We examined the association between serum aflatoxin B1-lysine adduct (AFB1-lys) levels in pregnant women and adverse pregnancy outcomes (low birthweight, miscarriage and stillbirth) through a nested matched case-control study of pregnant women enroled at ≤28 weeks' gestation in Mombasa, Kenya, from 2017 to 2019. Cases comprised women with an adverse birth outcome, defined as either delivery of a singleton infant weighing <2500 g, or a miscarriage, or a stillbirth, while controls were women who delivered a singleton live infant with a birthweight of ≥2500 g. Cases were matched to controls at a ratio of 1:2 based on maternal age at enrolment, gestational age at enrolment and study site. The primary exposure was serum AFB1-lys. The study included 125 cases and 250 controls. The median gestation age when serum samples were collected was 23.0 weeks (interquartile range [IQR]: 18.1-26.0) and 23.5 (IQR: 18.1-26.5) among cases and controls, respectively. Of the 375 tested sera, 145 (38.7%) had detectable serum AFB1-lys: 36.0% in cases and 40.0% in controls. AFB1-lys adduct levels were not associated with adverse birth outcomes on multivariable analysis. Mid-upper arm circumference was associated with a 6% lower odds of adverse birth outcome for every unit increase (p = 0.023). Two-fifths of pregnant women had detectable levels of aflatoxin midway through pregnancy. However, we did not detect an association with adverse pregnancy outcomes, likely because of low serum AFB1-lys levels and low power, restricting meaningful comparison. More research is needed to understand the public health risk of aflatoxin in pregnant women to unborn children.

The COVID-19 pandemic caused widespread changes and disruptions to healthcare seeking behavior. There are limited studies on the effect of the COVID-19 pandemic on healthcare seeking patterns in low-and middle-income countries (LMICs), especially in settings with inequitable access to healthcare in rural and urban informal settlements. We investigated the effect of the COVID-19 pandemic on reported healthcare seeking at health facilities and chemists using morbidity data from participants in an ongoing population-based infectious disease surveillance platform in Asembo in Siaya County, a rural setting in western Kenya and Kibera, an urban informal settlement in Nairobi County. We described healthcare seeking patterns before (from 1st January 2016 to 12th March 2020) and during the pandemic (from 13th March 2020 to 31st August 2022) by gender and age for any reported illness and select clinical syndromes using frequencies and percentages. We used a generalized estimating equation with an exchangeable correlation structure to assess the effect of the pandemic on healthcare seeking adjusting for gender and age. Overall, there was a 19% (adjusted odds ratio, aOR: 0.81; 95% Confidence Interval, CI: 0.79-0.83) decline in odds of seeking healthcare at health facilities for any illness in Asembo during the pandemic, and a 30% (aOR: 0.70; 95% CI: 0.67-0.73) decline in Kibera. Similarly, there was a decline in seeking healthcare by clinical syndromes, e.g., for ARI, aOR: 0.76; 95% CI:0.73-0.79 in Asembo, and aOR: 0.68; 95% CI:0.64-0.72 in Kibera. The pandemic resulted in increased healthcare seeking at chemists (aOR: 1.23; 95% CI: 1.20-1.27 in Asembo, and aOR: 1.40; 95% CI: 1.35-1.46 in Kibera). This study highlights interruptions to healthcare seeking in resource-limited settings due to the COVID-19 pandemic. The pandemic resulted in a substantial decline in seeking care at health facilities, and an increase of the same at chemists.

BACKGROUND: SARS-CoV-2 has extensively spread in cities and rural communities, and studies are needed to quantify exposure in the population. We report seroprevalence of SARS-CoV-2 in two well-characterized populations in Kenya at two time points. These data inform the design and delivery of public health mitigation measures. METHODS: Leveraging on existing population based infectious disease surveillance (PBIDS) in two demographically diverse settings, a rural site in western Kenya in Asembo, Siaya County, and an urban informal settlement in Kibera, Nairobi County, we set up a longitudinal cohort of randomly selected households with serial sampling of all consenting household members in March and June/July 2021. Both sites included 1,794 and 1,638 participants in the March and June/July 2021, respectively. Individual seroprevalence of SARS-CoV-2 antibodies was expressed as a percentage of the seropositive among the individuals tested, accounting for household clustering and weighted by the PBIDS age and sex distribution. RESULTS: Overall weighted individual seroprevalence increased from 56.2% (95%CI: 52.1, 60.2%) in March 2021 to 63.9% (95%CI: 59.5, 68.0%) in June 2021 in Kibera. For Asembo, the seroprevalence almost doubled from 26.0% (95%CI: 22.4, 30.0%) in March 2021 to 48.7% (95%CI: 44.3, 53.2%) in July 2021. Seroprevalence was highly heterogeneous by age and geography in these populations-higher seroprevalence was observed in the urban informal settlement (compared to the rural setting), and children aged <10 years had the lowest seroprevalence in both sites. Only 1.2% and 1.6% of the study participants reported receipt of at least one dose of the COVID-19 vaccine by the second round of serosurvey-none by the first round. CONCLUSIONS: In these two populations, SARS-CoV-2 seroprevalence increased in the first 16 months of the COVID-19 pandemic in Kenya. It is important to prioritize additional mitigation measures, such as vaccine distribution, in crowded and low socioeconomic settings.

