Using sentinel surveillance system data to characterize severe malaria illness and quality of malaria case management among hospitalized patients in Kenya, 2017-2024

Using sentinel surveillance system data to characterize severe malaria illness and quality of malaria case management among hospitalized patients in Kenya, 2017-2024 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Using sentinel surveillance system data to characterize severe malaria illness and quality of malaria case management among hospitalized patients in Kenya, 2017-2024 Megumi Itoh, Naomi Lucchi, Jonathan Schultz, George Agogo, Peninah Munyua, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7321812/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Malaria Journal → Version 1 posted 11 You are reading this latest preprint version Abstract Background : In Kenya, limited clinical data on hospitalized malaria patients restricts insights into disease severity and care quality. Using data from the Integrated Facility-based Surveillance (IFBS) system—a sentinel surveillance platform for febrile illnesses across twelve facilities—we assessed risk factors for severe illness and mortality, diagnostic accuracy of microscopy, and adherence to severe malaria treatment guidelines. Methods : We analyzed IFBS data obtained from June 2017 to July 2024 using bivariable logistic regression to identify factors linked to severe illness and deaths. Microscopy results were compared with PCR results to assess diagnostic concordance. We also evaluated whether patients received parasitological confirmation before treatment and if severe cases received IV artesunate followed by artemether-lumefantrine (AL), per standard guidelines. Results : Among 8,487 inpatients, 2,197 (25.9%) tested positive for malaria; among malaria cases, 713 (32.5%) had severe disease and 16 (0.7%) died. Severe illness and deaths were associated with age under one year and delayed care-seeking. Positive routine microscopy was PCR negative in 21% of patients. Only 15% of severe cases were documented to have received both IV artesunate and AL, while 17% received IV quinine. Discussion : Risk factors associated with severe illness and mortality included young age < 1 as well as modifiable risk factors that suggested delayed care seeking. Despite IFBS data reliance on chart reviews, findings reveal critical gaps in diagnostic accuracy and adherence to treatment protocols for severe malaria. Trial registration: N/A Malaria Sentinel Surveillance Quality of Health Care Kenya Africa Figures Figure 1 Introduction Investments in malaria control measures — such as broader coverage and implementation of effective vector control activities and increased access to effective malaria case management — have led to marked reduction in malaria prevalence among children under 5 years old in Kenya, decreasing from 11% nationally in 2010 1 to 6% in 2020. 2 Kenya has heterogenous malaria epidemiology, with prevalence rates ranging from < 1% in the low-risk epidemic zone to 19% in the lake endemic zone. 2 Advancements in malaria surveillance have also resulted in improved data capture and use, with the quality and availability of routine malaria data steadily improving over the past several years. However, routine data remain primarily focused on the outpatient setting, emphasizing the treatment cascade of uncomplicated malaria – from testing suspected febrile cases to treatment of those with a parasitological diagnosis of malaria. Despite increased efforts to enhance data capture in the inpatient setting, information on patients hospitalized with malaria in Kenya and more broadly in Africa remain sparse and incomplete. Data on clinical presentation, treatments provided and clinical outcomes among admitted patients with malaria are limited. The only routinely reported malaria indicator on malaria-related hospitalizations in Kenya is the case fatality rate, calculated by dividing the total number of malaria-related deaths in health facilities that offer inpatient services by the total number of malaria admissions. This indicator has significant limitations due to incomplete data capture of admissions and discharge diagnoses in the inpatient setting. As a result, a detailed clinical picture of hospitalized malaria patients, including their risk factors for severe presentations and mortality, cannot be derived from routinely collected malaria surveillance data. Such comprehensive data are typically obtained only through research studies or enhanced surveillance activities conducted at a limited number of health facilities. The Integrated Facility-based Surveillance (IFBS) is a sentinel disease surveillance platform carried out across 12 facilities in Kenya across various malaria epidemiologic zones. Of note, there are about 1600 public inpatient facilities in Kenya. This surveillance platform collects clinical and laboratory information, as well as specimens for pathogen testing among patients who present with acute febrile illness (AFI), defined as an axillary temperature of ≥ 38.0 o C. Malaria is considered an AFI and all patients enrolled in IFBS are tested for malaria, initially by rapid diagnostic testing (mRDT), confirmed by expert microscopy, and in some cases, by multiplex TaqMan Array Cards PCR testing (TAC). As such, the information collected as part of IFBS provides a rich and unique dataset from multiple sites across Kenya, describing the clinical characteristics, treatments provided, and outcomes of inpatients diagnosed with malaria and other co-morbidities, enabling analyses of risk factors associated with severe disease presentation and mortality. As a pathogen surveillance platform, IFBS also identifies cases of co-infection with non- Plasmodium pathogens. In other studies of children with severe malaria, 6–8% have been reported to have bacterial co-infection (vs. 1% among adults with severe malaria), predominantly gram-negative enteric organisms, especially non-typhoidal Salmonella (NTS). 3 , 4 Literature varies regarding disease severity and mortality among patients co-infected with malaria and other pathogens, but up to one-third of deaths among children with severe malaria have been attributed to a bacterial co-infection. 5 These findings have influenced WHO guidelines for the treatment and management of severe malaria among children, which recommend co-administering broad-spectrum antibiotics in moderate to high malaria transmission settings. 6 However, this recommendation is inconsistently applied, and the widespread resistance of gram-negative bacteria (such as NTS) to commonly used empiric antibiotics, as well as laboratory limitations in culture-based diagnosis of bacterial infections, pose significant challenges to implementing this approach. Microscopy is the gold standard clinical diagnostic method for malaria in Kenya given its relative cost and implementation feasibility at the point-of-service in higher-level facilities. Malaria RDTs are used when microscopy is not possible, typically in lower-level facilities without laboratory infrastructure or adequately trained staff. Facility laboratory staff receive pre-service training in malaria diagnostics, including reading thick and thin blood smears, but in many malaria-endemic countries, the external quality assurance (EQA) program to maintain reliability and accuracy of microscopy-based diagnostic results struggles with resource constraints. 7 Since the IFBS platform also conducts its own malaria testing for all enrolled patients, it provides an opportunity to assess how routine clinical smears read by laboratory staff at the facilities (clinical smears) compare with smear results read by expert microscopists (surveillance smears). The information captured in IFBS in terms of treatments provided during hospitalization can also demonstrate the level of adherence to clinical guidelines in the inpatient setting, particularly regarding provision of antimalarials and empiric antibiotics. We used data from the IFBS platform across Kenya to examine two key objectives: First, to assess the factors associated with severe illness and mortality among patients with laboratory confirmed malaria; second, to describe the quality of malaria case management among hospitalized patients by evaluating the diagnostic accuracy of clinical malaria testing and assessing the implementation of appropriate malaria case management. Methodology IFBS procedures have been previously described. 8 Each morning, trained surveillance officers reviewed daily hospital admission logs for pediatric and adult medical wards for the past 24 hours and all new admissions were screened for eligibility based on information in the medical record. AFI was defined as a measured temperature ≥38.0 0 C for ≤14 days. Those eligible were approached for informed consent and enrolment into the surveillance system study. Additionally, at some sites, recruitment was done at outpatient department clinics for persons aged ≥13 years only. However, we limited our analysis to inpatients given the objectives of the project and relatively small number of outpatients enrolled. Following enrollment, the surveillance officer interviewed the patient, extracted information from their medical records, and recorded this information in a standardized digital form. Data included pertinent medical history, clinical presentation, vital signs and physical exam, clinical management, routine lab results (including mRDT or microscopy), discharge diagnosis, and clinical outcomes. Regardless of whether they had already undergone clinical testing for malaria at the health facility, trained laboratory staff collected 5 cc (2.5 cc for children under five years old) venous blood from all enrolled patients in Ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes and transported it to an onsite laboratory, where on the same day a mRDT (Abbott Bioline Malaria Ag P.f/Pan) was performed and thin and thick blood smears were prepared with Geimsa for microscopy following the WHO malaria microscopy standard operating procedure. 9 Microscopy slides from all sites were transferred weekly to Jaramogi Oginga Odinga Teaching & Referral Hospital in Kisumu, where certified expert microscopists analyzed the slides for malaria parasites. For a subset of patients who met undifferentiated fever ** criteria, whole blood samples were stored at -20˚ C for up to seven days and transported to the CDC-supported laboratory at the Kenya Medical Research Institute (KEMRI) in Nairobi for PCR testing using TAC PCR (ThermoFisher). [1] TAC PCR is intended for surveillance and was not approved for the diagnoses of patients for clinical purposes. From June 2023 onwards, patients who had a positive IFBS mRDT did not get a TAC PCR as a resource savings measure. IFBS data from June 2017 – July 2024 were analyzed to describe the clinical characteristics and hospitalization course of patients with AFI who were diagnosed with malaria. Data were analyzed from the 12 sentinel IFBS health facilities across Kenya (Figure 1), which were not constant during the analysis period (Supplemental table 1) For the analysis of factors associated with severe disease and mortality, a positive malaria diagnosis was defined as a patient who tested positive on mRDT or microscopy during IFBS enrollment (“surveillance” test). A positive malaria result on TAC PCR test only without a positive parasitological result was considered a case of submicroscopic malaria and not included in the analysis; this is aligned with the national guideline’s definition of parasitological confirmation for clinical diagnosis. For the analysis assessing adherence of clinical care to MOH guidelines, a positive malaria diagnosis was defined as a patient with a positive mRDT or microscopy performed by health facility staff as part of their routine care (“clinical” test), which were the results used by the clinician to manage the patient. While all inpatients technically meet the definition of “severe malaria” by virtue of being assessed by a clinician to require hospitalization, inclusion in the analysis of severe illness required documentation of at least one of the following characteristics: physical exam findings of severe respiratory disease (i.e., oxygen saturation of ≤90%, stridor, nasal flaring, lower chest indrawing, or grunting), severe neurological disease (i.e., level of consciousness below “alert” on the Alert/Verbal/Pain/Unresponsive [AVPU] scale, convulsions, photophobia, nuchal rigidity, and bulging fontanelle), jaundice, petechial rash, or severe anemia (hemoglobin <7g/dL). 