IMPACT OF INTEGRATED COMMUNITY CASE MANAGEMENT ON SEVERE MALARIA IN A HIGH TRANSMISSION AREA IN NORTHERN ZAMBIA

by Ben Kussin-Shoptaw

A thesis submitted to Johns Hopkins University in conformity with the requirements for the degree of Master of Science

Baltimore, Maryland April 2020

i

Abstract

Background: Mortality due to severe malaria remains elevated in rural, high transmission areas despite more than a decade of control measures. Progression from uncomplicated to severe malaria is rapid, with death occurring within one to two days after symptom onset if no treatment is given. Individuals living farther away from healthcare facilities have higher odds of death from severe malaria than those living closer, suggesting that inaccessibility to care may be a key driver of mortality within these populations. To alleviate this distance-related barrier to care, community health workers in northern Zambia were trained to diagnose, treat, and refer severe malaria cases to the nearest health center or hospital as part of a rollout of an integrated community case management program (iCCM). This thesis assessed the impact of iCCM in

Nchelenge District, Luapula Province, Zambia on severe malaria metrics.

Methods: The study population was a before-and-after cross sectional study of children ≤15 years old admitted for severe malaria (N=1,115) to the children’s ward of St. Paul’s General

Hospital from October 2017 - May 2019. Patient demographics, inpatient registry diagnoses, and hematological data were collated and examined for associations with severe malaria and severe malaria mortality in a pre-post analysis. A multivariable logistic model was constructed to look for potential changes in these associations over time. Time-matched case-control data (N=107 per group) were fitted to conditional logistic regression models to account for unmeasured confounders potentially obscuring the relationship between distance and severe malaria death.

Finally, kriging models were generated and mapped to visualize the relationship between

Euclidean distance to the hospital and in-hospital mortality.

Results: Of the 1,119 children hospitalized during the study period, a total of 1,115 had available outcome data. Demographically they were similar across the entire time period, with a median

ii age of 1.9 years (interquartile range [IQR] 1.1-3.0), 53% were male, and 10% were refugees from the Democratic Republic of Congo residing in the Kenani Transit Camp (pre) or Mantapala

Refugee Settlement (post) in Nchelenge District, Zambia. Case fatality did not significantly differ between the periods before and after iCCM implementation (13% vs. 15%, p=0.24). The most frequent complication of severe malaria was severe anemia (N=257, 23%), which was slightly reduced in the post-iCCM period from 25% to 20%. Consistent with this, the median hemoglobin concentration was higher after iCCM implementation (7.4 g/dL, IQR 4.1-9.8) than before (6.0 g/dL, IQR 4.1-8.7). The proportions of patients with sepsis physiology and thrombocytopenia followed a similar trend. Children admitted during the post-implementation period resided a median distance from the hospital of 11 km (IQR 2.3-25 km), a trending increase from the pre-iCCM median of 7 km (IQR 2.5-18 km). When controlling for age, sex, refugee status, and concomitant diagnoses (severe anemia, sepsis, pneumonia, gastroenteritis, meningitis, protein-calorie malnutrition), increased distance was no longer associated with severe malaria death after iCCM implementation (adjusted odds ratio [aOR] 1.01, 95% CI 0.99-1.04, p=0.18) despite being strongly associated with the outcome in the overall model (aOR 1.02, 95%

CI 1.01-1.02, p=0.003). In the case-control study, patients were being admitted from more distant villages in the post-iCCM period, with cases coming from villages a median of 17 km

(IQR 5-25 km) away from the hospital compared to 4 km (IQR 2-15 km) for controls (p=0.049).

Kriged maps visually depicted a reduction in case fatality at further distances from the hospital in the post- compared to pre-iCCM period and identified temporally changing hotspots of severe malaria mortality throughout the district.

Conclusions: Although no difference was seen in severe malaria mortality before and after iCCM implementation, findings support that iCCM led to earlier diagnosis and referral overall,

iii and a greater number of referrals of children from more distant villages. Clinicians may begin to see more severe patients as iCCM continues its rollout and previously hard to reach patients have access to the health system. Continual monitoring of this patient population will elucidate further the true impact of iCCM and its effectiveness in limiting severe malaria mortality.

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Primary Reader and Advisor: William J. Moss Secondary Ready: Matthew M. Ippolito Acknowledgements

I would first and foremost like to thank the people of Nchelenge District and St. Paul’s General Hospital for being so welcoming and helpful during my time there—natotela sana. I would also like to thank Dr. Jean-Bertin Kabuya, Dr. Mike Chaponda, Clifford Tende, and the rest of the Tropical Diseases Research Centre Team for allowing me the opportunity to work, live, and play alongside them in Kashikishi. In particular I’d like to extend my gratitude to James Lupiya, who made me feel like a member of the village by the time of my departure. I thank Dr. Clive Shiff for starting me on this pathway and sharing enough wonderful stories, life lessons, and inspiration to fuel my passion for years to come. I want to thank Dr. Matthew Ippolito for guiding me throughout the data collection, analysis, and writing processes while allowing me an opportunity to present this work at the highest levels of science. I thank him also for his constant support and advice—both professional and philosophical. I am eternally grateful to Dr. Bill Moss for allowing me the opportunity to go conduct this research and his guidance throughout my graduate school experience. I want to thank my partner Marissa Hetrich for her faith in me, her endless patience as I wrote deep into the night and into early morning hours, and most of all for her love. Finally, I would like to thank my mom, Jody Kussin, dad, Steve Shoptaw, my siblings, and the rest of my family and friends for their support of me throughout both this thesis process and my life in general.

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Table of Contents

Abstract ...... ii Acknowledgements ...... v Glossary ...... vii List of Tables ...... viii List of Figures ...... ix

I. Introduction ...... 1 1.1 Clinical and Epidemiologic Background ...... 1 1.2 Barrier of Distance and Integrated Community Case Management ...... 4 1.3 Objectives and Aims ...... 6 II. Patient Characterization and Risk Factor Analysis ...... 9 2.1 Introduction ...... 9 2.2 Methods ...... 9 2.3 Results ...... 13 2.4 Conclusions...... 25 III. Before-and-After Case-Control Study of iCCM Impact ...... 29 3.1 Introduction ...... 29 3.2 Methods ...... 29 3.3 Results ...... 31 3.4 Conclusions...... 37 IV. Geospatial Analysis of iCCM Impact ...... 38 4.1 Introduction ...... 38 4.2 Methods ...... 39 4.3 Results ...... 40 4.4 Conclusions...... 48 VI. References ...... 53 VII. Curriculum Vitae ...... 60

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Glossary

aOR Adjusted Odds Ratio

CI Confidence Interval

CFR Case Fatality Ratio

CHW Community Health Worker

FBC Full Blood Count iCCM Integrated Community Case Management

ICEMR International Centers of Excellence for Malaria Research

IQR Interquartile Range

IRS Indoor Residual Spray

ITN Insecticide Treated Bed-nets

NMEC National Malaria Elimination Centre

OR Odds Ratio

PAMO Program for the Advancement of Malaria Outcomes

PCM Protein-Calorie Malnutrition

PMI President’s Malaria Initiative

RDT Rapid Diagnostic Test

RHC Rural Health Center

SD Standard Deviation

TDRC Tropical Diseases Research Centre

WHO World Health Organization

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List of Tables

Table 2.1: Patient Characteristics Pre- and Post-iCCM Implementation...... 14

Table 2.2: Predictive Value of Registry Diagnosis of Severe Malaria...... 15

Table 2.3: Severe Malaria Crude Mortality Ratios...... 15

Table 2.4: Odds of Severe Malaria Death Pre- and Post-iCCM Implementation...... 21

Table 2.5: Adjusted Odds of Severe Malaria Death Pre- and Post-iCCM

Implementation...... 23

Table 3.1: Characteristics of Cases and Controls...... 33

Table 3.2: Conditional Adjusted Odds of Severe Malaria Death...... 36

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List of Figures

Figure 1.1: Map of Parasite Prevalence by Province of Zambia...... 3

Figure 1.2: Proposed Directed Acyclical Graph of iCCM on Severe Malaria Mortality...... 7

Figure 2.1: Weekly Distribution of Cases of Severe Malaria Pre- and Post-iCCM

Implementation...... 16

Figure 2.2: Mortality by Median Distance Pre- and Post-iCCM Implementation...... 17

Figure 2.3: Hematologic Abnormalities by iCCM Period and Outcome...... 19

Figure 2.4: Adjusted Odds of Severe Malaria Risk Factors in Pre- and Post-iCCM Periods by Subgroup...... 25

Figure 3.1: Case-Control Analytical Framework...... 32

Figure 3.2: Difference in Distance to Hospital Among Cases and Controls...... 34

Figure 4.1: Overall Case Fatality Ratios by Village...... 41

Figure 4.2: Overall Case Count by Village...... 42

Figure 4.3: Number of Severe Malaria Cases by Village Before and After iCCM

Implementation...... 43

Figure 4.4: Severe Malaria Death Risk Map for Nchelenge District,

October 2017 – May 2019...... 44

Figure 4.5: Severe Malaria Death Risk Map for Nchelenge District Pre-iCCM ...... 46

Figure 4. 6: Severe Malaria Death Risk Map for Nchelenge District Post-iCCM...... 47

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I. Introduction

1.1 Clinical and Epidemiologic Background

Malaria is one of the largest causes of mortality from infectious diseases globally, with

228 million cases and 405,000 deaths in 2018.1 The disease is caused by Plasmodium spp. parasites, with the majority of the mortality due to P. falciparum infections.1 Transmission of the parasite is by Anopheles spp. mosquitoes, ubiquitous throughout tropical and subtropical regions.1 After vector inoculation, the parasite travels to the liver, where it matures and multiplies rapidly within hepatocytes. The numerous hepatic merozoites burst out of the liver and invade red blood cells to begin another round of multiplication. The parasitization of red blood cells in such great numbers and the reactive immune response results in the clinical manifestations of disease.2 The signs and symptoms of malaria include high fever, headaches, vomiting, chills, sweats, aches, and an enlarged spleen.3 Current World Health Organization

