Abstract Background Climate change has caused more frequent and severe extreme weather events, threatening health system resilience worldwide. In April and May 2024, Kenya experienced unprecedented extensive floods with devastating outcomes. However, the quantitative impact of flooding on geographical access to healthcare remains unclear. This study, therefore, evaluates post-disaster accessibility to health facilities and quantifies geographical coverage losses resulting from flooding compounded by doctors’ strike in Kenya. Methods Geospatial datasets were assembled including health facility locations (public, private not-for-profit (PNfP), and private for-profit (PfP)), road network, land use/land cover, topography, population density, and flooding extents). A pre-flood baseline and three post-flood scenarios were defined using satellite-derived flooding extents (Sentinel 1 synthetic aperture radar (SAR) and National Oceanic and Atmospheric Administration – Visible Infrared Imaging Radiometer Suite (NOAA-VIIRS) satellites) and their combined maximal extents. Travel time (TT) to the nearest health facility by type was estimated using a least-cost path algorithm, accounting for ± 20% variations in travel speed and flood extent for sensitivity analysis. Population coverage was extracted within five 30-minute TT bands for each scenario, nationally and by subnational units (county). Results A total of 10,995 health facilities were assembled (public = 5,586; PNfP = 855; PfP = 4,554). Pre-floods, average TT to the nearest facility was 19.6 min, with public facilities at 20.7 min, PfP at 37.8 min, and PNfP at 49.2 min. Post-floods average TT increased across all sectors, longest across PNfP at 113.5 min and shortest for public facilities at 48.5 min. Pre-floods, 94.0% (52.5 million) of the population had access within 30-min and 20 out of 47 counties with an average TT of < 2 h. Under the maximal flood extents, coverage dropped to 73% (40.9 million) and only 5 counties retained < 2 h TT. County-level 30-min coverage losses ranged from 1.0% (Nairobi) to 51.0% (Narok). In several arid counties, populations facing 2 + hours TT rose to 15–31%, up from 4 to 12% pre-floods. Conclusion Kenya’s health system is highly vulnerable to floods, causing unequal disruptions in geographical access across subnational region. Incorporating disaster preparedness into county health care planning to strengthen health system resilience nationwide is needed.

Background Expectations for health-care quality affect how populations rate primary care, influencing tolerance of poor-quality care and demand for improvement, yet how expectations align with standards of practice remains poorly understood. We aimed to measure population expectations of primary care and their determinants within and across countries. Methods In this cross-sectional analysis, we used nationally representative data from the People's Voice Survey collected between May 9, 2022, and Dec 21, 2023, in Ethiopia, Nigeria, Kenya, South Africa, Colombia, Peru, Mexico, Argentina (Province of Mendoza), Uruguay, Laos, India, China, South Korea, Romania, Greece, Italy, the UK, and the USA. Adults aged 18 years and older with at least one health-care visit in the past 12 months were included. Expectations were assessed using two anchoring vignettes, one depicting poor-quality primary care and one depicting adequate-quality primary care, administered via a mobile telephone survey, household interview, or nationally representative web panels. Respondents rated each vignette on a Likert scale: poor, fair, good, very good, or excellent. Low expectations were defined as rating the poor-quality care vignette as fair, good, very good, or excellent, and high expectations were defined as rating the adequate-quality care vignette as poor, fair, or good. Multivariable logistic regression models identified determinants of expectations, including covariates covering demographics, health status, care utilisation and experience, and system competence. Findings Among 18 312 respondents (10 000 [54·6%] female and 8312 [45·4%] male; mean age 44 years [SD 17]), 2955 (16·1%) rated poor-quality primary care as fair or better and 11 787 (64·4%) rated adequate-quality primary care as less than very good. The proportion of respondents with low expectations of primary care ranged from 70 (6·3%) of 1112 in the UK to 530 (41·1%) of 1290 in South Korea. The proportion of respondents with high expectations of primary care ranged from 563 (40·1%) of 1404 in Nigeria to 494 (85·0%) of 581 in Italy. Older age, female gender, higher educational attainment, higher income, and use of private care (vs public care) were all associated with reduced odds of low expectations. Respondents with higher self-rated health had higher odds of low expectations (vs those with poor self-rated health). Activated patients (ie, those who were confident in raising concerns with their provider and that they were responsible for managing their own health) had lower odds of low expectations and lower odds of high expectations (vs non-activated patients). And positive perceptions of government management of COVID-19 were associated with high odds of low expectations and low odds of high expectations. Interpretation Populations can often misjudge primary care quality, with substantial cross-national variation. Recognising and measuring expectations are essential for accurately interpreting health system ratings and guiding efforts to improve primary care. Policy makers should invest in strategies that strengthen health literacy and patient activation, empowering populations to recognise and demand high-quality primary care. Funding Bill & Melinda Gates Foundation, the Swiss Federal Department of Foreign Affairs, Merck Sharp & Dohme, Inter-American Development Bank, the Eckenstein-Geigy Professorship, Initiative on the Future of Health and Economic Resiliency in Africa, National Natural Science Foundation of China, WHO Regional Office for Europe, and the Taejae Foundation.

