed. Modelling predicts that indoor residual spraying (IRS) and mass drug administration (MDA), when co-timed, have synergistic impact. This study investigated additional protective impact of population-based MDA on malaria prevalence, if any, when added to IRS, as compared with IRS alone and with standard of care (SOC).

\parttitle{Methods} 

The 32-month prospective controlled community trial enrolled an open cohort of
residents (46,765 individuals, 1st enumeration and  52,133, 4th enumeration)
in Katakwi District in northeastern Uganda. Consented participants
were assigned to three arms based on residential subcounty: MDA+IRS, IRS, and SOC (insecticide treated bednets and case management). IRS with pirimiphos methyl and MDA with dihydroartemisinin- piperaquine were delivered in 4
co-timed campaign-style rounds 8 months apart. The primary endpoint was
population prevalence of malaria, estimated by 6 cross-sectional surveys, starting at baseline and preceding each subsequent round. 

\parttitle{Results} 

Comparing malaria prevalence %by qPCR 
in MDA+IRS and IRS only arms over all 6
surveys (intention-to-treat analysis), roughly every 6 months,
post-interventions, a geostatistical model found a significant
additional 15.5\% (95\% confidence interval (CI): [13.7\%, 17.5\%], Z=9.6, p= 5e-20) decrease in the
adjusted odds ratio (aOR) due to MDA for all ages, a 13.3\% reduction in under
5’s (95\% CI: [10.5\%, 16.8\%], Z=4.02, p= 5e-5), and a 10.1\% reduction in
children 5-15 (95\% CI: [8.5\%, 11.8\%], Z=4.7, p= 2e-5).  All ages residents of the MDA + IRS arm enjoyed an overall
80.1\% reduction (95\% CI: [80.0\%,83.0\%, p¡.0001] in odds of qPCR confirmed
malaria compared with SOC residents.  Secondary difference-in-difference analyses comparing surveys
at different timepoints to baseline showed aOR (MDA + IRS vs IRS) of qPCR
positivity between 0.28 and 0.66 (p$<$.001). Of three serious adverse events, one (nonfatal) was considered related to study medications.  Limitations include the initial non-random assignment of MDA+IRS, which may have understated the impact of MDA, and lack of MDA-only arm, considered to violate equipoise.

\parttitle{Conclusions} 

Despite being assessed at long timepoints 5-7 months post-round, MDA plus IRS provided significant additional protection from malaria infection over IRS alone. Future cohort studies of impact on incidence recommended.

\parttitle{Trial registration}
This trial was retrospectively registered July 7th, 2018 with the Pan African Clinical Trials Registry (PACTR 201807166695568).
\end{abstract}

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\begin{keyword}
\kwd{MDA}
\kwd{malaria}
\kwd{IRS}
\kwd{high burden}
\kwd{Uganda}
\kwd{controlled trial}
\kwd{pirimiphos}
\kwd{dihydroartemisinin}
\end{keyword}

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\section*{Introduction}

\subsection*{Background}

Globally, progress against malaria morbidity has stalled, with the remaining
burden concentrated in eleven countries,
including Uganda\cite{world2018high}. Uganda is a stably endemic,
high burden country with, as of 2020, the third highest number of malaria cases
in the world\cite{world2019world,world2020world}. After some years of low
movement against key malaria metrics\cite{yeka2012malaria}, the country
made great strides in the decade between 2009 and 2019, including: a doubling of insecticide
treated net (ITN) ownership and use, maintaining a commitment to indoor
residual spraying (IRS) in high transmission
areas\cite{steinhardt2013effect,kigozi2012indoor,nankabirwa2020malaria,kamya2020impact},
emphasizing early diagnosis and prompt treatment of uncomplicated malaria with
artemisinin combination therapies (ACTs), intermittent preventive treatment for
pregnant women (IPTp)\cite{okethwangu2019factors,musoke2021malaria}, and
improving national surveillance\cite{nuwa2021district}. These efforts, along
with growing urbanization and greater access to improved
housing\cite{tusting2019mapping,tusting2015evidence,rek2018rapid,liu2014housing},
have resulted in dramatic national reduction in microscopy prevalence of
children under 5 from 42\% in 2009\cite{malariaIndSurv2009} to 9\% in
2018\cite{nmcd2020icf}. In recent years, however, this progress is slowing. Further gains must come from accurately targeting control combinations towards
remaining pockets of high transmission, where most national cases
occur\cite{nkumama2017changes,feachem2019malaria}.

While vector control remains the primary way to reduce malaria burden and
transmission, chemoprevention may also have a role in high burden regions where
progress has faltered \cite{rosenthal2019malaria,alonso2020role}. A growing
enthusiasm to use chemoprevention in control settings is emerging. It is
supported by evidence from, for example, China, who for nearly 40 years
(1960-1999) used both radical treatment and mass drug
administration (MDA) of antimalarials as primary malaria interventions.  MDA was specifically targeted to reduce disease burden, with annual
doses administered tracking the level of
burden, and reducing with it\cite{xu2021transition}. During this time, national malaria transmission fell
from hyperendemic to pre-elimination levels, and malaria cases shrank from an
estimated 30 million per annum in the 1940s to a little over 30,000 in
2000\cite{zu1981malaria}. In May of 2021, the WHO granted China official status
as a malaria free country\cite{chinaFree,chen202130}. More recently, there has
been a push for new evidence on MDA in high burden settings. In
2020, the Director of the World Health Program Global Malaria Program called
for new evidence for MDA in the context of the High Burden High Impact
initiative\cite{world2018high}, noting that it is one of the few tools whose
``full potential has yet to be realized.''

MDA is a full course of an antimalarial treatment
given to the whole population in a given area at approximately the same time,
irrespective of symptoms or infection status and with the exception of
individuals for whom the medicine is
contraindicated\cite{world2015role,world2017mass}. In order for MDA to be
effective, high coverage is essential\cite{gerardin2015mass,eisele2019mass} and
good engagement with stakeholders is required. For maximum impact, MDA should
be performed with an artemisinin-combination therapy
(ACT)\cite{sawa2013malaria}, with Dihydroartemisinin-piperaquine (DP) often
preferred for its efficacy, safety profile, and long post-treatment
prophylactic protection\cite{zani2014dihydroartemisinin,hung2004population}.

Absent of concurrent vector control, the duration of MDA impact in a high
transmission setting is expected to be quite limited; rebound is predicted
to occur rapidly after a transient period of chemoprophylactic prevention \cite{elliott2018medical,elliott2019synergy}. A recent,
%randomized placebo-controlled 
clinical trial of intermittent preventive treatment in schoolchildren (IPTsc),
conducted in a high transmission region of Uganda, underlined this effect:
chemoprevention was extremely protective when ACTs were administered at monthly
intervals, reducing incidence and prevalence in intervention subjects relative
to placebo by 96\% and 94\% respectively, but was accordingly much less
effective in suppressing clinical incidence if given
quarterly\cite{nankabirwa2014impact}. Similarly, in a large cluster-randomized
trial of MDA in Zambia, with asynchronous vector control present in the study
region, the effects of an MDA campaign after two rounds was estimated to last
for 3-4 months\cite{eisele2015assessing}. In this case, MDA was shown to reduce
cumulative incidence relative to control in a
cohort\cite{steketee2020implications,eisele2020impact} in a high transmission
setting; however, the differential impact of the MDA on prevalence was not
significant in high burden areas at survey points 3-4 months after the
campaign\cite{eisele2020impact}. 

Prior work noted a robust impact of combining MDA with vector control,
showing the strategy effective for burden reduction in high transmission
regions, though not for achieving complete
elimination\cite{molineaux1980garki}. Recent modeling has predicted a
complimentary impact of MDA+IRS, especially when applied at the same time, with
the IRS protecting and effectively extending the duration of the MDA's
prophylactic effect for six months or more\cite{elliott2018medical}. Because an
MDA campaign initially removes so much infection in high transmission, the
cooperative advantage of prolonging its impact is predicted to increase with
transmission intensity, making the strategy of particular interest in high
burden settings\cite{elliott2019synergy}. From a cost and compliance
standpoint, the advantage of a single annual population-wide MDA over monthly
or seasonal chemoprophylaxis in children is compelling, but to date an
experimental study has not investigated the impact of delivering IRS and
MDA simultaneously in a high-burden context. Here, we evaluated the impacts of a
co-timed MDA campaign with DP with IRS with pirimiphos methyl in a setting of high transmission and burden. Based on
modeled predictions of the benefit of co-timing interventions, especially at
long times after MDA implementation, the primary objective of this
study was to assess the impact on malaria prevalence of single rounds
of population MDA+IRS at intervals five to seven months post-intervention, and to evaluate any additional protective impact of MDA when added to IRS over IRS alone and over standard of care (SOC).
%This was compared to intervention impacts of rounds of IRS only, and, separately,
%with impacts of standard of care standard in all arms, \emph{i.e.} case management
%based at local health-facilities and ITNs distributed via population-wide campaign. 
The comparison of MDA+IRS to IRS provides a picture of additional MDA impact, if any exists, at times well beyond the chemoprophylactic prevention period of the MDA campaign. 
This paper presents an overview of the interventions, procedures and primary prevalence outcomes.

\section*{Methods}

\subsection*{Study design}

This was a prospective controlled community trial, conducted between October
2016 and June 2019, for a duration of 32 months. This study had three
assignment arms: MDA+IRS, IRS only, and SOC as the control arm,
which included ITNs and case management at facilities with commodity support
only. SOC interventions were present in all three arms. The MDA+IRS arm was not assigned randomly, but to the arm with the highest
number of malaria cases in the previous year.

\subsection*{Study setting}

The study took place in Toroma County in the Katakwi District of northeastern
Uganda, a high transmission region within the country deemed by the Uganda
Ministry of Health (MOH) National Malaria Control Division (NMCD) to be suitable for
targeted IRS. With a combined population of 46,765 in 2016, the project area
consisted of three adjacent rural farming sub-counties bordering Lake Bisina.
These three sub-counties served as the three arms of the study, with assignment
taking place before study enrollment. Malaria transmission in this marshy area
is dominated by \emph{Anopheles gambiae sensu lato} (s.l.) and \emph{Anopheles
funestus s.l.} vector groups\cite{republic2015uganda,oguttu2017rapid,okia2013bioefficacy}, and follows a bimodal
rainfall pattern.  The longer rainy season occurs between March-June, and a
shorter season between October-November; malaria is hyperendemic year-round,
with peak malaria transmission between May-July\cite{kilama2014estimating}. All
three sub-counties received free ITNs through national universal coverage
campaigns conducted in November 2013 and again in April 2017 by Uganda MOH. 
Public health clinics in the study area follow the national malaria
case management guidelines. None of the three targeted sub-counties had
received IRS in the 5 years prior to November 2016.