(WHO) guidelines advise administration of artemisinin drugs after clinical diagnosis via either microscopy or rapid diagnostic test (RDT).4 If a patient is not treated promptly, deterioration of their condition can begin. Severe malaria is associated with typical malaria symptoms plus one or more of eleven severe sequelae including respiratory distress, coma, convulsions, renal failure, abnormal hemorrhaging, and shock among others.5 Progression to severe malaria is also associated with changes in laboratory markers. Most common in high transmission settings is the development of severe malaria anemia, indicated by a hemoglobin concentration below 5 g/dL.4

Recent scholarship has further suggested that depletion of platelets to thrombocytopenic levels

(<150 platelets x 109 cells/L) may be indicative of the progression to late stage severe malaria and an increased risk in mortality.6-7 Despite malaria control efforts and widely available

1 treatments, disease prevalence remains high in sub-Saharan Africa with progression to severe malaria occurring in 1-2% of individuals, usually children, with Plasmodium infection.8

Malaria disproportionately affects sub-Saharan Africa, with the WHO reporting 93% of malaria cases and 94% of malaria deaths occurring there, of which over 60% occurs in children.1

Although the WHO has strongly encouraged governments in the region to institute various control and elimination strategies to reduce the health burden, the effectiveness of these measures can differ greatly between low and high transmission areas.9-11 The southern African nation of Zambia typifies this issue, with control measures in the low transmission southern provinces having nearly achieved elimination while northern regions have seen minimal reductions in transmission (Figure 1.1).12 Resources for control interventions have been allocated to high transmission areas like Luapula Province, but transmission has not fallen; population parasite prevalence remains high.11

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Figure 1.1: A map of Zambia highlighting the gradient of malaria transmission from the south, where coordinated control measures and interventions have effectively controlled the disease, to the north, where similar efforts have been unable to reduce transmission.

The degree of malaria endemicity in a population is reflected in the age distribution of patients and the predominant clinical manifestations. In high transmission regions like Nchelenge

District in Luapula Province, malaria most often presents in children from six months to two years of age.13 Progression to severe malaria is more common in children in this age range due to limited exposure and therefore weaker immune responses to infection with malaria parasites compared to older children.14 In lesser exposed children of hyper- and holoendemic regions such as Nchelenge District, severe malaria is characterized by severe anemia (hemoglobin < 5 g/dL) with or without other typical severe malaria signs and symptoms.4-5 As transmission intensity decreases, the age profile of infected individuals increases, moving the age of affected children

3 towards the two to five years range.15 With less exposure to the parasite and thus a less primed immune response, and other differences related to pathophysiology and ontology, children who progress to severe malaria are more likely to present with cerebral malaria—characterized by coma and convulsions—as opposed to severe anemia.13

Progression of signs and symptoms towards severe forms of disease are rapid in P. falciparum infections: high schizogony rates of the blood stage of the parasite can lead to parasitemia of over 100,000 parasites per microliter and cause death within 24 hours of symptom onset.3 Fortunately, test and treat procedures performed early enough mean that visibly sick patients can be identified and cured quickly prior to the development of severe disease.16 The outcome of uncomplicated malaria then relies heavily on the time from symptom onset to treatment.

1.2 Barrier of Distance and Integrated Community Case Management

Distance to healthcare sites in rural areas of Africa has long been linked with greater morbidity and mortality among children with malaria.17-8 Those residing farther away from healthcare centers have a greater risk of progressing from uncomplicated to severe malaria.19 A recent study performed by the Southern and Central Africa International Centers of Excellence for Malaria Research (ICEMR) team in Nchelenge District showed higher mortality in children with severe malaria from villages located further from the hospital.20 Each additional kilometer from the hospital was associated with a 4% increase in odds of death, with a median distance to hospital of 14 km in children who died from severe malaria compared to 3 km in those who survived.20 In order to alleviate this health burden faced by individuals living in remote villages, many nations have started integrated community case management (iCCM) programs as an attempt to bring evidenced-based healthcare services farther into previously unserviceable areas.

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The intervention is both logically simple and organizationally complex. Village representatives receive training on recognizing the signs and symptoms of the three greatest causes of global childhood mortality—diarrhea, pneumonia, and malaria.21 These newly trained community health workers (CHWs) are equipped to perform RDTs on suspected malaria cases and provide antimalarials to those with uncomplicated malaria.22 Cases presenting with more severe clinical features are referred to their nearest health facility capable of providing inpatient treatment.22 By reaching patients earlier in disease progression, uncomplicated malaria can be cured prior to needing more intensive medical care while more severe cases have a greater opportunity to receive timely treatment. The former reduces stress placed on formal healthcare centers to treat uncomplicated malaria patients as well as shrinks the number of patients who progress to severe malaria; the latter allows these same treatment centers access to children that would have otherwise died prior to being able to engage their health system.

The intervention was adapted by the National Malaria Elimination Centre of Zambia

(NMEC) from a successful home management of malaria program that reduced the barrier to access due to distance in rural Ghana. NMEC led the implementation of the program across

Zambia’s low transmission southern provinces.23-4 While the iCCM program in southern Zambia succeeded in its initial goals, implementation of the intervention in other malarious nations has been met with varying levels of success.25-6 Impact analyses showed reductions in mortality from diarrheal diseases and pneumonia, with conflicting reports on malaria burden decreases.25,27-8 A

2019 comprehensive review of sub-Saharan Africa CHW-based interventions found a small reduction of malaria mortality across the participating regions, but little agreement on decreases in indicators of transmission reduction such as case volume, fever prevalence, or prevalence of severe anemia.28

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More recently, the Ministry of Health of Zambia, with assistance from the President’s

Malaria Initiative (PMI) and its in-country subsidiary Program for the Advancement of Malaria

Outcomes (PAMO), expanded the Zambian iCCM program to high malaria transmission areas such as Luapula Province.24 The program focuses on four high transmission provinces including

Luapula and was budgeted $4.58 million USD under the FY2018 budget. Despite more than a decade of control measures in these regions, there have been limited or no? reduction seen in transmission intensity.29-30 The funding helped support training of 256 CHWs in Nchelenge

District as a malaria control measure and a means of reducing distance-related barriers to health care access. CHWs were active by October of 2018.24

1.3 Objectives and Aims

The objective for this thesis is to expand upon ongoing epidemiological studies of malaria led by the Southern and Central Africa International Centers of Excellence for Malaria

Research (ICEMR) in the northern Zambia study site located in Nchelenge District, Luapula

Province.31 The ICEMR is an international collaboration that includes the Johns Hopkins Malaria

Research Institute in the United States as well as the Tropical Diseases Research Centre (TDRC) in Zambia. Studies performed in Nchelenge have led to advances in knowledge of malaria epidemiology, vector biology and the viability of control strategies in an area with high malaria transmission.29-30 This thesis evaluated the impact of the iCCM program, implemented in

October 2018, on severe malaria in Nchelenge District. The hypothesis is displayed below in visual form through a Directed Acyclic Graph (Figure 1.2).

iCCM is hypothesized to affect several risk factors of severe malaria and associated mortality. Broadly, iCCM increases access to health care by stationing CHWs throughout the district, thereby decreasing the distance of an individual to a health care provider. CHWs

6 deployed under iCCM are able to diagnose and treat patients with uncomplicated malaria and refer those with severe malaria to local rural health centers (RHCs), reducing delays in care.

CHWs are also trained to diagnose and treat diarrheal disease with oral rehydration solution.

This would lower the dehydration levels of the patient at intake, a feature associated with increased mortality as well as a lowered ability to tolerate treatment.7,32 Better health care access would lead to timelier diagnosis and treatment, reducing progression to severe malaria or accelerating time-to-treatment for those already with severe malaria, and thus reduce the probability of death.

Figure 1.2: Proposed hypothesis of the factors associated with severe malaria deaths in Nchelenge

District. If effective, the iCCM program should reduce deaths due to disease progression to septic shock, dehydration due to concomitant diarrhea, or severe anemia. It should reduce delays to care of severe cases, as well as diagnosing uncomplicated malaria patients prior to their progression to severe disease.

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The following analyses measured the impact that iCCM had on severe malaria through the following aims:

1. To characterize the demographics and risk factors associated with severe malaria

mortality for children admitted to St. Paul’s General Hospital pre- and post-

implementation of iCCM

2. To assess the impact iCCM had on severe malaria mortality through hypothesized

mediators such as age and distance, as well as comorbidities such as severe anemia,

sepsis, pneumonia, gastroenteritis, and protein-calorie malnutrition

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II. Patient Characterization and Risk Factor Analysis

2.1 Introduction

Previous analysis of hospital data in Nchelenge District showed an increasing odds of severe malaria mortality as distance from St. Paul’s General Hospital increased.20 One of the goals of iCCM implementation in Nchelenge was to address this issue by bringing health care closer to people by placing 256 community health workers throughout the district. Should iCCM have an effect, demographic and clinical profiles of patients with severe malaria should change over time before and after iCCM rollout. At the lowest level of program success, patient profiles should not differ significantly in any characteristic from the pre-iCCM implementation period to the post-period. Age, sex, and comorbidities would remain constant across the study period.

Comparatively at the highest level of success, implementers would hope to see signs of demographic changes related to how quickly children with malaria are identified and either treated by the CHW (for mild cases) or referred on to local health centers or district hospital (for more severe cases). Patients admitted to the district hospital post-implementation might be expected to present earlier in the course of their illness and display milder clinical phenotypes of malaria than in the pre-implementation period. At the same time, iCCM implementation may lead to increased hospitalizations of sicker children from more distant villages due to better access to care. The following section attempts to address Aim 1 by looking for differences in demographic and clinical profiles of patients across the two time periods.

2.2 Methods

2.2.1 Ethical Approval

This study was approved by the Johns Hopkins Bloomberg School of Public Health

Institutional Review Board as part of ongoing work being conducted under the Southern and 9

Central Africa ICEMR. As part of the ICEMR, this work was also approved locally by the

Tropical Diseases Research Centre Ethics Review Committee in Ndola, Zambia. Collected data did not include any personal identifiers and thus should not be considered human subjects research.

