Kenya Sub Counties matched to former provinces and DHIS2 IDs. The files have been corrected for topological errors and are perfectly aligned to the wards. If you need the wards, go to https://data.humdata.org/dataset/administrative-wards-in-kenya-1450

Individual-level household survey dataset for one urban and one rural constituency in Kenya and Zambia, covering questions on media and communications habits, political behaviour and attitudes. The objective of the surveys was to obtain representative samples of two constituencies per country. Constituencies were selected according to their social and economic characteristics, in order to capture a wide variety of contexts. A random procedure was deployed in all stages of sampling, ensuring representativity of households and individuals of voting age in the four constituencies. The results of the survey can be generalised to the particular constituencies with a margin of error of approximately minus or plus 5% for a 95% confidence interval.Politics and Interactive Media in Africa (PIMA) examines whether and how Africans, particularly the poorest and least politically enfranchised, use new communication technologies to voice their opinion and to engage in a public debate on interactive broadcast media, and its effects on modes of political accountability. Through detailed qualitative case-studies in Kenya and Zambia, PIMA critically interrogates the heralded potential for digital communications and liberalised media sectors to promote more responsive and inclusive democratic governance, with a keen eye for turning project insights into relevance for policymakers, media houses, journalists and development organisations. By employing survey-based, qualitiative and ethnographic methods to comparatively analyse interactive radio and TV programmes in the context of electoral and everyday politics, we will probe whose voice counts, why and to what effects in these new digitally-enabled spaces of voice and accountability. The project takes into account local innovation in the use of ICTs and the interactions between different modes, venues and actors of information gathering and dissemination that are particularly prominent in African contexts. PIMA brings together researchers from the Universities of Cambridge, Nairobi and Zambia, working closely with select broadcast stations and other stakeholders. Data collection for the PiMA surveys took place during May 2013 (Kenya) and June-July 2013 (Zambia). In Kenya, surveys were conducted in Ruaraka: a peri-urban constituency in the capital city Nairobi, with mixed demographics including one of the city’s major slums; and Seme: a rural constituency settled around Lake Victoria in a largely fisher-agricultural community in the western Kenyan city of Kisumu. In Zambia, the surveys were conducted in Mandevu: an urban constituency in the capital city Lusaka with a mixed demographic including some of the city’s major slum settlements; and Chipangali: a rural constituency in the country’s largely agricultural Eastern Province. The four samples were designed as representative cross-sections of all households in those constituencies. Although no claim is made that the constituencies themselves were representative of the wider national population, they were selected based on the possibility of capturing variation in terms of socio-economic factors, political context and media landscape. A multi-stage sampling approach was deployed in the four sites, which involved selecting geographically defined units of decreasing size at each stage. The main four stages of the sampling strategy were: (1) cluster sampling for selection of wards; (2) simple random sampling for selection of enumeration areas (EAs) within wards; (3) systematic random sampling for selection of households within EAs (“random walk”); and (4) simple random (Kenya), or stratified by age and gender (Zambia) sampling for selection of individuals within households. Because there were no available lists of voting individuals living in those constituencies based on census data, the population was grouped into units from which reliable data was available, such as EAs. The lists of EAs constituted the sampling frame from which the primary sampling units (PSUs) were randomly selected. In Stages 2 and 3, selection was performed with probabilities proportional to population size. The purpose was to guarantee that more populated areas (wards, EAs) had a proportionally higher probability of being included in the sample. Within each household, individuals were selected using a random procedure. By employing random techniques in all stages of sampling, and using sampling with probability proportional to the population, it may be assumed that all individuals of voting age (18 and over) living in those four constituencies had a known and above zero chance of being included in the sample. The results of the survey allow inferences to the voting population in the four constituencies (macro-units) with some degree of accuracy (but not to the two countries). The sample sizes are 760 for Kenya (383 for Ruaraka and 377 for Seme) and 688 for Zambia (327 for Mandevu and 361 for Chipangali). The margins of error for a 95% confidence level are no more than plus or minus 5% for both Ruaraka and Seme, 5.41% for Mandevu and 5.12% for Chipangali. The response rate for Kenya was 90.4% (84.6% for Ruaraka and 96.3% for Seme). The response rate for Zambia was not available because the team did not record the number and reasons of unsuccessful calls.

This data were collected for a study seeking to examine and explain antimicrobial use in Kenyan public hospitals. Data were collected from 14 public hospitals in Kenya between February and April 2018. The data sets contain ward and patient-level data. Ward level data described the type of ward, specialty, number of admitted patients and number of beds in the ward. Patient data includes data on hospital ID, ward, Department, patients age, gender, weight, dates of admission and surgery, antimicrobials prescribed, the doses, route and duration of treatment and any laboratory investigations ( C reactive Protein or bacteriological cultures).

