Infectious Pathogens Associated With Stillbirth and Under-five Mortality in Western Kenya and the Effect of Sickle Cell Condition

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Children with sickle cell disease (SCD) are susceptible due to weakened immunity, influencing infection outcomes. This sub-study, nested within the Kenya Child Health and Mortality Prevention Surveillance (CHAMPS) network, investigated infectious pathogens associated with stillbirths and under-five deaths in Western Kenya, as well as the role of the sickle cell condition. Methods: We analysed stillbirths (n=387) and ≤ 5 child deaths (n=945) enrolled in CHAMPS in Karemo (Siaya) and Manyatta (Kisumu), both in western Kenya, between May 2017 and Dec 2024. Minimally invasive tissue sampling (MITS) specimens were processed on a TaqMan Array Card using the QuantStudio 7 real-time PCR (TAC qPCR) and cultured. All participants were screened for sickle cell status using the point-of-care Gazelle™ Hb Variant device, and confirmed by High Performance Liquid Chromatography (HPLC) and pathological diagnosis. Statistical analyses were performed using R programming software (version 4.5.1). Findings : A total of 1,332 cases were investigated, 499, 37.5% (95% CI:34.9-40.1) had an infectious pathogen (IP), with lower prevalence among stillbirths (4.1%, 95%CI:2.6-6.6) and higher prevalence among under-five deaths (51.1%, 95%CI:47.9-54.3). SCD was identified in 46 cases (3.5%) and sickle cell trait in 256 cases (19.2%), for a total of 302 cases (22.7%) with SCD/trait. IP prevalence was higher in SCD (60.9%, 95%CI: 46.3-73.9) than in sickle cell trait (25.8%, 95%CI: 20.6-31.6). Among SCD and sickle cell trait cases, the most frequently identified pathogens were Plasmodium falciparum (39.3% and 18.2%), Klebsiella pneumoniae (7.1% and 37.9%), and Streptococcus pneumoniae (21.4% and 9.1%), respectively. Overall, the leading pathogens associated with death in both SCD/trait and non-SCD/trait groups were K. pneumoniae (28.7% vs 25.5%), P. falciparum (24.5% vs 35.9%), S. pneumoniae (12.8% vs 12.1%), HIV (8.5% vs 8.7%), cytomegalovirus (7.4% vs 7.4%), and respiratory syncytial virus (3.2% vs 1.5%). Conclusions: Infectious pathogens contribute substantially to stillbirth and under-five mortality in Western Kenya, highlighting the need to strengthen infection prevention, diagnosis, and management during pregnancy and early childhood in line with WHO recommendations and targeted preventive care for vulnerable populations like those with SCD. Infectious Pathogens Stillbirth Under-Five mortality Sickle Cell Disease LMICs Figures Figure 1 INTRODUCTION In low- and middle- income countries (LMICs), particularly across sub-Saharan Africa, Infectious diseases remain a major public health challenge and continue to contribute significantly to adverse pregnancy outcomes and childhood mortality( 1 ). Stillbirths and under-five deaths account for a significant proportion of preventable mortality in the region. In 2022, the total under-five mortality globally reduced from 76 deaths per 1000 live births in 2000 to 37 deaths per 1000 live births. However, sub-Saharan Africa still records a high under-five mortality rate of 70 deaths per 1000 live births, 10 times higher than the under-five mortality rate in European countries, which is mainly contributed to by infectious diseases and SCD ( 2 ). In Kenya, despite under-five mortality reducing from 115 deaths per 1000 live births in 2003 to 41 deaths per live birth in 2021, it is still significantly high ( 3 ). The high death rates in resource-limited countries are mainly attributed to infections that cause severe sepsis, respiratory infections, malaria and diarrhoea, and complications that include pre-term births, asphyxia and congenital anomalies ( 4 – 6 ). However, the contribution of specific infectious pathogens to these outcomes is often poorly characterised due to limitations in diagnostic capacity and incomplete post-mortem investigations. Africa bears a disproportionately high burden of sickle cell disease and sickle cell trait, both of which are known to influence susceptibility to infection and disease severity, yet their role in pathogen-associated mortality has not been sufficiently examined ( 7 ). Infection is among the leading causes of stillbirths and under-five mortality, especially in children with sickle cell conditions ( 1 , 6 ). The predisposition of children living with SCD is brought about by immune incompetence resulting from auto-splenectomy and functional hypersplenism, making them susceptible to bacterial and viral infection ( 6 , 8 ). In low-resource countries, inadequate access to quality healthcare services and high costs of effective therapies like hydroxyurea and stem-cell transplantation exacerbate this vulnerability. Consequently, this increases the risk of dying from infectious diseases, underscoring the necessity of investigating infectious pathogens as a key contributor to children under 60 months with SCD. In 2021, Sub-Saharan Africa (SSA) recorded the highest mortality cases directly linked to SCD, with approximately 30000 deaths, representing a 30.1% increase from an estimated 2.3 million in 2000 to 7.8 million in 2021 ( 8 ). In Kenya, about 20000 to 30000 babies are born with sickle cell disease per year, and approximately 50% to 80% of them die before their fifth birthday ( 9 ). The high mortality of SCD infants in Kenya is mainly attributed to inadequate healthcare facilities, inappropriate use of penicillin prophylaxis, and unavailability of vaccines in some medical centres ( 8 ). Infectious pathogens associated with stillbirths and under-five deaths are preventable, yet the causes are poorly examined, hindering the efficient evaluation of this outcome, especially in SCD-related cases ( 10 ). Roughly 17 per cent of children in the lake region have sickle cell traits, and 0.6 per cent of them have sickle cell disease, amounting to 0.9 per cent of the overall prevalence of SCD across the nation ( 9 ). Despite a worldwide improvement in child mortality, inconsistencies persist across different regions. In low- and middle-income countries (LMICs), children aged below five are predisposed to mortality and morbidity due to malnutrition, underlying conditions like HIV, SCD and diabetes, inadequate access to quality healthcare, and poor sanitation. Infants born with SCD in low-resource countries have a higher risk of death before seeing their fifth birthday ( 2 ). Existing studies have primarily focused on general infectious pathogens in children, but few have examined the specific contribution of sickle cell conditions to deaths from infections. Lack of efficient mortality surveillance has led to underestimation and misinterpretation of infectious causes of death in existing studies. This sub-study, nested within the Kenya Child Health and Mortality Prevention Surveillance (CHAMPS) network, aimed to characterise infectious pathogens associated with stillbirth and under-five mortality and to examine the effect of sickle cell disease condition (SCD/trait). METHODS This study was a retrospective cross-sectional study utilising mortality surveillance data from the Kenya Child Health and Mortality Prevention Surveillance (CHAMPS) network. Kenya CHAMPS leverages two existing Health and Demographic Surveillance Systems (HDSS) in western Kenya: Karemo HDSS (Siaya County) and Manyatta HDSS (Kisumu County) ( 1 , 11 ). The catchment is characterised by high under-five mortality of 54 deaths per live birth ( 12 , 13 ), a substantial infectious-disease burden of 59.9% ( 14 ), and a high prevalence of sickle cell disease (SCD) of 0.6% ( 9 , 15 ). The study included all stillbirths and deaths among children aged ≤ 60 months enrolled in CHAMPS between May 2017 and December 2024, following the CHAMPS protocol and methods ( https://www.champshealth.org/ ) ( 11 , 16 ). Mortality events were identified through community and facility notifications within the surveillance areas. The study included deceased children under 60 months and stillbirths who met specific requirements as captured in the CHAMPS Protocol ( https://www.champshealth.org/ ) ( 11 , 15 , 16 ). Eligible decedents were those for whom the family consented, and post-mortem biological specimens were collected within 24 hours for pathogen testing, and for whom SCD status could be confirmed through laboratory testing. Stillbirths were defined as foetal deaths occurring at ≥ 28 weeks of gestation, or with birthweight ≥ 1000 g, or crown-heel length > 35 cm, with no sign of life, consistent with CHAMPS protocol. Enrolment required informed consent from a parent or guardian and availability of suitable post-mortem specimens for pathogen identification and SCD confirmation. However, exclusion criteria were also based on CHAMPS Protocol guidelines ( 11 , 16 ). CHAMPS protocol excludes cases where consent was not obtained; bodies had been cremated or embalmed before sampling; or the death was subject to medicolegal investigation or regulatory procedures that precluded post-mortem sampling (Salzberg et al., 2019; Taylor et al., 2020). These criteria were applied to preserve ethical standards, data integrity, and the reliability of laboratory analyses. Data Collection Methods Data collection consisted of both clinical and laboratory procedures according to the CHAMPS Protocol ( https://www.champshealth.org/ ). Clinical data such as demographic information, medical history, healthcare access patterns and co-morbidities were abstracted from medical records and verbal autopsy forms. Laboratory information was acquired through investigations of post-mortem biological specimens, including tissue and non-tissue sampling. These samples were processed to identify infectious pathogens using Multiplex PCR and microbiological procedures. In addition, sickle cell status was determined using the point-of-care GazelleTM Hb Variant device (Hemex Health) and confirmed by HPLC and by pathological diagnosis through the Determination of Cause of Death (DeCoDe) Panel. Data entry was performed using a secure KoBoToolbox and Research Electronic Data Capture (REDCap) software, which included built-in validation checks to minimise errors. Sample collection The samples were collected in a sterile environment using the standard Minimal Invasive Tissue Sampling (MITS) kits within 24 hours, which consisted of biopsy needles (14 G-18 G), forceps, sterile gloves, scalpels and blood culture bottles as per CHAMPS methods ( 17 ). The intended puncture sites of the body were externally disinfected with povidone-iodine. The blood samples were collected through percutaneous puncture of the heart and key vessels using a sterile syringe. Ten millilitres of blood were collected in paediatric blood culture bottles and EDTA tubes for microbiological examinations and TAC analysis ( 17 ). Cerebrospinal fluid (CSF) was collected through lumbar puncture between the L3 and L5 vertebrae, providing 5 ml of fluid into sterile tubes ( 1 , 18 ). Tissue samples from the lungs, liver and brain were collected by penetrating biopsy needles through anatomic structures: the right upper quadrants for the liver, the left subcostal margins for the spleen, and the mid-clavicular line for the lungs. Brain tissues were collected in addition to nasopharyngeal and rectal swabs to investigate respiratory and gastrointestinal pathogens. Laboratory Procedures Multiplex PCR The study used TAC testing on the ViiA7 and Quant-Studio 7 (QS7) Real-Time PCR (qPCR) machines to identify various pathogen types from post-mortem samples. TAC is a multi-pathogen identification system comprising a 384-well array vessel used to amplify nucleic acid based on TaqMan real-time PCR technology ( 19 , 20 ). TAC wells contained dried primers and hydrolysis probes for the detection of targeted infectious pathogens or control targets. The amplification levels were corrected to the fluorescence data generated and measured using the QS7 Flex qPCR instrument, which indicated the presence of targeted nucleic acid in the sample ( 21 ). The process for identification started with total nucleic acid extraction and was followed by TAC testing. Total nucleic acid (TNA) Extraction Nucleic acid was extracted from post-mortem samples, including blood, lung tissues, brain tissues, cerebrospinal fluids (CSF), rectal swabs, oropharyngeal/ nasopharyngeal (OP/NP) swabs, using a standardised procedure to maximise yield and maintain the integrity across different samples. CSF and blood, Anticoagulant (EDTA) tubes were used to collect whole blood, and 400 µl was used in the extraction. 1 ml of phosphate-buffered saline (PBS) was used to suspend the rectal swab and vortexed; 400 µl of the specimen was utilised ( 21 ). Respiratory swabs (OP/NP) were combined in universal transport medium, and 350 µl of the resultant specimen was used. 1 ml of bacteria lysis buffer (BLB) was used to soak lung tissues and then homogenised, maintaining a tissue to buffer ratio of 1:1. Infectious pathogens were inactivated in each sample by adding BLB, achieving a 1:1 ratio of buffer to specimen volume. Furthermore, RNases and Dnases were inactivated by adding 25 mg/ml Protease K at a 10% volume ratio. Mechanical lysis steps differed based on the sample type: CSF, blood, and rectal swabs were disrupted by 0.5 mm glass beads to achieve a maximum disruption of difficult microbes to lyse; tissue homogenisation was done on lung tissues using 7 mm stainless steel beads. Mechanical lysis was not necessary for respiratory swabs. Thereafter, in the inactivation and lysis steps, a 700 µL volume of each prepared sample was collected for further processing. TNA was then extracted using an automated machine that ensured high-quality TNA suitable for subsequent TAC testing. TAC Testing on Multiplex PCR The study utilised CHAMPS customised TAC plates organised into four unique designs that consisted of under-five assays; CSF/blood tier 1 and tier 2 designs. The samples were tested on 4 sets of TACS that included enteric, CSF/blood tier 1, respiratory, and CSF/blood tier 2, which accommodated six test samples and two controls in every run to enhance data integrity and quality ( 21 ). After the extraction of TNA, a reaction mixture was prepared by thawing qScript XLT 1-Step RT- qPCR Tough Mix (enzyme mix) and kept at 4°C. No template controls (NTC) containing sterile nuclease-free water and positive controls (PC), consisting of synthetic nucleic acid templates, were set up. The reaction mixes were placed in a designated sterile area by combining 10 µl of extracted TNA and enzyme mix per run. The study used aerosol-resistant tips to pipette 100 µl of the mixture for each specimen and control, respectively, into TAC ports, preventing cross-contamination and air bubbles. The TAC was then centrifuged twice at 1200 revolutions per minute for a minute each to enhance the even distribution of the mixture across the wells. The TAC was then trimmed off the fill reservoirs after being sealed with a mechanical sealer. The sealed cards were then inserted into the ViiA7 qPCR instrument for running. The setup of