6 Patient outcomes were dichotomized to either discharged in stable condition or died. Those with ambiguous outcomes -- such as discharged against medical advice, transferred to another hospital or missing outcome—were excluded from the mortality analysis. Statistical analysis Demographic and baseline characteristics of the study population were described using frequencies and percentages, and chi-square tests were used to test for statistical significance in differences. Bivariate logistic regression analyses were performed in R (v4.4.2). To ascertain factors associated with severe illness at enrollment and mortality, we looked at the following patient characteristics: sex, age, malaria epidemic zone of residence, number of days of fever prior to presentation, care-seeking prior to presentation (including referrals from other health facilities and antimalarials taken prior to admission), pregnancy, HIV status, and malnutrition. Pregnancy status was self-reported for women aged 15-49 years, and HIV status was ascertained by self-report or the medical chart but was not confirmed by HIV testing. Malnutrition status was determined by age-based mid-upper arm circumference (MUAC) cut-offs for children aged <5 years. Malaria co-infection was defined as a Plasmodium species infection plus another non- P lasmodium pathogen detected on TAC PCR. We determined the odds of developing severe illness or dying with malaria mono-infection vs. co-infection vs. other pathogens using logistic regression and quantified the effects with odds ratios. For the purposes of analyzing the TAC PCR results, we included data from all patients who had a TAC PCR, not limited to those with a positive malaria test. To assess the accuracy of parasitological testing by health facility staff, we assessed the PCR positivity for both clinical and surveillance smears and mRDTs. While PCR results were not used for malaria diagnosis, PCR has a higher sensitivity for detecting Plasmodium spp., making it possible to assess misdiagnoses by microscopy and mRDT. To assess the quality of care provided, we examined whether patients diagnosed with malaria by either clinical mRDT or microscopy test received appropriate antimalarial treatment per national guidelines: injectable artesunate followed by a course of artemether-lumefantrine (AL) for severe disease, AL alone for uncomplicated disease; and if artesunate was not available, IV quinine as an alternate choice for severe disease (per the Kenya National Malaria Treatment Guidelines). We also assessed the proportion of patients who received any antimalarial treatment without a documented parasitological diagnosis of malaria, as this would not be in accordance with national guidelines. Lastly, we assessed the proportion of patients with severe malaria illness who received empiric treatment with antibiotics per WHO guidelines, particularly in moderate to high transmission areas, which is applicable in Kenya’s malaria endemic zones in western Kenya as well as on the coast (WHO guidelines for malaria, 30 Nov 2024). ** Undifferentiated fever was defined as AFI without evidence of lower respiratory tract infection (cough or shortness of breath plus tachypnea or abnormalities on respiratory examination), diarrhea (≥3 loose stools in a 24-hour period), or another focus of fever based on history and physical examination (e.g., meningitis, skin/soft tissue infection). [1] From June 2017 to October 2022, the TAC assay included targets for Bartonella , Brucella , Ebola virus, Bundibugyo virus, Coxiella burnetii , Crimean-Congo hemorrhagic fever virus, chickungunya virus, dengue virus, hepatitis E virus, lassa virus, Leishmania , Leptospira , Marburg virus, Nipah virus, O'nyong'nyong virus, Plasmodium , Rickettsia , Rift Valley Fever virus, Salmonella , Salmonella typhi , HIV I, HIV II, Sudan (ebola) virus, Trypanosoma bruceii, West Nile virus, Yersinia pestis, yellow fever virus, and Zika virus. The TAC assay was updated in November 2020 to add additional targets, including: Burkholderia pseudomallei, Lassa virus, Orientia tsutsugamushi, Oropouche virus, Plasmodium falciparum, Plasmodium vivax, Streptococcus pneumoniae, Salmonella paratyphi A, and Zika virus that detects 32 pathogens including 15 viral, 12 bacterial, and 5 protozoal pathogens through real-time PCR. Results In total, 9,436 patient records were available from the IFBS platform from June 2017 to July 2024 (Supplemental figure 2). Of these, 9,212 had a surveillance smear or mRDT and 2,232 (23.7%) tested positive for malaria. Among 2,197 patients with malaria who were hospitalized, 713 (32.5%) met criteria for severe illness presentation and 16 (0.7%) died. Factors associated with severe illness and mortality Table 1 shows the results from bivariate analysis of factors associated with severe illness presentation and mortality. In summary, patients had greater odds of severe illness if they were very young (aged <1 year), had a longer fever duration before presentation, or had sought care at another facility or been referred from another health facility, including self-report of having received antimalarials in the last 7 days. Similarly, longer fever duration, being referred from another health facility, and having received antimalarials in the last 7 days were associated with greater odds of mortality. Severe illness was also associated with mortality among patients with malaria. Malaria co-infections and association with disease severity and mortality TAC PCR was done on 6,069 patients presenting with AFI; P. falciparum was detected in 2,093 (34.5%) cases (Table 2). Narrowing the results only to inpatients with a positive surveillance mRDT and/or positive blood smear, Plasmodium spp. was detected in 94.3%. Among those who tested positive with an mRDT or smear, the most common co-infection pathogen detected by TAC PCR was non-typhoid Salmonella (NTS) with 26 cases (2.0%), followed by HIV-1 (1.2%), Rickettsia (0.9%), and Dengue virus (0.9%). There was no clear pattern of association between TAC PCR test results and disease severity. However, compared to detection of Plasmodium alone, the odds of mortality were higher for malaria co-infection, detection of a non- P lasmodium pathogen(s), or a negative TAC result. Though only detection of a non- P lasmodium pathogen was statistically significant, there was near significance of patients who had a negative TAC result (Table 3). Table 2: Pathogens detected on TaqMan Array Card of persons with acute febrile illness in Kenya, 2017-2024 Pathogen* ,† All persons with AFI with TAC done (N = 6069) ‡ Inpatients with an mRDT and/or smear positive for malaria (N = 1293) Plasmodium spp. 2093 (34.5%) 1219 (94.3%) HIV-1 136 (2.2%) 16 (1.2%) Non-typhoid Salmonella 73 (1.2%) 26 (2.0%) Rickettsia 72 (1.2%) 12 (0.9%) Dengue 52 (0.9%) 11 (0.9%) Streptococcus pneumoniae 44 (0.7%) 5 (0.4%) Brucella 42 (0.7%) 6 (0.5%) Salmonella Typhi 38 (0.6%) 7 (0.5%) Leishmania 34 (0.6%) 1 (0.1%) Chikungunya 28 (0.5%) 4 (0.3%) Bartonella 22 (0.4%) 1 (0.1%) Leptospira 14 (0.2%) 2 (0.2%) Coxiella burnetii 10 (0.2%) 4 (0.3%) Plasmodium vivax 5 (0.1%) 0 (0.0%) Rift Valley Fever virus 3 (0.0%) 1 (0.1%) Burkholderia pseudomallei 2 (0.0%) 0 (0.0%) Negative TAC 3630 (59.8%) 66 (5.1%) * Results for targets for Plasmodium falciparum and Plasmodium vivax were not shown to avoid double-counting and because these targets were not added until November 2020. All P. falciparum and P. vivax detections were positive for the Plasmodium spp . target but not vice versa. † TAC assay was updated in November 2020 to add the following targets: Burkholderia pseudomallei, Lassa virus, Orientia tsutsugamushi, Oropouche virus, Plasmodium falciparum , Plasmodium vivax , Streptococcus pneumoniae , Salmonella paratyphi A, and Zika virus. ‡ Includes all patients (inpatient and outpatient) who received a TAC test as part of AFI surveillance in Kenya AFI: acute febrile illness; mRDT: rapid diagnostic test; TAC: TaqMan Array Card Of note, the following pathogens have not been detected on this platform in Kenya: Crimean-Congo hemorrhagic fever virus, Ebola virus (Zaire, Budibugyo, Sudan), Hepatitis E virus, Lassa virus, Marburg virus, Nipah virus, O'nyong-nyong virus, Orientia tsutsugamushi, Oropouche virus, Salmonella paratyphi A, Trypanosomiasis brucei, West Nile virus, Yellow Fever virus, Yersinia pestis, Zika virus Table 3: Risk of severe disease and mortality based on TAC results (N = 6069)* Category Severe illness presentation Mortality Not severe Severe OR 95% CI Discharged Died OR 95% CI Malaria only 1346 (32%) 572 (30%) Ref 1733 (35%) 23 (23%) Ref Malaria coinfection 130 (3%) 45 (2%) 0.81 0.57, 1.15 150 (3%) 4 (4%) 2.01 0.58, 5.31 Other pathogen(s) 237 (6%) 109 (6%) 1.08 0.84, 1.38 263 (5%) 15 (15%) 4.30 2.17, 8.27 Negative 2458 (59%) 1,172 (62%) 1.12 1.00, 1.27 2831 (57%) 59 (58%) 1.57 0.98, 2.60 *This analysis is among all persons with a TAC result regardless of malaria rapid diagnostic test or microscopy result. The malaria result reflects the TAC target result. CI: confidence interval; OR: odds ratio; TAC: TaqMan Array Card PCR positivity of positive clinical and surveillance malaria test results Of the inpatients assessed for AFI, 4,916 (57.9%) had documentation of malaria testing as part of their clinical care—3,097 by microscopy (smear) and 1,819 by mRDT. Among the 5,303 inpatients who had a TAC PCR test, positive test result concordance between PCR and smear as well as mRDTs, was higher for the surveillance test compared to clinical test results (smear: 94.4% vs. 78.6%; mRDT: 95.4% vs. 88.9%) (Table 4). PCR positivity of positive clinical smears varied by epidemic zone, ranging from 69.6% in highland epidemic-prone areas to 87.9% in low-risk areas. Table 4: Malaria PCR positivity of positive microscopy and mRDTs among inpatients by malaria epidemic zone Epidemic zone† Total* PCR percent positivity Positive Smear Positive mRDT Surveillance Clinical Surveillance Clinical Overall 5303 94.4% 78.6% 95.4% 88.9% Lake endemic 1068 92.7% 79.1% 96.6% 83.8% Coast endemic 715 96.2% 80.5% 92.5% 100.0% Highland epidemic prone 245 95.8% 69.6% 91.5% ** Seasonal 1615 95.8% 76.5% 96.5% 90.9% Low risk 1660 95.0% 87.9% 91.7% 80.8% * Total number of persons with acute febrile illness with a PCR test done by TaqMan Array Card. † Epidemic zones were defined as: Lake endemic (8): Bungoma, Busia, Homa Bay, Kakamega, Kisumu, Migori, Siaya, Vihiga; Coast endemic (5) : Kilifi, Kwale, Lamu, Mombasa, Taita Taveta; Highland epidemic prone (10) : Bomet, Elgeyo-Marakwet, Kericho, Kisii, Narok, Nandi, Nyamira, Trans Nzoia, Uasin Gishu, West Pokot; Seasonal (14) : Baringo, Embu, Garissa, Isiolo, Kajiado, Kitui, Mandera, Marsabit, Meru, Samburu, Tana River, Tharaka-Nithi, Turkana, Wajir; Low risk (10) : Kiambu, Kirinyaga, Laikipia, Machakos, Makueni, Murang’a, Nairobi, Nakuru, Nyandarua, Nyeri ** Only 2 mRDTs were formed in the highland epidemic prone zone during the study period. PCR: polymerase chain reaction; mRDT: rapid diagnostic test. Adherence to Kenya malaria clinical guidelines for management of severe malaria Among 4,916 inpatients who were tested for malaria with microscopy or mRDT as part of their routine care, 2,094 (44.6%) tested malaria positive. Of these, 1,847 (91.7%) had documentation of antimalarials given (Table 5). Two-hundred eighty-five (15.4%) received both IV artesunate and AL per the national guidelines for treatment of severe malaria. The majority (63.0%) were documented to have only received artesunate monotherapy. Quinine was used alone or in combination with artesunate or AL in 19.0% of patients and was primarily used in a facility in Kakuma, the site of a refugee camp, in the seasonal malaria epidemic zone (Table 6). Table 5: Antimalarials documented as used among those with positive malaria test Antimalarial(s) **N = 1847** Artesunate therapy Monotherapy 1164 (63.0%) + Artemether-lumefantrine (AL) 285 (15.4%) + Quinine 15 (0.8%) + AL and Quinine 15 (0.8%) Quinine therapy Monotherapy 128 (6.9%) + AL 193 (10.4%) AL monotherapy 46 (2.5%) Other 1 (0.1%) Table 6: Antimalarial usage by epidemic zone Epidemic zone† Artesunate N = 1479 AL* N = 539 Quinine* N = 351 Lake endemic 669 (45.2%) 68 (12.6%) 1 (0.3%) Coast endemic 86 (5.8%) 24 (4.5%) 0 (0.0%) Highland epidemic prone 109 (7.4%) 2 (0.4%) 2 (0.6%) Seasonal 499 (33.7%) 394 (73.1%) 348 (99.1%) Low risk 116 (7.8%) 51 (9.5%) 0 (0.0%) * Result not available for three patients † Epidemic zones were defined as: Lake endemic (8): Bungoma, Busia, Homa Bay, Kakamega, Kisumu, Migori, Siaya, Vihiga; Coast endemic (5) : Kilifi, Kwale, Lamu, Mombasa, Taita Taveta; Highland epidemic prone (10) : Bomet, Elgeyo-Marakwet, Kericho, Kisii, Narok, Nandi, Nyamira, Trans Nzoia, Uasin Gishu, West Pokot; Seasonal (14) : Baringo, Embu, Garissa, Isiolo, Kajiado, Kitui, Mandera, Marsabit, Meru, Samburu, Tana River, Tharaka-Nithi, Turkana, Wajir; Low risk (10) : Kiambu, Kirinyaga, Laikipia, Machakos, Makueni, Murang’a, Nairobi, Nakuru, Nyandarua, Nyeri Multiple discharge diagnoses were possible, but 90.1% of inpatients with a positive clinical malaria test included a discharge diagnosis of malaria, whereas the remainder (9.9%) had a non-malaria diagnosis such as pneumonia, meningitis, or gastroenteritis. Some of the patients with a malaria discharge diagnosis had a negative malaria test (15.0%) or no clinical malaria test (6.6%). Most (91.7%) patients that tested positive for malaria during clinical care were treated with antimalarials. Some patients who tested negative for malaria (17.2%) or were not tested for malaria (7.5%) also received an antimalarial medication (table 7). Approximately half (55.8%) of patients with a positive clinical malaria test also received antibiotics. In malaria endemic zones where transmission intensity is moderate to high, a higher proportion of patients diagnosed with malaria were also treated with antibiotics (61% in lake endemic and 60% in coast endemic zones). In general, a higher proportion of patients who tested negative for malaria (89.3%) or were not tested (93.8%) were treated with antibiotics. Table 7. Receipt of antimalarials, antibiotics and discharge diagnosis by clinical malaria test result Tested for malaria as part of clinical care Not tested for malaria Positive N = 2,094 Negative N = 2,598 Result not recorded N = 224 N = 3,569 Antimalarial medications given (n miss: 324) No 168 (8.3%) 2,039 (82.8%) 96 (46.6%) 3,216 (92.5%) Yes 1,849 (91.7%) 423 (17.2%) 110 (53.4%) 260 (7.5%) Antibiotic medications given (n miss: 324) No 892 (44.2%) 264 (10.7%) 58 (28.2%) 215 (6.2%) Yes 1,125 (55.8%) 2,198 (89.3%) 148 (71.8%) 3,261 (93.8%) Discharge diagnosis Malaria* 1,887 (90.1%) 390 (15.0%) 115 (51.3%) 235 (6.6%) Other diagnosis 207 (9.9%) 2,208 (85.0%) 109 (48.7%) 3,334 (93.4%) * Could include additional diagnoses such as pneumonia, meningitis, etc. Among those with a positive clinical malaria test, treatment with antimalarial medications was associated with lower odds of severe illness and mortality, although the latter association was not statistically significant (Table 8). Additionally, the odds of severe illness and mortality were higher among patients who had a non-malaria diagnosis compared to persons only diagnosed with malaria at discharge. Patients with malaria plus another discharge diagnosis had greater odds of severe illness, but not mortality, than those with malaria only. Table 8: Disease severity and outcomes by antimalarial use and discharge diagnosis among patients with acute febrile illness and a positive malaria test during clinical care Not severe, N = 1506 Severe, N = 588 OR (95% CI) Discharged, N = 2012 Dead, N = 15 OR (95% CI) Antimalarials given during hospitalization (n miss = 77) No 105 (7%) 63 (11%) Ref 127 (7%) 3 (20%) Ref