\subsection*{Study interventions and cluster assignment}

This study had two main study interventions, IRS with pirimiphos methyl (Actellic 300 CS, Syngenta) and MDA with DP (Eurartesim, Sigma Tau). Population-wide IRS was conducted in two
arms by the research team, and in one of the arms, a single round of MDA was
also administered and co-timed with the IRS.

IRS was conducted using Actellic 300 CS, a
WHO recommended non-pyrethroid insecticide formulation of an existing
organophosphate insecticide, pirimiphos-methyl. Its features include being a
broad spectrum mosquito control insecticide, non-staining after application,
possessing acceptable odor, safe in use for human and the environment and passes the
WHO pesticide evaluations scheme (WHOPES) risk assessment\cite{haji2015efficacy}. Actellic offers a number of
benefits over existing alternatives; it is not a pyrethroid or a carbamate, and so is
unaffected by pyrethroid or carbomate resistance, both of which can arise
quickly; it has a longer residual effect than all other non-pyrethroid IRS
formulations (over 6 months), and was during the study period the primary
insecticide used by MOH in partnership with Vectorlink.

DP (Eurartesim, Sigma Tau) was used for MDA. Eurartesim is a pre-qualified malaria treatment medicinal product according to the United Nations pre-qualification program managed by the WHO. 

The three contiguous sub-counties, shown in Fig. \ref{fig:site}, were assigned to the three study arms in the following non-random manner: prior to the
baseline study, the sub-county with the highest number of malaria cases reported
to District Helath Information Sytem 2 (DHIS2) in the year prior to the study start was assigned to MDA+IRS, and the
other two sub-counties were randomly assigned. Each arm received one of the
following three malaria control packages:

\begin{itemize}
\item{Arm A (Kapujan sub county): MDA with DP (Eurartesim) + IRS with pirimiphos methyl (Actellic) + SOC.}
\item{Arm B (Toroma sub county): IRS with pirimiphos methyl (Actellic) + SOC.}
\item{Arm C (Magoro sub county): SOC only, including ITNs
distributed through universal coverage campaign, case management at facilities,
and intermittent treatment of pregnant women IPTp.}
\end{itemize}


 
\subsection*{Study participants: eligibility and consent}

All residents in the three study sub-counties were assessed for inclusion in
the study. An enumeration and consenting visit was conducted in all three arms
prior to study start. In both intervention arms, an additional screening and
consenting visit was conducted before each intervention round, to assess
continued eligibility for receiving the intervention.  Eligibility and
exclusion criteria differed slightly by arm. In the SOC arm, all
residents were eligible, no screening was needed, written consent was sought
for participation in the study, and all consenting participants were enrolled
and assigned unique identification numbers. All residents in the IRS and
MDA+IRS arms were eligible for IRS, screened for possible adverse reactions to
the insecticides, and written consent was obtained from household heads on
behalf of the family; all household members of consenting households were
assigned unique identification numbers. All household residents in the MDA+IRS
arm were additionally screened for MDA intervention eligibility. Exclusion
criteria included: younger than 6 months, pregnant, current symptoms of severe
malaria, reported allergies to DP, history of cardiac problems or fainting,
family history of long QT syndrome, or currently taking medications known to
prolong the QT interval (e.g. antiretrovirals). Eligible adults
provided written consent for themselves and any minor children, and children
between 8 and 18 also provided written assents. Consented participants in this
arm were given unique identification cards that included name, geographical
location and demographic information as well unique study ID, and requested to
present them at the MDA site at time of treatment.  After the first round,
barcode scanners were used to confirm the identity, consent and screening
status of each participant. Prior to each round, eligibility and consent for
MDA+IRS was reassessed. At the MDA treatment sites, consents were confirmed and
current medical eligibility reconfirmed. Mapping, enumeration, screening,
consents, assents, and sample frames were updated prior to each of the four
rounds in both intervention arms.

 
\subsection*{Study Procedures}

\subsubsection*{Social and behavior change communication}

To create awareness and uptake of the intervention, and to obtain high coverage
rates for MDA and IRS, numerous innovative social and behavior change
communication (SBCC) methods were used, before, during and after
implementation. These included: inception meetings and engagement
workshops with key macro-level stakeholders to obtain buy-in and support for
the study; village meetings conducted at village venue sites to promote
dialogue, create community awareness, and describe the study objectives and
interventions; radio talk shows and radio messages to reinforce
continued awareness and to disseminate important study information; publicly posted posters describing the interventions; village health team
(VHT) coordinators were sensitized to strengthen interpersonal
communication; and a gender-sensitive community advisory board comprised of
community members was formed, to ensure continued engagement and community ownership for the interventions conducted over the 3-year period.

\subsubsection*{Household mapping, and individual enumeration and screening}

With the help of the local leaders, all households within the study area were
mapped to create a sampling frame for estimating coverage of interventions
and subsequent evaluations. A household was defined as any single permanent or
semi-permanent dwelling structure acting as the primary residence for a person
or group of people that generally cook and eat together. Household locations
were mapped using hand-held eTrex global positioning system (GPS) receivers
(Garmin Ltd., Olathe, KS) and readings were taken from the door of the
household, if possible, or from a point that was most representative of the
household. In addition to the mapping, all household residents living in the
mapped households were enumerated, screened if living in the intervention arms,
and informed consent for study participation was sought as described above.

\subsubsection*{Indoor residual spraying}

Spray operations were performed by the study team in compliance with WHO and
national standards. IRS was conducted every 8 months in the IRS and MDA+IRS
arms using both Hudson and Semco spray pumps fitted with pressure regulators.
Both IRS arms received four co-timed rounds of IRS. Approximately 150 spray
operators, 50 washpersons and 13 parish store operators were recruited from the
target sub-counties, medically screened (pregnant and nursing persons were
excluded), and trained on all spray operations including environmental
monitoring and compliance, use, and maintenance of spray equipment and personal
protective equipment (PPE). In advance of each spray round, the study team
marked eligible households with chalk or pen. All communities were alerted to
the spray campaign, spray dates, and special instructions in advance. Each
spraying exercise in a given parish of 5-10 villages was conducted within a
10-day period, and the entire spray round was conducted within a month. Both
VectorLink Uganda and visiting Innovative Vector Control Consortium (IVCC) East
African regional teams provided specialized spray quality intensives and
monitoring support. The numbers of houses/structures found and those sprayed by
the spray operators were counted per parish and cumulative totals sent by short message service (SMS)
on a daily basis.

Mosquito resistance to pirimiphos-methyl and other insecticides was monitored
via susceptibility testing, including field rearing of captured larvae and
exposure to insecticide. This was performed each year during planned spray
activities. Quality control testing for IRS was done by cone bioassay testing
of representative walls, with repeated cone bioassays providing information
about the residual efficacy and the decay rate of the insecticide.


\subsubsection*{Mass drug administration}
All eligible and consented residents in the MDA+IRS arm received a three-dose
treatment course of DP within a two week period of IRS implementation. A mix of
fixed campaign style and house-to-house sweeps approach was used to administer
DP by MDA. Details of this approach are documented in an earlier
publication\cite{mulebeke2019implementing}. In summary, the first dose of DP
was administered by direct observation (DOT) from 18 fixed village centers to
eligible participants. At these sites, each participant was provided
instructions of the MDA process, screened, and weighed. Accurate DP
dosing was provided according to weight-based guidelines in 8 weight
bands.  The second and third doses were given to the participants (in a distinctive easy to use packaging) with instructions on how the medication
should be taken from home on the second and third day.  For participants less
than 15 years, instructions were provided to their care-takers on how to
administer the medication.  To ensure compliance, study VHTs
conducted house-to-house follow-up sweeps to check if participants had taken
their second and third doses, by asking for the empty blister packs/medication
envelopes.

\subsubsection*{Standard of care}

All sub counties received ITNs by mass campaign conducted nation-wide by
Uganda’s MOH in April 2017. In addition, malaria case management
and provision of IPTp was performed in all three sub counties,
according to national guidelines, in 8 facilities located in the study
area. The study personnel slightly enhanced the commodity supply chain in the
study area by ensuring buffer stocks of rapid diagnostic tests (RDTs), ACTs and intravenous (IV) artesunate to facilities as needed to limit their stock-out. Fig. \ref{fig:interv} below provides a summary of the trial flow progress.


\subsection*{Evaluation Methods}

To evaluate the impact of the study interventions on malaria prevalence,
cross-sectional community surveys were conducted at baseline and every 5-7
months thereafter, with the exception of survey number three, which was
conducted three months after the second spray round. The surveys collectively provide a composite picture of the prevalence response to the intervention rounds at the $\sim$6(+3) month points post-intervention.

Household surveys consisted of three components: (1) a survey questionnaire
administered to heads of households, (2) a women’s survey questionnaire,
administered to women aged 15–49 years, adopted from the Roll Back Malaria
Monitoring and Evaluation Reference Group\cite{world2005malaria}, and (3) a
clinical survey including laboratory testing of all household residents present
at the time of the survey. Fig. \ref{fig:chron} shows the timing of the
interventions and surveys.



\subsubsection*{Survey sample size and selection}

Six surveys were conducted as shown in the timeline of Fig. \ref{fig:chron}.
The unit of survey selection was the household and sample size
estimations were based on estimates from disease prevalence
studies\cite{daniel2018biostatistics}. Using the following formula to calculate adequate sample size 
\begin{equation}
n = Z^2 \frac{P(1-P)}{d^2},
\end{equation}
where $Z=1.99$ is the critical value of the normal distribution, $P=0.4$ is the presumed prevalence at baseline, and $d=0.035$ is the effect size precision.
The choice of precision, d, should depend on expected prevalence, and the ideal precision to choose for cross-sectional surveys is one-fourth or one-fifth of the
prevalence\cite{pourhoseingholi2013sample}. Therefore, it was estimated that a sample size of 1200 persons per arm would detect an effect size of .175, or an
absolute difference between arms of $\sim \pm3.5$\%, with Z = 1.99 and P = 10\%. 
In order to create a non-biased sampling frame, all households in the
three sub-counties were enumerated before each survey. Thereafter a
computerized number generator using a probability proportion to village size
approach was used to randomly select approximately 200 households (for the
first four surveys) and 300 households (for the fifth and sixth survey) from
each of the sub-counties.