each run was based on template conditions appropriate for each TAC design. The run included amplification cycles, reverse transcription steps, and polymerase activation, adding up to 45 cycles. The amplification data were extracted and analysed using the QS qPCR Software v1.2, CDC. The threshold parameters were reviewed manually to distinguish between the real signal and background noise. A specimen was considered positive for the targeted infectious pathogen if a clear application curve crossed the expected cycle threshold (Ct) value between Ct 28–40, based on the target pathogen. Internal quality control of amplification, such as MS2 bacteriophage for stool samples and Rnase P for human samples, were examined for each specimen to ensure the absence of PCR inhibitors and nucleic acid integrity. Based on the amplification curves, the outputs were classified as negative, indeterminate or positive, and those runs that failed internal quality checks, like amplifications in negative control and unobservable amplification from positive controls, were discarded and repeated. Bacterial Culture This study used two culture methods: an automated blood culture system using the BD Bactec FX40 Machine and a conventional microbial culture on culture media as per CHAMPS methods ( https://www.champshealth.org/ ). Automated Blood Culture Blood collected in paediatric blood culture bottles from the field was immediately transported to the laboratory for incubation. These bottles contained media that supported the growth of a wide range of microorganisms, including both aerobic and anaerobic bacteria. The bottles were placed in the BD Bactec FX40 system, which was incubated and continuously monitored for microbial growth. The system uses fluorescence-based technology to detect changes in the carbon dioxide levels produced by microbial metabolism, indicating the presence of microorganisms ( 22 ). The system detected most bacteraemia cases within a 5- to 7-day incubation period. When microbial growth was detected, the system flagged the bottle as positive. The time to positivity (TTP) varied depending on the organism and the blood volume used. The system provided rapid detection, which was crucial for the timely diagnosis and further examination of bloodstream infections( 23 ). Positive cultures were subjected to further investigations, such as Gram staining and subculturing, to identify the specific microorganisms involved. This step was essential for guiding the appropriate antimicrobial panel ( 22 ). Bacterial Media Culture A positive blood culture from the Bactec machine was immediately processed for subculturing. The rubber septum of the bottles was aseptically disinfected using 70% Isopropyl alcohol. A sterile syringe and needle were then used to aspirate 5 ml of the blood broth mixture, which was inoculated on three primary media: Blood agar (BA), Chocolate agar (CHOC), and MacConkey agar (MAC). Blood agar was used to support a wide range of gram-negative microbes, and chocolate agar was incubated under 5–10% carbon dioxide conditions, which facilitated the growth of fastidious microorganisms like Haemophilus influenzae and Neisseria species. MacConkey agar was used to isolate gram-negative enteric bacteria and to differentiate non-lactose fermenters from lactose fermenters. The inoculated Petri dishes were incubated at 37°c with BA and MAC maintained at aerobic conditions, and CHOC incubated in a carbon dioxide-enriched environment. Plates were examined after 18–24 hours, with the second inspection at 48 hours of incubation to capture any slow-growing pathogens. Bacterial colonies were identified based on morphological features on the media plate and Gram-staining results. For CSF specimens collected through MITS, 0.5 ml was inoculated directly onto BA, MAC and CHOC plates after reception in the laboratory. The same incubation protocol was followed as for blood culture, with CHOC plates maintained in a carbon dioxide environment. In addition to culture, a direct Gram stain technique was done on CSF to provide rapid preliminary results on morphology, presence and Gram stain status of any microbe. Incubated plates were examined after 24 and 48 hours. Isolated microbes were identified using catalase and oxidase testing ( 24 ). Sickle Cell Testing This study used the point-of-care Gazelle™ Hb Variant device (Hemex Health) and the HPLC (Bio-Rad D-10) to describe the haemoglobin variants of all the participants’ samples. A standardised procedure was followed as per the manufacturer’s instructions to ensure reproducibility and reliability of results. The blood samples collected during the MITS procedure were loaded into the Gazelle disposable cartridge. Each cartridge was prefilled with a separation buffer, and a 20 µl blood sample was carefully loaded into the application area. The cartridge was then loaded into a Gazelle Hb Variant testing machine, a portable microchip-based electrophoresis platform. The machine separated the haemoglobin variants in the applied blood samples on cellulose acetate paper housed in the cartridge. The devices relied on the haemoglobin electrophoresis principle, where Hb variants comprising of Haemoglobin Sickle C (Hb SC), haemoglobin S (Hb S), haemoglobin A (Hb A), haemoglobin A2 (Hb A2), haemoglobin F (Hb F), and haemoglobin E (Hb E) have distinguished net negative charge in alkaline solution ( 25 ). This difference in negative charge allowed them to move across the paper at different speeds due to the applied voltage, separating Hb variants into visible bands on the paper. In approximately 8 minutes, the gazelle gave results that were visually represented as bands and quantitatively reported as the relative percentage of each haemoglobin type present. Data Processing and Analysis Data collection and organisation were conducted using the KoBo Toolbox and Research Electronic Data Capture (REDCap) software, which provided reliability and security for managing field and clinical information. The data collected was cleaned and securely stored. Data system access was limited to a few authorised research personnel by password-supported accounts, and physical access to study material was restricted. Personal study identifiers were used to maintain participant confidentiality. Statistical analyses were performed using R programming software (version 4.5.1). Descriptive statistics were summarised in demographic characteristics such as age and gender, types of infectious pathogens identified, and mortality trends. A comparative analysis assessed the differences and burdens of infectious pathogens between mortality cases with SCD/trait and those without SCD/trait. Statistical tests such as Pearson’s chi-squared and Fisher’s exact tests were used to test for independence of association between SCD status and the infectious pathogen identified, with a significance level maintained at p < 0.05. RESULTS The findings are from 1332 cases, of which 387 were stillbirths and 945 were from under-five cases. The overall prevalence of IP in the study population was 37.5% (95% CI: 34.9%, 40.1%), in the stillbirth cases was 4.1% (95% CI: 2.6%, 6.6%), and in under-five death was 51.1% (95% CI: 47.9%, 54.3%). Of the 1332 cases, 46 (3.5%) had sickle cell disease, and 256 (19.2%) had sickle cell trait, totalling 302 (22.7%) with SCD/trait. The prevalence of IP in sickle cell disease was 60.9% (95% CI: 46.3%, 73.9%) and in sickle cell traits was 25.8% (95% CI: 20.6%, 31.6%), with top pathogens being Plasmodium falciparum 11cases (39.3%), 12 cases (18.2%), Klebsiella pneumoniae 2 cases (7.1%), 25 cases (37.9%), Streptococcus pneumoniae , 6 cases (21.4%), 6 cases (9.1%) respectively. Findings on sex-specific analysis showed a balanced distribution, with females accounting for 46.5% of deaths among SCD/trait compared to 45.5% among non-SCD/trait, and males comprising 53.5% SCD/trait vs 54.5% non-SCD/trait deaths. The observable differences were not statistically significant (p = 0.8) (Table 1 ) The analysis of deaths stratified by SCD status showed notable variations across age categories (p = < 0.001). Among non-SCD/trait cases, stillbirths accounted for 29.8% of deaths as compared to 26.5% among those with SCD/traits. Contrary to deaths within the first 24 hours of life, which were proportionally higher among SCD/trait group (21.5%) than in non-SCD/trait cases (12.0%). 1 day to less than 7 days old comprised 9.0% of non-SCD/trait deaths and 10.6% of SCD/trait deaths, while 7 days to 27 days old were relatively comparable (4.7% vs. 5.3%). In 28 days to less than 12 months old, 23.5% were accounted for by non-SCD/traits and 17.5% SCD/trait deaths. In 12 months to less than 60 months old categories, there were relatively higher non-SCD/trait death rates (21.0%) compared to SCD/trait deaths 18.5%. (Table 1 ) Table 1 Characteristics of Stillbirths and Deaths Enrolled in the Study Characteristic N Overall Non SCD/trait, SCD/trait, p-value 1 N = 1,332 N (%) = 1030 (77.3%) N (%) = 302 (22.7%) Sickle Cell Disease type, n (%) 1,332 Non SCD 1,030 (77.3%) Sickle Cell disease 46 (3.5%) 46 (15.2%) Sickle Cell trait 256 (19.2%) 256 (84.8%) Age at death, n (%) 1,332 < 0.001 Stillbirth 387 (29.1%) 307 (29.8%) 80 (26.5%) Death in the first 24 hours 189 (14.2%) 124 (12.0%) 65 (21.5%) 1 to 6 days old 125 (9.4%) 93 (9.0%) 32 (10.6%) 7 to 27 days old 64 (4.8%) 48 (4.7%) 16 (5.3%) 28 days old to < 12 months old 295 (22.1%) 242 (23.5%) 53 (17.5%) 12 months old to < 60 months old 272 (20.4%) 216 (21.0%) 56 (18.5%) Catchment Area, n (%) 1,332 0.7 Karemo 679 (51.0%) 528 (51.3%) 151 (50.0%) Manyatta 653 (49.0%) 502 (48.7%) 151 (50.0%) Location of Death, n (%) 1,332 0.013 Community deaths 328 (24.6%) 270 (26.2%) 58 (19.2%) Health Facility deaths 1,004 (75.4%) 760 (73.8%) 244 (80.8%) Sex, n (%) 1,319 0.8 Female 603 (45.7%) 463 (45.5%) 140 (46.5%) Male 716 (54.3%) 555 (54.5%) 161 (53.5%) 1 Pearson’s Chi-squared test; Fisher’s exact test Infectious Pathogens Identified From the 1332 cases enrolled in the study, 499 had an infectious pathogen identified in their post-mortem samples, with a prevalence of 37.5% (95% CI: 34.9%, 40.1%). The distribution of infectious pathogens identified was as follows: bacteria accounted for the highest number of infections among the total cases, with 305,61.8% (95% CI 56.7%, 65.4%); parasites, 171, 34.3% (95% CI 30.1%, 38.6%); viruses, 118, 23.6% (95% CI 20.0%, 27,7%); and fungi, 9, 1.8% (95% CI 0.9%, 3.52%) (Table 2 ). Among 305 cases with SCD/trait enrolled in the study, pathogens were identified in 94 cases (31.1% [95% CI 26.0%, 36.7%]). Bacterial infections were the most prevalent, accounting for 60 out of 94 cases (63.8%). Viral infections were identified in 25 cases (26.6%), parasites in 23 cases (24.5%), and fungal pathogens were not identified in this group. Table 2 Prevalence of Infectious Pathogens in the Study Population Infectious Pathogen (IP) N Overall Non SCD/trait, SCD/trait, p-value 1 N = 1,332 N (%) = 1030 (77.3%) N (%) = 302 (22.7%) Infectious Conditions, n (%) 1,332 0.008 Infectious 823 (61.8%) 656 (63.7%) 167 (55.3%) Non-infectious 509 (38.2%) 374 (36.3%) 135 (44.7%) Infectious Pathogen Identified, n (%) 1,332 499 (37.5%) 405 (39.2%) 94 (31.1%) 0.011 Bacteria, n (%) 499 308 (61.8%) 248 (61.4%) 58 (61.7%) 0.9 Virus, n (%) 499 118 (23.6%) 90 (22.2%) 28 (26.6%) 0.12 Parasite, n (%) 499 171 (34.3%) 148 (36.6%) 23 (24.5%) 0.026 Fungi, n (%) 499 9 (1.9%) 9 (2.2%) 0.2 1 Pearson’s Chi-squared test; Fisher’s exact test In children without SCD/trait, bacterial pathogens were also the most common, accounting for 248 of 405 cases (61.4%). Parasitic infections were identified in 148 cases (36.6%), viral infections in 88 cases (21.8%), and fungal pathogens in 9 cases (2.2%). The parasitic pathogens were less prevalent among SCD/trait cases (24.5%) than among non-SCD/trait cases (36.6%) (Table 2 ). Table 3 Most Prevalent Pathogens identified from the Study Population Infectious Pathogens Overall No SCD/trait SCD/trait p-value 1 N = 499 N = 405 N = 94 Parasite Plasmodium falciparum , n (%) 168 (33.7%) 145 (35.9%) 23 (24.5%) 0.048 Ascaris lumbricoides , n (%) 2 (0.4%) 2 (0.5%) 0 (0.0%) > 0.9 Plasmodium malariae , n (%) 1 (0.2%) 1 (0.2%) 0 (0.0%) > 0.9 Bacteria Klebsiella pneumoniae , n (%) 130 (26.1%) 103 (25.5%) 27 (28.7%) 0.6 Streptococcus pneumoniae , n (%) 61 (12.2%) 49 (12.1%) 12 (12.8%) 0.8 Escherichia coli , n (%) 56 (11.2%) 50 (12.4%) 6 (6.4%) 0.14 Staphylococcus aureus , n (%) 30 (6.0%) 26 (6.4%) 4 (4.3%) 0.6 Streptococcus spp ., n (%) 17 (3.4%) 16 (4.0%) 1 (1.1%) 0.3 Haemophilus influenzae , n (%) 16 (3.2%) 13 (3.2%) 3 (3.2%) > 0.9 Pseudomonas aeruginosa , n (%) 11 (2.2%) 10 (2.5%) 1 (1.1%) 0.7 Streptococcus pyogenes , n (%) 11 (2.2%) 9 (2.2%) 2 (2.1%) > 0.9 Acinetobacter baumannii , n (%) 8 (1.6%) 5 (1.2%) 3 (3.2%) 0.4 Streptococcus agalactiae , n (%) 8 (1.6%) 7 (1.7%) 1 (1.1%) > 0.9 Aeromonas spp ., n (%) 5 (1.0%) 5 (1.2%) 0 (0.0%) 0.6 Enterobacter cloacae , n (%) 5 (1.0%) 4 (1.0%) 1 (1.1%) > 0.9 Salmonella spp ., n (%) 5 (1.0%) 4 (1.0%) 1 (1.1%) > 0.9 Escherichia coli/shigella spp ., n (%) 4 (0.8%) 4 (1.0%) 0 (0.0%) 0.7 Citrobacter freundi , n (%) 3 (0.6%) 3 (0.7%) 0 (0.0%) > 0.9 Klebsiella spp ., n (%) 3 (0.6%) 2 (0.5%) 1 (1.1%) > 0.9 Virus HIV, n (%) 43 (8.6%) 35 (8.7%) 8 (8.5%) > 0.9 Cytomegalovirus (CMV), n (%) 37 (7.4%) 30 (7.4%) 7 (7.4%) > 0.9 Adenovirus , n (%) 18 (3.6%) 15 (3.7%) 3 (3.2%) > 0.9 Respiratory syncytial virus (RSV), n (%) 9 (1.8%) 6 (1.5%) 3 (3.2%) 0.5 Rotavirus a , n (%) 6 (1.2%) 4 (1.0%) 2 (2.1%) 0.7 Rotavirus non-typable , n (%) 5 (1.0%) 4 (1.0%) 1 (1.1%) > 0.9 Human metapneumovirus (HMPV), n (%) 3 (0.6%) 1 (0.2%) 2 (2.1%) 0.2 Norovirus gii , n (%) 3 (0.6%) 3 (0.7%) 0 (0.0%) > 0.9 Norovirus gi , n (%) 2 (0.4%) 0 (0.0%) 2 (2.1%) 0.042 Parainfluenza virus type 1 , n (%) 2 (0.4%) 2 (0.5%) 0 (0.0%) > 0.9 Parainfluenza virus type 3 , n (%) 2 (0.4%) 1 (0.2%) 1 (1.1%) 0.8 Parvovirus b19 , n (%) 2 (0.4%) 1 (0.2%) 1 (1.1%) 0.8 Fungi Pneumocystis jirovecii , n (%) 6 (1.2%) 6 (1.5%) 0 (0.0%) 0.5 Candida albicans , n (%) 1 (0.2%) 1 (0.2%) 0 (0.0%) > 0.9 Yeast , n (%) 1 (0.2%) 1 (0.2%) 0 (0.0%) > 0.9 Yeast fungaemia , n (%) 1 (0.2%) 1 (0.2%) 0 (0.0%) > 0.9 1 2-sample test for equality of proportions with continuity correction Bacterial Pathogens The most commonly identified bacterial agent was Klebsiella pneumoniae , detected in 130 cases (26.1%) overall. Of these, 103 cases (25.5%) were isolated from non-SCD/trait deaths, while 27 cases (28.7%) were from participants with SCD/trait, showing a slightly higher proportion than in non-SCD/trait cases (p = 0.6) (Table 3 ). Streptococcus pneumoniae was the second most prevalent identified bacterium, detected in 61 cases (12.2%). Among these, 49 cases (12.1%) were detected in non-SCD/trait participants and 12 cases (12.8%) in SCD/trait. Escherichia coli was identified in 56 cases (11.2%), with a markedly higher prevalence in non-SCD/trait participants (50 cases, 12.4%) compared to the SCD/trait group (6 cases, 6.4%). Staphylococcus aureus was detected in 30 cases (6.0%), consisting of 26 non-SCD/trait cases (6.4%) and 4 SCD/trait cases (4.3%). For