Yes 1340 (93%) 509 (89%) 0.63 (0.46, 0.88) 1811 (93%) 12 (80%) 0.28 (0.08, 1.01) Discharge diagnosis Malaria diagnosis only † 1021 (68%) 220 (37%) Ref 1228 (61%) 5 (33%) Ref Malaria plus another diagnosis 365 (24%) 281 (48%) 3.57 (2.89, 4.43) 625 (31%) 5 (33%) 1.96 (0.57, 6.81) Other diagnosis 93 (6%) 73 (12%) 3.64 (2.59, 5.11) 157 (8%) 4 (27%) 6.26 (1.66, 23.55) Unknown diagnosis 27 (2%) 14 (2%) 2.41 (1.21, 4.59) 2 (0%) 1 (7%) 112.80 (9.53, 1582.32) * Outcome missing for 67 patients CI: Confidence interval; OR: odds ratio; Ref: referent level Discussion Factors associated with severe illness and mortality The IFBS platform in Kenya provides a rich clinical data source to characterize patients hospitalized with malaria. Using this data source, we were able to explore factors associated with severe disease and mortality among inpatients as well as benchmark clinical management against MOH guidelines. Unsurprisingly, the youngest age group (≤1 year) was associated with increased odds of developing severe illness. There was also a trend towards higher odds of severe illness and mortality among those with malnutrition and HIV-positive status, but the sample sizes were too small to detect statistically significant differences. This is consistent with the prevailing assumption that a weakened immune system resulted in worse clinical outcomes. While self-reporting being treated with an antimalarial in the previous week may be perceived to be protective from having severe presentation, it appeared as a risk factor for developing severe illness or dying. This could be due to inappropriate dosing, substandard or poor quality product, poor drug absorption, anti-malarial resistance or delays in care seeking behaviors. Similarly, history of having sought other care prior to presentation was associated with severe illness, and while not statistically significant due to small sample size, also death. In Kenya, approximately 40-50% of patients seek initial care for malaria in the private sector, 10 especially in unregulated drug dispensaries, where adherence to the national malaria treatment guidelines is not guaranteed and patients may receive substandard care—such as ineffective antimalarial therapy or an incomplete treatment course due to cost limitations. Most cases treated in the private sector will likely experience clinical resolution, thus never requiring hospitalization and potential recruitment into the IFBS database. However, others who develop clinical complications and severe malaria may become part of the IFBS database, resulting in a biased result where the self-report to have received care at another facility or taken antimalarials prior to admission appeared as a potential risk factor associated with severe illness or mortality. Delays in care seeking were also associated with severe illness and mortality, including reporting longer duration of fever, and history of being referred from a lower-level health facility. These findings underscore the importance of behavior change strategies as well as messaging in the community that promote timely care-seeking, referrals and follow-up, especially for the youngest children. It is also important to emphasize the need to complete a full 3-day course of an oral artemisinin-based combination therapy (ACT), ideally obtained at a regulated health facility. For healthcare providers in malaria endemic areas, diagnosing malaria is common, but for those in areas where malaria is less common, malaria diagnosis may not be considered initially, resulting in the development of severe disease. Further, patients who live in non-endemic areas do not have immunity to malaria, rendering them more susceptible to developing severe disease. The TAC results among those with parasitological confirmation of malaria demonstrated that non-typhoidal Salmonella is the most common co-infection with malaria, which has been extensively documented in the literature, though at 2%, the co-infection rate with the organism was lower than expected. The other co-infecting pathogens of interest include various zoonotic infections such as Rickettsia , Brucella , Bartonella , Leptospira , Coxiella burnetii, and Rift Valley Fever. Any one of these pathogens could result in severe illness, so a co-infection with malaria may result in more severe disease or higher mortality. However, our data showed co-infection with another non-falciparum pathogen was not definitively associated with a more severe presentation or mortality, though it should be noted that the odds ratio for mortality for malaria co-infections was 2.0, though non-significant. However, when expanding the analysis to all patients with AFI (not limited to malaria), severe illness and mortality odds were higher among patients who had a non-falciparum infection, as well as among those with a negative TAC result. Accuracy of malaria diagnostic testing and adherence to national treatment guidelines This project provided a unique opportunity to conduct an internal validation of clinical microscopy smears and mRDT results by comparing them with PCR testing as part of the surveillance project. Microscopy at the facility level suggests a substantial over-reading of smears as positive (about 1 in 5 being called positive when PCR was negative) in the routine clinical setting. Consistently higher PCR positive concordance with positive smears read by surveillance staff who are certified at expert level corroborates this finding. The quality of routine microscopy readings could be related to suboptimal equipment, reagents, contamination of stain and/or slides with dust or debris, and lack of adequate routine training of microscopists. The high smear false positivity in the lake endemic zone of about 20% is concerning considering that this area is responsible for over 80% of the malaria burden in Kenya and it is where significant investments have occurred in ensuring quality malaria diagnostics and case management. Our findings suggest that malaria might be over-diagnosed where diagnosis is made by microscopy alone, which is the case in many of the higher-level health facilities. These findings underscore the need for strengthening quality assured microscopy programs with adequate training of microscopists and properly functioning microscopes and reagents, as well as an EQA program that routinely provide feedback on performance and identify areas for improvement While false positive results, or over-diagnosis of malaria may be arguably better than significant under-diagnosing of patients who need malaria treatment, it still results in wasted resources and may result in masking of other diagnoses that would benefit from different therapies. The TAC PCR findings of higher odds of severe illness and mortality for non-falciparum (or unknown) infections demonstrates the potential risk of false positive microscopy or mRDT results if additional diagnostics and therapeutics are delayed until the patient deteriorates. In terms of adherence to the national malaria treatment guidelines in Kenya, the findings were both encouraging and revealing to inform improvements in malaria programming. A key advantage of this study was the opportunity to assess the level of adherence to the national treatment guidelines for treatment of patients hospitalized with malaria. The Kenya National Malaria Control Program (NMCP) conducts annual Health Facility Assessments (HFAs) to examine the level of adherence to malaria clinical guidelines by clinicians in both outpatient and inpatient settings. The most recent HFA in 2023 evaluated 2,073 files of patients admitted for suspected malaria and evaluated them for the following: 1) testing for malaria, 2) prescribing of recommended treatment based on severity criteria and malaria test results, (defined as either injectable artesunate for severe test positive patients, artemether-lumefantrine (AL) for non-severe test positive patients, or no antimalarial treatment for test negative patients). The composite score for adherence to guidelines was 55%, with 83% of patients tested for malaria on admission, 94% of test positive patients with severe malaria criteria receiving IV artesunate and only 4% of test positive patients without any severe malaria criteria treated with AL. For test negative patients with or without clinical criteria for severe disease, 46% and 33% still received IV artesunate, respectively. The composite performance in the HFA was higher in high-risk areas compared to low-risk areas (60% vs. 52%). However, there were major differences in the treatment of test negative patients, where in low-risk areas antimalarial treatments were less commonly prescribed for test negative patients, both for those with severe (34% vs. 57%) or non-severe criteria (14% vs. 31%). 11 Unfortunately, the HFA did not look at whether patients received a course of AL following IV artesunate. While our analysis of IFBS data cannot provide a direct comparison to the HFA results, it provides a sense of the critical gaps in adhering to treatment guidelines. First, the IFBS data showed only 58% of patients with AFI were tested clinically for malaria, which was much lower than 83% observed in the 2023 HFA. Further, IFBS showed 92% of those with a positive test were documented to have received an antimalarial, whereas 17% of those with a documented negative test and 10% of those who did not have a clinical malaria test documented nonetheless received an antimalarial. The Kenya clinical guidelines for management of severe malaria make it clear that the first line therapy should be IV artesunate, followed by a course of artemether-lumefantrine (AL). However, only 15.4% of those with a positive test who received antimalarials were documented to have received both artesunate and AL. In fact, when assessed by epidemic zone whether patients received AL, the highest proportion of patients who received it was only 47.0% in the seasonal epidemic zone, and only a minority were documented to have received AL in lake (9.9%) and coast endemic (27.9%) areas. Patients receiving artesunate alone is concerning considering monotherapy goes against standard treatment guidelines and could give rise to artemisinin resistance, a major emerging threat for global malaria control. 12 Also of note is that in the seasonal epidemic zone, there was a significant number of patients who received IV quinine instead of IV artesunate. The majority of these cases were from an IFBS site in Kakuma, which is home to a large refugee camp. Presumably, the Kakuma facility did not have IV artesunate in stock and had to rely on use of quinine, the second line drug for treatment of severe malaria. These commodity stock challenges highlight the importance of monitoring health facility readiness to provide life-saving treatments, especially where malaria is not endemic, as these are the areas where patients with malaria are more likely to develop severe disease because of their lower immunity to malaria and thus will require access to IV artesunate. While we recognize that this analysis depends on completeness of documentation in patients’ medical records, these findings on specific antimalarials received (or not received) in an inpatient setting is concerning because only a minority of patients appear to be receiving care per guidelines. It is plausible that patients were discharged on a 3-day course of AL and therefore did not have documentation of having received it. Training of healthcare workers who manage severe malaria in the inpatient setting to emphasize the need for ACTs after IV artesunate to ensure parasite clearance, as well as improving stock availability of IV artesunate, are critical to improve the quality of care and improve outcomes. Lastly, initiation of broad-spectrum antibiotics is part of the WHO recommendations in the management of severe malaria, especially in high to moderate transmission settings. In our data, antibiotics were widely used (82%) for patients admitted with AFI, although those who had a positive malaria test were less likely to receive antibiotics than those who had a negative test or no malaria test. This makes sense from a clinical decision-making standpoint, where patients with a diagnosis of malaria were less likely to receive empiric antibiotics compared to those lacking a clear diagnosis. Also expected is that antibiotics were more likely initiated among those who developed severe illness or died, presumably because of clinical deterioration. This project had several limitations. Using data extracted from patient records, we categorized patients as having severe presentation of malaria if they had any signs and symptoms suggestive of severe respiratory or neurologic involvement, as well as severe anemia based on hemoglobin level of <7g/dL. Other laboratory values typically used to diagnose severe malaria, such as lactate levels and chemistry panels were typically unavailable. As such, this approach may have resulted in underestimation of the number of patients with severe illness. Further, the number of deaths among those diagnosed with malaria was relatively small during the reporting period, making it difficult to draw statistically meaningful conclusions about risk factors of mortality. This could result from patients usually being recruited the day following admission, meaning those who died within the first hours of presentation were not enrolled. In the analysis of association between patient and clinical characteristics and disease severity and mortality, we did not account for clustering by surveillance site due to the low counts of some clusters. Lastly, our analysis took the abstracted data (e.g., antimalarial medication) at face value and assumed that it reflected the actual care received—thus any incomplete data in medical records or errors during data extraction would impact the accuracy of our findings. Overall, there are key findings that inform malaria programming from care-seeking, diagnosis to treatment of severe malaria. Delays in seeking higher level of care may be contributing to severe illness and mortality, underscoring the need to encourage early care-seeking behavior in formal healthcare settings. There might also be significant over-diagnosis of malaria by microscopy, requiring strengthening of microscopy training and external quality assessment programs. The AFI cases that are potentially misdiagnosed as malaria may result in higher mortality if not treated appropriately—for example, failure to initiate broad-spectrum antibiotics—since our data show that antibiotic initiation rates are lower among those with diagnosis of malaria. Once diagnosed, there are also concerning gaps in the antimalarials used for treatment of severe malaria, specifically the need to follow with a course of AL once the patient receives IV artesunate. There is also a need to ensure availability of IV artesunate for treatment of severe malaria, as IV quinine is still being widely used, particularly in seasonal epidemic zones. This analysis highlights the value of an integrated disease surveillance platform, even when implemented at a limited number of facilities, in identifying malaria-specific programmatic challenges. To enhance representativeness and long-term sustainability, routine inpatient data collected through electronic medical records should ideally be capable of capturing malaria case information to support both clinical and programmatic decision-making. Declarations Disclaimer : The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention. MI, VS and MS’s salaries were provided by the U.S. President’s Malaria Initiative at the time this work was conducted.” References Kenya Malaria Indicator Survey, 2010. https://dhsprogram.com/pubs/pdf/mis7/mis7.pdf. Accessed 22 July 2025. Kenya Malaria Indicator Survey, 2020. https://dhsprogram.com/pubs/pdf/MIS36/MIS36.pdf. Accessed 22 July 2025. Church, J., Maitland, K. Invasive bacterial co-infection in African children with Plasmodium falciparum malaria: a systematic review . BMC Med. 2014; doi:10.1186/1741-7015-12-31. Nguyen Hoan Phu, Nicholas P J Day, Phung Quoc Tuan, Nguyen Thi Hoang Mai, Tran Thi Hong Chau, Ly Van Chuong, et al. Concomitant Bacteremia in Adults With Severe Falciparum Malaria. Clinical Infectious Diseases. 