2.2.2 Study Design

A before-and-after cross-sectional study was performed using routinely collected hospital data from children ≤15 years old with severe malaria seen at St. Paul’s General Hospital between

October 2017 and May 2019. Lag was not added to the start date of the program as there would be minimal data in the post-period available to make inferences on the program’s performance.

2.2.3 Study Site and Population

Inpatient hospital admissions data were collected from St. Paul’s General Hospital, the district hospital of Nchelenge District. Patients whose information was to be retrieved were restricted to any child admitted to the St. Paul’s children’s ward from 1 October, 2017 through

31 May, 2019 with a discharge diagnosis listed on the inpatient registry that included malaria.

Cases of adult malaria were excluded from data collection as they were outside the scope of the study; the overwhelming majority of severe malaria in this region occurs in children.8

The definition of a child was set by the age limit of St. Paul’s children’s ward, children ≤

15 years old. Given the rural setting of the hospital, difficulties in receiving care and frequency of malaria cases in the region, admittance to the children’s ward for treatment for malaria was determined sufficient enough to classify the patient as having severe malaria.20

2.2.4 Data Sources and Variables

This analysis incorporated both hospital admissions registry data as well as data extracted from the hospital laboratory’s full blood count (FBC) logbooks. Variables extracted from the

10 registry for each patient include age, sex, date of admission, date of discharge, disposition at discharge, and discharge diagnosis. Discharge diagnoses addended to the malaria diagnosis were grouped into six categories: severe anemia, sepsis, pneumonia, acute gastroenteritis (vomiting, diarrhea, or both), meningitis, and protein-calorie malnutrition (PCM).

A refugee variable was created to reflect the large number of Congolese refugees resettled in Nchelenge District seen at the hospital originating from the Kenani Refugee Transit

Camp (pre-implementation) or Mantapala Refugee Settlement (mainly during post- implementation). The camp started to physically relocate in June 2018 from the Kenani camp alongside the lakeside to the Mantapala settlement situated in the swampy interior. Heterogeneity in malaria transmission between people living in refugee camps and local populations has been noted previously.33 Further, environmental and entomological factors within refugee camps increase risk of disease compared to local village life.34 Sensitivity analyses excluding refugees were performed because iCCM did not include placement of CHWs in the refugee camp or settlement. Refugee health care in the community was instead overseen by the Zambian Ministry of Home Affairs and United Nations High Commissioner for Refugees.

FBC logbook data were available for the period October, 2017 through mid-January,

2019. Variables were hemoglobin, white blood cells, platelets, neutrophils, and eosinophils. Only first blood draw information was recorded when multiple FBC draws were performed to best capture patient conditions prior to any major intervention such as blood transfusion or treatment.

2.2.5 Outcomes

The primary outcome was death. Secondary outcomes investigated relied on FBC data and include severe anemia (hemoglobin <5.0 g/dL), thrombocytopoenia (platelet count < 150 x

109 cells/L), leukocytosis (white blood cell count > 17.5 x 109 cells/L), leukopoenia (white blood

11 cell count < 5.5 x 109 cells/L), neutropenia (neutrophil count < 0.5 x 109 cells/L), and eosinophilia (eosinophil count > 0.81 x 109 cells/L).

2.2.6 Analyses

Patient demographics (age, sex, and village distance to hospital) were compared across pre- and post-iCCM implementation periods by the Student’s t-test for independent samples with equal variances.35 Crude case fatality ratios (CFRs) were compared between time periods.

Potential significant differences were assessed using Pearson’s chi-squared for categorical variables.36 Case counts were plotted over time in exploratory analyses to identify reductions or influxes of patients seen at the hospital after iCCM implementation.

Univariable analysis was performed to better understand the relationships between admissions registry variables. Analysis of the relationship between mortality and median distance to the hospital was done to look for a change in severe malaria outcome between the two time periods. A binary distance variable was generated using a threshold equal to the median distance to the hospital combined over both time periods. Covariance matrices were generated to detect potential collinear variables. For example, severe anemia diagnoses would be expected to be highly correlated with blood transfusions. Simple logistic regression of mortality on each variable was done to look for significant differences between pre- and post-iCCM periods.

Hematologic abnormalities were defined according to clinical handbook definitions for severe anemia, thrombocytopenia, neutropenia, leukocytosis, leukopenia, and eosinophilia.37

Significance of relationships between blood abnormality and outcome was tested again using

Pearson’s chi-squared.36

Finally, a multivariable logistic regression model was constructed by backward stepwise regression with an inclusion value of p<0.2. FBC data were excluded from multivariable

12 modelling due to missingness (data were only available through January 2019). Variables excluded from the stepwise model were reinstated if they had been previously implicated as being a risk factor or mediator of severe malaria death or were otherwise implicated as targets of care in the iCCM program. Results were displayed in a forest plot.

2.2.7 Statistical Assumptions and Software Used

No patient identifiers (e.g. national insurance number, unique patient identification number) were kept in the inpatient registry as means of tracking potential readmissions, so each individual case recorded was assumed to be independent of another. Case counts by week were plotted and visually inspected to test whether the pre- and post-iCCM periods could be assumed to be relatively similar with respect to potential seasonal variation of malaria cases. To account for potential misclassification (of non-malaria cases as malaria cases) due to transcription and other human error in the inpatient registry, chart data were extracted and compared to the final registry diagnosis for a selection of patients. Statistical significance was prespecified to a two- sided α and p-value < 0.05. All statistical analyses were performed in Stata 15.1 (StataCorp LLC,

College Station, TX).38

2.3 Results

2.3.1 Study Population

A total of 1,119 children were hospitalized with severe malaria during the study period.

Four were excluded due to incomplete outcome data. Patient demographic characteristics did not differ significantly between the pre- and post-implementation periods (Table 2.1). The median age of hospitalized children with severe malaria was just below two years. Distance of the patient’s home village to the hospital followed a bimodal distribution, with most patients residing

10 km or 25 km away. Patients in the post-implementation period tended to come from more

13 distant villages than in the pre-implementation period, although this difference was not significant (7 km before vs. 11 km after iCCM implementation, p=0.08). Refugee children accounted for 10% and 11% of the patient population in pre- and post-iCCM periods, respectively.

Table 2.1: Patient Characteristics Pre - and Post-iCCM Implementation Demographic and Clinical Characteristics Pre (N=734) Post (N=381) p-value^ (N=1,115)

Age, Yrs, Median (IQR) 1.92 (1.1-3.0) 1.92 (1.0-3.0) 0.63

Sex 0.81

Male, N (%) 387 (52.7) 198 (52.0) - Female, N (%) 347 (47.3) 183 (48.0)

Distance, Km, Median (IQR) 7.0 (2.5-18.0) 11.0 (2.3-25.0) 0.08 Children Living > 10 Km from Hospital, N (%) 301 (45.3) 188 (52.4) 0.03*

Refugees, N (%) 74 (10.1) 41 (10.8) 0.73

Concurrent Diagnosis (N=1,115)† Pre (N=734) Post (N=381) p-value None 377 (51) 210 (55) 0.23 Anemia 181 (24.7) 76 (20.0) 0.08 Sepsis 79 (10.8) 15 (3.94) <0.001** Pneumonia 25 (3.4) 11 (2.9) 0.64 Gastroenteritis 40 (5.5) 31 (8.1) 0.08 Meningitis 6 (0.8) 7 (1.8) 0.13 PCM 15 (2.0) 9 (2.4) 0.73 Other Diagnoses‡ 49 (6.7) 38 (10.0) 0.051

Laboratory Characteristics (N=438) Pre (N=373) Post (N=65) p-value Hemoglobin, g/dL, Median (IQR, N) 6.0 (4.1-8.7, 373) 7.4 (4.1-9.8, 64) 0.08 Platelets, 109 cells/L, Median (IQR, N) 160.5 (83-308, 304) 252.0 (127-348, 61) 0.03* Leukocytes, 109 cells/L, Median (IQR, N) 11.3 (7.4-17.8, 305) 10.9 (7.7-20.9, 64) 0.32 Neutrophils, 109 cells/L, Mean (SD, N) 6.3 (5.9, 222) 6.0 (4.8, 56) 0.74 Eosinophils, 109 cells/L, Median (IQR, N) 0.03 (0.01-0.10, 250) 0.05 (0.01-0.13, 64) 0.15

IQR = Interquartile Range, SD = Standard Deviation ^ p-values for continuous variables derived from Student's T-Test, categorical variables from Fisher's Exact Test † Some children had >1 additional IPD diagnosis, therefore n's sum greater than total * p-value significant at the α=0.05 level ** p-value significant at the α=0.001 level ‡ Refers to non-malarial diagnoses seen in children with severe malaria (i.e. abscess, fracture, etc.)

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The proportion of patients with severe anemia and/or sepsis decreased in the post-iCCM period (severe anemia: 25% to 20%, p = 0.08; sepsis: 11% to 4%, p < 0.001). Concurrent gastroenteritis trended toward a higher prevalence after iCCM implementation (8% vs. 6%, p=0.08). Concomitant meningitis, pneumonia, and PCM did not appreciably change.

Misclassification was fairly common. Among patients with a discharge diagnosis of malaria recorded in the inpatient register, 12% had a negative malaria RDT result recorded in the chart (Table 2.2).

Table 2.2: Predictive Value of Registry Diagnosis of Severe Malaria Chart RDT Registry Diagnosis Positive Negative Total Present 334 46 380 Hospitalization register diagnoses of malaria had a positive predictive value of 88% for a positive RDT test

2.3.2 Severe Malaria Mortality and Village Distance

Of the 1,115 hospitalized children with severe malaria, 151 (14%) died during the study period. There was no statistically significant difference in mortality after iCCM compared to before (Table 2.3). Plotting the registry cases over time, the weekly number of individuals admitted for severe malaria did not differ greatly between the two time periods (Figure 2.1). Of note, the pre-iCCM period fully captures the bimodal seasonal distribution of cases typical of

Nchelenge District whereas the post-period captured one of these malaria transmission peaks.