Streptococcus pyogenes , 9 cases (2.2%) were in non-SCD/traits groups and 2 cases (2.1%) in SCD/trait groups. Acinetobacter baumanii was noticed in 5 non-SCD/trait cases (1.2%) and 3 SCD/trait cases (3.2%), showing a notably higher proportion among SCD/trait participants (Table 3 ). Fungal Pathogens Fungal pathogens were relatively uncommon, accounting for 9 (1.1%) cases of the total infection-related deaths. They were only identified in non-SCD/trait participants, and no case was detected in SCD/trait. Pneumocystis jirovecii was detected in 1.2% (6/499) of the overall cohort, with all cases occurring in individuals without SCD/trait (1.5%, 6/405). In contrast, yeast fungaemia and Candida albicans were detected in 1 case (0.2%) each. (Table 3 ) Viral Pathogens Analysis of viral pathogens detected in the study population showed a distinct pattern based on sickle cell status (Table 3 ). The prevalence of major viruses, including HIV (8.7% vs 8.5%) and Cytomegalovirus (7.4% vs 7.4%), was comparable between individuals without and with SCD/traits. In contrast, significant differences were observed for specific respiratory and gastrointestinal viruses. The SCD/trait group exhibited markedly higher detection rates for Human Metapneumovirus (HMPV) (0.2% vs. 2.1%) and Respiratory Syncytial Virus (RSV) (1.5% vs. 3.2%). A similar trend was seen for Norovirus GI , which was detected exclusively in the SCD/trait group (2.1%), and Rotavirus A (1.0% vs. 2.1%). Other viruses, including Adenovirus and Parainfluenza types, showed minimal variation between groups. Parasitic Pathogens The predominant parasitic agent detected in this study was Plasmodium falciparum , the causative pathogen of the most severe form of malaria. It was identified in 33.7% (168/499) of the overall cohort, making it the most prevalent pathogen identified across all infectious agent classifications. Among non-SCD/trait cases, Plasmodium falciparum was detected in 35.9% (145/405), while 24.5% (23/94) were in the SCD/trait group. The findings highlight that Plasmodium falciparum infection was slightly less common in the SCD/trait decedents in comparison to the burden in the non-SCD/trait group, though it still presented a significant burden in both groups (p = 0.048) (Table 3 ). Other parasitic infections were infrequently detected. Ascaris lumbricoides was identified in two individuals (0.4%), both in the group without SCD/trait. Plasmodium malariae was detected in one individual (0.2%), also in the group without SCD/trait. Distribution of Infectious Pathogens by Age Group among SCD/trait Cases Figure 2 provides details on infectious pathogens across different age groups among SCD/trait cases. Among viral agents, CMV and HIV were most frequently identified, especially among infants and children. RSV and Adenoviruses were also prevalent, with moderate representation in infants and neonates. In addition, respiratory viruses such as Influenza A , parainfluenza virus type 3 , and Rotavirus A were dominantly detected in infant groups. For parasitic infections, Plasmodium falciparum, the causative agent for malaria, was highly dominant, with the majority of deaths occurring in child and infants’ categories (Fig. 2 ). Moreover, in bacterial infections, Klebsiella pneumoniae was the most predominant pathogen across all age groups, with a high burden observed among infants, followed by neonates and child categories (Fig. 2 ). Streptococcus pneumoniae was the second most prevalent bacterium detected, dominantly causing deaths in infants and children. Staphylococcus aureus, Haemophilus influenzae and Acinetobacter baumannii also appeared prominently, particularly in neonates and infants. Escherichia coli was detected in all the categories, including child, neonates, infants and stillbirths. (Fig. 2 ). DISCUSSION The study aimed to characterise infectious pathogens associated with stillbirth and under-five deaths among decedents with SCD/trait in Western Kenya, and to compare their distribution with their counterparts without SCD/traits. The findings highlight infection as a significant contributor to adverse pregnancy outcomes and child deaths, with distinct infectious pathogen profiles observed among SCD/trait cases. Among the infectious agents detected, Plasmodium falciparum was the most prevalent overall, accounting for 33.7% (168 cases). This insight shows a significant burden of malaria within the study setting. This underscores the continued public health concerns imposed by Plasmodium falciparum in endemic regions, especially in association with neonatal and maternal complications leading to stillbirths and mortality of the under-fives ( 26 , 27 ). Bacterial agents were predominantly presented in the findings, with Klebsiella pneumoniae 130 cases (26.1%), Streptococcus pneumoniae 61 cases (12.2%), and Escherichia coli 56 cases (11.2%) among the top three most prevalent bacterial pathogens in both groups with and without SCD/trait. These microbes are mainly associated with severe invasive infections like meningitis, pneumonia and sepsis, particularly in neonates and immunocompromised infants ( 28 – 30 ). In addition, Staphylococcus aureus , Haemophilus influenzae , and Streptococcus spp . further reflects the diversity of bacterial agents implicated in children's infections and stillbirths. Viral agents also contributed significantly, with Cytomegalovirus (CMV) amounting to 7.4% (37 cases), and HIV 8.6% (43 cases) being the most frequently identified. These infections are particularly linked to potential vertical transmission and chronic complications ( 31 , 32 ). The insights detecting Adenovirus in 18 cases (3.6%) and Respiratory Syncytial Virus in 9 cases (1.8%) pointed out the role of respiratory viral infections in paediatric morbidity and mortality, especially in neonates and infants. Generally, the findings demonstrated a broad array of infectious pathogens contributing to stillbirths and deaths of children below 60 months, giving an overview of the complex interplay between host susceptibilities and environmental exposures. Infectious Pathogens Distribution (SCD/trait vs. non-SCD/trait) The study findings on the distribution of infectious agents among stillbirths and under-five mortality cases with SCD/trait and those without showed notable differences in prevalence patterns. reflecting the Among bacterial infections, Klebsiella pneumoniae was frequently detected in both groups, and was predominantly identified in the among SCD/trait cases (28.7%) compared to the non-SCD/trait group (25.5%); however, this difference was not statistically significant (p = 0.6) This higher infection may be associated with impaired immune responses and frequent hospitalisation in SCD/trait patients ( 33 ). Similarly, Streptococcus pneumoniae revealed a slightly higher relative prevalence among SCD/trait cases (12.8%) than among those without SCD/trait (12.1%), but the difference was not statistically significant (p = 0.8) While previous studies have reported increased susceptibility to certain bacterial infections among individuals with sickle cell disease, particularly pneumococcal infections related to functional asplenia, our findings did not demonstrate statistically significant differences in pathogen prevalence by sickle cell status ( 6 , 8 , 34 ). In Kenya, the National Guideline for the Prevention and Control of Common Childhood Illnesses stresses the importance of early diagnosis and management of febrile diseases in susceptible individuals, including SCD children ( 35 ). However, our study shows continued vulnerability among this group, highlighting a policy-practice gap, especially in Western Kenya. Strengthening routine screening for bacterial and parasitic infections in SCD clinics could reduce preventable mortalities. Plasmodium falciparum was identified as the leading parasitic agent and overall, the most prevalent. The high burden is attributable to the parasite’s propensity to cause severe disease manifestations such as cerebral malaria and severe anaemia, both of which are associated with high case fatality rates in young children ( 36 ). Plasmodium falciparum infections exhibit distinct characteristics in patients with sickle cell disease (SCD) and traits compared to those without. The study findings showed that children with SCD/traits were less likely to have malaria as a contributing factor to mortality compared to their counterparts without SCD/traits (P = 0.048). These findings support the existing knowledge that SCD/trait confers varying degrees of protection against malaria, primarily through mechanisms that affect parasite density and cytoadherence ( 37 ). This protection is attributed to impaired cytoadherence of Plasmodium falciparum -infected erythrocytes containing sickle haemoglobin, reducing parasite sequestration in microvascular tissues ( 37 ). Additionally, existing studies have indicated that HbAS is associated with a reduced risk of symptomatic malaria, although this protective effect can vary with age ( 38 ). The World Health Organisation (WHO) recommends an integrated approach to malaria control, focusing on early diagnosis, effective treatment with artemisinin-based combination therapies, and preventive measures that include insecticide-treated nets and intermittent preventive treatment to pregnant women ( 39 ). For high-risk groups such as children living with SCD, WHO guidelines indicate the need for intensified surveillance and tailored interventions, including regular screening and timely management of febrile illness ( 39 ). The observed lower prevalence of severe malaria among SCD/trait patients aligns with existing studies for the partial protective effects of sickle haemoglobin (HbS), but it warrants the importance of maintaining comprehensive malaria prevention strategies in this vulnerable population. This ensures both effective prevention and management of malaria-related morbidity and mortality in vulnerable individuals. This study reveals a distinct pattern of viral pathogen detection among individuals with SCD/trait compared to those without. While the prevalence of major viruses like HIV and CMV was nearly identical between groups, a markedly higher detection rate of specific respiratory and gastrointestinal viruses, namely Human Metapneumovirus (HMPV), Respiratory Syncytial Virus (RSV), Norovirus GI , and Rotavirus A , in the SCD/trait cohort. These findings show that an immunologic landscape of sickle cell conditions may confer a unique susceptibility to particular acute viral infections, even as it may leave responses to other persistent viruses unaffected. This slightly higher prevalence of HIV in the SCD/trait group may be due to their increased exposure to parenteral interventions such as blood transfusions and an underlying immunocompromised state. Rotavirus A accounted for 1.2% of cases, with a slightly higher prevalence in SCD/traits groups (2.1%) compared to non-SCD/traits groups (1.0%), which may indicate an increased predisposition due to impaired mucosal immunity. Notably, there was a higher prevalence of RSV infections in SCD/trait cases (3.2%) compared to the non-SCD/trait group (1.8%). This insight supports previous studies on the vulnerability of SCD patients to respiratory viral infections, due to chronic anaemia and functional asplenia ( 6 , 8 ). Distribution of Infectious Pathogens by Age Group among SCD/trait Cases The findings from a comprehensive analysis of the distribution of infectious pathogens by age among SCD/trait cases showed a diverse microbial landscape that contributes to stillbirth and mortality of children under 60 months. Streptococcus pneumoniae and Haemophilus influenzae , microbes specifically targeted by penicillin prophylaxis and Hib vaccines, were more prominent, particularly in the infant and child categories. These insights reinforce current pieces of evidence that individuals with SCD/trait have a high risk of invasive bacterial infections, particularly in early life ( 40 ). These results are relevant in LMICs, especially in Kenya, where routine childhood immunisation programs, including the pneumococcal conjugate and Hib vaccines, are essential components of the Expanded Program of Immunisation (EPI). The continuous detection of these infectious pathogens may be attributed to suboptimal coverage of the vaccine, waning immunity, and the presence of non-vaccine serotypes in the population. Klebsiella pneumoniae was the most prevalent bacterium detected across all age groups, including a notable number of infant and neonate deaths. This may be due to maternal colonisation and subsequent vertical transmission, and nosocomial acquisition in the perinatal care setting. The high burden of Acinetobacter baumanii and Staphylococcus aureus among SCD/traits, both known to be linked with hospital-acquired infections, especially in neonatal units, further supports the contribution of hospital-acquired infections in the neonatal deaths ( 41 , 42 ). The detection of Plasmodium falciparum across infants, children and neonates highlights the importance of malaria prevention strategies in SCD/trait individuals. Malaria is a known trigger for severe complications in SCD/trait individuals, including haemolysis, anaemia and increased mortality risks ( 43 ). This data supports existing studies indicating that SCD children in malaria-endemic regions need targeted chemoprophylaxis and urgent treatment protocols ( 44 ). Furthermore, CMV, HIV, and RSV were predominantly detected in children's and infants’ samples. This dominance of CMV in infants may be linked to either congenital infection or reactivation in immunocompromised hosts. RSV was among the top detected viral pathogens in children and infants’ groups; its death-related cases are associated with its role in severe lower respiratory tract infections in early childhood. CONCLUSIONS The findings of the study showed that infectious pathogens remain a significant contributor to both stillbirths and deaths of children aged 1–59 months, particularly among vulnerable groups like those with SCD/traits. While many of the detected pathogens, Plasmodium falciparum, Klebsiella pneumoniae and Streptococcus pneumoniae , align with regional epidemiological trends, a few notable patterns emerged. The study demonstrates that infectious pathogens continue to drive preventable stillbirths and under-five deaths, with sickle cell disease and trait further increasing vulnerability. Recommendations include rigorous adherence to WHO management guidelines for pregnant women with SCD or trait, enhanced screening and treatment of infectious agents, and systematic post-mortem investigation of deaths to clarify causality. Addressing these combined risks is essential to reducing mortality and improving maternal and child health outcomes in Kenya and similar LMIC contexts. Furthermore, this study provides new insights into the pathogen-specific vulnerability of SCD/traits individuals, highlighting the complex interaction between host factors and infectious pathogens. By identifying the most prevalent pathogens associated with foetal outcomes in this population, the study offers practical knowledge to inform clinical prioritisation, guide critical treatment choices and shape targeted prevention strategies. Abbreviations CHAMPS Child Health and Mortality Prevention Surveillance CI Confidence Interval DNA Deoxyribonucleic Acid KEMRI Kenya Medical Research Institute MITS Minimal Invasive Tissue Sampling NTC No Template Control PC Positive Control PCR Polymerase Chain Reaction RNA Ribonucleic Acid SCD Sickle Cell Disease SCT Sickle Cell Trait TAC TaqMan Array Cards UCC University of Cape Coast Declarations ETHICAL APPROVAL AND CONSENT FROM PARTICIPANTS Ethical approval was obtained from the Scientific Ethical Review Unit of Kenya Medical Research Institute (KEMRI), with registration number SERU 3308. Written informed consent was sought from the parents or next of kin of the deceased participants before any post-mortem procedure was done through MITS. Clinical trial number: Not Applicable AVAILABILITY OF DATA AND MATERIALS Additional materials are available on the CHAMPS website (https://www.champshealth.org/) and upon request. COMPETING INTEREST The authors declared no competing interests FUNDING The study was supported by the Gates Foundation and Partnering for Health Professional Training in African Universities (P4HPT) AUTHOR’S CONTRIBUTIONS MOW: Conceptualisation, Methodology, Investigation, Data curation, Formal Analysis, Writing-original draft, Writing-review and editing, VS: Investigation, Writing-review and editing, FO: Data curation, Formal Analysis, Writing-review and editing, EK: Data curation, Formal Analysis, Writing-review and editing, MN: Investigation, Writing-review and editing, KO: Methodology, Investigation, Data curation, Writing-review and editing, HM: Investigation, Formal Analysis, Writing-review and editing, RO: Conceptualisation, Methodology, Investigation, Formal Analysis, Supervision, Writing-review and editing, VA: Conceptualisation, Methodology, Investigation, Data curation, Formal Analysis, Supervision, Writing-review and editing, DOY: Conceptualisation, Methodology, Investigation, Data curation, Formal Analysis, Supervision, Writing-review and editing, All the authors read and approved for the final manuscript. 