Disease modelling for low-income settings often lacks reliable data, which leads to modelled outputs that contrast with empirical data and local knowledge

Background:Evidence-based global guidance on safe travel time for small or sick newborns who require transfer to health facilities after birth is lacking. A two-hour threshold is frequently cited in low- and middle-income countries (LMICs), while 30 minutes is commonly used in high-income countries (HICs). Although these thresholds are widely referenced, their empirical basis and consistency across levels of newborn care and journey types have not been systematically examined. This study synthesises the evidence linking travel time and perinatal outcomes. Methods:We conducted a systematic review with narrative synthesis and meta-analysis to assess the impact of travel time or distance (home-to-facility or interfacility) on stillbirth, perinatal mortality, and neonatal mortality. We searched Embase, MEDLINE, and Cochrane Central Register of Controlled Trials for published studies from 2014 to 2023. Given substantial methodological heterogeneity, we used narrative synthesis as the primary analytical approach and conducted random-effects meta-analyses where studies were sufficiently comparable (≥2 with similar definitions and outcome windows), pooling effect estimates for travel time thresholds of 30 minutes, 1 hour, or 2 hours and travel distances of 5, 10, and 15 km. We assessed bias using the Newcastle-Ottawa Scale for cohort and case-control studies. Results:Of 8317 screened records, 166 were eligible for full-text review, with 37 studies meeting the inclusion criteria- All but two had low or moderate risk of bias. Most studies (n = 26) came from LMICs and documented higher perinatal survival with shorter journeys. Studies from HICs demonstrated lower out-of-hospital birth, lower morbidity, and lower mortality with shorter journeys though associations were weaker. Across the narrative synthesis, shorter travel times were consistently associated with better outcomes. Exploratory pooling suggested a greater than 3-fold higher odds of survival for interfacility journeys under 30 minutes (odds ratio (OR) = 3.25; 95% confidence interval (CI) = 1.90-5.57) and over 2-fold higher odds of survival for journeys from any location to hospitals at all thresholds (OR = 2.06; 95% CI = 1.60-2.65 for 2 hours; OR = 2.20; 95% CI = 1.46-3.33 for 1 hour; OR = 1.92; 95% CI = 1.10-3.34 for 30 minutes), though prediction intervals were wide, reflecting methodological and contextual diversity. Conclusions:We found that shorter journeys were associated with better perinatal outcomes, with the highest survival rates observed for journeys under 30 minutes to the hospital. Due to substantial contextual and methodological heterogeneity, pooled estimates should be interpreted as illustrative, rather than definitive. A travel time norm of 30 minutes or one hour is preferable to the two-hour threshold currently used in LMICs. To safeguard perinatal survival rates, any travel-time standard should be balanced with corresponding quality of care standards. Registration:PROSPERO: CRD42023460423.