2020; doi: 10.1093/cid/ciaa191. J Anthony G Scott, James A Berkley, Isaiah Mwangi, Lucy Ochola, Sophie Uyoga, et al. Relation between falciparum malaria and bacteraemia in Kenyan children: a population-based, case-control study and a longitudinal study. The Lancet. 2011; doi:10.1016/S0140-6736(11)60888-X. World Health Organization. WHO guidelines for malaria. 2024. https://www.who.int/publications. Accessed 23 July 2025. World Health Organization. Malaria microscopy quality assurance manual: version 2. 2016. https://apps.who.int/iris/handle/10665/204266Top of FormBottom of Form. Accessed 23 July 2025 Verani JR, Eno EN, Hunsperger EA, Munyua P, Osoro E, Marwanga D, et al. (2024) Acute febrile illness in Kenya: Clinical characteristics and pathogens detected among patients hospitalized with fever, 2017–2019. PLoS ONE. 2024; doi:10.1371/journal.pone.0305700. World Health Organization. Giemsa Staining of Malaria Blood Films: SOP. 2016. https://apps.who.int/iris/handle/10665/204458. Accessed 23 July 2025. Odhiambo FO, O'Meara WP, Abade A, Owiny M, Odhiambo F, Oyugi EO. Adherence to national malaria treatment guidelines in private drug outlets: a cross-sectional survey in the malaria-endemic Kisumu County, Kenya. Malar J. 2023; doi: 10.1186/s12936-023-04744-7. Kenya Ministry of Health Division of National Malaria Program, Malaria Health Facility Assessment. 2023. Final report, Nairobi, Kenya (Unpublished) World Health Organization. Artemisinin resistance and artemisinin-based combination therapy efficacy: Status report. (2018) https://www.who.int/docs/default-source/documents/publications/gmp/who-cds-gmp-2018-26-eng.pdf. Accessed 24 July 2025. Additional Declarations No competing interests reported. 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Health","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"","lastName":"Onyango","suffix":""},{"id":504617059,"identity":"221cc500-e5b7-4f9e-b641-33e6e37dbf46","order_by":12,"name":"Jonas Hines","email":"","orcid":"","institution":"Centers for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jonas","middleName":"","lastName":"Hines","suffix":""}],"badges":[],"createdAt":"2025-08-07 20:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7321812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7321812/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12936-025-05738-3","type":"published","date":"2026-01-14T16:29:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89978137,"identity":"c2f1ba1b-a9e0-4505-ba87-46808e396f07","added_by":"auto","created_at":"2025-08-27 06:13:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of surveillance sites and malaria epidemic zones in Kenya\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7321812/v1/f342aa961df6ec2385b79a56.png"},{"id":100617487,"identity":"825b1685-66d8-4f7f-bf66-db29c9efb4b9","added_by":"auto","created_at":"2026-01-19 17:53:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1335323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7321812/v1/2a3e4279-955b-4118-aba2-b107d5fffd5a.pdf"},{"id":89978139,"identity":"387dc1ec-7772-4acf-96e7-45eaffdcb33e","added_by":"auto","created_at":"2025-08-27 06:13:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":332205,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7321812/v1/54108e84509ee70f3090cf38.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using sentinel surveillance system data to characterize severe malaria illness and quality of malaria case management among hospitalized patients in Kenya, 2017-2024","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInvestments in malaria control measures — such as broader coverage and implementation of effective vector control activities and increased access to effective malaria case management — have led to marked reduction in malaria prevalence among children under 5 years old in Kenya, decreasing from 11% nationally in 2010 \u003csup\u003e1\u003c/sup\u003e to 6% in 2020.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Kenya has heterogenous malaria epidemiology, with prevalence rates ranging from \u0026lt; 1% in the low-risk epidemic zone to 19% in the lake endemic zone.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAdvancements in malaria surveillance have also resulted in improved data capture and use, with the quality and availability of routine malaria data steadily improving over the past several years. However, routine data remain primarily focused on the outpatient setting, emphasizing the treatment cascade of uncomplicated malaria – from testing suspected febrile cases to treatment of those with a parasitological diagnosis of malaria. Despite increased efforts to enhance data capture in the inpatient setting, information on patients hospitalized with malaria in Kenya and more broadly in Africa remain sparse and incomplete. Data on clinical presentation, treatments provided and clinical outcomes among admitted patients with malaria are limited.\u003c/p\u003e\u003cp\u003eThe only routinely reported malaria indicator on malaria-related hospitalizations in Kenya is the case fatality rate, calculated by dividing the total number of malaria-related deaths in health facilities that offer inpatient services by the total number of malaria admissions. This indicator has significant limitations due to incomplete data capture of admissions and discharge diagnoses in the inpatient setting. As a result, a detailed clinical picture of hospitalized malaria patients, including their risk factors for severe presentations and mortality, cannot be derived from routinely collected malaria surveillance data. Such comprehensive data are typically obtained only through research studies or enhanced surveillance activities conducted at a limited number of health facilities.\u003c/p\u003e\u003cp\u003eThe Integrated Facility-based Surveillance (IFBS) is a sentinel disease surveillance platform carried out across 12 facilities in Kenya across various malaria epidemiologic zones. Of note, there are about 1600 public inpatient facilities in Kenya. This surveillance platform collects clinical and laboratory information, as well as specimens for pathogen testing among patients who present with acute febrile illness (AFI), defined as an axillary temperature of ≥ 38.0\u003csup\u003eo\u003c/sup\u003eC. Malaria is considered an AFI and all patients enrolled in IFBS are tested for malaria, initially by rapid diagnostic testing (mRDT), confirmed by expert microscopy, and in some cases, by multiplex TaqMan Array Cards PCR testing (TAC). As such, the information collected as part of IFBS provides a rich and unique dataset from multiple sites across Kenya, describing the clinical characteristics, treatments provided, and outcomes of inpatients diagnosed with malaria and other co-morbidities, enabling analyses of risk factors associated with severe disease presentation and mortality.\u003c/p\u003e\u003cp\u003eAs a pathogen surveillance platform, IFBS also identifies cases of co-infection with non-\u003cem\u003ePlasmodium\u003c/em\u003e pathogens. In other studies of children with severe malaria, 6–8% have been reported to have bacterial co-infection (vs. 1% among adults with severe malaria), predominantly gram-negative enteric organisms, especially non-typhoidal \u003cem\u003eSalmonella\u003c/em\u003e (NTS).\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Literature varies regarding disease severity and mortality among patients co-infected with malaria and other pathogens, but up to one-third of deaths among children with severe malaria have been attributed to a bacterial co-infection.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e These findings have influenced WHO guidelines for the treatment and management of severe malaria among children, which recommend co-administering broad-spectrum antibiotics in moderate to high malaria transmission settings.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e However, this recommendation is inconsistently applied, and the widespread resistance of gram-negative bacteria (such as NTS) to commonly used empiric antibiotics, as well as laboratory limitations in culture-based diagnosis of bacterial infections, pose significant challenges to implementing this approach.\u003c/p\u003e\u003cp\u003eMicroscopy is the gold standard clinical diagnostic method for malaria in Kenya given its relative cost and implementation feasibility at the point-of-service in higher-level facilities. Malaria RDTs are used when microscopy is not possible, typically in lower-level facilities without laboratory infrastructure or adequately trained staff. Facility laboratory staff receive pre-service training in malaria diagnostics, including reading thick and thin blood smears, but in many malaria-endemic countries, the external quality assurance (EQA) program to maintain reliability and accuracy of microscopy-based diagnostic results struggles with resource constraints.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Since the IFBS platform also conducts its own malaria testing for all enrolled patients, it provides an opportunity to assess how routine clinical smears read by laboratory staff at the facilities (clinical smears) compare with smear results read by expert microscopists (surveillance smears). The information captured in IFBS in terms of treatments provided during hospitalization can also demonstrate the level of adherence to clinical guidelines in the inpatient setting, particularly regarding provision of antimalarials and empiric antibiotics.\u003c/p\u003e\u003cp\u003eWe used data from the IFBS platform across Kenya to examine two key objectives: First, to assess the factors associated with severe illness and mortality among patients with laboratory confirmed malaria; second, to describe the quality of malaria case management among hospitalized patients by evaluating the diagnostic accuracy of clinical malaria testing and assessing the implementation of appropriate malaria case management.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eIFBS procedures have been previously described.\u003csup\u003e8\u003c/sup\u003e Each morning, trained surveillance officers reviewed daily hospital admission logs for pediatric and adult medical wards for the past 24 hours and all new admissions were screened for eligibility based on information in the medical record. AFI was defined as a measured temperature \u0026ge;38.0 \u003csup\u003e0\u003c/sup\u003eC for \u0026le;14 days. Those eligible were approached for informed consent and enrolment into the surveillance system study. \u0026nbsp;Additionally, at some sites, recruitment was done at outpatient department clinics for persons aged \u0026ge;13 years only. However, we limited our analysis to inpatients given the objectives of the project and relatively small number of outpatients enrolled.\u003c/p\u003e\n\u003cp\u003eFollowing enrollment, the surveillance officer interviewed the patient, extracted information from their medical records, and recorded this information in a standardized digital form. Data included pertinent medical history, clinical presentation, vital signs and physical exam, clinical management, routine lab results (including mRDT or microscopy), discharge diagnosis, and clinical outcomes. Regardless of whether they had already undergone clinical testing for malaria at the health facility, trained laboratory staff collected 5 cc (2.5 cc for children under five years old) venous blood from all enrolled patients in Ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes and transported it to an onsite laboratory, where on the same day a mRDT (Abbott Bioline Malaria Ag P.f/Pan) was performed and thin and thick blood smears were prepared with Geimsa for microscopy following the WHO malaria microscopy standard operating procedure.\u003csup\u003e9\u003c/sup\u003e Microscopy slides from all sites were transferred weekly to Jaramogi Oginga Odinga Teaching \u0026amp; Referral Hospital in Kisumu, where certified expert microscopists analyzed the slides for malaria parasites. For a subset of patients who met undifferentiated fever\u003csup\u003e**\u003c/sup\u003e criteria, whole blood samples were stored at -20˚ C for up to seven days and transported to the CDC-supported laboratory at the Kenya Medical Research Institute (KEMRI) in Nairobi for PCR testing using TAC PCR (ThermoFisher).