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Supplementary Files PrevalenceofInfectiousPathogensand95ConfidenceIntervals.xlsx ComparisonbetweenDecedentswithSCDtraitvsnonSCDtraitbyagegroup.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor invited by journal 20 Feb, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 24 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Wasilwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYJCCAw/gzAogZmZuIKwlAc48A9LCSFgLA1wLYxuYxK/F4PjxhwcS2+yi+aWPX/xcOK82mr8dqOVHxTbcWs7kGAC1JOfO7Msplp657XjujMOMDYw9Z27j1GJ2IIfhQOI25twNZ3gSpHm3HcttAGphZmzDo+X88wdALfUgLcm/eeccy51PUMuNBKDDth0GamE/Js3bUJO7gZAW+xtvgFr+Hc+d2cPDZs1z7EDuRqCWg/j8Itmf/vjDhzPVuf087I9v89TU5c47f/jggx8VuLUgAR4DIHEYzDxAjHogYH8AJOqIVDwKRsEoGAUjCQAAsXBkp4NvgTcAAAAASUVORK5CYII=","orcid":"","institution":"University of Cape Coast","correspondingAuthor":true,"prefix":"","firstName":"Morgan","middleName":"Otundo","lastName":"Wasilwa","suffix":""},{"id":602559960,"identity":"65da7fd8-49cc-48db-ba8b-c705344dc13f","order_by":1,"name":"Valentine Simiyu","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Valentine","middleName":"","lastName":"Simiyu","suffix":""},{"id":602559961,"identity":"b219fc10-ec6b-41fa-89f5-7f5f62182f90","order_by":2,"name":"Fredrick Onduro","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Fredrick","middleName":"","lastName":"Onduro","suffix":""},{"id":602559962,"identity":"ad6df168-6507-401b-bd75-c43e13b57141","order_by":3,"name":"Edwin Kiplangat","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"","lastName":"Kiplangat","suffix":""},{"id":602559963,"identity":"4bb05be1-84b8-43bb-9431-fb6ca4ffde2c","order_by":4,"name":"Marryanne Nyanjom","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Marryanne","middleName":"","lastName":"Nyanjom","suffix":""},{"id":602559964,"identity":"e1a91f5f-049c-4497-9f43-e9d0d10d43c2","order_by":5,"name":"Kephas Otieno","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kephas","middleName":"","lastName":"Otieno","suffix":""},{"id":602559965,"identity":"f0ccf070-9ca7-4fdd-b63e-1023ce23ea81","order_by":6,"name":"Hellen Muttai","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Hellen","middleName":"","lastName":"Muttai","suffix":""},{"id":602559966,"identity":"57eaf8a6-f241-48d0-a0cf-471ceb529010","order_by":7,"name":"Richard Omore","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Omore","suffix":""},{"id":602559967,"identity":"c016109b-3eee-4e28-98a0-7ea2b1c5a704","order_by":8,"name":"Victor Akelo","email":"","orcid":"","institution":"Kenya Medical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Akelo","suffix":""},{"id":602559968,"identity":"e926b85f-97b7-448f-9127-1430b12b10f1","order_by":9,"name":"Dorcas Obiri-Yeboah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Dorcas","middleName":"","lastName":"Obiri-Yeboah","suffix":""}],"badges":[],"createdAt":"2026-01-25 00:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8689461/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8689461/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104343799,"identity":"28797bf6-be81-43be-bf7d-2b9aa682859a","added_by":"auto","created_at":"2026-03-10 17:16:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e: The Distribution of Infectious Pathogens by Age among SCD/trait cases\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8689461/v1/5a77d18213a0ee0f4e8bb11e.png"},{"id":104409689,"identity":"70bb4300-1949-4968-9cda-2ef4e53c0e07","added_by":"auto","created_at":"2026-03-11 12:46:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1231913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8689461/v1/0743cf9a-4d3d-409d-975f-2590b43950a3.pdf"},{"id":104343801,"identity":"2ce49d3e-44f2-409f-901b-0274c4413c1f","added_by":"auto","created_at":"2026-03-10 17:16:20","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13781,"visible":true,"origin":"","legend":"","description":"","filename":"PrevalenceofInfectiousPathogensand95ConfidenceIntervals.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8689461/v1/fefbc1059b6a7b2743eb14f4.xlsx"},{"id":104406317,"identity":"67602cf9-a6a2-4332-abc3-3784acbd0e38","added_by":"auto","created_at":"2026-03-11 12:25:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31031,"visible":true,"origin":"","legend":"","description":"","filename":"ComparisonbetweenDecedentswithSCDtraitvsnonSCDtraitbyagegroup.docx","url":"https://assets-eu.researchsquare.com/files/rs-8689461/v1/028d15846ca72d868be4eec2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInfectious Pathogens Associated With Stillbirth and Under-five Mortality in Western Kenya and the Effect of Sickle Cell Condition\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn low- and middle- income countries (LMICs), particularly across sub-Saharan Africa, Infectious diseases remain a major public health challenge and continue to contribute significantly to adverse pregnancy outcomes and childhood mortality(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Stillbirths and under-five deaths account for a significant proportion of preventable mortality in the region. In 2022, the total under-five mortality globally reduced from 76 deaths per 1000 live births in 2000 to 37 deaths per 1000 live births. However, sub-Saharan Africa still records a high under-five mortality rate of 70 deaths per 1000 live births, 10 times higher than the under-five mortality rate in European countries, which is mainly contributed to by infectious diseases and SCD (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Kenya, despite under-five mortality reducing from 115 deaths per 1000 live births in 2003 to 41 deaths per live birth in 2021, it is still significantly high (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The high death rates in resource-limited countries are mainly attributed to infections that cause severe sepsis, respiratory infections, malaria and diarrhoea, and complications that include pre-term births, asphyxia and congenital anomalies (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, the contribution of specific infectious pathogens to these outcomes is often poorly characterised due to limitations in diagnostic capacity and incomplete post-mortem investigations. Africa bears a disproportionately high burden of sickle cell disease and sickle cell trait, both of which are known to influence susceptibility to infection and disease severity, yet their role in pathogen-associated mortality has not been sufficiently examined (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Infection is among the leading causes of stillbirths and under-five mortality, especially in children with sickle cell conditions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The predisposition of children living with SCD is brought about by immune incompetence resulting from auto-splenectomy and functional hypersplenism, making them susceptible to bacterial and viral infection (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In low-resource countries, inadequate access to quality healthcare services and high costs of effective therapies like hydroxyurea and stem-cell transplantation exacerbate this vulnerability. Consequently, this increases the risk of dying from infectious diseases, underscoring the necessity of investigating infectious pathogens as a key contributor to children under 60 months with SCD. In 2021, Sub-Saharan Africa (SSA) recorded the highest mortality cases directly linked to SCD, with approximately 30000 deaths, representing a 30.1% increase from an estimated 2.3\u0026nbsp;million in 2000 to 7.8\u0026nbsp;million in 2021 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In Kenya, about 20000 to 30000 babies are born with sickle cell disease per year, and approximately 50% to 80% of them die before their fifth birthday (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The high mortality of SCD infants in Kenya is mainly attributed to inadequate healthcare facilities, inappropriate use of penicillin prophylaxis, and unavailability of vaccines in some medical centres (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Infectious pathogens associated with stillbirths and under-five deaths are preventable, yet the causes are poorly examined, hindering the efficient evaluation of this outcome, especially in SCD-related cases (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Roughly 17 per cent of children in the lake region have sickle cell traits, and 0.6 per cent of them have sickle cell disease, amounting to 0.9 per cent of the overall prevalence of SCD across the nation (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite a worldwide improvement in child mortality, inconsistencies persist across different regions. In low- and middle-income countries (LMICs), children aged below five are predisposed to mortality and morbidity due to malnutrition, underlying conditions like HIV, SCD and diabetes, inadequate access to quality healthcare, and poor sanitation. Infants born with SCD in low-resource countries have a higher risk of death before seeing their fifth birthday (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Existing studies have primarily focused on general infectious pathogens in children, but few have examined the specific contribution of sickle cell conditions to deaths from infections. Lack of efficient mortality surveillance has led to underestimation and misinterpretation of infectious causes of death in existing studies.\u003c/p\u003e \u003cp\u003eThis sub-study, nested within the Kenya Child Health and Mortality Prevention Surveillance (CHAMPS) network, aimed to characterise infectious pathogens associated with stillbirth and under-five mortality and to examine the effect of sickle cell disease condition (SCD/trait).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study was a retrospective cross-sectional study utilising mortality surveillance data from the Kenya Child Health and Mortality Prevention Surveillance (CHAMPS) network. Kenya CHAMPS leverages two existing Health and Demographic Surveillance Systems (HDSS) in western Kenya: Karemo HDSS (Siaya County) and Manyatta HDSS (Kisumu County) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The catchment is characterised by high under-five mortality of 54 deaths per live birth (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), a substantial infectious-disease burden of 59.9% (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and a high prevalence of sickle cell disease (SCD) of 0.6% (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The study included all stillbirths and deaths among children aged\u0026thinsp;\u0026le;\u0026thinsp;60 months enrolled in CHAMPS between May 2017 and December 2024, following the CHAMPS protocol and methods (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.champshealth.org/\u003c/span\u003e\u003cspan address=\"https://www.champshealth.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Mortality events were identified through community and facility notifications within the surveillance areas.\u003c/p\u003e \u003cp\u003eThe study included deceased children under 60 months and stillbirths who met specific requirements as captured in the CHAMPS Protocol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.champshealth.org/\u003c/span\u003e\u003cspan address=\"https://www.champshealth.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEligible decedents were those for whom the family consented, and post-mortem biological specimens were collected within 24 hours for pathogen testing, and for whom SCD status could be confirmed through laboratory testing. Stillbirths were defined as foetal deaths occurring at \u0026ge;\u0026thinsp;28 weeks of gestation, or with birthweight\u0026thinsp;\u0026ge;\u0026thinsp;1000 g, or crown-heel length\u0026thinsp;\u0026gt;\u0026thinsp;35 cm, with no sign of life, consistent with CHAMPS protocol. Enrolment required informed consent from a parent or guardian and availability of suitable post-mortem specimens for pathogen identification and SCD confirmation.\u003c/p\u003e \u003cp\u003eHowever, exclusion criteria were also based on CHAMPS Protocol guidelines (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). CHAMPS protocol excludes cases where consent was not obtained; bodies had been cremated or embalmed before sampling; or the death was subject to medicolegal investigation or regulatory procedures that precluded post-mortem sampling (Salzberg et al., 2019; Taylor et al., 2020). These criteria were applied to preserve ethical standards, data integrity, and the reliability of laboratory analyses.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Methods\u003c/h2\u003e \u003cp\u003eData collection consisted of both clinical and laboratory procedures according to the CHAMPS Protocol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.champshealth.org/\u003c/span\u003e\u003cspan address=\"https://www.champshealth.