Table 2.3: Severe Malaria Crude Mortality Rat ios Stratified by Pre-and Post-iCCM Periods Outcome Pre Post Overall N=734 N=381 N=1,115 Died, N (%) 93 (13) 58 (15) 151 (14) p = 0.24

15

50

45

40

35

30

25

20

Weekly Weekly Case Count 15

10

5

0 1 5 9 13 17 21 25 29 33 37 41 45 Week Beyond October 1st

Pre-iCCM Count Post-iCCM Count

Figure 2.1: Weekly distribution of cases of severe malaria over the course of a full year stratified comparing pre- and post-iCCM periods.

Disaggregating CFRs begins to show the effects iCCM had during the limited time it was implemented in the study period (Figure 2.2). Individuals were stratified by whether they resided within the median distance to St. Paul’s Hospital and time period. There were proportionally more individuals coming from villages beyond the 7 km median distance in the post-iCCM period than in the pre-period, although this difference was not significant. The CFR for those living near the hospital (11%) was not appreciably different than for those living beyond the median (15%) in the pre-period. In the post-period, mortality dropped slightly for those living within the median and was found to be significantly elevated for those coming to the hospital from beyond the median distance (9% vs. 20%, p=0.007).

16

Figure 2.2: Difference in crude mortality by distance to the hospital between time periods.

Individuals categorized as living near the hospital were within 7 km of St. Paul’s while those living far lived beyond that radius.

17

2.3.3 Severe Malaria Mortality and Hematologic Abnormalities

Severe anemia (hemoglobin < 5.0 g/dL) was present more often than any other abnormality (173/437, 40%). There were no significant associations between anemia, thrombocytopoenia, leukocytosis, neutropoenia, or eosinophilia with mortality. However, having a lower platelet count was associated with greater odds of death; for each decrement of 100 x

109 platelets/L the odds of death increased 1.8 times (95% CI 2.0-3.4, p=0.025).

Comparisons between the pre- and post-iCCM periods by age, distance, and hematologic abnormalities showed that children who survived in the post-iCCM period had evidence of less progressive disease, specifically lower prevalences of severe anemia or thrombocytopoenia

(Figure 2.3). Among patients with severe malaria who died, laboratory parameters suggested that those in the post-iCCM period were more likely to have more advanced disease. The proportion of patients from more remote villages trended higher in the post-iCCM period among non-survivors.

18

Figure 2.3: Percentage of cases with hematologic abnormalities surviving or dying before and after iCMM. Age and distant village values were analyzed here among those receiving FBC lab work.

2.3.4 Unadjusted and Adjusted Odds of Severe Malaria Death by Patient Characteristics

Age appeared to be a mildly protective factor, with each additional year of age providing a 12% reduction of odds (OR 0.88, 95% CI 0.80-0.97, p=0.008) of severe malaria death (Table

2.4). The majority of this protection was afforded to children older than 2 years of age. Children under 2 years old had 1.9 times the odds of dying from severe malaria (95% CI 1.1-3.5, p=0.03) than children 5-15 years old and 1.4 times the odds of dying (95% CI 0.98-2.1, p=0.06) compared to children 2 to <5 years old.

Increased Euclidean distance to a health center was associated with an increased odds of severe malaria death but did not differ in the pre-and post-iCCM periods. There was an overall

19 addition of 2% higher odds of death for each kilometer of distance from the hospital to the patient’s village (OR 1.02; 95% CI 1.01-1.03). Independent of distance, refugees were almost twice as likely to die from severe malaria than non-refugee patients (OR 1.94; 95% CI 1.20-

3.14). The proportion of refugees attending St. Paul’s Hospital did not differ in the pre- and post- periods (Table 2.1). Among refugees, case fatality was higher in the post-iCCM period than in the pre-period (15% pre vs. 34% post, p = 0.02).

20

Table 2.4 Results of simple logistic regression of mortality on demographic and laboratory features of hospitalized children with malaria, including posited mediators of the effect of iCCM on severe malaria mortality.

Overall Pre Post Demographic and Clinical (N=1,115) OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value

Age 0.88 (0.80, 0.97) 0.008** 0.86 (0.75, 0.98) 0.03* 0.90 (0.80, 1.03) 0.12 Age 5-15 ref - ref - ref Age 2-5 1.35 (0.73, 2.54) 0.34 1.14 (0.50, 2.60) 0.76 1.88 (0.71, 4.95) 0.20 Age 0-2 1.94 (1.07, 3.52) 0.03* 1.86 (0.85, 4.05) 0.12 2.11 (0.84, 5.29) 0.11

Sex

Male ref - ref - ref -

Female 1.19 (0.85, 1.69) 0.31 1.08 (0.70, 1.67) 0.73 1.40 (0.80, 2.46) 0.24

Distance (N=1,021) per km distance 1.02 (1.01, 1.03) 0.001** 1.02 (1.00, 1.03) 0.04* 1.03 (1.01, 1.04) 0.01*

Refugee Status 1.94 (1.20, 3.14) 0.007** 1.26 (0.63, 2.46) 0.53 3.49 (1.70, 7.16) 0.001**

Concurrent Diagnoses None 0.75 (0.53, 1.05) 0.10 0.75 (0.49, 1.16) 0.20 0.72 (0.41, 1.27) 0.26 Anemia 1.34 (0.91, 1.98) 0.14 1.14 (0.70, 1.87) 0.60 1.86 (0.99, 3.49) 0.06 Sepsis 0.66 (0.32, 1.33) 0.24 0.64 (0.29, 1.44) 0.29 0.85 (0.19, 3.88) 0.84 Pneumonia 1.29 (0.53, 3.15) 0.58 1.76 (0.65, 4.82) 0.27 0.55 (0.07, 4.37) 0.57 Gastroenteritis 1.76 (0.97, 3.19) 0.07 2.37 (1.12, 5.01) 0.02* 1.08 (0.40, 2.93) 0.88 Meningitis 2.89 (0.88, 9.50) 0.08 1.38 (0.16, 11.97) 0.77 4.35 (0.95, 19.97) 0.06 PCM 3.31 (1.39, 7.89) 0.007** 3.59 (1.20, 10.73) 0.02* 2.88 (0.70, 11.86) 0.14 Other Diagnoses‡ 0.54 (0.24, 1.19) 0.12 1.16 (0.51, 2.67) 0.73 - - Laboratory (N=438) Hemoglobin Concentration per 1.0 g/dL 0.95 (0.86, 1.06) 0.35 0.99 (0.88, 1.11) 0.89 0.77 (0.58, 1.02) 0.07 Platelet Count per 103 cells/L 0.99 (0.99, 1.00) 0.02* 0.99 (0.99, 1.00) 0.13 0.98 (0.96, 0.99) 0.03* White Cell Count per 103 cells/L 1.00 (0.97, 1.03) 0.87 0.99 (0.96, 1.03) 0.83 1.02 (0.97, 1.07) 0.49 Severe Anemia (hb < 0.5g/dL) 0.84 (0.46, 1.55) 0.58 0.61 (0.31, 1.22) 0.16 4.56 (0.97, 21.44) 0.06 12.19 (1.32, Thrombocytopoenia (plt < 150 x 109 cells/L) 1.94 (0.98, 3.87) 0.06 1.45 (0.69, 3.03) 0.33 0.03* 112.72) Leukocytosis (wbc > 17.5 x 109 cells/L) 1.28 (0.63, 2.59) 0.49 0.89 (0.38, 2.06) 0.78 5.42 (0.96, 30.62) 0.06 Leukopoenia (wbc < 5.5 x 109 cells/L) 1.37 (0.50, 3.76) 0.54 1.25 (0.41, 3.83) 0.69 2.21 (0.21, 23.12) 0.51 Neutropoenia (n < 0.5 x 109 cells/L) 8.52 (0.52, 139.84) 0.13 8.17 (0.49, 134.84) 0.14 - - Eosinophilia (e > 0.81 x 109 cells/L) ------* p-value significant at the α = 0.05 level ** p-value significant at the α = 0.01 level

Gastroenteritis was a risk factor for mortality in the pre-iCCM period but not in the post- period (OR 2.37; 95% CI 1.12-5.01, p = 0.02 in the pre-period). Comorbid PCM followed a similar pattern (OR 3.59, 95% CI 1.20-10.73, p = 0.02 in the pre-period). Thrombocytopoenia was more strongly associated with malaria-related mortality than anemia. The association was more pronounced in the post- than pre-iCCM period.

21

Adjusted models yielded similar results. The relationships of age and distance with mortality were significant in the overall sample but not in the subdivided pre- and post-iCCM samples controlling for distance, refugee status, and diagnoses of severe anemia, sepsis, pneumonia, gastroenteritis, and PCM. After conditioning on these same variables, coming from a refugee camp still conferred risk of mortality but the association was attenuated (OR = 1.85, 95%

CI 1.13-3.02, p = 0.02).