INTRODUCTION: Measurement error in gestational age (GA) may bias the association of GA with a health outcome. Ultrasound-based GA is considered the gold standard and is not readily available in low-resource settings. We corrected for measurement error in GA based on fundal height (FH) and date of last menstrual period (LMP) using ultrasound from the sub-cohort and adjusted for the bias in associating GA with neonatal mortality and low birth weight (< 2,500 grams, LBW). METHODS: We used data collected from 01/2015 to 09/2019 from pregnant women enrolled at two public hospitals in Siaya county, Kenya (N = 2,750). We used regression calibration to correct for measurement error in FH- and LMP-based GA accounting for maternal and child characteristics. We applied logistic regression to associate GA with neonatal mortality and low birth weight, with and without calibrating FH- and LMP-based GA. RESULTS: Calibration improved the precision of LMP (correlation coefficient, ρ from 0.48 to 0.57) and FH-based GA (ρ from 0.82 to 0.83). Calibrating FH/LMP-based GA eliminated the bias in the mean GA estimates. The log odds ratio that quantifies the association of GA with neonatal mortality increased by 29% (from -0.159 to -0.205) by calibrating FH-based GA and by more than twofold (from -0.158 to -0.471) by calibrating LMP-based GA. CONCLUSION: Calibrating FH/LMP-based GA improved the accuracy and precision of GA estimates and strengthened the association of GA with neonatal mortality/LBW. When assessing GA, neonatal public health and clinical interventions may benefit from calibration modeling in settings where ultrasound may not be fully available.

Robust data on the impact of the COVID-19 pandemic on mortality in Africa are relatively scarce. Using data from two well-characterized populations in Kenya we aimed to estimate excess mortality during the COVID-19 pandemic period. The mortality data arise from an ongoing population-based infectious disease surveillance (PBIDS) platform, which has been operational since 2006 in rural western Kenya (Asembo, Siaya County) and an urban informal settlement (Kibera, Nairobi County), Kenya. PBIDS participants were regularly visited at home (2-3 times a year) by field workers who collected demographic data, including deaths. In addition, verbal autopsy (VA) interviews for all identified deaths are conducted. We estimated all-cause and cause-specific mortality rates before and during the height of the COVID-19 pandemic, and we compared associated mortality rates between the periods using incidence rate ratios. Excess deaths during the COVID-19 period were also estimated by modelling expected deaths in the absence of COVID-19 by applying a negative binomial regression model on historical mortality data from January 2016. Overall and monthly excess deaths were determined using the P-score metric. Spearman correlation was used to assess whether there is a relationship between the generated P-score and COVID-19 positivity rate. The all-cause mortality rate was higher during the COVID-19 period compared to the pre-COVID-19 period in Asembo [9.1 (95% CI, 8.2-10.0) vs. 7.8 (95% CI, 7.3-8.3) per 1000 person-years of observation, pyo]. In Kibera, the all-cause mortality rate was slightly lower during the COVID-19 period compared to the pre-COVID-19 period [2.6 (95% CI, 2.2-3.2 per 1000 pyo) vs. 3.1; 95% CI, 2.7-3.4 per 1000 pyo)]. An increase in all-cause mortality was observed (incidence rate ratio, IRR, 1.16; 95% CI, 1.04-1.31) in Asembo, unlike in Kibera (IRR, 0.88; 95% CI, 0.71-1.09). The notable increase in mortality rate in Asembo was observed among persons aged 50 to 64 years (IRR, 2.62; 95% CI, 1.95-3.52), persons aged 65 years and above (5.47; 95% CI, 4.60-6.50) and among females (IRR, 1.25; 95% CI, 1.07-1.46). These age and gender differences were not observed in Kibera. We observed an increase in the mortality rate due to acute respiratory infection, including pneumonia (IRR, 1.45;95% CI, 1.03-2.04), and a reduction in the mortality rate due to pulmonary tuberculosis (IRR, 0.22; 95% CI, 0.05-0.87) among older children and adults in Asembo. There was no statistically significant change in mortality rates due to leading specific causes of death in Kibera. Overall, during the COVID-19 period observed deaths were higher than expected deaths in Asembo (P-score = 6.0%) and lower than expected in Kibera (P-score = -22.3%).Using well-characterized populations in the two diverse geographic locations, we demonstrate a heterogenous impact of the COVID-19 pandemic on all-cause and cause-specific mortality rates in Kenya. We observed more deaths than expected during the COVID-19 period in our rural site in western Kenya contrary to the urban site in Nairobi, the capital city in Kenya.