Dengue virus (DENV) and chikungunya virus (CHIKV), transmitted primarily by Aedes mosquitoes, represent growing public health threats in Kenya and across sub-Saharan Africa. In the absence of licensed vaccines for either disease in Africa, targeted vector control remains the primary prevention strategy, making accurate knowledge of Aedes species distributions essential. Global inventories of Aedes species are severely under-representative for Kenya, with existing repositories documenting fewer than 200 unique locations. No comprehensive, geocoded national inventory integrating published, unpublished, and digital sources has previously been assembled. Three complementary approaches were used to assemble a national geocoded inventory of Aedes species in Kenya including (i) a systematic review of peer-reviewed literature, theses, and conference abstracts; (ii) digitisation of unpublished archival reports from the Ministry of Health, Division of Insect-Borne/Vector-Borne Diseases (DIBD/DVBD), regional and municipal councils; and (iii) cross-referencing with Global Biodiversity Information Facility (GBIF) and Walter Reed Biosystematics Unit’s (WRBU) VectorMap repositories. Data were extracted using a standardised template, de-duplicated, and geocoded using Global Positioning System (GPS) coordinates, Google Earth, or census enumeration area shapefiles. Aedes species were classified as principal or possible secondary vectors based on confirmed field detection and, for the principal vector, an established role in transmission during dengue and chikungunya outbreaks reported in Kenya. A total of 1,802 time-site records were identified across 855 unique locations between 1901 and 2024, documenting 104 Aedes species. Unpublished archival records contributed 51

Background Equitable access to paediatric emergency care (EC) is fundamental to reducing child mortality, yet its conventional measures rely largely on travel time, overlooking barriers in availability, affordability, accommodation and acceptability that shape care seeking. Existing composite indices also often aggregate access dimensions additively, allowing well-performing components to mask critical deficits, and rarely account for local variation in barrier importance. We address these gaps by developing a high-resolution, multidimensional composite index of access to inpatient paediatric EC in Kenya and testing its relevance for child survival and policy targeting. Methodology: Drawing on the Penchansky & Thomas framework, we quantified five access dimensions - availability, geographic accessibility, affordability, accommodation, acceptability - as continuous 1 km × 1 km surfaces using national health facility censuses, demographic surveys, and spatial datasets. Availability was modelled using an Enhanced Two-Step Floating Catchment Area method; geographic accessibility via least-cost path modelling; affordability, accommodation and acceptability via Bayesian geostatistical methods. Spatially adaptive weights were derived using geographically weighted principal component analysis, and the composite index constructed using ordered weighted averaging (OWA), a non-compensatory method penalising severe deficits. OWA was compared with arithmetic and geometric means and validated against under-five mortality using quantile regression as a plausibility check. Results Access to inpatient paediatric EC was unevenly distributed (median composite access index 0.45, IQR: 0.35,0.57). Although 70% of children resided within 1-hour of a facility offering inpatient paediatric EC services, this threshold misclassified access: 9/20 counties within 1-hour fell into the lowest composite quartiles due to deficits in other dimensions. OWA produced systematically lower scores than arithmetic/geometric means in unbalanced areas, avoiding overestimation from compensatory effects. In quantile regression, OWA-based access showed a protective association with under-five mortality, strongest at higher mortality quantiles, whereas compensatory methods produced slopes near zero or inconsistent across quantiles. Conclusions Access barriers cluster and combine differently across space, and these patterns matter for child survival. The index and underlying approach offer a practical advance beyond travel time alone, providing a transferable way to measure access and target investments in Kenya and other sub-Saharan African settings where EC resources are scarce and inequities are wide.‌