\subsubsection*{Laboratory procedures}

The clinical survey component included a finger-prick blood sample for a thick
blood smear and for an RDT, measurement of hemoglobin, and preparation of
filter paper blood samples for later analysis by quantitative polymerase chain
reaction (qPCR). All specimens were barcoded and linked to their corresponding
surveys. Thick and thin blood smears were prepared in the field for microscopy.
At the laboratory the blood smears were stained with 2\% Giemsa for 30 minutes
and evaluated for the presence of asexual and sexual parasites (gametocytes). A
thick blood smear was considered negative if examination of 100 high-power
fields revealed no asexual parasites.  For quality control, all slides were
read by a second microscopist, and a third reviewer settled any discrepant
readings. qPCR was performed on the filter-paper samples. Hemoglobin estimation
was carried out on site using a battery-operated portable HemoCue analyzer
(HemoCue, Anglom, Sweden). Rapid diagnostic testing was also conducted by the
field team using lateral flow assay from SD-Bioline. Filter paper blood samples
taken off and stored at -20$^\circ$C before being assessed for parasitemia by
qPCR. During qPCR deoxyribonucleic acid (DNA) was extracted using Qiagen spin
columns. Parasite DNA was detected using nested PCR targeting the 18S rRNA
gene. Parasite density was estimated for all positive PCR samples. Duplex 10mL
reactions amplifying both human ($\beta$-tubulin) and Plasmodium (Met transfer ribonucleic acid (tRNA)
gene) targets were run for each sample in a 384-well format ABI qPCR machine,
model 7500. Delta Ct values between the two targets were estimated for each
sample and the mean delta Ct of duplicate wells normalized to the within-run
quantitative standard, comprising the WHO International Standard for \emph{P.
falciparum} DNA (IS) and representing 500 parasites/mL. The ratio of parasite
density in the sample relative to the IS was then multiplied by 500
parasites/mL to obtain the estimate of parasite density. Any samples negative
by qPCR but positive by nested PCR were assigned an arbitrary parasite density
value of half the minimum density detected.

Participants with a temperature of greater than 37.5$^\circ$C were treated with
paracetamol as appropriate and those with a positive malaria RDT and no
evidence of severe malaria were treated with artemether-lumefantrine (AL)
according to national guidelines. Children and adults with a positive malaria
RDT plus any signs of severe disease were referred to the nearest health
facility for further evaluation and treatment.

\subsubsection*{Analytical Methods}

\paragraph*{Prevalence outcomes.}

All comparisons of prevalence outcomes followed the intention to treat
analysis. Population prevalence for each arm was estimated at each survey time
point by qPCR positivity, and reported, with a 95\% confidence interval, for
three age groups of interest: all ages, children under 5, and children between
5 and 15. The baseline survey results for RDT, microscopy, and qPCR were
examined separately and qPCR results subsequently compared with the follow-up
surveys in the three age categories.

\paragraph*{Geostatistical analysis.}

In order to compare the impact on prevalence for residence in the IRS+MDA arm
versus residence in the IRS arm, qPCR prevalence data from the six
cross-sectional surveys in each of the three arms was modeled using a
spatiotemporal Gaussian model with random effects, adjusted for age and
seasonality. Model-based geostatistical methods\cite{diggle2019book} provide a
principled likelihood-based approach to carry out spatio-temporal predictive
inferences of geo-referenced health outcomes. More importantly, this approach
combines all survey data mapped in space and time during the study period, and
allows modeling of the residual spatio-temporal variation in disease prevalence
unexplained by the measured covariates. This model is described in detail in
Supporting Information, Appendix I. The spatio-temporal geostatistical model allows for a joint analysis of the data across all surveys and quantifies the effects of control interventions more reliably, by borrowing strength of information in space and time.


\paragraph*{Difference-in-differences analysis.}

For comparison, secondary difference-in-differ\-ences (DiD) analyses were
performed comparing all follow-up survey timepoints with baseline for qPCR.
Using SAS 9.4, the model used binomial logistic regression models that compared
trends in malaria prevalence in the MDA+IRS arm to trends in malaria prevalence
in the IRS arm, adjusting for potential confounding variables, (age, ITN use,
and gender). Statistical significance threshold was set at $\alpha=0.05$ using
two-tailed tests. Log odds were converted to odds ratios by calculating the
exponentiated regression coefficient of the interaction terms in the model.
Comparisons between each of the surveys and baseline, and MDA+IRS vs. IRS are
presented, and additional results are provided in Supporting Information,
Appendix II.


\section*{Results}

\subsection*{Participant characteristics}

A total of 46,765 individuals and 8,004 households across all three study arms
were enumerated at baseline. Roughly 51\% of the population were females, and
16\% were children under 5 years of age. Table \ref{tablDemog} shows study
population demography enumerated through household visits prior to the baseline
survey.

%
%\begin{table}[!ht]
%  \begin{center}
%%\begin{adjustwidth}{-1.25in}{0in} 
%    \caption{Enumerated study population characteristics at baseline, November %2016.}
%    \label{tablDemog}
%    \begin{tabular}{| l | l | l | l |}
%    \hline
%    \bf{Characteristic} & \multicolumn{3}{ |c|
%    }{\bf{Study Arms}} \\
%    \hline \hline
%    Demographics & Arm A & Arm B & Arm C \\ \hline
%    Population & 15,738 & 10, 503 & 20,524 \\ \hline
%    Number of households & 2,755 & 1,929 & 3,320 \\ \hline
%    %Ave household residency & 4.2 & 4.1 & 4.4 \\ \hline
%    Male & 7,568 & 5,217 & 9,904 \\ \hline
%    Female & 8,170 & 5,286 & 10,620 \\ \hline
%    Children $<$5 years & 2,551 & 1,606 & 3,501 \\ \hline
%    Children 5-15 years & 5,666 & 3,585 & 7,169 \\ \hline %\hline
%    %\bf{Malaria control interventions} & \multicolumn{3}{ |c| 
%    %}{ } \\ \hline \hline
%    %IRS coverage, 11/2016 & None & None & None \\ \hline
%    %ITN coverage, ($<$5 yrs) & Universal & Universal & Universal \\ \hline \hline
%    %\bf{Baseline $<$5yr qPCR malaria prevalence (\%)} & 58.4 & 52.6 & 48.5 \\ %\hline
%    %\bf{Baseline 2-10yps qPCR malaria prevalence (\%)} & 73.1 & 64.6 & 64.3 \\ %\hline
%    %\bf{Baseline all ages qPCR malaria prevalence (\%)} & 66.0 & 65.0 & 62.0 \\
%     % \hline
%   % \bf{Study intervention arm} & MDA+IRS: Arm A & IRS-only: Arm B & Standard of %Care: Arm C\\
%    %  \hline
%     % \hline
%    \end{tabular}
% %  \end{adjustwidth}
%  \end{center}
%\end{table}
%


\subsection*{Baseline prevalence}

The initial survey was performed in November 2016, before the first IRS and MDA
rounds in December 2016. All arms were found to be highly infected at baseline,
with all ages qPCR prevalence of 62-66\%, and between 73-75\% in children ages
5-15 (Table 1). Described further in Supporting Information, Appendix III, microscopy prevalence was between 21-26\%, with the MDA+IRS arm most
highly infected and the SOC arm least infected. In contrast, RDT
prevalence was between 36-47\%, and showed the opposite trend, with the MDA+IRS
arm least infected and the SOC arm most infected. The qPCR trends
tracked the microscopy trends more closely than they tracked the RDT trends,
but were considerably more sensitive than either, and showed less overall
variation between arms than did either the microscopy or the RDT. Baseline
malaria prevalence by age group and study arm is summarized in Supporting Information, Appendix III.
%\ref{prevArm}.

%\begin{table}[!ht]
%\begin{center}
%\includegraphics[scale=0.95]{Table2BaselinePrev.png}
%\caption{
%Baseline malaria prevalence by study arm, diagnostic tool and age %category.}
%\end{center}
%\label{prevArm}
%\end{table}
 
\subsection*{Intervention Coverage}

\subsubsection*{IRS coverage}

IRS coverage in both arms in all 4 rounds was over 97\% of structures sprayed over
structures found. This method of measuring coverage can be inaccurate when used
for targeted spray operations by spray operators who are unfamiliar with the
area they are spraying; however in this study, spray operators sprayed their
own villages and neighboring villages, and all houses in the designated area
were mapped, enumerated and targeted for spraying, which created positive
social pressure not to miss compounds. Due to strong SBCC programs prior to
spraying, almost all residents were home with belongings already prepared
outside their home when spray operators arrived. As noted previously, IRS was
extremely welcome in this community\cite{wanzira2018community} and very few
homes were found locked.  Table 2 %\ref{irsCov} 
contains household information on structures sprayed by intervention arm.