\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e[1]\u003c/a\u003e\u0026nbsp; TAC PCR is intended for surveillance and was not approved for the diagnoses of patients for clinical purposes. From June 2023 onwards, patients who had a positive IFBS mRDT did not get a TAC PCR as a resource savings measure.\u003c/p\u003e\n\u003cp\u003eIFBS data from June 2017 \u0026ndash; July 2024 were analyzed to describe the clinical characteristics and hospitalization course of patients with AFI who were diagnosed with malaria. Data were analyzed from the 12 sentinel IFBS health facilities across Kenya (Figure 1), which were not constant during the analysis period (Supplemental table 1)\u003c/p\u003e\n\u003cp\u003eFor the analysis of factors associated with severe disease and mortality, a positive malaria diagnosis was defined as a patient who tested positive on mRDT or microscopy during IFBS enrollment (\u0026ldquo;surveillance\u0026rdquo; test). A positive malaria result on TAC PCR test only without a positive parasitological result was considered a case of submicroscopic malaria and not included in the analysis; this is aligned with the national guideline\u0026rsquo;s definition of parasitological confirmation for clinical diagnosis. For the analysis assessing adherence of clinical care to MOH guidelines, a positive malaria diagnosis was defined as a patient with a positive mRDT or microscopy performed by health facility staff as part of their routine care (\u0026ldquo;clinical\u0026rdquo; test), which were the results used by the clinician to manage the patient.\u003c/p\u003e\n\u003cp\u003eWhile all inpatients technically meet the definition of \u0026ldquo;severe malaria\u0026rdquo; by virtue of being assessed by a clinician to require hospitalization, inclusion in the analysis of severe illness required documentation of at least one of the following characteristics: physical exam findings of severe respiratory disease (i.e., oxygen saturation of \u0026le;90%, stridor, nasal flaring, lower chest indrawing, or grunting), severe neurological disease (i.e., level of consciousness below \u0026ldquo;alert\u0026rdquo; on the Alert/Verbal/Pain/Unresponsive [AVPU] scale, convulsions, photophobia, nuchal rigidity, and bulging fontanelle), jaundice, petechial rash, or severe anemia (hemoglobin \u0026lt;7g/dL).\u003csup\u003e6\u003c/sup\u003e Patient outcomes were dichotomized to either discharged in stable condition or died. \u0026nbsp;Those with ambiguous outcomes -- such as discharged against medical advice, transferred to another hospital or missing outcome\u0026mdash;were excluded from the mortality analysis.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eDemographic and baseline characteristics of the study population were described using frequencies and percentages, and chi-square tests were used to test for statistical significance in differences. Bivariate logistic regression analyses were performed in R (v4.4.2). To ascertain factors associated with severe illness at enrollment and mortality, we looked at the following patient characteristics: sex, age, malaria epidemic zone of residence, number of days of fever prior to presentation, care-seeking prior to presentation (including referrals from other health facilities and antimalarials taken prior to admission), pregnancy, HIV status, and malnutrition. Pregnancy status was self-reported for women aged 15-49 years, and HIV status was ascertained by self-report or the medical chart but was not confirmed by HIV testing. Malnutrition status was determined by age-based mid-upper arm circumference (MUAC) cut-offs for children aged \u0026lt;5 years. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMalaria co-infection was defined as a \u003cem\u003ePlasmodium\u003c/em\u003e species infection plus another non-\u003cem\u003eP\u003c/em\u003e\u003cem\u003elasmodium\u003c/em\u003e pathogen detected on TAC PCR. We determined the odds of developing severe illness or dying with malaria mono-infection vs. co-infection vs. other pathogens using logistic regression and quantified the effects with odds ratios. For the purposes of analyzing the TAC PCR results, we included data from all patients who had a TAC PCR, not limited to those with a positive malaria test. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess the accuracy of parasitological testing by health facility staff, we assessed the PCR positivity for both clinical and surveillance smears and mRDTs. While PCR results were not used for malaria diagnosis, PCR has a higher sensitivity for detecting \u003cem\u003ePlasmodium\u0026nbsp;\u003c/em\u003espp., making it possible to assess misdiagnoses by microscopy and mRDT. To assess the quality of care provided, we examined whether patients diagnosed with malaria by either clinical mRDT or microscopy test received appropriate antimalarial treatment per national guidelines: injectable artesunate followed by a course of artemether-lumefantrine (AL) for severe disease, AL alone for uncomplicated disease; and if artesunate was not available, IV quinine as an alternate choice for severe disease (per the Kenya National Malaria Treatment Guidelines). \u0026nbsp; We also assessed the proportion of patients who received any antimalarial treatment without a documented parasitological diagnosis of malaria, as this would not be in accordance with national guidelines. Lastly, we assessed the proportion of patients with severe malaria illness who received empiric treatment with antibiotics per WHO guidelines, particularly in moderate to high transmission areas, which is applicable in Kenya\u0026rsquo;s malaria endemic zones in western Kenya as well as on the coast (WHO guidelines for malaria, 30 Nov 2024). \u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"ftn1\"\u003e\n \u003cp\u003e\u003csup\u003e**\u003c/sup\u003e Undifferentiated fever was defined as AFI without evidence of lower respiratory tract infection (cough or shortness of breath plus tachypnea or abnormalities on respiratory examination), diarrhea (\u0026ge;3 loose stools in a 24-hour period), or another focus of fever based on history and physical examination (e.g., meningitis, skin/soft tissue infection).\u003c/p\u003e\n \u003cp\u003e[1] From June 2017 to October 2022, the TAC assay included targets for \u003cem\u003eBartonella\u003c/em\u003e, \u003cem\u003eBrucella\u003c/em\u003e, Ebola virus, Bundibugyo virus, \u003cem\u003eCoxiella burnetii\u003c/em\u003e, Crimean-Congo hemorrhagic fever virus, chickungunya virus, dengue virus, hepatitis E virus, lassa virus, \u003cem\u003eLeishmania\u003c/em\u003e, \u003cem\u003eLeptospira\u003c/em\u003e, Marburg virus, Nipah virus, O\u0026apos;nyong\u0026apos;nyong virus, \u003cem\u003ePlasmodium\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e, Rift Valley Fever virus, \u003cem\u003eSalmonella\u003c/em\u003e, \u003cem\u003eSalmonella typhi\u003c/em\u003e, HIV I, HIV II, Sudan (ebola) virus, \u003cem\u003eTrypanosoma bruceii,\u003c/em\u003e West Nile virus, \u003cem\u003eYersinia pestis,\u003c/em\u003e yellow fever virus, and Zika virus. The TAC assay was updated in November 2020 to add additional targets, including: Burkholderia pseudomallei, Lassa virus, Orientia tsutsugamushi, Oropouche virus, Plasmodium falciparum, Plasmodium vivax, Streptococcus pneumoniae, Salmonella paratyphi A, and Zika virus that detects 32 pathogens including 15 viral, 12 bacterial, and 5 protozoal pathogens through real-time PCR.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 9,436 patient records were available from the IFBS platform from June 2017 to July 2024 (Supplemental figure 2). Of these, 9,212 had a surveillance smear or mRDT and 2,232 (23.7%) tested positive for malaria. Among 2,197 patients with malaria who were hospitalized, 713 (32.5%) met criteria for severe illness presentation and 16 (0.7%) died.\u003c/p\u003e\n\u003ch2\u003eFactors associated with severe illness and mortality\u003c/h2\u003e\n\u003cp\u003eTable 1 shows the results from bivariate analysis of factors associated with severe illness presentation and mortality. In summary, patients had greater odds of severe illness if they were very young (aged \u0026lt;1 year), had a longer fever duration before presentation, or had sought care at another facility or been referred from another health facility, including self-report of having received antimalarials in the last 7 days. Similarly, longer fever duration, being referred from another health facility, and having received antimalarials in the last 7 days were associated with greater odds of mortality. Severe illness was also associated with mortality among patients with malaria.\u003c/p\u003e\n\u003ch2\u003eMalaria co-infections and association with disease severity and mortality\u003c/h2\u003e\n\u003cp\u003eTAC PCR was done on 6,069 patients presenting with AFI; \u003cem\u003eP. falciparum\u003c/em\u003e was detected in 2,093 (34.5%) cases (Table 2). Narrowing the results only to inpatients with a positive surveillance mRDT and/or positive blood smear, \u003cem\u003ePlasmodium spp.\u0026nbsp;\u003c/em\u003ewas detected in 94.3%. Among those who tested positive with an mRDT or smear, the most common co-infection pathogen detected by TAC PCR was non-typhoid Salmonella (NTS) with 26 cases (2.0%), followed by HIV-1 (1.2%), \u003cem\u003eRickettsia\u003c/em\u003e (0.9%), and Dengue virus (0.9%). There was no clear pattern of association between TAC PCR test results and disease severity. However, compared to detection of \u003cem\u003ePlasmodium\u003c/em\u003e alone, the odds of mortality were higher for malaria co-infection, detection of a non-\u003cem\u003eP\u003c/em\u003e\u003cem\u003elasmodium\u003c/em\u003e pathogen(s), or a negative TAC result. Though only detection of a non-\u003cem\u003eP\u003c/em\u003e\u003cem\u003elasmodium\u003c/em\u003e pathogen was statistically significant, there was near significance of patients who had a negative TAC result (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: \u0026nbsp;Pathogens detected on TaqMan Array Card of persons with acute febrile illness in Kenya, 2017-2024\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"534\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003ePathogen*\u003csup\u003e,\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eAll persons with AFI with TAC done (N = 6069)\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eInpatients with an mRDT and/or smear positive for malaria (N = 1293)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlasmodium spp.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;2093 (34.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;1219 (94.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eHIV-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;136 (2.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;16 (1.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eNon-typhoid Salmonella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;73 (1.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;26 (2.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eRickettsia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;72 (1.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;12 (0.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDengue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;52 (0.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;11 (0.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;44 (0.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;5 (0.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eBrucella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;42 (0.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;6 (0.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eSalmonella Typhi\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;38 (0.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;7 (0.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eLeishmania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;34 (0.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;1 (0.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eChikungunya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;28 (0.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;4 (0.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eBartonella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;22 (0.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;1 (0.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eLeptospira\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;14 (0.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;2 (0.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eCoxiella\u0026nbsp;\u003c/em\u003e\u003cem\u003eburnetii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;10 (0.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;4 (0.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlasmodium vivax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;5 (0.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;0 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eRift Valley Fever virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;3 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;1 (0.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;2 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;0 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eNegative TAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;3630 (59.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;66 (5.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 534px;\"\u003e\n \u003cp\u003e* Results for targets for \u003cem\u003ePlasmodium falciparum\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePlasmodium vivax\u0026nbsp;\u003c/em\u003ewere not shown to avoid double-counting and because these targets were not added until November 2020. All \u003cem\u003eP. falciparum\u0026nbsp;\u003c/em\u003eand \u003cem\u003eP. vivax\u0026nbsp;\u003c/em\u003edetections were positive for the\u0026nbsp;\u003cem\u003ePlasmodium spp\u003c/em\u003e. target but not vice versa.