Typhoid fever burden can vary over time. Long-term data can inform prevention strategies; however, such data are lacking in many African settings. We reexamined typhoid fever incidence and antimicrobial resistance (AMR) over a 10-year period in Kibera, a densely populated urban informal settlement where a high burden has been previously described. We used data from the Population Based Infectious Diseases Surveillance platform to estimate crude and adjusted incidence rates and prevalence of AMR in nearly 26,000 individuals of all ages. Demographic and healthcare-seeking information was collected through household visits. Blood cultures were processed for patients with acute fever or lower respiratory infection. Between 2010 and 2019, 16,437 participants were eligible for blood culture and 11,848 (72.1%) had a culture performed. Among 11,417 noncontaminated cultures (96.4%), 237 grew Salmonella enterica serovar Typhi (2.1%). Overall crude and adjusted incidences were 95 and 188 cases per 100,000 person-years of observation (pyo), respectively. Annual crude incidence varied from 144 to 233 between 2010 and 2012 and from 9 to 55 between 2013 and 2018 and reached 130 per 100,000 pyo in 2019. Children 5-9 years old had the highest overall incidence (crude, 208; adjusted, 359 per 100,000 pyo). Among isolates tested, 156 of 217 were multidrug resistant (resistant to chloramphenicol, ampicillin, and trimethoprim/sulfamethoxazole [71.9%]) and 6 of 223 were resistant to ciprofloxacin (2.7%). Typhoid fever incidence resurged in 2019 after a prolonged period of low rates, with the highest incidence among children. Typhoid fever control measures, including vaccines, could reduce morbidity in this setting.

INTRODUCTION: More persons with dementia are residing in the community as many countries shift from residential care to home and community care. Although there are many forms of care and support available to avoid crisis situations and prolong community living, it remains unclear how these are valued by community-dwelling persons with dementia and their informal caregivers. Understanding perspectives of persons with dementia and informal caregivers on care characteristics is a vital step in valuing care services. This study aims to prioritize care characteristics for community-dwelling persons with dementia and informal caregivers with the use of an innovative mixed-methods approach. METHODS: Six mixed focus groups were conducted in The Netherlands with persons with dementia (n = 23) and informal caregivers (n = 20), including a quantitative ranking exercise that prioritized seven care and support characteristics from "most important" to "least important," followed by a group discussion about the prioritization. Audio recordings were transcribed and analyzed using thematic analysis. RESULTS: The ranking exercise and discussion showed that persons with dementia favored in-home care, help with daily activities, and social activities, whereas informal caregivers favored social activities, information about dementia, navigating the health care system, and emotional support. DISCUSSION: Persons with dementia prioritized day-to-day activities, whereas informal caregivers preferred assistance with organizing care and coping with caregiving. This study created a method to capture the care preferences of persons with dementia and informal caregivers.

Exposure measurement error (ME) biases exposure-outcome associations. Calibration dietary intake data used in the regression calibration (RC) response to adjust for ME are usually right-skewed, heteroscedastic and with excess zeroes. We proposed three-part RC models to handle these distributional complexities simultaneously, while correcting for ME in fish intake. We applied data from the National Health and Nutrition Examination Survey (NHANES), where long-term intake was measured with food frequency questionnaire (FFQ) in the main study and short-term intake with 24-hour recall (24HR) in the calibration study. In the three-part RC models, never consumers were modelled using two approaches: a zero distribution (Three-part RC-het-det), and logistic distribution (Three-part RC-het-prob); heteroscedasticity using an exponential distribution and right-skewness using generalized gamma distribution. The proposed models were compared with two-part RC model that ignores never consumers, and with methods that estimate intakes using FFQ and 24HR. The models were evaluated in a simulation study. With NHANES data, mean increase in the mercury level (in (Formula presented.)) was 1.20 using FFQ-method, 0.4 using 24HR-method, 1.87 using two-part RC and 2.02 using three-part RC-het-prob method. The three-part RC estimated the association with the least bias in the simulation study.