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Table 2.5: Adjusted Odds Ratios of Severe Malaria Death Pre- and Post-iCCM Implementation (N=1, 021)

Overall Pre Post Predictors aOR (95% CI) p-value aOR (95% CI) p-value aOR (95% CI) p-value

Age 0.90 (0.82, 0.99) 0.03* 0.89 (0.79, 1.02) 0.10 0.90 (0.79, 1.03) 0.14

Sex

Male ref - ref - ref -

Female 1.29 (0.89, 1.86) 0.18 1.21 (0.75, 1.94) 0.43 1.49 (0.81, 2.72) 0.20

Distance 1.02 (1.01, 1.03) 0.003** 1.02 (0.99, 1.03) 0.05 1.01 (0.99, 1.04) 0.18

Refugee Status 1.85 (1.13, 3.02) 0.02* 1.20 (0.59, 2.43) 0.61 3.04 (1.40, 6.57) 0.005**

Severe Malaria Feature

Severe Anemia 1.26 (0.83, 1.91) 0.28 1.14 (0.66, 1.94) 0.64 1.66 (0.82, 3.34) 0.16

Sepsis 0.67 (0.31, 1.44) 0.31 0.59 (0.24, 1.43) 0.24 1.25 (0.26, 6.13) 0.78

Pneumonia 1.01 (0.38, 2.70) 0.98 1.29 (0.41, 4.02) 0.66 0.54 (0.07, 4.43) 0.56

Gastroenteritis 1.79 (0.94, 3.42) 0.08 2.41 (1.08, 5.37) 0.03* 1.19 (0.37, 3.82) 0.76 PCM 2.64 (1.04, 6.71) 0.04* 2.44 (0.72, 8.32) 0.15 2.78 (0.63, 23.36) 0.18 * p-value significant at the α = 0.05 level ** p-value significant at the α = 0.01 level Adjusted for age, sex, distance, refugee status, and concomitant severe anemia, sepsis, pneumonia, gastroenteritis, and PCM

23

Gastroenteritis retained its significant association with mortality in the before period, with slightly greater odds of severe malaria death reported (OR 2.41 for those with gastroenteritis compared to those without; 95% CI 1.08-5.37, p = 0.03) than when looking at its univariable relationship. However, there was no significant association between gastroenteritis and severe malaria-related death in the post-period. After adjustment, the role of PCM in severe malaria outcomes was better described, with a significant increase in odds (OR 2.64; 95% CI

1.04-6.71, p = 0.04) but with no difference between the before and after periods.

2.3.5 Pre- and Post-iCCM Mortality in Patient Subgroups

Results of subgroup analyses are summarized in the forest plot below (Figure 2.5).

Subgroup analysis demonstrated an increased odds of severe malaria mortality in refugee compared to non-refugee children in the post- but not the pre-iCCM period. There was no significant reduction in the odds of mortality in children coming from more distant villages in the post- compared to the pre-iCCM era; conversely, there was a trend towards increased mortality in children from remote villages. There were trends toward decreased mortality in the post- compared to pre-iCCM period in children with severe malaria with concurrent diagnoses of pneumonia and gastroenteritis, reflective of the trend also seen in the primary analysis.

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Figure 2.4: Forest plot summarizing results of logistic regression of mortality on pre/post-iCCM among subgroups of children hospitalized with severe malaria

2.4 Conclusions

An exploratory before-and-after analysis of iCCM implementation in a high malaria transmission region of northern Zambia was performed and found no significant difference in in- hospital malaria mortality. The overall case fatality (13.5%) was similar to other rural, malarious areas.39 All-cause mortality from malaria in Zambia regardless of having sought treatment was measured most recently in 2010-12 through verbal autopsy to be 6.6%.40 The elevated observed rate is expected as in-hospital mortality is typically higher.41 Although there was no detectable difference in mortality before and after iCCM, comparisons of demographic, clinical, and

25 laboratory characteristics of patients pre- and post-iCCM, and their associations with mortality identified several patterns indicative of a potential impact of iCCM on malaria case management.

Findings support the notion that iCCM implementation led both to earlier referrals and increased referrals of sicker cases from more remote areas. Fewer patients in the post period had a sepsis diagnosis, and there was a trend toward lower prevalence of anemia and higher hemoglobin concentrations in the post compared to pre periods, markers of reduced disease severity. In addition, we observed significantly lower prevalences of severe anemia and thrombocytopenia among survivors of severe malaria after implementation compared to before.

These observations are consistent with earlier referral to the hospital from CHWs and rural health centers.

At the same time, there appeared to be a greater number of sicker cases from more distant communities, suggested by trends seen in the data on village distance and laboratory markers of severe infection. Patients in the post period were arriving sicker from farther away than in the pre-iCCM period. Among those who died, laboratory markers showed more severe disease progression in the post period and a statistical trend toward greater village distances. In fact, case fatality may have increased in the post-iCCM period for patients with markers of severe disease.

Case fatality of patients with thrombocytopoenia before iCCM was 12% compared to

24% after iCCM implementation (p = 0.008). One potential explanation for this finding is that iCCM implementation led to a greater number of referrals of children with more advanced infections who otherwise would have died at home and remained uncounted among malaria- related deaths. Verbal autopsies taken between 2010-12 found that over half (63.5%) of all-cause deaths in Luapula Province occurred at home of which approximately 10% were due to malaria.41

26

Eosinophilia was present in a small proportion of patients (18/314, 6%) and may indicate co-infection with soil helminths or Schistosoma spp., both highly endemic to the study area. Case fatality was low in these individuals (0%). One potential explanation for this finding is that the primary infection responsible for their hospitalization was due to one or the other of these parasites, with incidental parasitemia or Plasmodium antigenemia detected by RDT, causing them to be misclassified as severe malaria cases.

Subgroup analyses which were done to help identify patient groups that might have experienced improved outcomes after iCCM implementation failed to show a significant difference in any of the subgroups. The analysis did, however, reveal greater mortality after iCCM than before in the refugee patient population. We hypothesize that this may be the result of differing malaria ecology, and distance to the hospital, in the refugee camp and settlement sites in the pre and post periods. Before iCCM, many if not most refugees were situated 7 km north of the hospital along the main lakeside road where malaria ecology was similar to other areas in proximity to the hospital. By the time of iCCM rollout, refugees were relocated to a distant settlement in a previously uninhabited, newly deforested, swampy part of the district located >25 km over difficult road conditions. Other factors including nutrition status, varying degree of premunition in a non-local population, and decreased health access are other potential contributors to this observation but which we have insufficient data to examine.

This study focused on the association of iCCM program implementation with malaria outcomes, while the two other main components of iCCM are case management of pneumonia and acute diarrheal disease. In our sample of hospitalized children with severe malaria, some had concurrent diagnoses of pneumonia and acute gastroenteritis (the latter subsumes acute diarrhea).

In subgroup analyses, there was a potential trend toward improved survival in these two patient

27 groups (pneumonia, gastroenteritis) in the post- relative to the pre-ICCM era. In adjusted models, pre-ICCM patients with comorbid gastroenteritis had greater odds of dying than those without comorbid gastroenteritis (aOR 2.41, 95% CI 1.08-5.37, p = 0.03), but not post-iCCM patients.

Additionally, gastroenteritis was more prevalent in hospitalized malaria patients after iCCM implementation, suggesting that more of these patients were being identified and referred to treatment in the community.

There are limitations to this study. All data were recorded from handwritten hospital records, therefore transcription errors of certain variables (e.g. numeric values, village names, diagnoses, disposition) due to illegibility, variations in spelling, or other human error are possible. To minimize this, source documents were reviewed with local hospital staff when able.

The format of data sources precluded a reliable way of capturing patient readmissions, therefore biases that could result from the inability to account for intra-individual correlation are possible, including the over- and underestimation of effect sizes and statistical significance of associations. Anecdotally, readmission for severe malaria is an infrequent occurrence in contrast to sickle cell crisis and other pediatric diseases with higher observed rates of repeat admission.

There were severe unmeasured potential confounders. Periodic stock-outs of artesunate, blood products, or other vital resources are known to occur and impact mortality but were not accounted for in our models. Hospital staffing, varying road conditions with rainfall, and other temporal trends are also unaccounted for. The next section presents results of a case-control study, designed to address some of these limitations.

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III. Before-and-After Case-Control Study of iCCM Impact

3.1 Introduction

To address potential confounding by clinical care and stockouts of hospital resources, a case-control study was conducted to look for the effect of the risk factors and mediators described above on severe malaria mortality before and after iCCM implementation. This analysis focused on identifying differences in mortality before and after iCCM and differences in risk factors such as distance from the hospital in the before and after periods.20

Matching allowed for the control of unmeasured confounders—in this case, matching within a day of the date of admission allowed for the removal of the effect of medication and other hospital stockouts as well as variations in clinical care.42 Different providers may have different thresholds for initiating certain treatments or ordering diagnostic studies that can impact care. Higher or lower nurse-to-patients ratios, which vary with patient census, also are likely to affect patient care. Stockouts of artesunate and other medications, intravenous fluids and whole blood for transfusion, oxygen tanks for respiratory support, and other hospital supplies were known to occur. Laboratory reagents become exhausted and instrumentation malfunctions, causing diagnostic tests that might inform clinical management to become unavailable. Temporal matching was one way to help control for these potential confounders of malaria mortality.

3.2 Methods

3.2.1 Selection of Cases and Controls

Cases and controls were selected from among the 1,115 children ≤ 15 years old admitted to St. Paul’s General Hospital for severe malaria between 1 October, 2017 and 31 May, 2019.

29

Ninety-five children were excluded for missing distance data, of whom 69 did not have village data in the registry or chart, and 26 had village data that were indiscernible due to unrecognized of indecipherable spellings, or residence in a village with no known geolocation to compute distance.

There were 138 deaths due to severe malaria during the study period, with 83 individuals dying pre-iCCM implementation and a further 55 patients dying post-iCCM. These cases were matched 1:1 to controls based on date of admission to account for clinical care and available hospital resources. When no control was available on the exact admission day of a case patient, potential controls were identified first on the day prior to admission before considering the selection of an individual admitted on the day after the case patient’s admission.

3.2.2 Exposures of Interest

Exposures of interest were identical to those in the cross-sectional analysis. These included age, sex, date of admission, village distance to St. Paul’s General Hospital, whether the patient was a refugee, and the malaria comorbidities of severe anemia, sepsis, pneumonia, PCM, and gastroenteritis. Hematologic variables were not included in this analysis due to high missingness.