%
%\begin{table}[!ht]
%%\begin{adjustwidth}{-1.25in}{0in} 
%  \begin{center}
%    \caption{SMS/Sprayer operator-based IRS coverage: houses sprayed/houses found}
%    \begin{tabular}{| M{25mm} | c | M{15mm} | M{15mm} | M{15mm} | M{15mm} |}
%    \hline
%    \bf{Characteristic} & \bf{Sub-County} & \bf{Houses sprayed}  & \bf{Houses %found} & \bf{Coverage (\%)} & \bf{Population protected} \\ \hline
%    \multirow{3}*{Round 1: Dec, 2016}& Arm A & 6483 & 6509 & 99.6 & %\multirow{3}{*}{30,741} \\ 
%    & Arm B & 5179 & 5339 & 97.0 & \\  
%     & \bf{Total} & \bf{11,662}  & \bf{11,852} & \bf{98.4} & \\
%     \hline
%      \hline
%    \multirow{3}*{Round 2: Aug, 2017}& Arm A & 6173 & 6235& 99.0 & %\multirow{3}{*}{31,663} \\ 
%    & Arm B & 5429 & 5597 & 97.0 & \\ 
%     & \bf{Total} & \bf{11,602}  & \bf{11,827} & \bf{98.1} & \\
%     \hline
%      \hline
%    \multirow{3}*{Round 3: Apr, 2018}& Arm A & 6658  & 6718 & 99.1 & %\multirow{3}{*}{32,979} \\ 
%    & Arm B & 5541  & 5712 & 97.0 & \\ 
%     & \bf{Total} & \bf{12,199}  & \bf{12,435} & \bf{98.1} & \\
%     \hline
%      \hline
%     \multirow{3}*{Round 4: Dec, 2018}& Arm A & 6355  & 6426 & 98.9 & %\multirow{3}{*}{32,438 } \\  
%    & Arm B & 5280 & 5399 & 97.8 & \\
%     & \bf{Total} & \bf{11,635}  & \bf{11,824} & \bf{98.4} & \\
%     \hline
%      \hline
%    \end{tabular}
%  \end{center}
%\label{irsCov}
%%\end{adjustwidth}
%\end{table}
%


\subsubsection*{MDA coverage}

High MDA compliance was the main goal for the SBCC programs, and the investment
in these activities was robust, with high community participation resulting. The denominator for MDA coverage included all persons normally resident in the study site, whether eligible or not. For rounds 1,3 and 4,  MDA directly observed first dose coverage was approximately 80\% or more, with
< 1\% fall off for second and third dose compliance ascertained by blister pack and verbal confirmation (See Table 3.) Round 2 MDA coverage was slightly higher at 83\% for directly observed first dose, and 82.2\% after second and third dose compliance.


%
%\begin{table}[!ht]
%  \begin{center}
%    \caption{MDA coverage by method and round (Arm A), 
%    December 2016–December 2018. }
%    \label{mdaCov}
%    \begin{tabular}{| M{25mm} | M{20mm} | M{20mm} | M{20mm} | %M{20mm} |}
%    \hline
%    & \multicolumn{4}{ |c| }{\bf{Round (Month, Year)}} \\
%    \hline
%    \bf{Coverage by Dose and Method} & \bf{Round 1, Dec 2016 %n(\%)} & \bf{Round 2, Aug 2017 n(\%)} & \bf{Round 3, Apr 2018 %n(\%)} & \bf{Round 4, Dec 2018 n(\%)} \\ \hline
%    Dose 1 - Fixed distribution& 12,528 (79.6)& 12,637 (81.2) & %12,366 (80.1) & 12,461 (80.1) \\  
%\hline
%    Dose 2 - Door-to-door monitoring & NA & 12,458 (80.0) & %12,366 (80.1) & 12,399 (79.7) \\
%\hline
%    Dose 3 - Door-to-door monitoring & NA & 12,454 (80.0) & %12,366 (80.1) & 12,399 (79.7) \\
%\hline
%    \bf{Total (N)} & \bf{15,737} & \bf{15,563} & \bf{15,447} & %\bf{15,559} \\
%      \hline
%    \end{tabular}
%  \end{center}
%\end{table}
%

\subsubsection*{Cross-sectional surveys and qPCR testing}

Primary analysis was based on qPCR results, because the qPCR and
microscopy results proved more consistent across arms than RDT findings, and because
qPCR was more sensitive throughout than either microscopy or RDT, while qPCR results were more tightly clustered at baseline than those from microscopy. During the six cross-sectional community
surveys, 15925 qPCR tests for \emph{P. falciparum} were conducted, of which
6730 (42.3\%) were positive. Cross-sectional qPCR prevalence results by
survey, number, date and intervention arm are presented for different age
categories for all 6 surveys in Fig. \ref{survPrev}.



\subsubsection*{Differential impact on malaria qPCR positivity of IRS over
standard of care, and of MDA+IRS over IRS, measured over all surveys:
geospatial analysis}

The differential impact of IRS over standard care, and of MDA+IRS over IRS
alone, over all 6 surveys, was analyzed using a geostatistical model with
results summarized in Fig. \ref{oddsInfctn}. Residence in the IRS arm was
associated with a 70\% reduction in odds of being found infected relative to
the standard care arm for children of age 5-15, and a 65\% reduction for all
ages as well as for children under 5. A resident of any age in the arm with
both IRS and MDA had an average 80\% decrease in odds of being found infected
as compared with a person under the same conditions living in the standard care
arm. This result, with one exception the result of surveys conducted more than
half a year after the MDA campaign, was consistent across age groups, with
children under 5 and 5-15 resident in the MDA+IRS arm having a 79\% decrease.
Comparing the MDA+IRS and IRS only arms, we estimate a significant additional
15.5\% (95\% CI: [13.4\%, 17.9\%], p=5e-20) decrease in the adjusted odds ratio
(aOR). For children under 5 and children 5-15 residence in the MDA+IRS arm was
also associated with significant additional protection over IRS only, measured on average 5-7 months
post-campaign. For children 5-15: aOR reduced 13.3\% (95\% CI: [10.5-16.7\%],
p=5e-5) and for children under 5: aOR reduced 10.1\% (95\% CI: [8.5-11.8\%],
p=2e-5). The geostatistical model estimated the temporal and spatial
correlation for the qPCR results based on the date and geolocation of each
sample. The estimated range of the spatial correlation was found to be
$\sim$1.0km, beyond which the spatial correlation took on values smaller than
0.05. Fig. \ref{heatMaps} shows the predicted geography of malaria prevalence,
including non-surveyed locations, for children under 5 at the six
survey time points of Fig. \ref{fig:chron}.



\subsubsection*{Differential impact of MDA+IRS over IRS on malaria qPCR
positivity measured after each survey: DiD Analyses}

Fig. \ref{didOdds} summarizes results from a secondary DiD analysis comparing
the change in the MDA+IRS arm to the change in the IRS arm at different survey
points from baseline. The aOR of qPCR malaria confirmation was 45\% lower in
residents who received one round of MDA+IRS compared to the IRS arm (DiD
aOR=0.55, 95\% CI: [0.40, 0.76], p$<$.001), and remained consistent when measured
six months following round 2 (aOR=0.58, 95\% CI: [0.43, 0.80], p$<$.001).
When measured three months following round 2, the adjusted odds of qPCR malaria infection in residents who received
MDA+IRS were 66\% lower than residents who received IRS, (aOR=0.34, 95\% CI: [0.23, 0.50], p$<$.001).
Six months following rounds 3 and 4, the adjusted odds of qPCR malaria confirmation were not as low: (aOR=0.74, 95\% CI: [0.57, 0.98],
p$<$.05) and (aOR 0.72, 95\% CI: [0.54, 0.98], p$<$.05) respectively.
Details of this analysis and these comparisons  
%adjusted odds ratio for qPCR and microscopy for residents who
%received MDA+IRS compared to standard of care, and residents who received IRS
%compared to standard of care 
may be found in Supporting Information, Appendix II.


\subsubsection*{Adverse events}

Both adverse events and serious adverse events (SAEs) were monitored by
the study team and SAEs reported in writing to Sigma Tau as well as to
University of Makerere's internal review board (IRB) and the study's project
advisory committee (PAC), a subset of whose members functioned as a data safety
and monitoring board (DSMB). Three SAE's were reported. The only one with a high likelihood of being related to the study medication was a case
of toxic epidermal necrolysis in a two year old girl who fully recovered. 

\section*{Discussion}

At present, MDA is considered to be a malaria control intervention appropriate for lower transmission environments,
especially for elimination settings, although there is recent interest in investigating the untapped potential of chemoprevention as a control tool  \cite{rosenthal2019malaria,alonso2020role}. 
We have demonstrated here that %this role may be expanded to include a vital new
%purpose as an augmentation, or ``accelerant'' to an IRS program.  
co-timed MDA and IRS campaigns may provide a new role for MDA as an augmentation for a vector control campaign in high burden environments.
%The protective mechanism for co-timed MDA and IRS campaigns is apparent: 
The quick,
initial impact of the MDA collectively reduces the parasite reservoir
of hosts, and the concurrent IRS extends protection against re-infection
to times well beyond initial chemoprophylaxis  %The IRS offers protection
%against resurgent malaria well after the medical campaign's effects expire,
\cite{elliott2018medical,elliott2019synergy}. This study examines the impact of these co-timed
interventions, MDA+IRS versus IRS, and MDA+IRS and IRS versus standard of care, on
 population malaria prevalence %many months post-MDA.%th the campaign well in advance of the survey.
%The study was designed specifically to address the question of  %potential: would there be
%e impact, 
at long time points (5-7 months) past a single MDA round.


Visual inspection of measured qPCR results over all 6 surveys, depicted in Fig.
\ref{survPrev}, shows that every
demographic category was highly infected at baseline in November 2016,
especially children 5-15, who have been shown to account for more than half of
the infectious reservoir in a similar transmission
environment in Uganda\cite{andolina2021sources}.   %with the MDA+IRS measurements for all ages clearly and
%significantly lower than the IRS arm for every survey after baseline. 
 However, after interventions, the arms clearly separated. The SOC arm, which like all the others received new ITNs in April 2017, was consistently higher than the others, with seasonal and/or annual fluctuations. Some possible intervention impact in the SOC arm, presumably from nets, is seen at Survey 3. Both IRS arms measured much lower prevalence than SOC at all surveys post-baseline, while the MDA+IRS arm consistently contained fewer infected individuals than the IRS arm.
The differential gains from MDA+IRS over IRS at the single survey that measured prevalence three months after the round, Survey 3, are particularly striking when compared with the seasonally equivalent baseline:  in children under 5 qPCR
positivity dropped more than an order of magnitude, from 58\% at baseline
to 5\% one year later.  By comparison, positivity for children under 5 in the IRS-only arm
dropped from 53\% to 15\% (Fig. \ref{survPrev}), a factor of roughly 3.5. Additional tables of malaria prevalence by study arm, age and diagnostic tool can be found in Supporting Information, Appendix III.