Introduction Registration of all births and deaths in sub-Saharan Africa remains inadequate. In Kenya, significant strides have been made, but progress has stalled in recent years. While some studies have examined factors influencing birth registration, national-level analytical assessments of these factors are limited. This study evaluates the progress and determinants of birth registration for children under 3 years in Kenya. Methods We used cross-sectional data from the 2014 and 2022 Kenya Demographic and Health Surveys complemented by geospatial covariates of travel time and urbanicity. We computed the percentage of children (<3 years) registered with the civil authority at the national and subnational level (counties) and compared temporal changes against the United Nations (UN) ≥ 90% target. Binary and multivariable logistic regressions were used to assess determinants of birth registration in 2022, accounting for survey design. Results National birth registration coverage improved from 69.0% (2014) to 76.0% (2022). Subnational coverage in 2014 ranged from 20.8% (West Pokot) to 96.4% (Nyeri) and from 46.4% (Marsabit) to 95.5% (Nyeri) in 2022. By 2022, only 17% (8/47) of counties met the UN’s ≥ 90% goal, with counties in the central region consistently performing better. Higher odds of birth registration were linked to health-facility births (adjusted OR (AOR)=2.44; 95% CI 2.07 to 2.89), immunisation (AOR=1.76; 95% CI 1.25 to 2.49), maternal age (35–39 years: AOR=1.61; 95% CI 1.22 to 2.12), higher education (AOR=1.39; 95% CI 1.03 to 1.89), media access (AOR=1.47; 95% CI 1.06 to 2.04), affiliation with the Catholic religion and the Kikuyu ethnic group. Households without bank accounts had lower odds (AOR=0.78; 95% CI 0.66 to 0.93). Notably, urbanicity and travel time to civil registration centres were not significantly associated with birth registration. Conclusion National birth registration coverage has improved, but subnational disparities persist. Our findings show that in 2022, the health, education, financial and media sectors are associated with a higher likelihood of birth registration. These results underscore the need for the government and stakeholders to implement multisectoral strategies to strengthen Civil Registration and Vital Statistics and address socioeconomic and geographic inequalities critical to achieving universal birth registration coverage.

Background Across the globe, rates of depression and anxiety have risen substantially since the COVID pandemic. Consequently, poor mental health is now a top health policy priority in many countries and more people than ever are seeking treatment. While the segment of people with poor mental health is large and growing, there is a dearth of data about their demographics and health needs and their use of and experience in the health system. Health systems require this information to effectively organize and provide services.Methods and findings We investigated population prevalence of fair or poor mental health and compared health system experience and quality of care among adults with poor versus good mental health in 18 high-, middle-, and low-income countries using data from the People's Voice Survey (n = 32,419). Data were collected in 2022 and 2023 through a combination of nationally representative telephone, online, and in-person surveys. Prevalence of self-reported poor mental health ranged from 4.7% in Nigeria to 39.6% in China and was unrelated to national income per capita. More women than men reported poor mental health in most countries. Across all countries, people with poor mental health had worse self-rated overall health and more chronic illness. Between 0.9% (Lao PDR) and 52.4% (UK) of those with poor mental health had received mental healthcare in the past year. People with poor mental health reported lower patient activation, worse care quality, and lower confidence in the health system. A study limitation is that results are based on self-reported mental health rather than clinical diagnoses.Conclusions People with poor mental health have markedly different health profiles and health system experience. These findings should prompt health systems to re-assess their services to better serve this growing patient group. Comparison of user experience and quality over time and across countries with similar health systems may assist in benchmarking performance.

Climate change and extreme weather events (EWEs) have an adverse impact on both populations and their surrounding environment. These effects span regions and sectors, with varying impacts, some of which are irreversible. The changing climate, accompanied by an increasing frequency of EWEs, necessitates assessment of climate vulnerability as an important applied instrument to identify populations and systems at risk and guide decision-makers in prioritising targeted interventions. Africa exhibits considerable climatic variability and is particularly susceptible to the impacts of climate change. This review aims to identify key concepts and metrics previously used to define climate vulnerability in Africa facilitating a regional understanding of approaches across various sectors that can be adopted to understand the gaps and limitations as a basis to improve future methods. We searched literature from 1st January 2003 to 31st December 2023, restricted to publications in English. We analysed the extracted data using both descriptive and thematic approaches, consistent with established scoping review frameworks (Arksey O’Malley, 2005). Specifically, we used descriptive statistics to summarise study characteristics (e.g., year, location, and type of method) and thematic analysis to identify approaches and frameworks used to assess climate vulnerability in Africa. We retrieved 94 articles in the review. Most studies were conducted in South Africa (14/94, 15