3.2.3 Statistical Methods

Student’s t-tests were performed to compare continuous variables to examine difference in means between cases and controls stratified by time period.29 Categorical variables were compared using Fisher’s exact test and Pearson’s chi-squared test.30,43 Conditional logistic regression models were generated, starting with a backwards stepwise conditional regression model with a threshold p-value of 0.2. A model incorporating all variables from the final

30 adjusted model of the cross-sectional study above was compared against a null model that excluded PCM diagnosis and the constructed stepwise model via Akaike’s information criteria.44

The final model incorporated age, sex, village distance, the refugee variable, and diagnoses of severe anemia, sepsis, pneumonia, and gastroenteritis. Univariable analysis for associations and effect sizes were not repeated. Statistical significance was prespecified to a two-sided α and p- value < 0.05. All statistical analyses were performed in Stata 15.1 (StataCorp LLC, College

Station, TX).38

3.3 Results

3.3.1 Study Population

After applying exclusion criteria and matching eligible cases and controls on date of admission ±1 day, there were 107 cases and 107 controls for a total of 214 patients. Of these there were 66 cases and controls (n=132, 62%) from the pre-iCCM implementation period and

41 cases and controls (n=82, 38%) from the post-period (Figure 3.1). Thirty-one cases were not able to be matched to an eligible control. Inability to match was due to temporal clusters of case patients with insufficient numbers of controls on certain admission days.

31

Figure 3.1: Case-control analysis framework depicting the process of the selection of cases and controls and divided into before and after iCCM periods. Cropped title above

Individuals included in the case-control analysis did not greatly differ in demographic and clinical characteristics either when stratified by outcome or time period (Table 3.1). Cases were more likely to be refugees than controls during the post-iCMM period but not pre-iCCM.

Severe anemia was more prevalent in non-survivors than survivors in the post period (32% vs.

7%, p=0.005).

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Table 3.1: Characteristics of Cases and Controls Before and After iCCM Implementation Overall (N=214) Pre (N=132) Post (N=82) Case Control Case Control Case Control Demographics p-value p-value p-value (N=107) (N=107) (N=66) (N=66) (N=41) (N=41) Age, Yrs, Mean (SD) 2.05 (1.88) 2.63 (2.59) 0.26 1.99 (2.00) 2.55 (2.36) 0.17 2.16 (1.68) 2.78 (2.94) 0.93 Sex‡ 0.27 0.49 0.38 Male N (%) 50 (46.7) 58 (54.2) - 33 (50.0) 37 (56.1) - 21 (51.2) 17 (41.5) - Female N (%) 57 (53.3) 49 (45.8) - 33 (50.0) 29 (43.9) - 20 (48.8) 24 (58.5) - Distance, Km, Mean (SD) 16.8 (13.8) 12.2 (12.8) 0.01* 15.4 (13.3) 12.9 (12.7) 0.38 19.1 (14.5) 11.2 (12.9) 0.004** Refugees N (%) 17 (15.9) 12 (11.2) 0.32 7 (10.6) 9 (13.6) 0.60 10 (24.4) 3 (7.3) 0.03* Malaria comorbidity N (%)

Severe Anemia 30 (28.0) 25 (23.4) 0.43 17 (25.8) 22 (33.3) 0.34 13 (31.7) 3 (7.3) 0.005**

Sepsis 5 (4.7) 11 (10.3) 0.12 4 (6.1) 9 (13.6) 0.14 1 (2.4) 2 (4.9) 0.56

Pneumonia 4 (3.7) 2 (1.9) 0.31 3 (4.6) 1 (1.5) 0.31 1 (2.4) 1 (2.4) 1.00 Gastroenteritis 11 (10.3) 7 (6.5) 0.33 8 (12.1) 4 (6.1) 0.23 3 (7.3) 3 (7.3) 1.00 ‡ p-values for categorical variables derived from Fisher's Exact Test, continuous variables normalized through log-transformation to conduct Student's T-Test SD = Standard Deviation * p-value significant at the α = 0.05 level ** p-value significant at the α = 0.01 level

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3.3.2 Severe Malaria Mortality and Village Distance

The distribution of patient distance to the hospital lost its bimodal distribution. As such, mean distances were analyzed here as opposed to median distances reported in the previous section. Mean case and control distances to the hospital differed significantly with cases coming from more remote villages (16.8 km) compared to their matched controls (12.2 km). During the pre-iCCM period, there was no significant difference in mean distance to the hospital. In contrast, cases in the post-iCCM period lived significantly farther away than their matched controls, with mean village distance for cases much farther than that of controls (19.1 km to 11.2 km, p = 0.004).

Figure 3.2: Proportion of individuals living beyond 12.2 km from St. Paul’s Hospital by case status and time period. Change notation to * <0.05, ** p<0.001)

34

The role of distance in severe malaria death was further investigated by stratifying by the mean distance from the hospital for controls (Figure 3.2). In the pre-iCCM period, the proportion of controls living beyond 12.2 km was 39% in controls and 52% in cases but the difference was not significant (p = 0.16). After iCCM implementation, twice as many cases as controls were from villages beyond the mean distance (34.2% to 70.7%, p < 0.001). The proportion of cases coming from beyond the mean in the post-period was also significantly greater than the same proportion for the pre-period (70.3% to 51.5%, p = 0.050).

3.3.3 Conditional Adjusted Odds of Severe Malaria Death by Patient Characteristics

The differences in adjusted odds of severe malaria death according to patient characteristics shown in Table 3.2. Distance was significantly associated with severe malaria mortality. Odds of death increased 1.03 times (aOR 1.03, 95% CI 1.00-1.05, p = 0.03) for each additional kilometer an individual’s village was from St. Paul’s General Hospital. This relationship was strongest during the post period, with an increase in odds of death 1.05 times per kilometer from the hospital (aOR 1.05, 95% CI 1.00-1.10, p = 0.049). This relationship was attenuated in the pre-period.

35

Overall Pre Post Demographics OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value

Age 0.91 (0.78, 1.06) 0.24 0.86 (0.69, 1.07) 0.19 1.03 (0.76, 1.38) 0.86 Sex Male ref - ref - ref - Female 1.58 (0.88, 2.87) 0.13 1.88 (0.83, 4.28) 0.13 1.68 (0.53, 5.33) 0.38 Distance 1.03 (1.00, 1.05) 0.03* 1.02 (0.99, 1.05) 0.28 1.05 (1.00, 1.10) 0.049* Refugee 1.19 (0.52, 2.73) 0.69 0.77 (0.25, 2.37) 0.65 2.79 (0.45, 17.34) 0.27

Diagnoses Severe Anemia 1.13 (0.57, 2.26) 0.63 0.69 (0.30, 1.62) 0.32 6.77 (1.06, 43.32) 0.02* Sepsis 0.44 (0.14, 1.37) 0.16 0.44 (0.12, 1.57) 0.21 0.73 (0.01, 55.07) 0.91 Pneumonia 2.27 (0.38, 13.71) 0.36 4.94 (0.46, 52.83) 0.19 3.27 (0.02, 452.10) 0.56 Gastroenteritis 1.44 (0.52, 4.02) 0.48 1.97 (0.53, 7.38) 0.32 0.70 (0.10, 5.15) 0.73 * p-value significant at the α=0.05 level

Table 3.2: Conditional adjusted odds of severe malaria death controlling for demographic and diagnostic risk factors overall and in the pre- and post-iCCM periods.

36

Refugees had the same odds of severe malaria mortality as non-refugees after controlling for age, sex, distance, severe anemia, sepsis, pneumonia and gastroenteritis. Cases had 6.8 times the odds (95% CI 1.1-43.3, p = 0.02) of receiving a severe anemia diagnosis than controls. Other diagnostic variables had no relationship with severe malaria mortality at either time point.

3.4 Conclusions

The main objective of the case-control study was to examine differences in risk factors of severe malaria mortality before and after iCCM implementation to assess its potential impact.

Mean distances from the hospital of 12.2 km and 16.8 km for cases and controls, respectively, were higher than the median distances from the overall cross-sectional analysis of 7.0 km and

14.0 km because of the skewed nature of the data. These values also exceeded those reported in an earlier case-control study at the same site where the authors found median distances for controls of 3.2 km and 13.5 km for cases admitted for severe malaria at St. Paul’s General

Hospital.20 The difference in village distance to the hospital between cases and controls was more pronounced in the post-iCCM period, with mean distances of 11.2 km for controls and 19.1 km for cases. This supports the hypothesis that iCCM increased accessibility to care. Individuals in the post-period surviving severe malaria lived on average only 2 km closer to St. Paul’s

General Hospital than those that died in the aforementioned earlier study.

The association between distance and severe malaria mortality remained even when controlling for refugee status and therefore the additional distance added by the highly serviced distant camp. Both the overall adjusted odds (aOR 1.03, 95% CI 1.00-1.05, p = 0.03) and post period odds (aOR 1.05, 95% CI 1.00-1.10, p = 0.049) showed similar increases in odds of severe malaria mortality to the 1.04 times increase reported in the previous case control analysis.20 The

37 relationship did not appear during the pre-iCCM period, indicating that increases in the odds of malaria mortality due to distance were driven by cases admitted during the post-period. These patients may have had more severe conditions, as demonstrated by the elevated odds of death among those with comorbid severe anemia (aOR 6.77, 95% CI 1.06-43.32, p = 0.02). The large confidence intervals reflect the low frequency of severe anemia diagnoses in the post-period

(n=16). However, the association may be evidence of more severely ill individuals being brought in from these farther distances. Further, the study design permits evaluation of exposures, not outcomes, therefore this study did not assess the impact of iCCM on severe malaria mortality.

IV. Geospatial Analysis of iCCM Impact

4.1 Introduction

The prior analyses codify distance as a linear Euclidean marker from the village of residence to St. Paul’s General hospital. While this provides some clarity towards the effect of distance on severe malaria mortality, it also assumes spatial independence as distance from the hospital increases. In reality, that assumption cannot be met. For large scale trends, there are geographic determinants that make different areas more or less prone to infection and provide differential access to care. For instance, individuals living on the main road 10 km south of St.

Paul’s hospital should have more direct access to care than those living 10 km east, deep into difficult to access bush areas. At smaller scales, individuals residing in villages geospatially close to each other should share more similar characteristics and therefore more similar odds of severe malaria mortality than individuals residing in villages geospatially distant from each other. The following analysis accounts for this lack of independence and generates expected

38 severe malaria mortality rates across Nchelenge District by allowing for differential spatial variation.