\u0026nbsp;\u003cbr\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e TAC assay was updated in November 2020 to add the following targets: Burkholderia pseudomallei, Lassa virus, Orientia tsutsugamushi, Oropouche virus,\u003cem\u003e\u0026nbsp;Plasmodium falciparum\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Plasmodium vivax\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Streptococcus pneumoniae\u003c/em\u003e, Salmonella paratyphi A, and Zika virus.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026Dagger;\u0026nbsp;\u003c/sup\u003eIncludes all patients (inpatient and outpatient) who received a TAC test as part of AFI surveillance in Kenya\u003c/p\u003e\n \u003cp\u003eAFI: acute febrile illness; mRDT: rapid diagnostic test; TAC: TaqMan Array Card\u003c/p\u003e\n \u003cp\u003eOf note, the following pathogens have \u003cstrong\u003enot\u003c/strong\u003e been detected on this platform in Kenya: \u0026nbsp;Crimean-Congo hemorrhagic fever virus, Ebola virus (Zaire, Budibugyo, Sudan), Hepatitis E virus, Lassa virus, Marburg virus, Nipah virus, O\u0026apos;nyong-nyong virus, \u003cem\u003eOrientia\u003c/em\u003e\u003cem\u003e\u0026nbsp;tsutsugamushi,\u0026nbsp;\u003c/em\u003eOropouche virus, \u003cem\u003eSalmonella paratyphi\u003c/em\u003e\u003cem\u003e\u0026nbsp;A, Trypanosomiasis brucei,\u0026nbsp;\u003c/em\u003eWest Nile virus, Yellow Fever virus, \u003cem\u003eYersinia pestis,\u0026nbsp;\u003c/em\u003eZika virus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: \u0026nbsp;Risk of severe disease and mortality based on TAC results (N = 6069)*\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 114px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere illness presentation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eNot severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eDischarged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eDied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eMalaria only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1346 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e572 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1733 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e23 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMalaria coinfection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e130 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e45 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.57, 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e150 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.58, 5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eOther pathogen(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e237 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e109 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.84, 1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e263 (5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15 (15%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.17, 8.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2458 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1,172 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.00, 1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2831 (57%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59 (58%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.98, 2.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 623px;\"\u003e\n \u003cp\u003e*This analysis is among all persons with a TAC result regardless of malaria rapid diagnostic test or microscopy result. The malaria result reflects the TAC target result.\u003c/p\u003e\n \u003cp\u003eCI: confidence interval; OR: odds ratio; TAC: TaqMan Array Card\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePCR positivity of positive clinical and surveillance malaria test results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the inpatients assessed for AFI, 4,916 (57.9%) had documentation of malaria testing as part of their clinical care\u0026mdash;3,097 by microscopy (smear) and 1,819 by mRDT. Among the 5,303 inpatients who had a TAC PCR test, positive test result concordance between PCR and smear as well as mRDTs, was higher for the surveillance test compared to clinical test results (smear: 94.4% vs. 78.6%; mRDT: 95.4% vs. 88.9%) (Table 4). PCR positivity of positive clinical smears varied by epidemic zone, ranging from 69.6% in highland epidemic-prone areas to 87.9% in low-risk areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Malaria PCR positivity of positive microscopy and mRDTs among inpatients by malaria epidemic zone\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEpidemic zone\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 72px;\"\u003e\n \u003cp\u003eTotal*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 393px;\"\u003e\n \u003cp\u003ePCR percent positivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 195px;\"\u003e\n \u003cp\u003ePositive Smear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePositive mRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eSurveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eClinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eSurveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eClinical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e94.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e78.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e95.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e88.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Lake endemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e92.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e79.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e83.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Coast endemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e96.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e80.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e92.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Highland epidemic prone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e95.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e69.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e91.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Seasonal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e95.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e76.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e96.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e90.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e95.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e87.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e91.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e80.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 622px;\"\u003e\n \u003cp\u003e* Total number of persons with acute febrile illness with a PCR test done by TaqMan Array Card.\u003c/p\u003e\n \u003cp\u003e\u0026dagger; Epidemic zones were defined as: Lake endemic (8): Bungoma, Busia, Homa Bay, Kakamega, Kisumu, Migori, Siaya, Vihiga; \u003cem\u003eCoast endemic (5)\u003c/em\u003e: Kilifi, Kwale, Lamu, Mombasa, Taita Taveta; \u003cem\u003eHighland epidemic prone (10)\u003c/em\u003e: Bomet, Elgeyo-Marakwet, Kericho, Kisii, Narok, Nandi, Nyamira, Trans Nzoia, Uasin Gishu, West Pokot; \u003cem\u003eSeasonal (14)\u003c/em\u003e: Baringo, Embu, Garissa, Isiolo, Kajiado, Kitui, Mandera, Marsabit, Meru, Samburu, Tana River, Tharaka-Nithi, Turkana, Wajir; \u003cem\u003eLow risk (10)\u003c/em\u003e: Kiambu, Kirinyaga, Laikipia, Machakos, Makueni, Murang\u0026rsquo;a, Nairobi, Nakuru, Nyandarua, Nyeri\u003c/p\u003e\n \u003cp\u003e** Only 2 mRDTs were formed in the highland epidemic prone zone during the study period.\u003cbr\u003e\u0026nbsp;PCR: polymerase chain reaction; mRDT: rapid diagnostic test.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Adherence to Kenya malaria clinical guidelines for management of severe malaria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 4,916 inpatients who were tested for malaria with microscopy or mRDT as part of their routine care, 2,094 (44.6%) tested malaria positive. Of these, 1,847 (91.7%) had documentation of antimalarials given (Table 5). Two-hundred eighty-five (15.4%) received both IV artesunate and AL per the national guidelines for treatment of severe malaria. The majority (63.0%) were documented to have only received artesunate monotherapy. Quinine was used alone or in combination with artesunate or AL in 19.0% of patients and was primarily used in a facility in Kakuma, the site of a refugee camp, in the seasonal malaria epidemic zone (Table 6). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Antimalarials documented as used among those with positive malaria test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003eAntimalarial(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e**N = 1847**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003eArtesunate therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Monotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1164 (63.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;+ Artemether-lumefantrine (AL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e285 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;+ Quinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e15 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;+ AL and Quinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e15 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003eQuinine therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Monotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e128 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;+ AL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e193 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003eAL monotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e46 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eTable 6: Antimalarial usage by epidemic zone\u003c/strong\u003e\u003c/h2\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eEpidemic zone\u0026dagger;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eArtesunate\u003cbr\u003e\u0026nbsp; N = 1479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eAL*\u003cbr\u003e\u0026nbsp; N = 539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eQuinine*\u003cbr\u003e\u0026nbsp; N = 351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Lake endemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e669 (45.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e68 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Coast endemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e86 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e24 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Highland epidemic prone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e109 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e2 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Seasonal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e499 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e394 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e348 (99.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e116 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e51 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 570px;\"\u003e\n \u003cp\u003e* Result not available for three patients\u003c/p\u003e\n \u003cp\u003e\u0026dagger; Epidemic zones were defined as: Lake endemic (8): Bungoma, Busia, Homa Bay, Kakamega, Kisumu, Migori, Siaya, Vihiga; \u003cem\u003eCoast endemic (5)\u003c/em\u003e: Kilifi, Kwale, Lamu, Mombasa, Taita Taveta; \u003cem\u003eHighland epidemic prone (10)\u003c/em\u003e: Bomet, Elgeyo-Marakwet, Kericho, Kisii, Narok, Nandi, Nyamira, Trans Nzoia, Uasin Gishu, West Pokot; \u003cem\u003eSeasonal (14)\u003c/em\u003e: Baringo, Embu, Garissa, Isiolo, Kajiado, Kitui, Mandera, Marsabit, Meru, Samburu, Tana River, Tharaka-Nithi, Turkana, Wajir; \u003cem\u003eLow risk (10)\u003c/em\u003e: Kiambu, Kirinyaga, Laikipia, Machakos, Makueni, Murang\u0026rsquo;a, Nairobi, Nakuru, Nyandarua, Nyeri\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMultiple discharge diagnoses were possible, but 90.1% of inpatients with a positive clinical malaria test included a discharge diagnosis of malaria, whereas the remainder (9.9%) had a non-malaria diagnosis such as pneumonia, meningitis, or gastroenteritis. Some of the patients with a malaria discharge diagnosis had a negative malaria test (15.0%) or no clinical malaria test (6.6%).\u003c/p\u003e\n\u003cp\u003eMost (91.7%) patients that tested positive for malaria during clinical care were treated with antimalarials. Some patients who tested negative for malaria (17.2%) or were not tested for malaria (7.5%) also received an antimalarial medication (table 7). \u0026nbsp;Approximately half (55.8%) of patients with a positive clinical malaria test also received antibiotics. In malaria endemic zones where transmission intensity is moderate to high, a higher proportion of patients diagnosed with malaria were also treated with antibiotics (61% in lake endemic and 60% in coast endemic zones). In general, a higher proportion of patients who tested negative for malaria (89.3%) or were not tested (93.8%) were treated with antibiotics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Receipt of antimalarials, antibiotics and discharge diagnosis by clinical malaria test result\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 323px;\"\u003e\n \u003cp\u003eTested for malaria as part of clinical care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNot tested for malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003ePositive \u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 2,094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 2,598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003eResult not recorded\u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eN = 3,569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eAntimalarial medications given (n miss: 324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e168 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,039 (82.