The suspension and/or termination of many programmes funded through the United States Agency for International Development (USAID) by the new US administration has severe short- and long-term negative impacts on the health of people worldwide. We draw attention to the termination of the Demographic and Health Surveys (DHS) Program, which includes nationally representative surveys of households, DHS, Malaria Indicator Surveys [MIS]) and health facilities (Service Provision Assessments [SPA]) in over 90 low- and middle-income countries. USAID co-funding and provision of technical support for these surveys has been shut down. The impact of these disruptions will reverberate across local, regional, national, and global levels and severely impact the ability to understand the levels and changes in population health outcomes and behaviours. We highlight three key impacts on (1) ongoing data collection and data processing activities; (2) future data collection and consequent lack of population-level health indicators; and (3) access to existing data and lack of support for its use. We call for immediate action on multiple fronts. In the short term, universal access to existing data and survey materials should be restored, and surveys which were planned or in progress should be completed. In the long term, this crisis should serve as a tipping point for transforming these vital surveys. We call on national governments, regional organisations, and international partners to develop sustainable alternatives that preserve the principles (standardised questionnaires, backward compatibility, open access data with rigorous documentation) which made the DHS Program an invaluable global health resource.

Background Everyone deserves legal recognition, yet millions of children remain unregistered, with the majority (87%) residing in sub-Saharan Africa and southern Asia. Despite global efforts to improve birth registration coverage, sub-national disparities persist. Across Kenya's 47 counties, birth registration completeness rates varies from nearly 100% to as low as 12.2%, suggesting local contextual factors are important. This study explores the influence of contextual factors on the spatially heterogeneous rates of birth registration in Kenya. Methods We utilized data from the 2022 Kenya Demographic and Health Survey. The association between registered births and its determinants (child factors, health care indicators, maternal, household and geographical factors) was assessed at the cluster level (villages) using four regression models: ordinary least square (OLS) and spatial local regression using Geographically Weighted Regression (GWR-single spatial scale for all predictors), Multiscale GWR (MGWR-each predictor operates at different spatial scale) and Similarity GWR (SGWR-single spatial scale for all predictors) models. Best-fit models were assessed using adjusted R2, AICc and Moran’s I (residual spatial autocorrelation). The key difference between GWR and SGWR lies in how spatial dependency is measured between locations. Results A total of 1673 survey clusters were analysed. MGWR was the best-fitting model (AICc = 14,870.57, adjusted R2 = 0.40, Moran’s I = -0.04 (p-value = 0.999)) and identified localised significant relationships for all variables examined. Evidence of spatially varying relationship (local influence) was observed between birth registration, bank account ownership, and unemployment. Regional influence was observed for female-headed households, while other associations maintained a uniform relationship across the study area (global influence). Conclusion Determinants of birth registration vary spatially at different geographical scales, necessitating context-specific targeted strategies to boost registration coverage across diverse areas and populations.