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Clinical data such as demographic information, medical history, healthcare access patterns and co-morbidities were abstracted from medical records and verbal autopsy forms. Laboratory information was acquired through investigations of post-mortem biological specimens, including tissue and non-tissue sampling. These samples were processed to identify infectious pathogens using Multiplex PCR and microbiological procedures. In addition, sickle cell status was determined using the point-of-care GazelleTM Hb Variant device (Hemex Health) and confirmed by HPLC and by pathological diagnosis through the Determination of Cause of Death (DeCoDe) Panel. Data entry was performed using a secure KoBoToolbox and Research Electronic Data Capture (REDCap) software, which included built-in validation checks to minimise errors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eThe samples were collected in a sterile environment using the standard Minimal Invasive Tissue Sampling (MITS) kits within 24 hours, which consisted of biopsy needles (14 G-18 G), forceps, sterile gloves, scalpels and blood culture bottles as per CHAMPS methods (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The intended puncture sites of the body were externally disinfected with povidone-iodine. The blood samples were collected through percutaneous puncture of the heart and key vessels using a sterile syringe. Ten millilitres of blood were collected in paediatric blood culture bottles and EDTA tubes for microbiological examinations and TAC analysis (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Cerebrospinal fluid (CSF) was collected through lumbar puncture between the L3 and L5 vertebrae, providing 5 ml of fluid into sterile tubes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Tissue samples from the lungs, liver and brain were collected by penetrating biopsy needles through anatomic structures: the right upper quadrants for the liver, the left subcostal margins for the spleen, and the mid-clavicular line for the lungs. Brain tissues were collected in addition to nasopharyngeal and rectal swabs to investigate respiratory and gastrointestinal pathogens.\u003c/p\u003e\n\u003ch3\u003eLaboratory Procedures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMultiplex PCR\u003c/h2\u003e \u003cp\u003eThe study used TAC testing on the ViiA7 and Quant-Studio 7 (QS7) Real-Time PCR (qPCR) machines to identify various pathogen types from post-mortem samples. TAC is a multi-pathogen identification system comprising a 384-well array vessel used to amplify nucleic acid based on TaqMan real-time PCR technology (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). TAC wells contained dried primers and hydrolysis probes for the detection of targeted infectious pathogens or control targets. The amplification levels were corrected to the fluorescence data generated and measured using the QS7 Flex qPCR instrument, which indicated the presence of targeted nucleic acid in the sample (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The process for identification started with total nucleic acid extraction and was followed by TAC testing.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTotal nucleic acid (TNA) Extraction\u003c/h3\u003e\n\u003cp\u003eNucleic acid was extracted from post-mortem samples, including blood, lung tissues, brain tissues, cerebrospinal fluids (CSF), rectal swabs, oropharyngeal/ nasopharyngeal (OP/NP) swabs, using a standardised procedure to maximise yield and maintain the integrity across different samples. CSF and blood, Anticoagulant (EDTA) tubes were used to collect whole blood, and 400 \u0026micro;l was used in the extraction. 1 ml of phosphate-buffered saline (PBS) was used to suspend the rectal swab and vortexed; 400 \u0026micro;l of the specimen was utilised (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Respiratory swabs (OP/NP) were combined in universal transport medium, and 350 \u0026micro;l of the resultant specimen was used. 1 ml of bacteria lysis buffer (BLB) was used to soak lung tissues and then homogenised, maintaining a tissue to buffer ratio of 1:1. Infectious pathogens were inactivated in each sample by adding BLB, achieving a 1:1 ratio of buffer to specimen volume.\u003c/p\u003e \u003cp\u003eFurthermore, RNases and Dnases were inactivated by adding 25 mg/ml Protease K at a 10% volume ratio. Mechanical lysis steps differed based on the sample type: CSF, blood, and rectal swabs were disrupted by 0.5 mm glass beads to achieve a maximum disruption of difficult microbes to lyse; tissue homogenisation was done on lung tissues using 7 mm stainless steel beads. Mechanical lysis was not necessary for respiratory swabs. Thereafter, in the inactivation and lysis steps, a 700 \u0026micro;L volume of each prepared sample was collected for further processing. TNA was then extracted using an automated machine that ensured high-quality TNA suitable for subsequent TAC testing.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTAC Testing on Multiplex PCR\u003c/h2\u003e \u003cp\u003eThe study utilised CHAMPS customised TAC plates organised into four unique designs that consisted of under-five assays; CSF/blood tier 1 and tier 2 designs. The samples were tested on 4 sets of TACS that included enteric, CSF/blood tier 1, respiratory, and CSF/blood tier 2, which accommodated six test samples and two controls in every run to enhance data integrity and quality (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter the extraction of TNA, a reaction mixture was prepared by thawing qScript XLT 1-Step RT- qPCR Tough Mix (enzyme mix) and kept at 4\u0026deg;C. No template controls (NTC) containing sterile nuclease-free water and positive controls (PC), consisting of synthetic nucleic acid templates, were set up. The reaction mixes were placed in a designated sterile area by combining 10 \u0026micro;l of extracted TNA and enzyme mix per run. The study used aerosol-resistant tips to pipette 100 \u0026micro;l of the mixture for each specimen and control, respectively, into TAC ports, preventing cross-contamination and air bubbles.\u003c/p\u003e \u003cp\u003eThe TAC was then centrifuged twice at 1200 revolutions per minute for a minute each to enhance the even distribution of the mixture across the wells. The TAC was then trimmed off the fill reservoirs after being sealed with a mechanical sealer. The sealed cards were then inserted into the ViiA7 qPCR instrument for running. The setup of each run was based on template conditions appropriate for each TAC design. The run included amplification cycles, reverse transcription steps, and polymerase activation, adding up to 45 cycles. The amplification data were extracted and analysed using the QS qPCR Software v1.2, CDC. The threshold parameters were reviewed manually to distinguish between the real signal and background noise. A specimen was considered positive for the targeted infectious pathogen if a clear application curve crossed the expected cycle threshold (Ct) value between Ct 28\u0026ndash;40, based on the target pathogen. Internal quality control of amplification, such as MS2 bacteriophage for stool samples and Rnase P for human samples, were examined for each specimen to ensure the absence of PCR inhibitors and nucleic acid integrity. Based on the amplification curves, the outputs were classified as negative, indeterminate or positive, and those runs that failed internal quality checks, like amplifications in negative control and unobservable amplification from positive controls, were discarded and repeated.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBacterial Culture\u003c/h3\u003e\n\u003cp\u003eThis study used two culture methods: an automated blood culture system using the BD Bactec FX40 Machine and a conventional microbial culture on culture media as per CHAMPS methods (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.champshealth.org/\u003c/span\u003e\u003cspan address=\"https://www.champshealth.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAutomated Blood Culture\u003c/h3\u003e\n\u003cp\u003eBlood collected in paediatric blood culture bottles from the field was immediately transported to the laboratory for incubation. These bottles contained media that supported the growth of a wide range of microorganisms, including both aerobic and anaerobic bacteria. The bottles were placed in the BD Bactec FX40 system, which was incubated and continuously monitored for microbial growth. The system uses fluorescence-based technology to detect changes in the carbon dioxide levels produced by microbial metabolism, indicating the presence of microorganisms (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The system detected most bacteraemia cases within a 5- to 7-day incubation period.\u003c/p\u003e \u003cp\u003eWhen microbial growth was detected, the system flagged the bottle as positive. The time to positivity (TTP) varied depending on the organism and the blood volume used. The system provided rapid detection, which was crucial for the timely diagnosis and further examination of bloodstream infections(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Positive cultures were subjected to further investigations, such as Gram staining and subculturing, to identify the specific microorganisms involved. This step was essential for guiding the appropriate antimicrobial panel (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBacterial Media Culture\u003c/h2\u003e \u003cp\u003eA positive blood culture from the Bactec machine was immediately processed for subculturing. The rubber septum of the bottles was aseptically disinfected using 70% Isopropyl alcohol. A sterile syringe and needle were then used to aspirate 5 ml of the blood broth mixture, which was inoculated on three primary media: Blood agar (BA), Chocolate agar (CHOC), and MacConkey agar (MAC). Blood agar was used to support a wide range of gram-negative microbes, and chocolate agar was incubated under 5\u0026ndash;10% carbon dioxide conditions, which facilitated the growth of fastidious microorganisms like \u003cem\u003eHaemophilus influenzae\u003c/em\u003e and Neisseria species. MacConkey agar was used to isolate gram-negative enteric bacteria and to differentiate non-lactose fermenters from lactose fermenters. The inoculated Petri dishes were incubated at 37\u0026deg;c with BA and MAC maintained at aerobic conditions, and CHOC incubated in a carbon dioxide-enriched environment. Plates were examined after 18\u0026ndash;24 hours, with the second inspection at 48 hours of incubation to capture any slow-growing pathogens. Bacterial colonies were identified based on morphological features on the media plate and Gram-staining results.\u003c/p\u003e \u003cp\u003eFor CSF specimens collected through MITS, 0.5 ml was inoculated directly onto BA, MAC and CHOC plates after reception in the laboratory. The same incubation protocol was followed as for blood culture, with CHOC plates maintained in a carbon dioxide environment. In addition to culture, a direct Gram stain technique was done on CSF to provide rapid preliminary results on morphology, presence and Gram stain status of any microbe. Incubated plates were examined after 24 and 48 hours. Isolated microbes were identified using catalase and oxidase testing (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSickle Cell Testing\u003c/h2\u003e \u003cp\u003eThis study used the point-of-care Gazelle\u0026trade; Hb Variant device (Hemex Health) and the HPLC (Bio-Rad D-10) to describe the haemoglobin variants of all the participants\u0026rsquo; samples. A standardised procedure was followed as per the manufacturer\u0026rsquo;s instructions to ensure reproducibility and reliability of results. The blood samples collected during the MITS procedure were loaded into the Gazelle disposable cartridge. Each cartridge was prefilled with a separation buffer, and a 20 \u0026micro;l blood sample was carefully loaded into the application area. The cartridge was then loaded into a Gazelle Hb Variant testing machine, a portable microchip-based electrophoresis platform. The machine separated the haemoglobin variants in the applied blood samples on cellulose acetate paper housed in the cartridge. The devices relied on the haemoglobin electrophoresis principle, where Hb variants comprising of Haemoglobin Sickle C (Hb SC), haemoglobin S (Hb S), haemoglobin A (Hb A), haemoglobin A2 (Hb A2), haemoglobin F (Hb F), and haemoglobin E (Hb E) have distinguished net negative charge in alkaline solution (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This difference in negative charge allowed them to move across the paper at different speeds due to the applied voltage, separating Hb variants into visible bands on the paper. In approximately 8 minutes, the gazelle gave results that were visually represented as bands and quantitatively reported as the relative percentage of each haemoglobin type present.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData Processing and Analysis\u003c/h2\u003e \u003cp\u003eData collection and organisation were conducted using the KoBo Toolbox and Research Electronic Data Capture (REDCap) software, which provided reliability and security for managing field and clinical information. The data collected was cleaned and securely stored. Data system access was limited to a few authorised research personnel by password-supported accounts, and physical access to study material was restricted. Personal study identifiers were used to maintain participant confidentiality.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using R programming software (version 4.5.1). Descriptive statistics were summarised in demographic characteristics such as age and gender, types of infectious pathogens identified, and mortality trends. A comparative analysis assessed the differences and burdens of infectious pathogens between mortality cases with SCD/trait and those without SCD/trait. Statistical tests such as Pearson\u0026rsquo;s chi-squared and Fisher\u0026rsquo;s exact tests were used to test for independence of association between SCD status and the infectious pathogen identified, with a significance level maintained at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe findings are from 1332 cases, of which 387 were stillbirths and 945 were from under-five cases. The overall prevalence of IP in the study population was 37.5% (95% CI: 34.9%, 40.1%), in the stillbirth cases was 4.1% (95% CI: 2.6%, 6.6%), and in under-five death was 51.1% (95% CI: 47.9%, 54.3%). Of the 1332 cases, 46 (3.5%) had sickle cell disease, and 256 (19.2%) had sickle cell trait, totalling 302 (22.7%) with SCD/trait. The prevalence of IP in sickle cell disease was 60.9% (95% CI: 46.3%, 73.9%) and in sickle cell traits was 25.8% (95% CI: 20.6%, 31.6%), with top pathogens being \u003cem\u003ePlasmodium falciparum\u003c/em\u003e 11cases (39.3%), 12 cases (18.2%), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e 2 cases (7.1%), 25 cases (37.9%), \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, 6 cases (21.4%), 6 cases (9.1%) respectively. Findings on sex-specific analysis showed a balanced distribution, with females accounting for 46.5% of deaths among SCD/trait compared to 45.5% among non-SCD/trait, and males comprising 53.5% SCD/trait vs 54.5% non-SCD/trait deaths. The observable differences were not statistically significant (p\u0026thinsp;=\u0026thinsp;0.8) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe analysis of deaths stratified by SCD status showed notable variations across age categories (p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among non-SCD/trait cases, stillbirths accounted for 29.8% of deaths as compared to 26.5% among those with SCD/traits. Contrary to deaths within the first 24 hours of life, which were proportionally higher among SCD/trait group (21.5%) than in non-SCD/trait cases (12.0%). 