This work was carried in Kenyan public hospital the main aim was to assess hospitals Infection Prevention and Control (IPC) and Antibiotic Stewardship(ABS) capacity as part of tracking and tackling efforts to limit antimicrobial resistance in Kenya. We redesigned an existing WASH facility improvement tool to collect data across 16 county hospitals with a total of 116 wards. There were 65 indicators in 4 domains used for this assessment that is 14 indicators for water, Sanitation 22 indicators, hygiene 18 indicators and 11 for organisational management domain. 32 of these indicators were also assessed at ward level. Addition modifications on the tool allowed us to contrast performance by assessing infrastructural, material and human resources to support WASH services, We the WASH facility tool to to allocate responsibilities at a more health systems level allowing for different levels of hospital leadership to be accountable for the implementation and subsequent improvement of WASH in hospitals. Antibiotic Stewardship - We examined prescription patterns and explored to what extent guidelines are available and how they might influence treatment appropriateness in Kenya. Data on antimicrobial usage were collected from hospitalised patients using a point prevalence survey across 14 Kenyan public hospitals spanning antimicrobials prescribed, laboratory investigations, clinical diagnoses and physical availability of treatment guidelines.Global under-5 deaths have halved in the last 20 years(1). However, reduction in the neonatal mortality rate has lagged greatly behind other advances, and now contributes over 40% of all child mortality in many countries (1). Yet, prior research in low and middle income countries (LMICs) suggests sick newborns often do not receive the interventions they need to ensure their disability free survival. Infections are estimated to cause 40% of all neonatal deaths in LMICs (2), where the burden health care-associated infections (HCAIs) is also up to 20 times higher than in industrialised countries (3) and where antibiotic resistant HCAIs are rapidly increasing (4) due to increases in antibiotic use, rising rates of hospitalisation, and high prevalence HCAIs (5) not matched with increases in hospital resources and measures to prevent these. Resistant infections often lead to longer hospitalizations (6), thus increasing opportunity for transmission to other inpatients in care, and subsequent transmission into the community following hospital discharge. The potential societal impact of bacterial antibiotic resistance (BAR) infections in sick newborns in LMICs, is reflected in the 58,000 deaths attributable to antibiotic resistant neonatal sepsis in India alone (5) compared to the 23,000 deaths each year across all population age groups in the United States (7). The much-needed attention to improve newborn health, has triggered multiple stakeholders to propose the 'Every Newborn: an action plan to end preventable deaths' (8), which seeks to improve the quality of care to ultimately end preventable newborn deaths. HCAIs, reflect breakdown in infection prevention and control (IPC) measures, which combined with injudicious use of antibiotics contribute to emergence of resistant HCAIs in neonatal units (9), and are the most frequent preventable adverse event in healthcare delivery worldwide (3). Intervention bundles comprising behavioural, environmental and antibiotic stewardship components (10), could prevent many HCAIs (11-13), and improved provision of high-quality, basic care in resource-limited hospitals could deliver up to a 71% reduction in neonatal mortality (14,15). Initiatives to improve quality and safety in healthcare, however, too often result in limited changes for the better and are often hard to replicate in new contexts (16). In this pump-priming grant, we seek to address key formative stages of the MRC framework for complex interventions (17,18) by generating contextual knowledge of the health system traits and behaviours that need to be understood prior to formulation and implementation of behavioural/integrated interventions to attain best IPC and antibiotic stewardship (IPC-ABS) practice required to reduce HCAIs and BAR in resource-limited healthcare facilities delivering care to sick newborns. In our approach, we draw from elements of the theory of change (ToC) (19,20), by first identifying the desired long-term goals and then working back from these to identify all the conditions that must be in place for the goals to occur. This proposed pump-priming grant includes research that aims to: a. Facilitate the development of appropriate, evidence based interventions based on a critical analysis of the policy, organisational and practice environments and current management, team and individual behaviours relevant to IPC-ABS, aimed at limiting BAR in high-risk populations in Kenyan facilities; b. Help identify context-appropriate clinical and performance indicators for use in monitoring and evaluation of IPC-ABS interventions; c. Highlight challenges in the uptake of policy into effective IPC-ABS practice; d. Increase capability and motivation to limit BAR and improve safety in hospitals; e. Initiate a process of building research capacity around IPC-ABS in Kenya. We expect proposed interventions to be generalizable to other inpatient settings in East African hospitals that share similar challenges. The WASH assessment was carried out at ward level and at facility level in a sample of 16 public hospitals in Kenya. The selection of these hospitals was purposeful and based on links developed from ongoing work to improve clinical information as part of a collaboration between the Kenya Medical Research Institute -Wellcome Trust Research Programme and the Ministry of Health. The data collection tool was the Water Sanitation and Hygiene Facility Improvement tool (WASH FIT) developed by the World Health Organization. The assessment included inpatient wards in the paediatric, medical, surgical and neonatal departments but excluded units not present in all hospitals (i.e. critical care, Ear Nose and Throat (ENT), eye, renal and psychiatric units). In each eligible ward, ward assessment forms were completed. Once these ward level inspections were complete, there was an inspection of the entire facility, including the laundry, kitchen, outpatient areas and the external environment. At facility level, there were a total of 65 WASH indicators to be assessed spread across 4 domains. Each indicator was assessed by direct observation and the score determined by team consensus on a three-point scale (meets = 2, partially meets = 1, or does not meet = 0 the required standard). At ward level, 34 of the 65 indicators were assessed and scored with the same three point system. Through a process of stakeholder engagement, each of the 65 indicators was assigned to one of three persons/groups who would be responsible to improve these indicators. These are the county government, Hospital Management or the hospital infection prevention and control committee. Antibiotics Stewardship Patient-Level Data collection At the patient level, data were collected on the patients' age, sex, weight, hospital department, date of admission or of surgery in the case of surgical patients, date of survey and diagnoses. For the diagnosis, there were a total of 46 possible options provided, 45 of these were categorised by the anatomical system involved. Data are provided on the antimicrobial type, posology, start and stop dates among others. Microbiology, antibiotic susceptibility and biomarker (C-reactive protein; procalcitonin or other) test results used to inform the diagnosis and treatment choice, were also collected for each patient where available.