Background:Missed opportunities for key vaccinations continue to exacerbate disease outbreaks. Accurately monitoring immunisation coverage is fundamental to identifying gaps in vaccine delivery and informing timely action. This study assesses the agreement between routine and survey-based coverage estimates for the second dose of the measles vaccine (MCV2) in Western Kenya. Methods:This study utilised model-based geostatistics estimates MCV2 coverage from the 2022 Kenya Demographic and Health Survey (DHS), monthly immunisation data from routine health information systems (2019-2022) imputed for missingness and population data from WorldPop for 2019 across 62 Western Kenyan subnational areas (sub-counties). Routine MCV2 coverage was computed using MCV2 doses as a numerator and two separate denominators: (i) Pentavalent 1 doses to account for children already receiving prior vaccines at health facilities (service-based coverage) and (ii) surviving infants to account for all eligible children (population-based coverage). Concordance was assessed using the 95% confidence intervals (CIs) of survey-modelled estimates, intra-class correlation coefficient (ICC), and Bland-Altman (BA) plots. Results:Survey-modelled estimates differed substantially in 55 (89%) and 39 (63%) sub-counties compared to population and service-based coverage estimates respectively. The different approaches showed poor congruence in survey-modelled vs. population-based coverage estimates (ICC: 0.10, p = 0.229) and survey-modelled vs. service-based coverage estimates (ICC: 0.42, p = <0.001); there was moderate congruence of population vs. service-based coverage estimates (ICC: 0.65, p = <0.001). Survey-modelled vs. population-based coverage estimates showed the highest bias in BA plots of 18.80 percent points (p.p) compared to 11.02 p.p. and 7.79 p.p. between survey-modelled vs. service-based coverage and population vs. service-based coverage estimates, respectively. Conclusions:Substantial discrepancies among survey-modelled, routine population, and service-based coverage estimates expose important variations in each approaches' results. While all approaches offer distinct insights, improving survey models, routine data quality and refining estimates of population catchment is imperative for reliable fine-scale vaccine delivery monitoring.

Many national development strategies are implemented at the subnational administrative level, serving as critical units for service delivery. Some subnational levels remain underserved and face significant obstacles to achieving equitable development. In Sub-Saharan Africa, underserved regions are often called hardship areas; however, there is no clarity on how such areas are defined across various contexts. Therefore, this scoping review aimed to delineate the definitions of hardship areas across countries in Sub-Saharan Africa and develop a unified typology of their features. This scoping review followed the framework by Arksey and O’Malley, aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. We searched Ovid Embase, Ovid MEDLINE, CINAHL via EBSCOhost, Ovid Global Health, and Scopus from 1st January 2010 to 8th October 2024, as well as grey literature from websites. We imported the retrieved articles into Covidence. One reviewer conducted the title and abstract screening. For full-text review, one reviewer assessed all the articles, whereas the second reviewer independently evaluated a random sample of 20

BACKGROUND:Healthcare service provision, planning, and management depend on the availability of a geolocated, up-to-date, comprehensive health facility database (HFDB) to adequately meet a population's healthcare needs. HFDBs are an integral component of national health system infrastructure forming the basis of efficient health service delivery, planning, surveillance, and ensuring equitable resource distribution, response to epidemics and outbreaks, as well as for research. Despite the value of HFDBs, their availability remains a challenge in sub-Saharan Africa (SSA). Many SSA countries face challenges in creating a HFDB; existing facility lists are incomplete, lack geographical coordinates, or contain outdated information on facility designation, service availability, or capacity. Even in countries with a HFDB, it is often not available open-access to health system stakeholders. Consequently, multiple national and subnational parallel efforts attempt to construct HFDBs, resulting in duplication and lack of governmental input, use, and validation. MAIN BODY:In this paper, we advocate for a harmonized SSA-wide HFDB. To achieve this, we elaborate on the steps required and challenges to overcome. We provide an overview of the minimum attributes of a HFDB and discuss past and current efforts to collate HFDBs at the country and regional (SSA) levels. We contend that a complete HFDB should include administrative units, geographic coordinates of facilities, attributes of service availability and capacity, facilities from both public and private sectors, be updated regularly, and be available to health system stakeholders through an open access policy. We provide historical and recent examples while looking at key issues and challenges, such as privacy, legitimacy, resources, and leadership, which must be considered to achieve such HFDBs. CONCLUSION:A harmonized HFDB for all SSA countries will facilitate efficient healthcare planning and service provision. A continental, cross-border effort will further support planning during natural disasters, conflicts, and migration. This is only achievable if there is a regional commitment from countries and health system stakeholders to open data sharing. This SSA-wide HFDB should be a government-led initiative with contributions from all stakeholders, ensuring no one is left behind in the pursuit of improved health service provision and universal health coverage.