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e96 (46.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,216 (92.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,849 (91.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e423 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e110 (53.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e260 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 265px;\"\u003e\n \u003cp\u003eAntibiotic\u0026nbsp;medications given (n miss: 324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e892 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e264 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e58 (28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e215 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,125 (55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,198 (89.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e148 (71.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,261 (93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eDischarge diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Malaria*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,887 (90.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e390 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e115 (51.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e235 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u0026nbsp; Other diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e207 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,208 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e109 (48.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3,334 (93.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 589px;\"\u003e\n \u003cp\u003e* Could include additional diagnoses such as pneumonia, meningitis, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAmong those with a positive clinical malaria test, treatment with antimalarial medications was associated with lower odds of severe illness and mortality, although the latter association was not statistically significant (Table 8). Additionally, the odds of severe illness and mortality were higher among patients who had a non-malaria diagnosis compared to persons only diagnosed with malaria at discharge. Patients with malaria plus another discharge diagnosis had greater odds of severe illness, but not mortality, than those with malaria only. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Disease severity and outcomes by antimalarial use and discharge diagnosis among patients with acute febrile illness and a positive malaria test during clinical care \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eNot severe,\u003cbr\u003e\u0026nbsp;N = 1506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eSevere,\u003cbr\u003e\u0026nbsp;N = 588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eDischarged,\u003cbr\u003e\u0026nbsp;N = 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eDead,\u003cbr\u003e\u0026nbsp;N = 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 298px;\"\u003e\n \u003cp\u003eAntimalarials given during hospitalization (n miss = 77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e105 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e63 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e127 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1340 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e509 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.63 (0.46, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1811 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e12 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.28 (0.08, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eDischarge diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malaria diagnosis only\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1021 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e220 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1228 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e5 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Malaria plus another diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e365 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e281 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3.57 (2.89, 4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e625 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e5 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.96 (0.57, 6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e93 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e73 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3.64 (2.59, 5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e157 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e4 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e6.26 (1.66, 23.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e27 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e14 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.41 (1.21, 4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e112.80 (9.53, 1582.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 374px;\"\u003e\n \u003cp\u003e* Outcome missing for 67 patients\u003cbr\u003e\u0026nbsp;CI: Confidence interval; OR: odds ratio; Ref: referent level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003ch2\u003eFactors associated with severe illness and mortality\u003c/h2\u003e\n\u003cp\u003eThe IFBS platform in Kenya provides a rich clinical data source to characterize patients hospitalized with malaria. Using this data source, we were able to explore factors associated with severe disease and mortality among inpatients as well as benchmark clinical management against MOH guidelines.\u003c/p\u003e\n\u003cp\u003eUnsurprisingly, the youngest age group (\u0026le;1 year) was associated with increased odds of developing severe illness. There was also a trend towards higher odds of severe illness and mortality among those with malnutrition and HIV-positive status, but the sample sizes were too small to detect statistically significant differences. This is consistent with the prevailing assumption that a weakened immune system resulted in worse clinical outcomes.\u003c/p\u003e\n\u003cp\u003eWhile self-reporting being treated with an antimalarial in the previous week may be perceived to be protective from having severe presentation, it appeared as a risk factor for developing severe illness or dying. This could be due to inappropriate dosing, substandard or poor quality product, poor drug absorption, anti-malarial resistance or delays in care seeking behaviors. Similarly, history of having sought other care prior to presentation was associated with severe illness, and while not statistically significant due to small sample size, also death. In Kenya, approximately 40-50% of patients seek initial care for malaria in the private sector,\u003csup\u003e10\u003c/sup\u003e especially in unregulated drug dispensaries, where adherence to the national malaria treatment guidelines is not guaranteed and patients may receive substandard care\u0026mdash;such as ineffective antimalarial therapy or an incomplete treatment course due to cost limitations. Most cases treated in the private sector will likely experience clinical resolution, thus never requiring hospitalization and potential recruitment into the IFBS database. However, others who develop clinical complications and severe malaria may become part of the IFBS database, resulting in a biased result where the self-report to have received care at another facility or taken antimalarials prior to admission appeared as a potential risk factor associated with severe illness or mortality. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDelays in care seeking were also associated with severe illness and mortality, including reporting longer duration of fever, and history of being referred from a lower-level health facility. These findings underscore the importance of behavior change strategies as well as messaging in the community that promote timely care-seeking, referrals and follow-up, especially for the youngest children. It is also important to emphasize the need to complete a full 3-day course of an oral artemisinin-based combination therapy (ACT), ideally obtained at a regulated health facility. For healthcare providers in malaria endemic areas, diagnosing malaria is common, but for those in areas where malaria is less common, malaria diagnosis may not be considered initially, resulting in the development of severe disease. Further, patients who live in non-endemic areas do not have immunity to malaria, rendering them more susceptible to developing severe disease.\u003c/p\u003e\n\u003cp\u003eThe TAC results among those with parasitological confirmation of malaria demonstrated that \u003cem\u003enon-typhoidal Salmonella\u0026nbsp;\u003c/em\u003eis the most common co-infection with malaria, which has been extensively documented in the literature, though at 2%, the co-infection rate with the organism was lower than expected. The other co-infecting pathogens of interest include various zoonotic infections such as \u003cem\u003eRickettsia\u003c/em\u003e, \u003cem\u003eBrucella\u003c/em\u003e, \u003cem\u003eBartonella\u003c/em\u003e, \u003cem\u003eLeptospira\u003c/em\u003e, \u003cem\u003eCoxiella burnetii,\u003c/em\u003e and Rift Valley Fever. Any one of these pathogens could result in severe illness, so a co-infection with malaria may result in more severe disease or higher mortality. However, our data showed co-infection with another non-falciparum pathogen was not definitively associated with a more severe presentation or mortality, though it should be noted that the odds ratio for mortality for malaria co-infections was 2.0, though non-significant. However, when expanding the analysis to all patients with AFI (not limited to malaria), severe illness and mortality odds were higher among patients who had a non-falciparum infection, as well as among those with a negative TAC result. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAccuracy of malaria diagnostic testing and adherence to national treatment guidelines\u003c/h2\u003e\n\u003cp\u003eThis project provided a unique opportunity to conduct an internal validation of clinical microscopy smears and mRDT results by comparing them with PCR testing as part of the surveillance project. Microscopy at the facility level suggests a substantial over-reading of smears as positive (about 1 in 5 being called positive when PCR was negative) in the routine clinical setting. Consistently higher PCR positive concordance with positive smears read by surveillance staff who are certified at expert level corroborates this finding. The quality of routine microscopy readings could be related to suboptimal equipment, reagents, contamination of stain and/or slides with dust or debris, and lack of adequate routine training of microscopists. \u0026nbsp;The high smear false positivity in the lake endemic zone of about 20% is concerning considering that this area is responsible for over 80% of the malaria burden in Kenya and it is where significant investments have occurred in ensuring quality malaria diagnostics and case management. Our findings suggest that malaria might be over-diagnosed where diagnosis is made by microscopy alone, which is the case in many of the higher-level health facilities. These findings underscore the need for strengthening quality assured microscopy programs with adequate training of microscopists and properly functioning microscopes and reagents, as well as an EQA program that routinely provide feedback on performance and identify areas for improvement \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile false positive results, or over-diagnosis of malaria may be arguably better than significant under-diagnosing of patients who need malaria treatment, it still results in wasted resources and may result in masking of other diagnoses that would benefit from different therapies. The TAC PCR findings of higher odds of severe illness and mortality for non-falciparum (or unknown) infections demonstrates the potential risk of false positive microscopy or mRDT results if additional diagnostics and therapeutics are delayed until the patient deteriorates. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of adherence to the national malaria treatment guidelines in Kenya, the findings were both encouraging and revealing to inform improvements in malaria programming. \u0026nbsp;A key advantage of this study was the opportunity to assess the level of adherence to the national treatment guidelines for treatment of patients hospitalized with malaria. The Kenya National Malaria Control Program (NMCP) conducts annual Health Facility Assessments (HFAs) to examine the level of adherence to malaria clinical guidelines by clinicians in both outpatient and inpatient settings. The most recent HFA in 2023 evaluated 2,073 files of patients admitted for suspected malaria and evaluated them for the following: 1) testing for malaria, 2) prescribing of recommended treatment based on severity criteria and malaria test results, (defined as either injectable artesunate for severe test positive patients, artemether-lumefantrine (AL) for non-severe test positive patients, or no antimalarial treatment for test negative patients). The composite score for adherence to guidelines was 55%, with 83% of patients tested for malaria on admission, 94% of test positive patients with severe malaria criteria receiving IV artesunate and only 4% of test positive patients without any severe malaria criteria treated with AL. For test negative patients with or without clinical criteria for severe disease, 46% and 33% still received IV artesunate, respectively. The composite performance in the HFA was higher in high-risk areas compared to low-risk areas (60% vs. 52%). \u0026nbsp; However, there were major differences in the treatment of test negative patients, where in low-risk areas antimalarial treatments were less commonly prescribed for test negative patients, both for those with severe (34% vs. 57%) or non-severe criteria (14% vs. 31%).