Geostatistical modeling analysis using all six cross-sectional surveys 
and an intention to treat approach showed that, over the 32 months of the study after baseline,
residents of the IRS arm were $\sim$65\% less likely to be found infected with
malaria than those living in the standard of care arm with universal ITN coverage only, a large
preventative difference. %with universal ITN coverage only for vector control. 
These results highlight the strong additional protection in this high transmission setting provided by an IRS campaign
 over ITNs alone. Significant additional protection was observed when a co-timed MDA campaign was included, as
residents in the MDA+IRS arm were $\sim$80\% less likely to be found infected than those resident in the standard of care arm,
and had a 15.5\% (95\% CI: [13.4\%, 17.9\%], p=5e-20) decrease in the adjusted
odds ratio as compared with those resident in the IRS only arm, measured roughly six months after the campaigns. For child
populations, residence in the MDA+IRS arm was also associated with significant
additional protection over the IRS-only arm. For children 5-15: aOR reduced
13.3\% (95\% CI: [10.5-16.7\%], p=5e-5) and for children under 5: aOR reduced
10.1\% (95\% CI: [8.5-11.8\%], p=2e-5) (Fig. \ref{oddsInfctn}). 

Secondary DiD analysis, also intention to treat, 
compared single prevalence survey timepoints to baseline, and 
confirmed the significant protective impact of MDA+IRS over IRS alone (Fig.
\ref{didOdds}). These comparisons showed significantly reduced
aOR for the MDA+IRS arm compared with the IRS, with regression coefficients
ranging from 0.28 to 0.66 for surveys 2-6. MDA+IRA appears even more
protective when compared with IRS by DiD analysis than by geospatial modeling, but as the DiD analyses
involve a necessarily more limited selection of the data, the geospatial analysis is
presented as primary. Some of the variation between survey points could be seasonal: the DiD analysis was
adjusted for age, gender and net use, but not for seasonality.  The smallest protective advantage for MDA+IRS over IRS was noted in survey six (an expected seasonal low point), compared to survey one (an expected seasonal high point). Survey three, on the other hand, is an expected seasonal high point, conducted at the same time of year as the baseline survey, but because it was conducted only 3 months rather than 6 months after the MDA round, provided an alternate window into the dynamic preventive effect of the MDA.  
Additional DiD comparisons between MDA+IRS and SOC and between IRS and SOC can be found in Supporting Information, Appendix II.


It's striking that survey data, measured 5-7 months after a single treatment round of MDA in this high transmission environment, still show significant protective advantage from MDA+IRS over IRS. However, cross-sectional measurements of prevalence would almost certainly be lower and even more differentiated at points in time closer to the MDA round. %The dynamics are expected: 
High-coverage MDA immediately lowers population prevalence which then slowly rebounds, as chemoprophylaxis expires and re-infection occurs. %This is evidenced by survey
%three in November 2017, the only survey with a shorter three
%month delay between the MDA round and the follow-up survey. 
Under the assumption of ideal DP
protection and compliance with medication regimen, in the first month after the intervention,  $\sim$79\% of the population presumably enjoyed protection through the
$\sim$28 day chemoprophylactic period of DP\cite{agarwal2013randomized}. %This protection must have waned over the following months, although the waning was slowed by the concurrent mosquito control.
Survey three, performed three months after the round, showed the lowest prevalence captured in the MDA+IRS arm, and also a much more
pronounced differential impact for the co-timed MDA, with 66\%
reduced aOR of infection for residents in the MDA+IRS arm compared with
residents in the IRS arm, as opposed to reductions of 28-45\% for other survey
times points (see Fig. \ref{didOdds}). This protection waned steadily in the months following the MDA round, as treated individuals became reinfected.
Survey four, taken several months later, indicate the resurgent return of
parasitaemia in the study area as expected.  %This point is also important because the
%protection conferred by the addition of 
%An MDA campaign is %one that is continuous in time after its deployment, 
%though in this investigation is found
%to be significant at just one time of measurement, many months later.  and due to the cooperative nature of the interventions, a number of infections were almost certainly prevented in the (roughly) six month period that elapsed between the campaigns and the surveys. 
Because prevalence in most of the surveys was measured at 5-7 months after the
MDA, the deepest impacts of MDA almost certainly occurred outside the survey
observation window. 
Health facility catchment areas are not constrained, so it is difficult to reliably quantify this effect through facility based case surveillance trends. Monthly cross-sectional surveys following an
MDA+IRS and an IRS intervention, or a cohort study of incidence following the
interventions, could help capture the full protective impact of MDA+IRS.


%The high-coverage MDA should quickly drive prevalence to low levels which then
%slowly rebound, as first prophylaxis expires in the community, and then
%re-infection ensues. 

%RCE: is this paragraph necessary? What is it accomplishing?
%The employed geostatistical model also estimated the temporal and spatial
%correlation for the qPCR results based on the date and geolocation of each
%sample. The estimated range of the spatial correlation was about 1.0km, beyond
%which the spatial correlation took values smaller than 0.05. Fig. 6 shows a
%summary of the spatial maps of malaria prevalence for children under 5
%predicted at non-surveyed locations at the 6 survey time points. The higher
%prevalence clusters are somewhat consistent across the three years, with higher
%numbers of positives found bordering either water or study site boundaries.

 %\textcolor{red}{The highest differential reduction of survey three of November 2017---again the singular survey with the short three month delay between the campaign and survey---indicated an aOR of being found infected was 66\% lower in the MDA+IRS arm than in the IRS arm. This timing with respect to the campaigns suggests the greatest MDA protection happens at this time, but, this is also measured at a time of the year in apparent opposition to the seasonal timing of infections. The DiD analysis was adjusted for age, gender and net use, but not for this seasonal effect. In the area generally, infections tend to increase out of the elapsing dry era of the first quarter of the calendar year, growing through the spring rains to then eventually wane at the end of the year when all rains cease (cite). For this matter, survey six, in the Spring of 2019, is expected to be a point in time which is seasonally lower in infections compared to the high infection rates of survey one and three, both taken in the Fall. In this way, seasonal effects act to reduce differences between the arms, and despite this, robust reductions from the interventions are still found in Survey three. }


\section*{Conclusion}


This trial showed that a significant protective impact of MDA was sustained for
long times (5-7 months) after a treatment round, a surprising result in high
transmission.  The 2013 Cochrane review of MDA explicitly evaluated studies for
sustained impact beyond 6 months, finding only a few low transmission trials
that met this criterion\cite{poirot2013mass,newby2015review}. Similar findings
from a large comparative modelling study suggested that MDA was less likely to
have durable impact in high transmission\cite{brady2017role}. This finding is
also in distinction to other trials of MDA impact in similar
settings\cite{eisele2020impact}, and to cohort studies of individual treatment
in absence of vector control\cite{nankabirwa2014impact}. The results suggest
that MDA impact in this high transmission setting was protracted by co-timed
vector control as predicted by modelling.

Limitations of this study include the fact that population-based interventions
were assigned without blinding. In addition, the MDA+IRS arm was non-randomly
assigned to the subcounty with the highest number of clinical cases in the year
preceding the study start, which may have reduced the relative impact of MDA +
IRS compared with IRS alone. Due to prohibitive cost constraints in creating
large-scale IRS clusters, and the concern of contamination through mosquito
flight at smaller scales (for instance, at village level), a single large
community intervention cluster (an entire subcounty, with 18- 30 villages) was
used for each arm. As equipoise precluded the establishment of a control arm
without any malaria prevention in place, and also the use of MDA without IRS,
it is not possible to separately distinguish the effect of ITNs vs no control
or to evaluate the impact of MDA in the absence of IRS. Only the first dose of
MDA was observed, with the second and third dose inferred from collection of
empty blister packs. If compliance was imperfect, MDA coverage would be lower than assumed,
which would reduce the additional impact of MDA. Intervention impact on
prevalence, with one exception, was measured at timepoints 5-7 months from each
of the four rounds of intervention; these measures are unable to provide a
comprehensive window into the population response to the intervention at
shorter intervening time periods.

As previously reported, campaign-style all-ages MDA was both easy to
implement\cite{mulebeke2019implementing}, and highly acceptable in this high transmission environment\cite{wanzira2018community}, and achieved high coverage
of $\sim$79\% for all four rounds. IRS for vector control requires extensive
community engagement and mobilization just as MDA does, and the two activities
can benefit from joint investment in SBCC activities and joint community
momentum. MDA's possible contribution to antimalarial resistance\cite{von2003mass}
is a valid concern, but in Uganda can be mitigated by the use of DP
for MDA while artemether-lumefantrine (AL) is used for frontline therapy;
the observed counter-selection between DP and
AL\cite{conrad2014comparative,okell2018emerging,rasmussen2017changing} may
offer some protection against the development of resistance. 

%Sustainability is often a concern for MDA in a control context. Sustainability may not be as important a question as cost effectiveness, however. 
Sustainability and cost effectiveness may be additional concerns for the use of MDA. Absent a
change to the underlying forces of infection in a high transmission area,
resurgence will reliably occur when control interventions
cease\cite{cohen2012malaria}. Moreover, the more reduction a control tool
achieves, the swifter will be the rebound when it is
withdrawn\cite{elliott2018medical}. But even where control cannot be maintained and resurgence occurs, the
gains may be greater than the losses. Recently, a multi-year examination of
surveillance data at reference health centers in Uganda showed that while
stopping IRS was associated with a 5-fold increase in infections in the ensuing
10 months, starting IRS was associated with a more than 5-fold decrease in 8
months, a reduction that increased still further after 4 years of continuous
control\cite{namuganga2021impact}. Effective interventions are also associated
with a rapid shift in the remaining burden of malaria from younger people to
older people, an advantage not captured in burden data
alone\cite{kigozi2020rapid}. 

To continue to make global gains against malaria, its necessary to optimize and
target tools for burden reduction specifically to high transmission areas.
Vector control is still the mainstay of prevention, but chemoprevention
strategies are an important additional tool for high burden settings.
Increased control impact from careful co-timing is predicted for all forms of
vector control and has been modeled for MDA+ITNs as well as
MDA+IRS\cite{elliott2018medical}. Because vector control is usually delivered
less frequently than chemoprevention, a co-timed use of MDA represents a
low-frequency use of chemoprevention, and though maintenance and
continuity are always needed in control settings, the reapplication demands,
and therefore the associated costs, are modest. In highly infected communities,
an annual round of MDA does not represent additional drug pressure on a
community, but instead a demographic reorganization (as most treated clinical
infections are in children) and synchronization of treatment. Considered not as an elimination tool, but as a control tool in this
high burden setting, co-timed IRS+MDA is an effective, practical and acceptable
intervention for sustained burden reduction. Co-timed MDA is 
likely to be cost effective, to
offer low pressure on the development of drug resistance, and could be combined
with ITN distribution as easily as with IRS.