Background:Access to emergency care (EC) services is crucial for severe anaemia outcome. Limited information exists on the association between travel times to EC services and the presentation and severity of anaemia upon hospital admission. Here, we investigate the association between travel time and presentation of severe anaemia (compared to mild/moderate anaemia) at admission in western Kenya. Methods:Data from January 2020 to July 2023 from Busia County Referral Hospital were assembled for paediatric admissions aged 1-59 months residing in Busia County. Travel time from a patient's village to the hospital was calculated using a least cost path algorithm. Anaemia severity was categorised as mild (Hb ≥ 7-<10 g dl-1), moderate (Hb ≥ 5-<7 g dl-1) and severe (Hb < 5 g dl-1). We fitted a geostatistical model accounting for covariates to estimate the association between travel times to EC services and severe anaemia presentation. Results:Severe anaemia admissions had the highest median travel time of 36 min (IQR: 25,54) (p-value: <0.001). Compared to children living within a 30 min travel time to the hospital, the adjusted odds ratio (AOR) of severe anaemia presentation relative to mild/moderate anaemia was 2.44 (95% CI: 1.63-3.55) for those residing within 30-59 min. For travel times of 60-89 min, the AOR was 3.55 (95% CI: 1.86-6.10) and for ≥90 min, the AOR was 3.41 (95% CI: 1.49-7.67). Conclusion:Travel time is significantly associated with the severity of paediatric anaemia presentations at hospitals. Addressing disparities in travel times such as strategic bolstering of lower-level facilities to offer EC services, is crucial for implementing new interventions and optimizing existing hospital-linked interventions to enhance healthcare delivery.

Access to quality healthcare services is key to achieving Universal Health Coverage (UHC). The multidimensional nature of access (availability, accessibility, accommodation, affordability and acceptability) makes it challenging to quantify the level of access. Current approaches focus predominantly on single dimensions, limiting the comprehensive monitoring and evaluation of access to healthcare facilities. Here, we conduct a systematic literature review on the methodological approaches and data used to construct multidimensional composite indices of healthcare facility access, globally. We undertook a literature search in eight databases including EBSCOhost (CINAHL), Google Scholar, Ovid (Embase and MEDLINE), PubMed, Scopus, Web of Science and Web of Science (MEDLINE) adhering to the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. Studies that incorporated multiple dimensions of access to healthcare facilities to construct a composite index were considered and quality assessment performed. Methodological approaches to measuring access and their supporting conceptual frameworks were synthesised using descriptive summaries and thematic analysis. Out of 4,291 articles retrieved,19 met inclusion criteria with an average quality score of 19.6 out of 26. Most of the studies (68

Background:To evaluate the human population at risk of arboviral illnesses and improve vector and disease surveillance, it is crucial to model the probability of occurrence of impactful mosquitoes such as Aedes aegypti sensu lato (s.l.) which transmits dengue and Chikungunya etc. While majority of studies on Aedes distributions have focused on global ecological niche modelling (ENM), there is need to build local vector niche models using national data to design targeted vector surveillance and control strategies. Here, we built a spatial inventory of Aedes aegypti s.l. and applied a national-wide ENM approach to predict the probability of occurrence of Ae. aegypti s.l. across Kenya. Methods:Occurrence data on Aedes aegypti s.l. from 2000 to 2024 were assembled from the Global Biodiversity Information Facility (GBIF), Walter Reed Biosystematics Unit's (WRBU) VectorMap, and online literature searches. A maximum entropy approach was used to predict Ae. aegypti s.l. probability of occurrence in Kenya for 2024 at ~5 x 5 km resolution, using the occurrence data assembled and environmental covariates: population density, daytime and nighttime land surface temperature (LST), enhanced vegetation index (EVI), elevation, and land cover. Model performance was evaluated using the area under the curve (AUC) metric. Results:A total of 291 unique locations reported positive identification of Ae. aegypti s.l. Population density, daytime and nighttime LST were the most influential predictors. The models predicted high probabilities of occurrence of Ae. aegypti s.l. along the coast, northeastern and western Kenya, and in urban centres, while lower probabilities were predicted in sparsely populated areas. The models achieved a mean AUC value of 0.732 (0.653-0.779), indicating a moderate performance. Conclusion:The predicted distribution of Ae. aegypti s.l. can guide vector surveillance in high-risk areas and help identify populations at risk of arboviral diseases like dengue fever and Chikungunya, aiding in future outbreak preparedness.