\u003csup\u003e11\u003c/sup\u003e\u0026nbsp; Unfortunately, the HFA did not look at whether patients received a course of AL following IV artesunate. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile our analysis of IFBS data cannot provide a direct comparison to the HFA results, it provides a sense of the critical gaps in adhering to treatment guidelines. First, the IFBS data showed only 58% of patients with AFI were tested clinically for malaria, which was much lower than 83% observed in the 2023 HFA. Further, IFBS showed 92% of those with a positive test were documented to have received an antimalarial, whereas 17% of those with a documented negative test and 10% of those who did not have a clinical malaria test documented nonetheless received an antimalarial. The Kenya clinical guidelines for management of severe malaria make it clear that the first line therapy should be IV artesunate, followed by a course of artemether-lumefantrine (AL). However, only 15.4% of those with a positive test who received antimalarials were documented to have received both artesunate and AL. In fact, when assessed by epidemic zone whether patients received AL, the highest proportion of patients who received it was only 47.0% in the seasonal epidemic zone, and only a minority were documented to have received AL in lake (9.9%) and coast endemic (27.9%) areas. Patients receiving artesunate alone is concerning considering monotherapy goes against standard treatment guidelines and could give rise to artemisinin resistance, a major emerging threat for global malaria control.\u003csup\u003e12\u003c/sup\u003e Also of note is that in the seasonal epidemic zone, there was a significant number of patients who received IV quinine instead of IV artesunate. The majority of these cases were from an IFBS site in Kakuma, which is home to a large refugee camp. Presumably, the Kakuma facility did not have IV artesunate in stock and had to rely on use of quinine, the second line drug for treatment of severe malaria. These commodity stock challenges highlight the importance of monitoring health facility readiness to provide life-saving treatments, especially where malaria is not endemic, as these are the areas where patients with malaria are more likely to develop severe disease because of their lower immunity to malaria and thus will require access to IV artesunate.\u003c/p\u003e\n\u003cp\u003eWhile we recognize that this analysis depends on completeness of documentation in patients\u0026rsquo; medical records, these findings on specific antimalarials received (or not received) in an inpatient setting is concerning because only a minority of patients appear to be receiving care per guidelines. It is plausible that patients were discharged on a 3-day course of AL and therefore did not have documentation of having received it. Training of healthcare workers who manage severe malaria in the inpatient setting to emphasize the need for ACTs after IV artesunate to ensure parasite clearance, as well as improving stock availability of IV artesunate, are critical to improve the quality of care and improve outcomes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lastly, initiation of broad-spectrum antibiotics is part of the WHO recommendations in the management of severe malaria, especially in high to moderate transmission settings. \u0026nbsp;In our data, antibiotics were widely used (82%) for patients admitted with AFI, although those who had a positive malaria test were less likely to receive antibiotics than those who had a negative test or no malaria test. This makes sense from a clinical decision-making standpoint, where patients with a diagnosis of malaria were less likely to receive empiric antibiotics compared to those lacking a clear diagnosis. Also expected is that antibiotics were more likely initiated among those who developed severe illness or died, presumably because of clinical deterioration.\u003c/p\u003e\n\u003cp\u003eThis project had several limitations. Using data extracted from patient records, we categorized patients as having severe presentation of malaria if they had any signs and symptoms suggestive of severe respiratory or neurologic involvement, as well as severe anemia based on hemoglobin level of \u0026lt;7g/dL. Other laboratory values typically used to diagnose severe malaria, such as lactate levels and chemistry panels were typically unavailable. As such, this approach may have resulted in underestimation of the number of patients with severe illness. Further, the number of deaths among those diagnosed with malaria was relatively small during the reporting period, making it difficult to draw statistically meaningful conclusions about risk factors of mortality. This could result from patients usually being recruited the day following admission, meaning those who died within the first hours of presentation were not enrolled. In the analysis of association between patient and clinical characteristics and disease severity and mortality, we did not account for clustering by surveillance site due to the low counts of some clusters. Lastly, our analysis took the abstracted data (e.g., antimalarial medication) at face value and assumed that it reflected the actual care received\u0026mdash;thus any incomplete data in medical records or errors during data extraction would impact the accuracy of our findings.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Overall, there are key findings that inform malaria programming from care-seeking, diagnosis to treatment of severe malaria. Delays in seeking higher level of care may be contributing to severe illness and mortality, underscoring the need to encourage early care-seeking behavior in formal healthcare settings. There might also be significant over-diagnosis of malaria by microscopy, requiring strengthening of microscopy training and external quality assessment programs. The AFI cases that are potentially misdiagnosed as malaria may result in higher mortality if not treated appropriately\u0026mdash;for example, failure to initiate broad-spectrum antibiotics\u0026mdash;since our data show that antibiotic initiation rates are lower among those with diagnosis of malaria. Once diagnosed, there are also concerning gaps in the antimalarials used for treatment of severe malaria, specifically the need to follow with a course of AL once the patient receives IV artesunate. \u0026nbsp;There is also a need to ensure availability of IV artesunate for treatment of severe malaria, as IV quinine is still being widely used, particularly in seasonal epidemic zones.\u003c/p\u003e\n\u003cp\u003eThis analysis highlights the value of an integrated disease surveillance platform, even when implemented at a limited number of facilities, in identifying malaria-specific programmatic challenges. \u0026nbsp;To enhance representativeness and long-term sustainability, routine inpatient data collected through electronic medical records should ideally be capable of capturing malaria case information to support both clinical and programmatic decision-making. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e: \u0026nbsp;The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention.\u003c/p\u003e\n\u003cp\u003eMI, VS and MS\u0026rsquo;s salaries were provided by the U.S. President\u0026rsquo;s Malaria Initiative at the time this work was conducted.\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKenya Malaria Indicator Survey, 2010. https://dhsprogram.com/pubs/pdf/mis7/mis7.pdf. Accessed 22 July 2025.\u003c/li\u003e\n\u003cli\u003eKenya Malaria Indicator Survey, 2020. https://dhsprogram.com/pubs/pdf/MIS36/MIS36.pdf. Accessed 22 July 2025.\u003c/li\u003e\n\u003cli\u003eChurch, J., Maitland, K. Invasive bacterial co-infection in African children with\u003cem\u003e Plasmodium falciparum\u003c/em\u003e malaria: a systematic review\u003cem\u003e. \u003c/em\u003eBMC Med. 2014; doi:10.1186/1741-7015-12-31.\u003c/li\u003e\n\u003cli\u003eNguyen Hoan Phu, Nicholas P J Day, Phung Quoc Tuan, Nguyen Thi Hoang Mai, Tran Thi Hong Chau, Ly Van Chuong, et al. Concomitant Bacteremia in Adults With Severe Falciparum Malaria. Clinical Infectious Diseases. 2020; doi: 10.1093/cid/ciaa191.\u003c/li\u003e\n\u003cli\u003eJ Anthony G Scott, James A Berkley, Isaiah Mwangi, Lucy Ochola, Sophie Uyoga, et al. Relation between falciparum malaria and bacteraemia in Kenyan children: a population-based, case-control study and a longitudinal study. The Lancet. 2011; doi:10.1016/S0140-6736(11)60888-X.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO guidelines for malaria. 2024. https://www.who.int/publications. Accessed 23 July 2025.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Malaria microscopy quality assurance manual: version 2. 2016. https://apps.who.int/iris/handle/10665/204266Top of FormBottom of Form. Accessed 23 July 2025\u003c/li\u003e\n\u003cli\u003eVerani JR, Eno EN, Hunsperger EA, Munyua P, Osoro E, Marwanga D, et al. (2024) Acute febrile illness in Kenya: Clinical characteristics and pathogens detected among patients hospitalized with fever, 2017\u0026ndash;2019. PLoS ONE. 2024; doi:10.1371/journal.pone.0305700.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Giemsa Staining of Malaria Blood Films: SOP. 2016. https://apps.who.int/iris/handle/10665/204458. Accessed 23 July 2025.\u003c/li\u003e\n\u003cli\u003eOdhiambo FO, O\u0026apos;Meara WP, Abade A, Owiny M, Odhiambo F, Oyugi EO. Adherence to national malaria treatment guidelines in private drug outlets: a cross-sectional survey in the malaria-endemic Kisumu County, Kenya. Malar J. 2023; doi: 10.1186/s12936-023-04744-7. \u003c/li\u003e\n\u003cli\u003eKenya Ministry of Health Division of National Malaria Program, Malaria Health Facility Assessment. 2023. Final report, Nairobi, Kenya (Unpublished)\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Artemisinin resistance and artemisinin-based combination therapy efficacy: Status report. (2018) https://www.who.int/docs/default-source/documents/publications/gmp/who-cds-gmp-2018-26-eng.pdf. Accessed 24 July 2025.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, Sentinel Surveillance, Quality of Health Care, Kenya, Africa","lastPublishedDoi":"10.21203/rs.3.rs-7321812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7321812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eIn Kenya, limited clinical data on hospitalized malaria patients restricts insights into disease severity and care quality. Using data from the Integrated Facility-based Surveillance (IFBS) system—a sentinel surveillance platform for febrile illnesses across twelve facilities—we assessed risk factors for severe illness and mortality, diagnostic accuracy of microscopy, and adherence to severe malaria treatment guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe analyzed IFBS data obtained from June 2017 to July 2024 using bivariable logistic regression to identify factors linked to severe illness and deaths. Microscopy results were compared with PCR results to assess diagnostic concordance. We also evaluated whether patients received parasitological confirmation before treatment and if severe cases received IV artesunate followed by artemether-lumefantrine (AL), per standard guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAmong 8,487 inpatients, 2,197 (25.9%) tested positive for malaria; among malaria cases, 713 (32.5%) had severe disease and 16 (0.7%) died. Severe illness and deaths were associated with age under one year and delayed care-seeking. Positive routine microscopy was PCR negative in 21% of patients. Only 15% of severe cases were documented to have received both IV artesunate and AL, while 17% received IV quinine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eRisk factors associated with severe illness and mortality included young age \u0026lt; 1 as well as modifiable risk factors that suggested delayed care seeking. Despite IFBS data reliance on chart reviews, findings reveal critical gaps in diagnostic accuracy and adherence to treatment protocols for severe malaria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003eN/A\u003c/p\u003e","manuscriptTitle":"Using sentinel surveillance system data to characterize severe malaria illness and quality of malaria case management among hospitalized patients in Kenya, 2017-2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:13:43","doi":"10.21203/rs.3.rs-7321812/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-04T12:57:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T08:30:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T15:23:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213241197329748220368121423173299387045","date":"2025-09-02T10:39:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168225779075406331984924400865545554128","date":"2025-09-01T06:10:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61622911252087657531735068797663082100","date":"2025-08-31T13:06:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148743759927329683557724911400631448113","date":"2025-08-18T19:41:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-16T16:26:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-08T17:46:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T17:44:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-08-07T20:36:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad36d6b8-ece5-4a3a-b3ef-7e0e49acaf71","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:23:09+00:00","versionOfRecord":{"articleIdentity":"rs-7321812","link":"https://doi.org/10.1186/s12936-025-05738-3","journal":{"identity":"malaria-journal","isVorOnly":false,"title":"Malaria Journal"},"publishedOn":"2026-01-14 16:29:58","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-08-27 06:13:43","video":"","vorDoi":"10.1186/s12936-025-05738-3","vorDoiUrl":"https://doi.org/10.1186/s12936-025-05738-3","workflowStages":[]},"version":"v1","identity":"rs-7321812","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7321812","identity":"rs-7321812","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}