4.2 Methods

4.2.1 Study Population and Coordinate System

Of the 1,119 children seen during the study period, 904 children from 106 villages had available geospatial data and were eligible for inclusion in the analysis. Location data were missing for the remainder of participants from 56 villages due to unrecognized village spellings, illegible handwriting, or residence in a village with no available location data. Village locations were plotted in the Universal Transverse Mercator, Zone 35 south coordinate system.

4.2.2 Geospatial Methods

The analytic set was uploaded into ArcGIS Pro v2.4.0 along with coordinates for villages in Nchelenge District recorded by local ICEMR partners.45 Patients were mapped to the village level. Case fatality ratios were generated by taking village death counts and dividing by the total number of cases originating from the village. A map of CFRs for villages in Nchelenge was generated. Three additional maps were constructed displaying the case count distribution both overall as well as before and after iCCM implementation. Villages with a CFR of 0 were excluded from use in subsequent spatial statistical analysis.

After geostatistical processing, ordinary kriged models were overlaid onto a map of

Nchelenge District to create heat maps of expected CFRs across the study region. The locations of the newly trained CHWs as well as households surveyed throughout an ongoing ICEMR-led longitudinal cohort study operational since 2012 were added to all maps to provide greater context towards severe malaria mortality risk and access to healthcare.46-7

4.2.3 Geostatistical Methods and Assumptions 39

Village CFR data generated in ArcGIS Pro was brought into Rstudio for geostatistical analysis.48 Villages for analysis were restricted to those on the mainland of Nchlenge District as

CFRs on the nearby islands were assumed independent of those on the mainland. For islanders, the limiting factor to receiving care at the hospital remains the often lengthy process of reaching the mainland. This process and the differing malaria ecology on the islands would make them independent from the mainland—however, the models used would force a relationship upon them where none exists.

Trends were explored for spatial dependence of CFR due to easterly or northerly coordinates both overall and in the pre- and post-iCCM implementation periods. Semivariograms comparing spatial data up to half the maximum distance between two villages were constructed to look for small scale spatial variation. Lines of best fit were generated through weighted least squares methods after manually setting initial range, sill and nugget parameters. Lines were then plotted using an exponential correlation function. A term to account for potential spatial dependence of higher CFR values on the north-south axis was added into the models. The resulting semivariograms of the residuals of the universal models were compared to those of the original ordinary models for their ability to explain spatial variation. These ordinary and universal models were then kriged to predict CFR based on coordinates, with the universal models accounting for potential spatial dependency in the northerly direction. Heatmaps of expected case fatality ratios across Nchelenge District from the ordinary models were created and uploaded back into ArcGIS.

4.3 Results

4.3.1 Village-Level Severe Malaria CFRs and Case Counts

40

There were a total of 106 villages mapped across Nchelenge District. Case count data were available for 67 villages, with an average CFR of 13% (Figure 4.1). The majority of patients (754/904, 83%) admitted for severe malaria at St. Paul’s General Hospital were from shoreside villages along Lake Mweru. Patient villages were concentrated in the northern part of the district, along the main road up to the Chiengi border. In contrast, CHW locations appeared to be concentrated in southern areas. Villages nearer the hospital appeared to have lower CFRs, with higher ratios observed in villages farther from St. Paul’s General Hospital.

Figure 4.1: Case fatality ratios for villages that had individuals seek care at St. Paul’s General

Hospital for severe malaria.

41

Villages with higher case counts in the overall map appear to cluster around St. Paul’s

General Hospital (Figure 4.2). The majority of cases came from the localized region around the hospital, with the exception of Mantapala Refugee Camp to the southeast. Large concentrations of cases were also seen immediately north and southeast of the hospital. These areas are associated with higher and lower CFRs, respectively.

Figure 4.2: Overall case count by village during the study period. Locations of surveyed households and other mapped villages provided for reference.

A high clustering of cases in villages closest to St. Paul’s General Hospital was observed for the pre-iCCM period, whereas more cases came from the Mantapala Refugee Settlement in

42 the post-iCCM period (Figure 4.3). There was also an increase in number of cases coming from the more isolated inland villages after CHWs were deployed.

Figure 4.3: Cases of severe malaria admitted to St. Paul’s Hospital before and after iCCM implementation.

4.3.2 Expected Case Fatality Ratios Across Nchelenge District

Ordinary kriging models outperformed universal models, so expected CFR maps were created without accounting for the northerly coordinate of the village.Much like in the overall

CFR map above, the overall ordinary kriging model prediction maps shows lower severe malaria mortality in areas surrounding St. Paul’s General Hospital (Figure 4.4). The kriged model

43 reveals two pockets of low predicted CFR to the north and south of the main population area around the hospital. There appeared to be a hotspot of severe malaria mortality in the easternmost villages of Nchelenge District—namely in the regions surrounding the villages of

Napemba, Cheswa, Mukanda, and Soldier—with predicted case fatality ratios above 40%. In areas with fewer overall case counts and therefore higher uncertainty, the model regresses to the mean CFR.

44

Figure 4.4: Ordinary kriged expected case fatality rates for severe malaria mortality across the entire study period more details here, see comments in text. The figure legend should also summarize the main finding

Maps of expected case fatality ratios before and after iCCM implementation were generated to check for spatiotemporal changes in risk of severe malaria mortality (Figures 4.5-

6). The protection afforded the areas surrounding St. Paul’s General Hospital was not as apparent in the pre-iCCM prediction map, with limited coverage of the nearby lakeside villages. There was a small hotspot of severe malaria mortality just south of the hospital in the areas that the overall map modelled as having very low risk of severe malaria mortality. The hotspot identified in the eastern areas of Figure 4.4 is readily apparent in the pre-iCCM prediction map. Standard errors appreciate faster in the pre-iCCM prediction map than they do in the overall map as distance from the hospital increases.

45

Figure 4.5: Ordinary kriged expected case fatality ratios for severe malaria mortality for those seen in the pre-iCCM period.

The post-iCCM expected severe malaria risk map captured some of the same trends seen in the overall map. The low expected CFRs in northern villages of the overall map is apparent in the post-iCCM period. Of note, the average expected CFR value decreased in the post-iCCM period compared to the pre-iCCM period. Thus, Case fatality was lower in more southerly villages after iCCM compared to before. The easterly hotspot also disappeared in the post period.

Notably, CHWs were deployed in both of these regions. The hotspot located in the southeasterly

46 area of the map corresponds to Mantapala Refugee Settlement and its influence on the region may not be truly indicative of the expected CFRs for individuals in surrounding villages.

Uncertainty is more consistent throughout Nchelenge District in the post-iCCM than in the pre- iCCM implementation period.

Figure 4.6: Ordinary kriged expected case fatality ratios for severe malaria mortality for those seen in the post-iCCM period. While the area of low case fatality surrounding St. Paul’s

Hospital as seen in the overall and pre-iCCM maps is not readily apparent here, the average expected CFR in this same region is equivalent across all three maps.

47

4.4 Conclusions

Geospatial and geostatistical analyses showed that iCCM implemented as associated with a chance in the distribution of severe malaria mortality of Nchelenge District. The areas surrounding St. Paul’s General Hospital appeared to experience lower malaria mortality throughout the study period, with lower expected and observed case fatality rates. These regions correspond with the households of individuals participating in ICEMR community surveys, suggesting that people living in nearby villages may be more likely to engage with their healthcare system. These individuals form the core of the initial catchment area of St. Paul’s

General Hospital and iCCM implementation led to greater care access and therefore better severe malaria outcomes. Further, the radius of this core region expanded in the post-iCCM period. The increase in access to care is illustrated by the change in hotspots of both low and high mortality between the two time periods.

The implementation of iCCM in Nchelenge District may have helped reverse expected severe malaria mortality in identified hotspots. Villages in hotspots of severe malaria mortality in the pre-iCCM period—the easterly and southerly areas with expected CFRs over 40%—had several CHWs dispatched to nearby posts. Expected CFRs in the post period decreased in these areas. The hotspot appearing just north of the hospital in the post-iCCM period is also suggestive of iCCM increasing access to care for individuals living far away from the hospital. Villages clustering in this area lie just across the other side of the Kenani River from the hospital and commuting across can be difficult; there is only a dirt road that crosses the stream, and it is often flooded during the rainy season. As such, villagers residing here may have been previously discouraged from seeking care for severe malaria due to inaccessibility. Implementing iCCM may have reduced this reluctance by expanding the number of referrals to the hospital, as

48 referring practices of iCCM in sub-Saharan Africa have been shown to increase health seeking behaviors among those referred to hospital for severe malaria.50 Thus a greater number of sicker patients was seen during the post-iCCM period from this area, thereby driving up the predicted

CFRs.

The impact of iCCM on severe malaria mortality across greater distances can be seen by looking at the uncertainty maps for the pre and post periods. Uncertainty appreciates much faster in the pre period map than in the post period. This is indicative of greater amounts of data at farther distances in the post than pre period. There was a larger number of villages farther from

St. Paul’s General Hospital with more robust CFR values in the post-iCCM period, indicating a greater number of patients being able to access the healthcare system from these more distant regions post-iCCM implementation. Cases in the pre period were more clustered around the hospital, making expected CFRs for areas farther from the main population center less certain.

An important limitation of this study is that geostatistical level analysis was performed as opposed to point pattern or other count-reliant analytical techniques. Village level population estimates were not available. Identification and geolocation for these villages onto a formal map had not been performed prior to this analysis, and a comprehensive census of the local population was last done in 2010 but does not include village-level data. A proxy for population could be used instead—for instance, the number of CHW referrals in a given area combined with the number of referrals from their correspondent RHCs—but these data were not available at the time of analysis. A second limitation is that data rely solely on known village information. The

116 individuals from named villages without coordinate data may reveal other hotspots or change expected CFRs in the less populated areas of the map. Greater collection of location data is required to understand how care is being apportioned throughout the district.

