Healthcare-seeking behavior for respiratory illnesses in Kenya: implications for burden of disease estimation

Estimating Influenza Illnesses Averted by Year-Round and Seasonal Campaign Vaccination for Young Children, Kenya

In Kenya, influenza virus circulates year-round, raising questions about optimum strategies for vaccination. Given national interest in introducing influenza vaccination for young children 6-23 months of age, we modeled total influenza-associated illnesses (inclusive of hospitalizations, outpatient illnesses, and non‒medically attended illnesses) averted by multiple potential vaccination strategies: year-round versus seasonal-campaign vaccination, and vaccination starting in April (Southern Hemisphere influenza vaccine availability) versus October (Northern Hemisphere availability). We modeled average vaccine effectiveness of 50% and annual vaccination coverage of 60%. In the introduction year, year-round vaccination averted 6,410 total illnesses when introduced in October and 7,202 illnesses when introduced in April, whereas seasonal-campaign vaccination averted 10,236 (October) to 11,612 (April) illnesses. In the year after introduction, both strategies averted comparable numbers of illnesses (10,831-10,868 for year-round, 10,175-11,282 for campaign). Campaign-style vaccination would likely have a greater effect during initial pediatric influenza vaccine introduction in Kenya; however, either strategy could achieve similar longer-term effects.

The effects of climatic and non-climatic factors on malaria mortality at different spatial scales in western Kenya, 2008-2019

BACKGROUND

Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya.

METHODS

We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality.

RESULTS

Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level.

CONCLUSION

Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.

Molecular and serological diagnosis of multiple bacterial zoonoses in febrile outpatients in Garissa County, north-eastern Kenya

Bacterial zoonoses are diseases caused by bacterial pathogens that can be naturally transmitted between humans and vertebrate animals. They are important causes of non-malarial fevers in Kenya, yet their epidemiology remains unclear. We investigated brucellosis, Q-fever and leptospirosis in the venous blood of 216 malaria-negative febrile patients recruited in two health centres (98 from Ijara and 118 from Sangailu health centres) in Garissa County in north-eastern Kenya. We determined exposure to the three zoonoses using serological (Rose Bengal test for Brucella spp., ELISA for C. burnetti and microscopic agglutination test for Leptospira spp.) and real-time PCR testing and identified risk factors for exposure. We also used non-targeted metagenomic sequencing on nine selected patients to assess the presence of other possible bacterial causes of non-malarial fevers. Considerable PCR positivity was found for Brucella (19.4%, 95% confidence intervals [CI] 14.2-25.5) and Leptospira spp. (1.7%, 95% CI 0.4-4.9), and high endpoint titres were observed against leptospiral serovar Grippotyphosa from the serological testing. Patients aged 5-17 years old had 4.02 (95% CI 1.18-13.70, p-value = 0.03) and 2.42 (95% CI 1.09-5.34, p-value = 0.03) times higher odds of infection with Brucella spp. and Coxiella burnetii than those of ages 35-80. Additionally, patients who sourced water from dams/springs, and other sources (protected wells, boreholes, bottled water, and water pans) had 2.39 (95% CI 1.22-4.68, p-value = 0.01) and 2.24 (1.15-4.35, p-value = 0.02) times higher odds of exposure to C. burnetii than those who used unprotected wells. Streptococcus and Moraxella spp. were determined using metagenomic sequencing. Brucellosis, leptospirosis, Streptococcus and Moraxella infections are potentially important causes of non-malarial fevers in Garissa. This knowledge can guide routine diagnosis, thus helping lower the disease burden and ensure better health outcomes, especially in younger populations.

Prevalence of Endemic Respiratory Viruses During the COVID-19 Pandemic in Urban and Rural Malawi

Background

We investigated endemic respiratory virus circulation patterns in Malawi, where no lockdown was imposed, during the COVID-19 pandemic.

Methods

Within a prospective household cohort in urban and rural Malawi, adult participants provided upper respiratory tract (URT) samples at 4 time points between February 2021 and April 2022. Polymerase chain reaction (PCR) was performed for SARS-CoV-2, influenza, and other endemic respiratory viruses.

Results

1626 URT samples from 945 participants in 542 households were included. Overall, 7.6% (n = 123) samples were PCR- positive for >1 respiratory virus; SARS-CoV-2 (4.4%) and rhinovirus (2.0%) were most common. No influenza A virus was detected. Influenza B and respiratory syncytial virus (RSV) were rare. Higher virus positivity were detected in the rural setting and at earlier time points. Coinfections were infrequent.

Conclusions

Endemic respiratory viruses circulated in the community in Malawi during the pandemic, though influenza and RSV were rarely detected. Distinct differences in virus positivity and demographics were observed between urban and rural cohorts.