\section*{Abbreviations}

    ACT:	 	artemisinin combination therapy

AL:	 	artemether-lumefantrine

aOR:	 	adjusted odds ratio

CI: 		confidence interval

DHIS2:		District Health Information System Version 2

DiD:		difference-in-differences 

DOT:		directly observed therapy

DP:		dihydroartemisinin- piperaquine

DNA:		deoxyribonucleic acid

DSMB:		data safety and monitoring board

GPS:	 	global positioning system

IPTp:		intermittent preventive treatment for pregnant women

IPTsc:		intermittent preventive treatment in schoolchildren

IRB:		internal review board

IRS:		indoor residual spraying

ITN:		insecticide treated net 

IV:		intravenous

IVCC:		Innovative Vector Control Consortium

MDA:		mass drug administration

MOH:		Ministry of Health

PAC:		project advisory committee

PMI:		President's Malaria Initiative

PPE:		personal protective equipment

qPCR:		quantitative polymerase chain reaction

RDT:		rapid diagnostic test

SAE:		serious adverse events

SBCC:		social and behavior change communication

SMS:		short message service

SOC:		standard of care

tRNA:		transfer ribonucleic acid

U5:		under 5

VHT:		village health team

WHO:		World Health Organization

WHOPES:	World Health Organization Pesticide Evaluation Scheme


%
%Text for this sub-heading \ldots \subsubsection*{Sub-sub heading for section}
%Text for this sub-sub-heading \ldots \paragraph*{Sub-sub-sub heading for
%section} Text for this sub-sub-sub-heading \ldots In this section we examine
%the growth rate of the mean of $Z_0$, $Z_1$ and $Z_2$. In
%addition, we examine a common modeling assumption and note the
%importance of considering the tails of the extinction time $T_x$ in
%studies of escape dynamics.
%We will first consider the expected resistant population at $vT_x$ for
%some $v>0$, (and temporarily assume $\alpha=0$)
%%
%\[
 %E \bigl[Z_1(vT_x) \bigr]= E
%\biggl[\mu T_x\int_0^{v\wedge
%1}Z_0(uT_x)
%%\exp \bigl(\lambda_1T_x(v-u) \bigr)\,du \biggr].
%\]
%%
%If we assume that sensitive cells follow a deterministic decay
%$Z_0(t)=xe^{\lambda_0 t}$ and approximate their extinction time as
%$T_x\approx-\frac{1}{\lambda_0}\log x$, then we can heuristically
%estimate the expected value as
%%
%\begin{eqnarray}\label{eqexpmuts}
%E\bigl[Z_1(vT_x)\bigr] &=& \frac{\mu}{r}\log x
%\int_0^{v\wedge1}x^{1-u}x^{({\lambda_1}/{r})(v-u)}\,du
%\nonumber\\
%&=& \frac{\mu}{r}x^{1-{\lambda_1}/{\lambda_0}v}\log x\int_0^{v\wedge
%1}x^{-u(1+{\lambda_1}/{r})}\,du
%\nonumber\\
%&=& \frac{\mu}{\lambda_1-\lambda_0}x^{1+{\lambda_1}/{r}v} \biggl(1-\exp \biggl[-(v\wedge1) \biggl(1+
%\frac{\lambda_1}{r}\biggr)\log x \biggr] \biggr).
%\end{eqnarray}
%%
%Thus we observe that this expected value is finite for all $v>0$ (also see \cite{koon,khar,zvai,xjon,marg}).
%%\nocite{oreg,schn,pond,smith,marg,hunn,advi,koha,mouse}

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\begin{backmatter}

\section*{Declarations}

%MJ format: Ethics approval and consent to participate
%Consent for publication
%Availability of data and materials
%Competing interests
%Funding
%Authors' contributions
%Acknowledgements
%Authors' information (optional)
%\paragraph*{Ethical considerations.}

\section*{Ethics approval and consent to participate}
The study was approved by the Makerere University School of Biomedical Sciences
Higher Degrees Research and Ethics Committee (SBS-HDREC) and the Uganda
National Council for Science and Technology (UNCST) ethics bodies. All study
team members were annually trained in Good Clinical Practices (GCP) compliance to assure following
guidelines for participant privacy and informed consent. All participants
interviewed gave their informed consent or assent (for those above 8 years of
age) to participate in this survey and for the information derived to be
published. The datasets used in this analysis are de-identified. 


\section*{Consent for publication}
Not applicable.
\section*{Availability of data and materials}
The datasets supporting the conclusions of this article are available in the figshare repository, [https://figshare.com/account/articles/19470125].
\section*{Competing interests}
  The authors declare that they have no competing interests.
  
\section*{Funding}
The study was supported by a grant from the Bill \& Melinda Gates Foundation to Pilgrim Africa (OPP1148566). This study also received financial support from US-based Rotary clubs, The Rotary Foundation and Malaria Partners International through a Rotary Global Grant (GG1423515) for which Pilgrim Africa was the implementing organization. Pirimiphos-methyl (Actellic) was purchased at a reduced price from Sigma-Tau made possible by Pilgrim Africa’s inclusion in IVCC’s subsidy agreement with next generation IRS providers.  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


\section*{Author's contributions}
 DE, AY, RE and JO contributed to the overall conceptualization of the study; DE and AY were responsible for study oversight; DE, AY, TE, WO, HW, RE, RM, SG, and JO were integral in the study design and execution; TE, WO, FB, HW, MK, RM, JN, OO and SG supported the field activities, data acquisition, and management; DE, AY, BA, HW, KC, TE, WO, RE, SP, EG and MR contributed to the analytical approaches for the study, various analyses, validation, interpretation and visualization of the data. DE wrote the original manuscript draft;  BA and SP made contributions to Methods and Supporting Information; and BA, HW, RE, SP, JN and EG reviewed and edited drafts of the manuscript. All authors read and approved the final manuscript. 

\section*{Acknowledgements}

This project would not have been successful without the support, dedication and
passion of our administrative, data management and financial staff members:
Angella Amuron, Claire Ogullei, Samson Ebalu, Jude Emenyu, JaneKakai, Doris
Akello, Dorcus Achan, James Ocan, Patrick Aliau, Robert Emugu, RichardKirunda,
Ketty Acan, John Bakibinga, James Adiga, Sally Opus, Emmanuel Data, Hellen
Alamo, Nicholas Wendo, Dr. Mary Kyohere, Stephen Odeke, and Elizabeth Aigi. We
would also like to acknowledge our CAB members: MartinOkello, Jackline Ibiara,
and Fr. Paul Ekwenyu, whose gracious service to this project was invaluable.
Finally, we acknowledge and thank all of the District Health Officers, Local
Council members, Health Facility Professionals, In-Charge officers, HMIS focal
points and Data Volunteers for their tireless dedication to not only this
project, but also towards their continued perseverance in reducing malaria
burden in Katakwi District. We thank you.


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\clearpage
\section*{Figures}
%  \begin{figure}[h!]
%  \caption{\csentence{Sample figure title.}
%      A short description of the figure content
%      should go here.}
%      \end{figure}
%
\begin{figure}[!ht]
\begin{center}
\includegraphics[width=0.95\textwidth]{Fig1map.png}
\caption{
The study site with mapped households and arm assignments. Katakwi district on
a map of Uganda, with an inset map showing the study area bordering Lake
Bisina. The points on the inset map show geolocated households in 85 villages,
color coded by adjacent intervention arms: green for Arm A with MDA+IRS, blue
for Arm B with IRS, red for Arm C, standard of care. All sites have ITNs, case
management, and IPTp which constitutes standard of care.}
\label{fig:site}
\end{center}
\end{figure}

\begin{figure}[!ht]
\begin{center}
\includegraphics[width=0.95\textwidth]{Fig.2 MDAIRSflow1.png}
\caption{Flow chart of study intervention implementation.}
\label{fig:interv}
\end{center}
\end{figure}
 
 \begin{figure}[!ht]
\includegraphics[width=0.95\textwidth]{Fig3Phase1Timeline.png}
\caption{
Chronology of the study interventions and monitoring activities. The bottom six
green diamonds represent the six cross-sectional surveys in November 2016, June
2017, November 2017, March 2018, November 2018 and April 2019. The four top
blue diamonds represent the co-timed intervention rounds (Arms A B) in late
November/ early December 2016, August 2017, April 2018, and late November/early
December 2018. The top yellow diamond shows the universal ITN coverage campaign
conducted in April 2017 by MOH. The bottom two orange
diamonds show ongoing entomological and health facility epidemiological
surveillance.}
\label{fig:chron}
\end{figure}


%\begin{figure}[!htb]
%\centering
%\includegraphics[width=0.75\textwidth]{Fig4PrevPanel.png}
%\caption{Cross-sectional malaria prevalence by arm, at six time points (all
%ages, under 5, and children 5-15). The vertical dashed red lines show the
%co-timed IRS + MDA and IRS only intervention rounds in the IRS arms, while the
%vertical grey dashed line shows the universal ITN coverage campaign in all
%three arms.  The points show the cross-sectional survey sample percent qPCR
%positivity at the times of the surveys, while the bars represent the associated
%95\% confidence intervals for population prevalence.}
%\label{survPrev}
%\end{figure}
 
\begin{figure}[!htb]
\centering
  \begin{tabular}{@{}cc@{}}
    \includegraphics[width=0.9\textwidth]{AllAges.png} \\
    \includegraphics[width=0.9\textwidth]{U5.png} \\
    \includegraphics[width=0.9\textwidth]{5-15yrs.png}\\
  \end{tabular}
\caption{Cross-sectional malaria prevalence by arm, at six time points (all
ages, under 5, and children 5-15). The vertical dashed red lines show the
co-timed IRS + MDA and IRS only intervention rounds in the IRS arms, while the
vertical grey dashed line shows the universal ITN coverage campaign in all
three arms.  The points show the cross-sectional survey sample percent qPCR
positivity at the times of the surveys, while the bars represent the associated
95\% confidence intervals for population prevalence.}
\label{survPrev}
\end{figure}

\begin{figure}[!ht]
\centering
\includegraphics[width=0.9\textwidth]{Fig5aORGeospatial.png}
\caption{Adjusted odds of being found infected if resident in IRS + MDA or IRS
arm relative to resident in standard care (children under 5, children 5-15, and
all ages in panels left to right).}
\label{oddsInfctn}
\end{figure}


\begin{figure}[!htb]
\centering
  \begin{tabular}{@{}cc@{}}
    \includegraphics[width=0.45\textwidth]{survey1.pdf} &
    \includegraphics[width=0.45\textwidth]{survey2.pdf} \\
    \includegraphics[width=0.45\textwidth]{survey3.pdf} &
    \includegraphics[width=0.45\textwidth]{survey4.pdf} \\
    \includegraphics[width=0.45\textwidth]{survey5.pdf} &
    \includegraphics[width=0.45\textwidth]{survey6.pdf}
  \end{tabular}
\caption{Heat maps of predicted spatial heterogeneity in malaria prevalence in
children under 5 in surveys 1-6.}
\label{heatMaps}
\end{figure}

\begin{figure}[!ht]
\centering
\includegraphics[width=0.6\textwidth]{Fig7aORMDAvsIRSqPCR.png}
\caption{
DiD adjusted odds ratio of qPCR confirmed malaria (all ages), MDA+IRS vs IRS,
by survey timing compared to baseline.
}
\label{didOdds}
\end{figure}