1 day to less than 7 days old comprised 9.0% of non-SCD/trait deaths and 10.6% of SCD/trait deaths, while 7 days to 27 days old were relatively comparable (4.7% vs. 5.3%). In 28 days to less than 12 months old, 23.5% were accounted for by non-SCD/traits and 17.5% SCD/trait deaths. In 12 months to less than 60 months old categories, there were relatively higher non-SCD/trait death rates (21.0%) compared to SCD/trait deaths 18.5%. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Stillbirths and Deaths Enrolled in the Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon SCD/trait,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSCD/trait,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,332\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u0026thinsp;=\u0026thinsp;1030 (77.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN (%)\u0026thinsp;=\u0026thinsp;302 (22.7%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSickle Cell Disease type, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon SCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,030 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSickle Cell disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSickle Cell trait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e256 (84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e387 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e307 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath in the first 24 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 6 days old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 to 27 days old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28 days old to \u0026lt;\u0026thinsp;12 months old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 months old to \u0026lt;\u0026thinsp;60 months old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatchment Area, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaremo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e679 (51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e528 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e151 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManyatta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e653 (49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e502 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e151 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of Death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Facility deaths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,004 (75.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e760 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e244 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e603 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e463 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e140 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e716 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e555 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e161 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;\u003cem\u003ePearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInfectious Pathogens Identified\u003c/h2\u003e \u003cp\u003eFrom the 1332 cases enrolled in the study, 499 had an infectious pathogen identified in their post-mortem samples, with a prevalence of 37.5% (95% CI: 34.9%, 40.1%). The distribution of infectious pathogens identified was as follows: bacteria accounted for the highest number of infections among the total cases, with 305,61.8% (95% CI 56.7%, 65.4%); parasites, 171, 34.3% (95% CI 30.1%, 38.6%); viruses, 118, 23.6% (95% CI 20.0%, 27,7%); and fungi, 9, 1.8% (95% CI 0.9%, 3.52%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among 305 cases with SCD/trait enrolled in the study, pathogens were identified in 94 cases (31.1% [95% CI 26.0%, 36.7%]). Bacterial infections were the most prevalent, accounting for 60 out of 94 cases (63.8%). Viral infections were identified in 25 cases (26.6%), parasites in 23 cases (24.5%), and fungal pathogens were not identified in this group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of Infectious Pathogens in the Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInfectious Pathogen (IP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon SCD/trait,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSCD/trait,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,332\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u0026thinsp;=\u0026thinsp;1030 (77.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN (%)\u0026thinsp;=\u0026thinsp;302 (22.7%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious Conditions, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e823 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e656 (63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e167 (55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-infectious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e509 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e374 (36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious Pathogen Identified, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e499 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e405 (39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e248 (61.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58 (61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVirus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParasite, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;\u003cem\u003ePearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn children without SCD/trait, bacterial pathogens were also the most common, accounting for 248 of 405 cases (61.4%). Parasitic infections were identified in 148 cases (36.6%), viral infections in 88 cases (21.8%), and fungal pathogens in 9 cases (2.2%). The parasitic pathogens were less prevalent among SCD/trait cases (24.5%) than among non-SCD/trait cases (36.6%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMost Prevalent Pathogens identified from the Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInfectious Pathogens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo SCD/trait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSCD/trait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eParasite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlasmodium falciparum\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAscaris lumbricoides\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlasmodium malariae\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003e\u003cb\u003eBacteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus spp\u003c/em\u003e., n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHaemophilus influenzae\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pyogenes\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus agalactiae\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAeromonas spp\u003c/em\u003e., n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalmonella spp\u003c/em\u003e., n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli/shigella spp\u003c/em\u003e., n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCitrobacter freundi\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella spp\u003c/em\u003e., n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eVirus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIV, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCytomegalovirus\u003c/em\u003e (CMV), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAdenovirus\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRespiratory syncytial virus\u003c/em\u003e (RSV), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRotavirus a\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRotavirus non-typable\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHuman metapneumovirus\u003c/em\u003e (HMPV), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNorovirus gii\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNorovirus gi\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParainfluenza virus type 1\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParainfluenza virus type 3\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParvovirus b19\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFungi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePneumocystis jirovecii\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYeast\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYeast fungaemia\u003c/em\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;2-sample test for equality of proportions with continuity correction\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBacterial Pathogens\u003c/h2\u003e \u003cp\u003eThe most commonly identified bacterial agent was \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, detected in 130 cases (26.1%) overall. Of these, 103 cases (25.5%) were isolated from non-SCD/trait deaths, while 27 cases (28.7%) were from participants with SCD/trait, showing a slightly higher proportion than in non-SCD/trait cases (p\u0026thinsp;=\u0026thinsp;0.6) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e was the second most prevalent identified bacterium, detected in 61 cases (12.2%). Among these, 49 cases (12.1%) were detected in non-SCD/trait participants and 12 cases (12.8%) in SCD/trait. \u003cem\u003eEscherichia coli\u003c/em\u003e was identified in 56 cases (11.2%), with a markedly higher prevalence in non-SCD/trait participants (50 cases, 12.4%) compared to the SCD/trait group (6 cases, 6.4%). \u003cem\u003eStaphylococcus aureus\u003c/em\u003e was detected in 30 cases (6.0%), consisting of 26 non-SCD/trait cases (6.4%) and 4 SCD/trait cases (4.3%). For \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e, 9 cases (2.2%) were in non-SCD/traits groups and 2 cases (2.1%) in SCD/trait groups. \u003cem\u003eAcinetobacter baumanii\u003c/em\u003e was noticed in 5 non-SCD/trait cases (1.2%) and 3 SCD/trait cases (3.2%), showing a notably higher proportion among SCD/trait participants (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFungal Pathogens\u003c/h2\u003e \u003cp\u003eFungal pathogens were relatively uncommon, accounting for 9 (1.1%) cases of the total infection-related deaths. They were only identified in non-SCD/trait participants, and no case was detected in SCD/trait. \u003cem\u003ePneumocystis jirovecii\u003c/em\u003e was detected in 1.2% (6/499) of the overall cohort, with all cases occurring in individuals without SCD/trait (1.5%, 6/405). In contrast, \u003cem\u003eyeast fungaemia\u003c/em\u003e and \u003cem\u003eCandida albicans\u003c/em\u003e were detected in 1 case (0.2%) each. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eViral Pathogens\u003c/h2\u003e \u003cp\u003eAnalysis of viral pathogens detected in the study population showed a distinct pattern based on sickle cell status (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The prevalence of major viruses, including HIV (8.7% vs 8.5%) and \u003cem\u003eCytomegalovirus\u003c/em\u003e (7.4% vs 7.4%), was comparable between individuals without and with SCD/traits. In contrast, significant differences were observed for specific respiratory and gastrointestinal viruses. The SCD/trait group exhibited markedly higher detection rates for \u003cem\u003eHuman Metapneumovirus\u003c/em\u003e (HMPV) (0.2% vs. 2.1%) and \u003cem\u003eRespiratory Syncytial Virus\u003c/em\u003e (RSV) (1.5% vs. 3.2%). A similar trend was seen for \u003cem\u003eNorovirus GI\u003c/em\u003e, which was detected exclusively in the SCD/trait group (2.1%), and Rotavirus A (1.0% vs. 2.1%). Other viruses, including \u003cem\u003eAdenovirus\u003c/em\u003e and \u003cem\u003eParainfluenza\u003c/em\u003e types, showed minimal variation between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eParasitic Pathogens\u003c/h2\u003e \u003cp\u003eThe predominant parasitic agent detected in this study was \u003cem\u003ePlasmodium falciparum\u003c/em\u003e, the causative pathogen of the most severe form of malaria. It was identified in 33.7% (168/499) of the overall cohort, making it the most prevalent pathogen identified across all infectious agent classifications. Among non-SCD/trait cases, \u003cem\u003ePlasmodium falciparum\u003c/em\u003e was detected in 35.9% (145/405), while 24.5% (23/94) were in the SCD/trait group. The findings highlight that \u003cem\u003ePlasmodium falciparum\u003c/em\u003e infection was slightly less common in the SCD/trait decedents in comparison to the burden in the non-SCD/trait group, though it still presented a significant burden in both groups (p\u0026thinsp;=\u0026thinsp;0.048) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther parasitic infections were infrequently detected. \u003cem\u003eAscaris lumbricoides\u003c/em\u003e was identified in two individuals (0.4%), both in the group without SCD/trait. \u003cem\u003ePlasmodium malariae\u003c/em\u003e was detected in one individual (0.2%), also in the group without SCD/trait.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of Infectious Pathogens by Age Group among SCD/trait Cases\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides details on infectious pathogens across different age groups among SCD/trait cases. Among viral agents, CMV and HIV were most frequently identified, especially among infants and children. RSV and \u003cem\u003eAdenoviruses\u003c/em\u003e were also prevalent, with moderate representation in infants and neonates. In addition, respiratory viruses such as \u003cem\u003eInfluenza A\u003c/em\u003e, \u003cem\u003eparainfluenza virus type 3\u003c/em\u003e, and \u003cem\u003eRotavirus A\u003c/em\u003e were dominantly detected in infant groups. For parasitic infections, \u003cem\u003ePlasmodium falciparum, the\u003c/em\u003e causative agent for malaria, was highly dominant, with the majority of deaths occurring in child and infants\u0026rsquo; categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, in bacterial infections, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e was the most predominant pathogen across all age groups, with a high burden observed among infants, followed by neonates and child categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e was the second most prevalent bacterium detected, dominantly causing deaths in infants and children. \u003cem\u003eStaphylococcus aureus, Haemophilus influenzae and Acinetobacter baumannii\u003c/em\u003e also appeared prominently, particularly in neonates and infants. \u003cem\u003eEscherichia coli\u003c/em\u003e was detected in all the categories, including child, neonates, infants and stillbirths. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study aimed to characterise infectious pathogens associated with stillbirth and under-five deaths among decedents with SCD/trait in Western Kenya, and to compare their distribution with their counterparts without SCD/traits. The findings highlight infection as a significant contributor to adverse pregnancy outcomes and child deaths, with distinct infectious pathogen profiles observed among SCD/trait cases. Among the infectious agents detected, \u003cem\u003ePlasmodium falciparum\u003c/em\u003e was the most prevalent overall, accounting for 33.7% (168 cases). This insight shows a significant burden of malaria within the study setting. This underscores the continued public health concerns imposed by \u003cem\u003ePlasmodium falciparum\u003c/em\u003e in endemic regions, especially in association with neonatal and maternal complications leading to stillbirths and mortality of the under-fives (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBacterial agents were predominantly presented in the findings, with \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e 130 cases (26.1%), \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e 61 cases (12.2%), and \u003cem\u003eEscherichia coli\u003c/em\u003e 56 cases (11.2%) among the top three most prevalent bacterial pathogens in both groups with and without SCD/trait. These microbes are mainly associated with severe invasive infections like meningitis, pneumonia and sepsis, particularly in neonates and immunocompromised infants (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In addition, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, and \u003cem\u003eStreptococcus spp\u003c/em\u003e. further reflects the diversity of bacterial agents implicated in children's infections and stillbirths.\u003c/p\u003e \u003cp\u003eViral agents also contributed significantly, with \u003cem\u003eCytomegalovirus\u003c/em\u003e (CMV) amounting to 7.4% (37 cases), and HIV 8.6% (43 cases) being the most frequently identified. These infections are particularly linked to potential vertical transmission and chronic complications (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The insights detecting \u003cem\u003eAdenovirus\u003c/em\u003e in 18 cases (3.6%) and \u003cem\u003eRespiratory Syncytial Virus\u003c/em\u003e in 9 cases (1.8%) pointed out the role of respiratory viral infections in paediatric morbidity and mortality, especially in neonates and infants. Generally, the findings demonstrated a broad array of infectious pathogens contributing to stillbirths and deaths of children below 60 months, giving an overview of the complex interplay between host susceptibilities and environmental exposures.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInfectious Pathogens Distribution (SCD/trait vs. non-SCD/trait)\u003c/h2\u003e \u003cp\u003eThe study findings on the distribution of infectious agents among stillbirths and under-five mortality cases with SCD/trait and those without showed notable differences in prevalence patterns. reflecting the Among bacterial infections, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e was frequently detected in both groups, and was predominantly identified in the among SCD/trait cases (28.7%) compared to the non-SCD/trait group (25.5%); however, this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.6) This higher infection may be associated with impaired immune responses and frequent hospitalisation in SCD/trait patients (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Similarly, \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e revealed a slightly higher relative prevalence among SCD/trait cases (12.8%) than among those without SCD/trait (12.1%), but the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.8) While previous studies have reported increased susceptibility to certain bacterial infections among individuals with sickle cell disease, particularly pneumococcal infections related to functional asplenia, our findings did not demonstrate statistically significant differences in pathogen prevalence by sickle cell status (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Kenya, the National Guideline for the Prevention and Control of Common Childhood Illnesses stresses the importance of early diagnosis and management of febrile diseases in susceptible individuals, including SCD children (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). However, our study shows continued vulnerability among this group, highlighting a policy-practice gap, especially in Western Kenya. Strengthening routine screening for bacterial and parasitic infections in SCD clinics could reduce preventable mortalities.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePlasmodium falciparum\u003c/em\u003e was identified as the leading parasitic agent and overall, the most prevalent. The high burden is attributable to the parasite\u0026rsquo;s propensity to cause severe disease manifestations such as cerebral malaria and severe anaemia, both of which are associated with high case fatality rates in young children (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). \u003cem\u003ePlasmodium falciparum\u003c/em\u003e infections exhibit distinct characteristics in patients with sickle cell disease (SCD) and traits compared to those without. The study findings showed that children with SCD/traits were less likely to have malaria as a contributing factor to mortality compared to their counterparts without SCD/traits (P\u0026thinsp;=\u0026thinsp;0.048). These findings support the existing knowledge that SCD/trait confers varying degrees of protection against malaria, primarily through mechanisms that affect parasite density and cytoadherence (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This protection is attributed to impaired cytoadherence of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e-infected erythrocytes containing sickle haemoglobin, reducing parasite sequestration in microvascular tissues (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Additionally, existing studies have indicated that HbAS is associated with a reduced risk of symptomatic malaria, although this protective effect can vary with age (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe World Health Organisation (WHO) recommends an integrated approach to malaria control, focusing on early diagnosis, effective treatment with artemisinin-based combination therapies, and preventive measures that include insecticide-treated nets and intermittent preventive treatment to pregnant women (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). For high-risk groups such as children living with SCD, WHO guidelines indicate the need for intensified surveillance and tailored interventions, including regular screening and timely management of febrile illness (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The observed lower prevalence of severe malaria among SCD/trait patients aligns with existing studies for the partial protective effects of sickle haemoglobin (HbS), but it warrants the importance of maintaining comprehensive malaria prevention strategies in this vulnerable population. This ensures both effective prevention and management of malaria-related morbidity and mortality in vulnerable individuals.\u003c/p\u003e \u003cp\u003eThis study reveals a distinct pattern of viral pathogen detection among individuals with SCD/trait compared to those without. While the prevalence of major viruses like HIV and CMV was nearly identical between groups, a markedly higher detection rate of specific respiratory and gastrointestinal viruses, namely \u003cem\u003eHuman Metapneumovirus\u003c/em\u003e (HMPV), \u003cem\u003eRespiratory Syncytial Virus\u003c/em\u003e (RSV), \u003cem\u003eNorovirus GI\u003c/em\u003e, and \u003cem\u003eRotavirus A\u003c/em\u003e, in the SCD/trait cohort. These findings show that an immunologic landscape of sickle cell conditions may confer a unique susceptibility to particular acute viral infections, even as it may leave responses to other persistent viruses unaffected. This slightly higher prevalence of HIV in the SCD/trait group may be due to their increased exposure to parenteral interventions such as blood transfusions and an underlying immunocompromised state. \u003cem\u003eRotavirus A\u003c/em\u003e accounted for 1.2% of cases, with a slightly higher prevalence in SCD/traits groups (2.1%) compared to non-SCD/traits groups (1.0%), which may indicate an increased predisposition due to impaired mucosal immunity. Notably, there was a higher prevalence of RSV infections in SCD/trait cases (3.2%) compared to the non-SCD/trait group (1.8%). This insight supports previous studies on the vulnerability of SCD patients to respiratory viral infections, due to chronic anaemia and functional asplenia (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDistribution of Infectious Pathogens by Age Group among SCD/trait Cases\u003c/h2\u003e \u003cp\u003eThe findings from a comprehensive analysis of the distribution of infectious pathogens by age among SCD/trait cases showed a diverse microbial landscape that contributes to stillbirth and mortality of children under 60 months. \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e and \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, microbes specifically targeted by penicillin prophylaxis and Hib vaccines, were more prominent, particularly in the infant and child categories. These insights reinforce current pieces of evidence that individuals with SCD/trait have a high risk of invasive bacterial infections, particularly in early life (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These results are relevant in LMICs, especially in Kenya, where routine childhood immunisation programs, including the pneumococcal conjugate and Hib vaccines, are essential components of the Expanded Program of Immunisation (EPI). The continuous detection of these infectious pathogens may be attributed to suboptimal coverage of the vaccine, waning immunity, and the presence of non-vaccine serotypes in the population.\u003c/p\u003e \u003cp\u003e \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e was the most prevalent bacterium detected across all age groups, including a notable number of infant and neonate deaths. This may be due to maternal colonisation and subsequent vertical transmission, and nosocomial acquisition in the perinatal care setting. The high burden of \u003cem\u003eAcinetobacter baumanii\u003c/em\u003e and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e among SCD/traits, both known to be linked with hospital-acquired infections, especially in neonatal units, further supports the contribution of hospital-acquired infections in the neonatal deaths (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The detection of \u003cem\u003ePlasmodium falciparum\u003c/em\u003e across infants, children and neonates highlights the importance of malaria prevention strategies in SCD/trait individuals. Malaria is a known trigger for severe complications in SCD/trait individuals, including haemolysis, anaemia and increased mortality risks (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This data supports existing studies indicating that SCD children in malaria-endemic regions need targeted chemoprophylaxis and urgent treatment protocols (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Furthermore, CMV, HIV, and RSV were predominantly detected in children's and infants\u0026rsquo; samples. This dominance of CMV in infants may be linked to either congenital infection or reactivation in immunocompromised hosts. RSV was among the top detected viral pathogens in children and infants\u0026rsquo; groups; its death-related cases are associated with its role in severe lower respiratory tract infections in early childhood.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe findings of the study showed that infectious pathogens remain a significant contributor to both stillbirths and deaths of children aged 1\u0026ndash;59 months, particularly among vulnerable groups like those with SCD/traits. While many of the detected pathogens, \u003cem\u003ePlasmodium falciparum, Klebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, align with regional epidemiological trends, a few notable patterns emerged.\u003c/p\u003e \u003cp\u003eThe study demonstrates that infectious pathogens continue to drive preventable stillbirths and under-five deaths, with sickle cell disease and trait further increasing vulnerability. Recommendations include rigorous adherence to WHO management guidelines for pregnant women with SCD or trait, enhanced screening and treatment of infectious agents, and systematic post-mortem investigation of deaths to clarify causality. Addressing these combined risks is essential to reducing mortality and improving maternal and child health outcomes in Kenya and similar LMIC contexts.\u003c/p\u003e \u003cp\u003eFurthermore, this study provides new insights into the pathogen-specific vulnerability of SCD/traits individuals, highlighting the complex interaction between host factors and infectious pathogens. By identifying the most prevalent pathogens associated with foetal outcomes in this population, the study offers practical knowledge to inform clinical prioritisation, guide critical treatment choices and shape targeted prevention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHAMPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChild Health and Mortality Prevention Surveillance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKenya Medical Research Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMITS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinimal Invasive Tissue Sampling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNo Template Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSickle Cell Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSickle Cell Trait\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTaqMan Array Cards\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversity of Cape Coast\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL AND CONSENT FROM PARTICIPANTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Scientific Ethical Review Unit of Kenya Medical Research Institute (KEMRI), with registration number SERU 3308. Written informed consent was sought from the parents or next of kin of the deceased participants before any post-mortem procedure was done through MITS. Clinical trial number: Not Applicable \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional materials are available on the CHAMPS website (https://www.champshealth.org/) and upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no competing interests \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Gates Foundation and Partnering for Health Professional Training in African Universities (P4HPT)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR\u0026rsquo;S CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMOW: Conceptualisation, Methodology, Investigation, Data curation, Formal Analysis, Writing-original draft, Writing-review and editing, VS: Investigation, Writing-review and editing, FO: Data curation, Formal Analysis, Writing-review and editing, EK: Data curation, Formal Analysis, Writing-review and editing, MN: Investigation, Writing-review and editing, KO: Methodology, Investigation, Data curation, Writing-review and editing, HM: Investigation, Formal Analysis, Writing-review and editing, RO: Conceptualisation, Methodology, Investigation, Formal Analysis, Supervision, Writing-review and editing, VA: Conceptualisation, Methodology, Investigation, Data curation, Formal Analysis, Supervision, Writing-review and editing, DOY: Conceptualisation, Methodology, Investigation, Data curation, Formal Analysis, Supervision, Writing-review and editing, All the authors read and approved for the final manuscript. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the Kenya Medical Research Institute CHAMPS Staff for their support in this study, from data and sample collection, processing and analysis. We also acknowledge the families that participated in this study, making data available for analysis. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBassat Q, Ogbuanu IU, Samura S, Kaluma E, Sow S, Keita AM, et al. Causes of Death Among Infants and Children in the Child Health and Mortality Prevention Surveillance (CHAMPS) Network. JAMA Netw Open. 2023;6(7):e2322494\u0026ndash;e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation WH. World health statistics 2024: monitoring health for the SDGs. sustainable development goals: World Health Organisation; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBovu A, Yirga A, Melesse S, Ayele D. The Impact of Socio-Economic, Demographic, and Geographic Factors on the Mortality of Children Under the Age of Five in Kenya, 2022. Iran J Public Health. 2024;53(11):2462\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBassat Q, Blau DM, Ogbuanu IU, Samura S, Kaluma E, Bassey IA, et al. Causes of Death Among Infants and Children in the Child Health and Mortality Prevention Surveillance (CHAMPS) Network. JAMA Netw Open. 2023;6(7):e2322494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimani RW, Gatimu SM. Child mortality in Africa and south Asia: a multidimensional research and policy framework. Lancet Global Health. 2022;10(5):e594\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRineer S, Walsh PS, Smart LR, Harun N, Schnadower D, Lipshaw MJ. Risk of Bacteremia in Febrile Children and Young Adults With Sickle Cell Disease in a Multicenter Emergency Department Cohort. JAMA Netw Open. 2023;6(6):e2318904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Vilchis Tella V, et al. 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Lancet Global Health. 2020;8(7):e909\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakislova N, Fernandes F, Lovane L, Jamisse L, Castillo P, Sanz A, et al. Standardization of Minimally Invasive Tissue Sampling Specimen Collection and Pathology Training for the Child Health and Mortality Prevention Surveillance Network. Clin Infect Dis. 2019;69(Supplement4):S302\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBassat Q, Blau DM, Ogbuanu IU, Samura S, Kaluma E, Bassey I-A, et al. Minimally Invasive Tissue Sampling as an Alternative to Complete Diagnostic Autopsies in the Context of Epidemic Outbreaks and Pandemics: The Example of Coronavirus Disease 2019 (COVID-19). Clin Infect Dis. 2021;73(Suppl5):S472\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgocho JS, Liu J, Kalengo NH, Kipengele AH, Maro A, Mujage B, et al. 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Subculturing and Gram staining of blood cultures flagged negative by the BACTEC\u0026trade; FX system: Optimizing the workflow for detection of Cryptococcus neoformans in clinical specimens. Front Microbiol. 2023;14:1113817.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYarbrough ML, Wallace MA, Burnham CD. Comparison of Microorganism Detection and Time to Positivity in Pediatric and Standard Media from Three Major Commercial Continuously Monitored Blood Culture Systems. J Clin Microbiol. 2021;59(7):e0042921.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaley E, Cockerill FR, Pesano RL, Festa RA, Luke N, Mathur M, et al. Pooled Antibiotic Susceptibility Testing Performs Within CLSI Standards for Validation When Measured Against Broth Microdilution and Disk Diffusion Antibiotic Susceptibility Testing of Cultured Isolates. Antibiotics. 2024;13(12):1214.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh N, Juneja R, Kant S, Seth T, Kaur R, Rai SK et al. Evaluation of Diagnostic Accuracy of Sickle SCAN\u0026trade;, and Gazelle\u0026trade; for Screening Hemoglobin S in india. Indian J Hematol Blood Transfus. 2025:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatapathy P, Khatib MN, Gaidhane S, Zahiruddin QS, Sharma RK, Rustagi S et al. Adverse pregnancy outcomes in maternal malarial infection: A systematic review and meta-analysis. New microbes and new infections. 2024:101474.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabe ZT, Yg T, Ak M, Db F, Ed N. Plasmodium falciparum neonatal malaria with atypical presentation: a case series from southwestern Ethiopia. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerani JR, Blau DM, Gurley ES, Akelo V, Assefa N, Baillie V, et al. Child deaths caused by \u0026lt;\u0026thinsp;em\u0026gt;Klebsiella pneumoniae\u0026thinsp;in sub-Saharan Africa and south Asia: a secondary analysis of Child Health and Mortality Prevention Surveillance (CHAMPS) data. Lancet Microbe. 2024;5(2):e131\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi D, Huang X, Rao H, Yu H, Long S, Li Y, et al. Klebsiella pneumoniae bacteremia mortality: a systematic review and meta-analysis. Front Cell Infect Microbiol. 2023;13:1157010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, Li L, Cheng P, Zhang H, Xu S. Clinical characteristics and mortality risk factors of premature infants with carbapenem-resistant Klebsiella pneumoniae bloodstream infection. Sci Rep. 2024;14(1):29486.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePontes KFM, Nardozza LMM, Peixoto AB, Werner H, Tonni G, Granese R, et al. Cytomegalovirus and pregnancy: a narrative review. J Clin Med. 2024;13(2):640.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta B, Gouveia MJ, Vale N. Oxidative Stress Induced by Antivirals: Implications for Adverse Outcomes During Pregnancy and in Newborns. Antioxidants. 2024;13(12):1518.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpoku-Asare B, Ntim OK, Awere-Duodu A, Donkor ES. Sickle Cell Disease and Antimicrobial Resistance: A Systematic Review and Meta-Analysis. Infect Disease Rep. 2025;17(2):32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoomer CA, Levin RA, Neri CM. Death from S. pneumoniae in HbSC Disease: Was Expanded Pneumococcal Vaccination Too Late? Hemoglobin. 2025:1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiambila W, Were F, Abuya T, Odwe G, Natecho A, Mungai S et al. Institutionalizing the Management of Sick Young Infants: Kenya\u0026rsquo;s Experience in Revising National Guidelines on Integrated Management of Newborn and Childhood Illnesses. Global Health: Science and Practice. 2023;11(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Fink G, Cohen J. Malaria Rapid Tests, Febrile Illness Management, and Child Mortality Across Sub-Saharan African Countries. JAMA. 2024;332(15):1270\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenrici RC, Sautter CL, Bond C, Opoka RO, Namazzi R, Datta D, et al. Decreased parasite burden and altered host response in children with sickle cell anemia and severe anemia with malaria. Blood Adv. 2021;5(22):4710\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZehner N, Adrama H, Kakuru A, Andra T, Kajubi R, Conrad M, et al. Age-Related Changes in Malaria Clinical Phenotypes During Infancy Are Modified by Sickle Cell Trait. Clin Infect Dis. 2021;73(10):1887\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation WH. World malaria report 2022. World Health Organization; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahtab S, Madewell ZJ, Baillie V, Dangor Z, Lala SG, Assefa N, et al. Etiologies and comorbidities of meningitis deaths in children under 5 years in high-mortality settings: Insights from the CHAMPS Network in the post-pneumococcal vaccine era. J Infect. 2024;89(6):106341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReddy K, Bekker A, Whitelaw AC, Esterhuizen TM, Dramowski A. A retrospective analysis of pathogen profile, antimicrobial resistance and mortality in neonatal hospital-acquired bloodstream infections from 2009\u0026ndash;2018 at Tygerberg Hospital, South Africa. PLoS ONE. 2021;16(1):e0245089.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJennings MR, Elhaissouni N, Colantuoni E, Prochaska EC, Johnson J, Xiao S, et al. Epidemiology and Mortality of Invasive Staphylococcus aureus Infections in Hospitalized Infants. JAMA Pediatr. 2025;179(7):747\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEleonore NLE, Cumber SN, Charlotte EE, Lucas EE, Edgar MML, Nkfusai CN, et al. Malaria in patients with sickle cell anaemia: burden, risk factors and outcome at the Laquintinie hospital, Cameroon. BMC Infect Dis. 2020;20:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor SM, Korwa S, Wu A, Green CL, Freedman B, Clapp S, et al. Monthly sulfadoxine/pyrimethamine-amodiaquine or dihydroartemisinin-piperaquine as malaria chemoprevention in young Kenyan children with sickle cell anemia: A randomized controlled trial. PLoS Med. 2022;19(10):e1004104.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Infectious Pathogens, Stillbirth, Under-Five mortality, Sickle Cell Disease, LMICs","lastPublishedDoi":"10.21203/rs.3.rs-8689461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8689461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eInfectious Pathogens (IP) remain a significant contributor to stillbirths and under-five deaths in resource-limited settings. Children with sickle cell disease (SCD) are susceptible due to weakened immunity, influencing infection outcomes. This sub-study, nested within the Kenya Child Health and Mortality Prevention Surveillance (CHAMPS) network, investigated infectious pathogens associated with stillbirths and under-five deaths in Western Kenya, as well as the role of the sickle cell condition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eMethods: \u003c/strong\u003eWe analysed stillbirths (n=387) and ≤ 5 child deaths (n=945) enrolled in CHAMPS in Karemo (Siaya) and Manyatta (Kisumu), both in western Kenya, between May 2017 and Dec 2024. Minimally invasive tissue sampling (MITS) specimens were processed on a TaqMan Array Card using the QuantStudio 7 real-time PCR (TAC qPCR) and cultured. All participants were screened for sickle cell status using the point-of-care Gazelle™ Hb Variant device, and confirmed by High Performance Liquid Chromatography (HPLC) and pathological diagnosis. Statistical analyses were performed using R programming software (version 4.5.1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: A total of 1,332 cases were investigated, 499, 37.5% (95% CI:34.9-40.1) had an infectious pathogen (IP), with lower prevalence among stillbirths (4.1%, 95%CI:2.6-6.6) and higher prevalence among under-five deaths (51.1%, 95%CI:47.9-54.3). SCD was identified in 46 cases (3.5%) and sickle cell trait in 256 cases (19.2%), for a total of 302 cases (22.7%) with SCD/trait. IP prevalence was higher in SCD (60.9%, 95%CI: 46.3-73.9) than in sickle cell trait (25.8%, 95%CI: 20.6-31.6). Among SCD and sickle cell trait cases, the most frequently identified pathogens were \u003cem\u003ePlasmodium falciparum\u003c/em\u003e (39.3% and 18.2%), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (7.1% and 37.9%), and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (21.4% and 9.1%), respectively. Overall, the leading pathogens associated with death in both SCD/trait and non-SCD/trait groups were \u003cem\u003eK. pneumoniae\u003c/em\u003e (28.7% vs 25.5%), \u003cem\u003eP. falciparum\u003c/em\u003e (24.5% vs 35.9%), \u003cem\u003eS. pneumoniae \u003c/em\u003e(12.8% vs 12.1%), HIV (8.5% vs 8.7%), \u003cem\u003ecytomegalovirus\u003c/em\u003e (7.4% vs 7.4%), and \u003cem\u003erespiratory syncytial virus\u003c/em\u003e (3.2% vs 1.5%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfectious pathogens contribute substantially to stillbirth and under-five mortality in Western Kenya, highlighting the need to strengthen infection prevention, diagnosis, and management during pregnancy and early childhood in line with WHO recommendations and targeted preventive care for vulnerable populations like those with SCD.\u003c/p\u003e","manuscriptTitle":"Infectious Pathogens Associated With Stillbirth and Under-five Mortality in Western Kenya and the Effect of Sickle Cell Condition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 17:16:10","doi":"10.21203/rs.3.rs-8689461/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-13T11:31:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T20:15:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59623408993415890102186776204424964890","date":"2026-03-12T16:54:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T16:11:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167728808904271380447361402208614664151","date":"2026-03-10T11:40:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255038560218757535906648704282203827070","date":"2026-03-07T18:43:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38385977778698498672083497263698849963","date":"2026-03-07T18:12:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122500508451162778119595080573885106765","date":"2026-03-07T05:23:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275728218220423399641804734519501235483","date":"2026-03-05T18:42:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29076640171366246405065059699087605021","date":"2026-03-05T12:36:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T11:48:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-20T16:07:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T07:55:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T07:50:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-01-24T23:59:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"473f9c1c-003e-4127-9eb3-84470dfbf8ca","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T14:11:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 17:16:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8689461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8689461","identity":"rs-8689461","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}