49

V. Discussion

This study has shown quasi-experimental evidence of reductions towards the burden of severe malaria in a rural high transmission area. Patients seen at St. Paul’s General Hospital were more likely to come from farther villages to receive treatment after iCCM was rolled out in. The increased access to care has had two contrasting effects. The majority of patients admitted to the hospital for severe malaria are coming in earlier on during their disease course, yet an important subset of patients with more advanced disease who would have not been able to access care prior to iCCM implementation are now able to receive care. The effect of iCCM on severe malaria outcomes was also demonstrated through case-control and geostatistical studies.

Should these gains indeed be true, then iCCM proves an effective tool in the arsenal of malaria control for this high transmission rural area where disease control has remained elusive despite years of interventions. Transmission intensity has remained high here even as insecticide treated bed-nets (ITN) and indoor residual spray (IRS) coverage has increased. As such, public health program officers at PMI, PAMO, and NMEC have been correct to roll out interventions targetting not only malaria transmission but severe malaria case management. Sustaining the program in Nchelenge District is predicted help alleviate further malaria mortality in the region as previously difficult-to-reach patients gain better access to the health care system. As CHWs become more ingrained in the local health care system, it is important to note that there may be a corresponding increase in mortality seen for severe malaria patients at St. Paul’s General

Hospital due to in-hospital deaths of children who otherwise would have died at home.

Implementers should not be discouraged and shrink the distribution of CHWs. Instead they

50 should be prepared for these numbers as newly captured patients may not have great expected survival rates as they arrive at the hospital already having progressed towards severe disease.

There are several ways to reduce the time from diagnosis to treatment among rural communities. One is to expand the training of CHWs to include the administration of pre-referral rectal artesunate. This has seen such great success across sub-Saharan Africa that it is now the

WHO recommendation for children under six years old presenting to a healthcare center with severe malaria.50 There are understandably concerns about the over-utilization of rectal artesunate for non-severe cases and the subsequent rise of parasite resistance in a given region.51

Other concerns are centered on anxieties over a decrease in case-seeking behavior post-rectal artesunate administration due to misconceptions, e.g., why should the long journey to the hospital be made when the child has already been treated?52

These concerns are valid, and care should be taken in the training of CHW workers. To ensure rational use of rectal artesunate, CHWs could be given an easy to use and interpret hemoglobin point of care test device to help assess malaria severity.53 In fact, CHWs should be given access and training to such a device regardless of rectal artesunate practices to ensure that children without more obvious signs and symptoms of severe malaria are not being incorrectly treated on an outpatient basis. By training CHWs to administer rectal artesunate, children referred to the hospital will experience less disease progression and therefore better odds of survival. However, this will not completely alleviate the burden of disease due to distance.

Transportation times remain a major obstacle to overcome in treating individuals in this rural setting. Personal motorized vehicles within this region are few and far between.39 St. Paul’s

Hospital has access to very few vehicles to be used as ambulances, and these are almost exclusively reserved for trauma or obstetric emergencies. The areas being targeted by iCCM—

51 namely, the more rural village not necessarily serviced by local road networks—are often not accessible for even the stoutest of all-terrain vehicles. Creative methods to solve this issue have been applied within the past few years, most notably in the Southern Province of Zambia where newly issued bicycle ambulances were combined with a pre-referral rectal artesunate program.

Results of the pilot study were promising, with a reduction of severe malaria CFR from 8% to

0.25%.54 However, the program was centered in a low transmission area and has since faded with drops in participation by the ambulance drivers due to payment concerns.

This thesis is limited by the lack of data collected in the post-iCCM period. Ideally, the same analyses could be repeated on an updated dataset encompassing cases admitted since May

2019. If more data were collected on hematological abnormalities beyond January 2019, covariates could be added to models exploring the role of different abnormalities on mortality, stratifying by time periods. Yet with the information available at this time, iCCM appears to be having a positive effect on severe malaria mortality in this high transmission area of northern

Zambia.

52

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59

VII. Curriculum Vitae

Ben Kussin-Shoptaw 22 N Wolfe St. Baltimore, MD 21231 +1-818-522-5138 [email protected]

AREAS OF INTEREST

Infectious disease epidemiology, humanitarian health, spatial distribution of disease

EDUCATION

Johns Hopkins University Master of Science (Infectious Disease Epidemiology) 2018 – 2020

American University Bachelor of Science (Biology) 2010 – 2014 Bachelor of Arts (History)

RESEARCH EXPERIENCE

2018 – present Research Assistant, Johns Hopkins University (Bill Moss, M.D., M.P.H.) • Collected data of severe malaria cases at rural general hospital in Nchelenge District, Zambia • Conducted matched case-control and geospatial analyses looking for changes in severe malaria outcomes pre- and post-implementation of an integrated community case management program • Created and maintained RedCAP surveillance database charting severe malaria cases in Northern Zambia 2017 – 2018 Research Assistant, Friends Research Institute (Jan Gryczynski, Ph.D.) • Recruited high school participants for a studying analyzing the efficacy of a computer-based substance use intervention compared to a nurse practitioner-administered intervention • Cleaned and analyzed preliminary study data using basic statistical methods • Performed a risk factor analysis on national survey data looking to better characterize substance use and abuse patterns

CLINICAL EXPERIENCE

2015 – 2016 Senior Medical Assistant, Metro Immediate and Primary Care of George Washington Medical Faculty Associates ● Provided continuing care to patients including consistently following up with patients, initiating prior authorizations, generating referrals, and scheduling specialty care appointments ● Educated patients with abnormal lab results as to the science and behavioral mechanisms most likely causing their elevated or depressed levels

60

2014 – 2015 Medical Assistant, Metro Immediate and Primary Care of George Washington Medical Faculty Associates ● Triaged patients, reported lab results to patients, registered patients, completed intakes with patients ● Provided basic phlebotomy and laboratory collection and testing

LABORATORY EXPERIENCE

2019 – 2019 Research Assistant, Johns Hopkins University (Clive Shiff, Ph.D.) • Worked towards novel diagnostic methods in potential neurocysticercosis patients • Used PCR, sequencing techniques to detect Tinnea solium parasites in dried urine specimens 2014 – 2016 American University, Microbiology (Jeffery Kaplan, M.D.) ● Isolated and categorized various aquatic bacteria using general microbiological techniques ● Examined bacteria for antibiofilm activity in combating the colonization of Staphylococcus aureus 2011 – 2014 American University, Neurobiology (Colin J. Saldanha, Ph.D.) ● Trained in surgical techniques for mechanical brain injuries to zebra finches ● Extracted whole brains for immunocytochemistry; sliced and mounted tissue for studies; performed quantitative polymerase chain reaction (qPCR) runs on additional extracted tissue

PAPERS IN PREPARATION

Kussin-Shoptaw B et al. [Impact of integrated community case management on severe malaria outcomes in a high transmission setting in Zambia]. Baltimore, MD: Johns Hopkins Bloomberg School of Public Health; 2020.

Ippolito MM, Kabuya JB, Hauser M, Kussin-Shoptaw B, Moss WJ et al. Thrombocytopenia and whole blood transfusion in children with severe falciparum malaria. Poster presented at: 68th American Society of Tropical Medicine and Hygiene; 23 November, 2019; Gaylord National Resort and Convention Center, National Harbor, MD.

POSTER PRESENTATIONS

Kussin-Shoptaw B, Kabuya JB, Lupiya JS, Kamavu LK, Ippolito MM et al. Severe malaria surveillance in a rural district hospital in northern zambia. Poster presented at: Global Health Day; 26 March, 2020; Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Kussin-Shoptaw B, Kabuya JB, Lupiya JS, Kamavu LK, Ippolito MM et al. Severe malaria surveillance in a rural district hospital in northern zambia. Poster presented at: 68th American Society of Tropical Medicine and Hygiene; 23 November, 2019; Gaylord National Resort and Convention Center, National Harbor, MD.

Kussin-Shoptaw B, Kabuya JB, Lupiya JS, Kamavu LK, Ippolito MM et al. Severe malaria surveillance in a rural district hospital in northern zambia. Poster presented at: Future of Malaria Research Symposium; 18 November, 2019; Johns Hopkins University Montgomery County Campus, Rockville, MD. 61

Kussin-Shoptaw B, Lupiya JS, Shields TM, Moss WJ, Ippolito MM. Distribution of severe malaria cases by village in a district in northern zambia. Poster presented at: Spatial Science for Public Health Center GIS Day; 13 November, 2019; Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

ORAL PRESENTATIONS

Kussin-Shoptaw B, Shields TM, Ippolito MM, Moss WJ. Impact of integrated community case management on severe malaria outcomes in a high transmission setting in zambia. Lecture presented at: Infectious Disease Epidemiology Research in Progress; 10 February, 2020; Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.

Kussin-Shoptaw B, Ippolito MM, Moss WJ. Impact of integrated community case management on severe malaria outcomes in a high transmission setting in zambia. Lecture presented at: Clinical Pharmacology Research in Progress; 16 October, 2019; Johns Hopkins University School of Medicine, Baltimore, MD.

Kussin-Shoptaw B. From electricity to pharmacy: examining the role of electroconvulsive therapy in the development of antidepressants. Lecture presented at: American University History Day; 24 April, 2014; American University, Washington, DC.

Kussin-Shoptaw B, Costanzi S, Saldanha CJ. Detection and Authentification of Classical and Non-Classical Steroid Receptors in the Song Bird Brain. (3/29/2014) Work presented at: the Robyn Rafferty Matthias Student Research Conference; 24 April, 2014; American University, Washington, DC.

LANGUAGES

Spanish Speaking: Beginner Reading: Beginner Writing: Beginner Japanese Speaking: Beginner Reading: Beginner Writing: Beginner Bemba Speaking: Beginner Reading: Beginner Writing: Beginner

AWARDS

Johns Hopkins Master’s Tuition Scholarship Award, September 2019-present Global Health Experience Field Placement Award, May 2019 Robyn Rafferty Matthias Student Research Award, June 2013 American University Presidential Scholarship Award, September 2010-June 2014

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