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\section*{Tables}
%\begin{table}[h!]
%\caption{Sample table title. This is where the description of the table should go.}
%      \begin{tabular}{cccc}
%        \hline
%           & B1  &B2   & B3\\ \hline
%        A1 & 0.1 & 0.2 & 0.3\\
%        A2 & ... & ..  & .\\
%        A3 & ..  & .   & .\\ \hline
%      \end{tabular}
%\end{table}
\begin{table}[!ht]
  \begin{center}
%\begin{adjustwidth}{-1.25in}{0in} 
    \caption{Baseline populations and qPCR malaria prevalence by study arm and age category}
    \label{tablDemog}
    \begin{tabular}{ M{38mm} r  r  r }
    \hline
     & \multicolumn{3}{ c }{\bf{Study Arms}} \\\cline{2-4}
    %\hline 
    {\bf Population \& Baseline qPCR Prevalence by Age}  & {\bf Arm A: MDA+IRS} & {\bf Arm B: IRS} & {\bf Arm C: SoC} \\ \hline
    \multicolumn{1}{l}{Total enumerated population} & 15,738 & 10,503 & 20,524 \\ %\hline
    \multicolumn{1}{l}{Total children under 5 years (U5), \%} & 2,551 (16.2) & 1,606 (15.3) & 3,501 (17.1) \\ %\hline
    \multicolumn{1}{l}{Total children 5-15 years, \%} & 5,666 (36.0) & 3,585 (34.1) & 7,169 (34.9) \\ \hline
  \multicolumn{1}{l}{\bf All Ages} & & & \\ %\hline
    \multicolumn{1}{r}{Sampled population} & 807 & 809 & 912 \\ %\hline
    \multicolumn{1}{r}{qPCR +/n$^{[1]}$} & 514/779 & 510/785 & 520/839 \\ %\hline
    \multicolumn{1}{r}{Malaria Prevalence, \% (95\% CI)} & 66.0 (62.6-69.3) & 65.0 (61.6-68.3) & 62.0 (58.9-65.3) \\ \hline
    \multicolumn{1}{l}{\bf U5} & & & \\ %\hline
    \multicolumn{1}{r}{Sampled population} & 155 & 141 & 213 \\ %\hline
    \multicolumn{1}{r}{qPCR +/n} & 87/149 & 72/137 & 95/196 \\ %\hline
    \multicolumn{1}{r}{Malaria Prevalence, \% (95\% CI)} & 58.4 (50.5-66.3) & 52.6 (44.2-60.9) & 48.5 (41.5-55.5) \\ \hline
    \multicolumn{1}{l}{\bf 5-15 Years} & & & \\ %\hline
    \multicolumn{1}{r}{Sampled population} & 319 & 323 & 348 \\ %\hline
    \multicolumn{1}{r}{qPCR +/n} & 231/312 & 234/317 & 229/316 \\ %\hline
    \multicolumn{1}{r}{Malaria Prevalence, \% (95\% CI)} & 75.0 (69.2-78.9) & 73.8 (69.0-78.7) & 72.5 (67.5-77.4) \\ \hline
\multicolumn{1}{l}{\footnotesize{$^{[1]}$ qPCR results missing, n=125.}} & & &
    \end{tabular}
 %  \end{adjustwidth}
  \end{center}
\end{table}

\begin{table}[!ht]
  \begin{center}
%\begin{adjustwidth}{-1.25in}{0in} 
    \caption{SMS/Sprayer operator-based IRS coverage: houses sprayed/houses found}
    \label{irsCov}
    \begin{tabular}{ M{38mm} M{15mm}  M{15mm}  r } 
    \hline
     & \multicolumn{3}{ c }{\bf{Study Arms}} \\\cline{2-4}
    %\hline 
    \bf{Intervention Round \& Characteristic}  & {\bf Arm A: MDA+IRS} & {\bf Arm B: IRS} & {\bf Total} \\ \hline
 \multicolumn{1}{l}{\bf Round 1: Dec, 2016} & & & \\ %\hline
    \multicolumn{1}{r}{Houses sprayed} & 6483 & 5179 & {\bf 11,662} \\ %\hline
    \multicolumn{1}{r}{Houses found} & 6509 & 5339 & {\bf 11,852} \\ %\hline
    \multicolumn{1}{r}{Coverage (\%)} & 99.6 & 97.0 & {\bf 98.4} \\ %\hline
    \multicolumn{1}{r}{Population protected} & & & {\bf 30,741}  \\ %\hline
    \hline 
 \multicolumn{1}{l}{\bf Round 2: Aug, 2017} & & & \\ %\hline
    \multicolumn{1}{r}{Houses sprayed} & 6173 & 5429 & {\bf 11,602} \\ %\hline
    \multicolumn{1}{r}{Houses found} & 6235 & 5597 & {\bf 11,827} \\ %\hline
    \multicolumn{1}{r}{Coverage (\%)} & 99.0 & 97.0 & {\bf 98.1} \\ %\hline
    \multicolumn{1}{r}{Population protected} & & & {\bf 31,663}  \\ %\hline
    \hline 
 \multicolumn{1}{l}{\bf Round 3: Apr, 2018} & & & \\ %\hline
    \multicolumn{1}{r}{Houses sprayed} & 6658 & 5541 & {\bf 12,199} \\ %\hline
    \multicolumn{1}{r}{Houses found} & 6718 & 5712 & {\bf 12,435} \\ %\hline
    \multicolumn{1}{r}{Coverage (\%)} & 99.1 & 97.0 & {\bf 98.1} \\ %\hline
    \multicolumn{1}{r}{Population protected} & & & {\bf 32,979}  \\ %\hline
    \hline
 \multicolumn{1}{l}{\bf Round 4: Dec, 2018} & & & \\ %\hline
    \multicolumn{1}{r}{Houses sprayed} & 6355 & 5280 & {\bf 11,635} \\ %\hline
    \multicolumn{1}{r}{Houses found} & 6426 & 5399 & {\bf 11,824} \\ %\hline
    \multicolumn{1}{r}{Coverage (\%)} & 98.9 & 97.8 & {\bf 98.4} \\ %\hline
    \multicolumn{1}{r}{Population protected} & & & {\bf 32,438}  \\ %\hline
    \hline
     \end{tabular}
 %  \end{adjustwidth}
  \end{center}
\end{table}


\begin{table}[!ht]
  \begin{center}
    \caption{MDA coverage by method and round (Arm A), December 2016–December
2018. } 
\label{mdaCov}
    \begin{tabular}{ M{30mm} M{15mm}  M{15mm} M{15mm} M{15mm} }
    \hline
     & \multicolumn{4}{ c }{\bf{Round (Month, Year)}}\\\cline{2-5}
    %\hline 
    \bf{Coverage by Dose \& Method}  & \bf{Round 1, Dec 2016 n(\%)} & \bf{Round 2, Aug 2017 n(\%)} & \bf{Round 3, Apr 2018 n(\%)} & \bf{Round 4, Dec 2018 n(\%)} \\ \hline
 \multicolumn{1}{l}{\bf Dose 1} & & & & \\ %\hline 
    \multicolumn{1}{r}{Fixed distribution} & 12,536 (80.0) & 12,586 (83.0) & 12,366 (80.0) & 12,449 (80.2) \\
\hline
 \multicolumn{1}{l}{\bf Dose 2} & & & & \\ %\hline
    \multicolumn{1}{r}{Door-to-door monitoring} & NA & 12,412 (81.8)& 12,344 (79.8) & 12,399 (79.9) \\  
\hline
 \multicolumn{1}{l}{\bf Dose 3} & & & & \\ %\hline
    \multicolumn{1}{r}{Door-to-door monitoring} & NA & 12,408 (81.8)& 12,343 (79.8) & 12,399 (79.9) \\
\hline
 \multicolumn{1}{l}{\bf Total (N)} & \bf{15,668} & \bf{15,170} & \bf{15,460} & \bf{15,525} \\
      \hline
    \end{tabular}
  \end{center}
\end{table}



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%  \subsection*{Additional file 2 --- Sample additional file title}
%    Additional file descriptions text.

\clearpage
\section*{Supporting Information}
  \subsection*{Appendix I. Spatiotemporal geostatistical model}

Let $p_j(x_i, t_i)$ denote the probability of a positive qPCR result from the
$j$-th individual sampled at $i$-th location $x_i$ and month $t_i$, where $t_i
\in \{1, \dots, 31 \}$, with $t_i=1$ representing November 2016. Conditionally
on a spatio-temporal Gaussian process $S(x_{i}, t_i)$ and unstructured random
effects $Z(x_{i}, t_i)$, we modeled the probability of positive qPCR test
$p_j(x_i, t_i)$ as logit-linear regression given by,
\begin{eqnarray}
\label{eqn:pr1}
\log \left\{ \frac{p_j(x_{i}, t_i)}{1 - p_j(x_{i}, t_i)}\right\} 
 &=& \beta_0 + s(a_{ij}) + \beta_4 t_i + \beta_5 I(t_i \in {HTS})   + \nonumber \\
 & & \beta_6I(t_i \in {Baseline}) + \beta_7I(t_i \in Kap_{base}) + \beta_8I(t_i \in Tor_{base})  +  \nonumber \\
 & & \alpha_1 I(x_i \in Tor) +  \alpha_2 I(x_i \in Kap) +   S(x_{i}, t_i) + Z(x_{i}, t_i), 
\end{eqnarray}
where $s(a_{ij})$ is a linear spline expressed by,
\begin{eqnarray}
\label{eqn:pr2}
s(a_{ij}) &=& \beta_1 \textrm{min}({a_{ij}, a_0}) +  \beta_2 I(a_{ij}>a_1)\textrm{min}({a_{ij}-a_1, a_1-a_0})  + \nonumber\\ 
& & \beta_3 \textrm{max}(a_{ij} - a_1, 0).  
\end{eqnarray}
In this expression, $a_{ij}$ denotes the age of person $j$ at location $x_i$,
and $a_0=10$ and  $a_1=29$ are ages where a change in the slope of the linear
spline is imposed. The values of 10 and 29 years were chosen based on a
deviance profile of all possible combinations for $a_0$ and $a_1$ using a
standard generalized linear model. In the above equation,  $\beta_1$, $\beta_2$
and $\beta_3$ are the slopes of age in different age groups, that is the
effects of age on the log-odds of prevalence in the age groups $0-10$, $10-29$,
and $30+$ respectively.