The social and behavioural determinants of COVID-19 vaccination have been described previously. However, little is known about how vaccinated people use and rate their health system. We used surveys conducted in 14 countries to study the health system correlates of COVID-19 vaccination. Country-specific logistic regression models were adjusted for respondent age, education, income, chronic illness, history of COVID-19, urban residence, and minority ethnic, racial, or linguistic group. Estimates were summarised across countries using random effects meta-analysis. Vaccination coverage with at least two or three doses ranged from 29% in India to 85% in Peru. Greater health-care use, having a regular and high-quality provider, and receiving other preventive health services were positively associated with vaccination. Confidence in the health system and government also increased the odds of vaccination. By contrast, having unmet health-care needs or experiencing discrimination or a medical mistake decreased the odds of vaccination. Associations between health system predictors and vaccination tended to be stronger in high-income countries and in countries with the most COVID-19-related deaths. Access to quality health systems might affect vaccine decisions. Building strong primary care systems and ensuring a baseline level of quality that is affordable for all should be central to pandemic preparedness strategies.

Population confidence is essential to a well functioning health system. Using data from the People's Voice Survey—a novel population survey conducted in 15 low-income, middle-income, and high-income countries—we report health system confidence among the general population and analyse its associated factors. Across the 15 countries, fewer than half of respondents were health secure and reported being somewhat or very confident that they could get and afford good-quality care if very sick. Only a quarter of respondents endorsed their current health system, deeming it to work well with no need for major reform. The lowest support was in Peru, the UK, and Greece—countries experiencing substantial health system challenges. Wealthy, more educated, young, and female respondents were less likely to endorse the health system in many countries, portending future challenges for maintaining social solidarity for publicly financed health systems. In pooled analyses, the perceived quality of the public health system and government responsiveness to public input were strongly associated with all confidence measures. These results provide a post-COVID-19 pandemic baseline of public confidence in the health system. The survey should be repeated regularly to inform policy and improve health system accountability.

Background Understanding diagnostic capacities is essential to addressing healthcare provision and inequity, particularly in low-income and middle-income countries. This study used routine data to assess trends in rapid diagnostic test (RDT) reporting, supplies and unmet needs across national and 47 subnational (county) levels in Kenya.Methods We extracted facility-level RDT data for 19 tests (2018–2020) from the Kenya District Health Information System, linked to 13 373 geocoded facilities. Data quality was assessed for reporting completeness (ratio of reports received against those expected), reporting patterns and outliers. Supply assessment covered 12 RDTs reported by at least 50% of the reporting facilities (n=5251), with missing values imputed considering reporting trends. Supply was computed by aggregating the number of tests reported per facility. Due to data limitations, demand was indirectly estimated using healthcare-seeking rates (HIV, malaria) and using population data for venereal disease research laboratory test (VDRL), with unmet need computed as the difference between supply and demand.Results Reporting completeness was under 40% across all counties, with RDT-specific reporting ranging from 9.6% to 89.6%. Malaria RDTs showed the highest annual test volumes (6.3–8.0 million) while rheumatoid factor was the lowest (0.5–0.7 million). Demand for RDTs varied from 2.5 to 11.5 million tests, with unmet needs between 1.2 and 3.5 million. Notably, malaria testing and unmet needs were highest in Turkana County, as well as the western and coastal regions. HIV testing was concentrated in the western and central regions, with decreasing unmet needs from 2018 to 2020. VDRL testing showed high volumes and unmet needs in Nairobi and select counties, with minimal yearly variation.Conclusion RDTs are crucial in enhancing diagnostic accessibility, yet their utilisation varies significantly by region. These findings underscore the need for targeted interventions to close testing gaps and improve data reporting completeness. Addressing these disparities is vital for equitably enhancing diagnostic services nationwide.