We used linear functions of time (corresponding to $\beta_4$) to account for
long-term trends. Seasonality was included by classifying the year into a high
transmission season (June-August), with prefactor $\beta_5$ and low
transmission season otherwise.

We accounted for differences in prevalence between different trial arms at
baseline by including indicator variables of whether or not a data point was
sampled at baseline in ($Baseline$), Kapujan during baseline ($Kap_{base}$)
and Toroma during baseline ($Tor_{base}$) corresponding to $\beta_6 \, \beta_7,
\beta_8$, respectively.

To account for the different intervention strategies, $Tor$ represents Toroma,
Arm B, which received IRS only while $Kap$ represents Arm A, Kapujan, which received
both IRS and MDA. The main regression parameters of interest are therefore
$\alpha_1, \alpha_2$, respectively, which when lesser, represents a greater
impact of the corresponding intervention. Arm C, Magoro, the SOC
arm, is the reference category.
\begin{table}[!ht]
  \begin{center}
%\begin{adjustwidth}{-1.25in}{0in} 
    \caption{Estimates of the geostatistical parameters.}
\label{tab:reg}
\begin{tabular}{lccccc}
\hline \hline
Variable & Parameter       & Point estimate   & 95\% CI    \\
\hline
\textit{ Other covariates effects} & & & \\
Intercept (Control, low trans.) & $\exp(\beta_0)$ & 0.4234 & (0.3405, 0.5264) \\ 
Age trend in $0-10$ y/o & $\exp(\beta_1)$ & 1.2006 & (1.1806, 1.2210) \\         
Age trend in $10-29$ y/o & $\exp(\beta_2)$ & 0.9368 & (0.9288, 0.9448) \\        
Age trend in $\geq 29$ y/o & $\exp(\beta_3)$ & 0.9913 & (0.9862, 0.9965) \\      
Time trend & $\exp(\beta_4)$ & 1.0107 & (1.0029, 1.0187) \\                      
Seasonal effect (High trans. season) & $\exp(\beta_5)$ & 1.7408 & (1.4697, 2.0619) \\

Baseline main effect & $\exp(\beta_6)$ & 1.6108 & (1.2547, 2.0680) \\            
Baseline effect in the arm with IRS Only & $\exp(\beta_7)$ & 2.5774 & (1.9520, 3.4033) \\
Baseline effect in the arm with MDA + IRS & $\exp(\beta_8)$ & 5.4293 & (4.0901, 7.2069) \\
\hline
\textit{Interventions effects} & & & \\
IRS Only & $\exp(\alpha_1)$ & 0.3533 & (0.3150, 0.3963) \\                       
MDA + IRS & $\exp(\alpha_2)$ & 0.1987 & (0.1768, 0.2233) \\                      
\hline
\textit{Residual spatiotemporal effects} & & & \\
Signal variance & $\sigma^2$ & 5.4445 & (4.8036, 6.1710) \\                      
Range parameter & $\phi$ & 1.0260 & (0.8526, 1.2347) \\                          
Nugget effect & $\tau^2$ & 0.4559 & (0.3611, 0.5757) \\                          
Temporal effect & $\psi$ & 2.3645 & (1.5550, 3.5954) \\
\hline
\end{tabular}
\end{center}
\end{table}


We modelled the spatially and temporally correlated random variations $S(x_{i},
t_i)$ as a stationary and isotropic Gaussian process with the separable
correlation function,
\begin{eqnarray}
\rho(u, v; (\sigma^2, \phi, \psi)) = 
\rho_1(u; (\sigma^2, \phi)) \rho_2(v; \psi),
\end{eqnarray}
where the spatial component of the correlation function is modelled as
$\rho_1(u; (\sigma^2, \phi)) = \exp(-u/\phi)$, where $\phi$ regulates the pace
at which the spatial correlation decays for increasing distance $u$ between any
two locations, and the temporal correlation function as $\rho_2(v; \psi) = 1/(1
+ v/\psi)$ with $v$ being the difference between any two time points. We
modelled the spatially unstructured random effects $Z(x_{i}, t_i)$ as
independent and identically distributed Gaussian random variables with variance
$\tau^2$.

The final data consisted of 15925  qPCR tests for \textit{P. falciparum}, of
which 6730 (42.3\%) were positive. Table \ref{tab:reg} shows parameter
estimates of the geostatistical model of equations \ref{eqn:pr1}-\ref{eqn:pr2}. As
expected, the odds ratio increases sharply in the early years of life until age
10, and it decreases moderately thereafter until around the age of 30, after
which there is very slow decrease in later years (Fig. \ref{fig:age_effect}).
The estimate range of the spatial correlation is about 1.0km, beyond which the
spatial correlation takes values smaller than 0.05. The estimated range of the
temporal correlation is about one month. As indicated by the larger estimate of
$\sigma^2$ than $\tau^2$, the spatial variation dominates substantially the
unexplained variation on a scale smaller than the minimum observed distance.
\begin{figure}[!ht]
\includegraphics[width=0.85\textwidth]{age_effect.pdf}
\caption{The effect of age on odds ratio. The solid line indicates the odds
ratio at different ages, and the shaded grey area is the associated 95\%
confidence interval.}
\label{fig:age_effect}
\end{figure}

\subsection*{Appendix II. Additional DiD Analyses}

The DiD design is a quasi-experimental method that relies on the assumption
that in the absence of any intervention, the study arms receiving the enhanced
malaria interventions have identical trends in outcomes as the SOC
(control) arm.

Using SAS 9.4, the model used a binomial logistic regression with the
proportion of positive malaria cases by diagnostic tool (qPCR, microscopy and
RDT) and age as the dependable variables. Indicator variables for survey timing
(baseline/post-intervention surveys) and study arms were used, with co-variates
determined through backwards stepwise procedure, and an interaction term
between survey timing and study arms. Statistical significance threshold was
set at alpha = .05 using two tailed tests. Log odds were converted to odds
ratios by calculating the exponentiated regression coefficient of the
interaction terms in the model. DiD analyses were not done
for positive malaria cases in the `all ages' group by RDT, since malaria
prevalence at baseline was significantly higher in the SoC (Arm C)
compared to Arms A and B by this metric.

After adjusting for age, ITN use and gender, Fig. \ref{mdaVSsoc} (upper left)
shows the odds of qPCR confirmed malaria in residents receiving MDA+IRS were
significantly lower compared to residents receiving SOC, at all
survey time points.  The aOR of qPCR malaria confirmation was 90\% lower in
residents who received 1 round of MDA+IRS compared to the SoC arm (DiD aOR =
.10 [95\% CI: .08 –.14], p$<.001$); adjusted odds remained consistent 6 months
following rounds 2 and 3 (DiD aOR = .18 [95\% CI: .07 – .15], p$<.001$) and
(DiD aOR = .17 [95\% CI: .13 – .31], p$<.001$), respectively. The odds of
malaria 6 months following 4 full rounds of MDA+IRS was 70\% lower than the SoC
arm (DiD aOR = .30 [95\% CI: .23 – .40], p$<.001$).

Residents in the IRS arm (Fig. \ref{mdaVSsoc},upper right) also showed
significantly lower odds of qPCR confirmed malaria in relation to SoC
residents. All surveys measured impact 5 – 7 months following intervention
rounds, with the exception of survey 3, which showed significant impact in
residents receiving MDA+IRS 3 months after receiving 2 rounds of the
intervention compared to residents receiving SoC (DiD aOR = .11 [95\% CI: .07 –
.15], p$<.001$) and residents receiving IRS (DiD aOR = .30 [95\% CI: .21 –
.41], p$<.001$). The lower panels of Fig. \ref{mdaVSsoc} show the same trends
in microscopy confirmed malaria for all ages.

These comparisons highlight the large protective impact of not only adding
chemoprevention to IRS, but also the protective impact IRS+ITNs have over ITNs
alone in this high transmission setting.
%\begin{figure}[!ht]
%\includegraphics[width=0.85\textwidth]{figSupplMDAvsSOCqPCR.png}
%\caption{The DiD adjusted odds ratio for qPCR confirmed malaria in residents of
%all ages receiving MDA+IRS versus those with standard of care (SoC). 95\%
%Confidence intervals are indicated.} 
%\label{mdaVSsoc}
%\end{figure}

\begin{figure}[!htb]
\centering
  \begin{tabular}{@{}cc@{}}
    \includegraphics[width=0.45\textwidth]{figSupplMDAvsSOCqPCR.png} &
    \includegraphics[width=0.45\textwidth]{figSupplIRSvsSoCqPCR.png} \\
    \includegraphics[width=0.45\textwidth]{figSupplMDAvsSoCmicro.png} &
    \includegraphics[width=0.45\textwidth]{figSupplIRSvsSoCmicro.png}
  \end{tabular}
\caption{Top left: the DiD adjusted odds ratio for qPCR confirmed malaria in
residents of all ages receiving MDA+IRS versus those with standard of care
(SoC). Top right: the same DiD adjusted odds ratio for qPCR confirmed malaria
in residents of all ages receiving IRS, without MDA, versus those with standard
of care. The lower panels display the same as the panels above except for
malaria in residents confirmed by microscopy. Horizontal bars indicate 95\%
confidence intervals.}
\label{mdaVSsoc}
\end{figure}

\subsection*{Appendix III. Malaria prevalence tables}

See included excel spreadsheet.








\end{backmatter}
\end{document}
