DOI: 10.5061/dryad.crjdfn3dj
title: Lake Victoria region block sub-county level risk data

description: The health risks of climate change need to be identified to inform the
 prioritization of adaptation efforts. This is particularly true within
 low- and middle-income countries (LMICs) with limited resources,
 heterogenous climates, and varying degrees of social vulnerability. In
 Kenya, diarrheal disease is one of the leading causes of death and
 identifying risk factors of diarrheal disease is critical. This research
 aims to characterize factors associated with a high risk of diarrheal
 disease in western Kenya by developing a risk index based on the
 Intergovernmental Panel on Climate Change (IPCC) risk framework.
  We developed a conceptual model of risk factors based on prior
 research with risk factors grouped into the four components of the IPCC
 risk framework: hazard, exposure, and vulnerability (which is comprised of
 sensitivity and adaptive capacity). We obtained 30 data elements
 corresponding to the four components for 99 sub-counties in 14 western
 Kenya counties. We conducted principal component analysis (PCA) to develop
 a risk index for diarrheal disease. Our risk index aligns with
 epidemiological literature, including precipitation, temperature, water
 sanitation and hygiene (WASH), sensitive populations, education, poverty,
 and health facilities. Within counties, we found that the risk varied
 substantially, and a geographic cluster of high-risk sub-counties was
 identified. Our findings should be useful for policymakers and health
 officials in Kenya to prioritize efforts to prepare communities for health
 impacts of climate change. The process may be useful for standardizing
 approaches to assessing the risk of climate-sensitive health outcomes.
description: # Lake Victoria region block sub-county level risk data
 [https://doi.org/10.5061/dryad.crjdfn3dj](https://doi.org/10.5061/dryad.crjdfn3dj) ## Description of the data and file structure In this research we aim to estimate a risk of diarrheal disease on a subnational scale in western Kenya. Following a search of publicly available data we were able to obtain 30 variables on the county or sub-county level. Weather data such as average, and extreme precipitation and temperature, were obtained from the Kenya Meteorological Department (KMD). The average monthly maximum temperature, minimum temperature, and total precipitation were obtained for 2010 to 2022 on a daily scale and averaged by month.  Data were not obtained for years before 2010 because changes in county boundaries that occurred between 2009 and 2010.  Climate variability was measured as the average standard deviation of the monthly maximum temperature, minimum temperature, and total precipitation from 2010 to 2022. Extreme events were measured as the frequency of days over the 95th percentile of precipitation, maximum temperature, and minimum temperature per month from 2014 to 2022, to match the time of adaptive capacity and sensitivity data sources. Adaptive capacity, sensitivity and exposure variables were abstracted from census data from the Kenya National Bureau of Statistics (KNBS), Kenya Ministry of Health, Food and Agriculture Organization, National Imagery and Mapping Agency of the US, and the peer reviewed literature. ### Files and variables #### File: schri_all.csv **Description:** CSV data set of variables used to calculate risk of diarrheal disease on a sub-county level in the Lake Victoria Region Block of Kenya  **Sub County Level Data:** | **Variable** | **Description** | | :----------------------------------- | :-------------------------------------------------------------------------------------------------------------- | | County | County | | Sub\_County | Sub\_county within the county | | Total\_Population | Total number of people in the sub county | | Rural\_Population | Number of people living in rural areas | | Urban\_Population | Number of people living in urban areas | | Child\_Population | Number of children under the age of 18 | | Elderly\_Population | Number of elderly people, over the age of 65 | | Housing\_Type\_permanent | Number of households with permanent housing – houses which are built with bricks and has iron roofs | | Housing\_Type\_\_non-permanent | Number of households with non-permanent housing – houses are built with either mud walls or grass thatched roof | | Education\_Level\_\_\_Primary | Number of adults with primary education | | Education\_Level\_\_\_Secondary | Number of adults with secondary education | | Education\_Level\_\_\_tvet | Number of adults with technical and vocational education and training (TVET) education | | Education\_Level\_\_\_University | Number of adults with university education | | Education\_Level\_\_\_adult\_basic | Number of adults with adult basic education | | Education\_Level\_\_\_madrasa\_duksi | Number of adults with madrasa duksi education | | Literacy\_Rate\_litrate\_ | Percentage of the population over 18 that can read and write | | Improved\_Sanitation\_Facility | Number of households with access to improved sanitation | | Electricity | Number of households with electricity | | Average\_Household\_size | Average size of a household | | Improved\_Drinking\_Water\_Source | Number of households with access to improved drinking water | | Population\_female | Number of females | | Population\_Density\_No\_per\_Sq\_ | Population per land area | | Number\_of\_Households | Number of households | | Land\_Area\_sq\_km\_ | Land area in the sub county per square kilometer | | Sex\_Ratio\_No\_of\_Males\_per\_100 | Number of males per 100 females | | Dispensary | Number of health facilities classified as a dispensary | | Health\_Centre | Number of health facilities classified as a health centre | | Hospitals | Number of health facilities classified as a hospital | | Medical \_Center | Number of health facilities classified as a medical centre | | Medical\_Clinic | Number of health facilities classified as a medical clinic | | Nursing\_Home | Number of health facilities classified as a nursing home | | Stand\_Alone | Number of health facilities classified as a stand alone | | Primary\_Health | Number of health facilities classified as primary health | | NoHealthFacilities | 1 represents that there are no health facilities in that sub county | | Total\_bedsandcots | Number of cots and beds in each sub county at any type of health facility | | Children | Proportion of the total population that is a child | | Elderly | Proportion of the total population that is elderly | | Female | Proportion of the total population that is female | | Rural | Proportion of the total population that is rural | | Urban | Proportion of the total population that is urban | | Adult\_Population | Count of the adult population, total pop minus child pop and elderly pop | | Adult | Proportion of the total population that is an adult | | Primary\_Ed | Proportion of the adult population with a primary education | | Secondary\_Ed | Proportion of the adult population with a secondary education | | Univ\_Ed | Proportion of the adult population with a university education | | Adult\_Ed | Proportion of the adult population with an adult basic education | | Madrasa\_duksi\_Ed | Proportion of the adult population with a madrasa Duksi education | | TVET\_Ed | Proportion of the adult population with a TVET education | | HDX | Whether or not this sub-county is present in the HDX shapefile, used for mapping and spatial data | | Distance\_to\_Urban\_Center | Average distance in minutes to an urban center in the subcounty | | Number\_of\_rivers | Number of distinct river segments in the sub-county | | Number\_of\_floodplains | Number of flood plains in the sub-county | | TP\_informal | Number of people living in informal settlements | | Pop\_InformalSettlements | Proportion of the total population living in informal settlements |   **County Level Only Data:** | **Variable** | **Description** | | :-------------------------------------------------- | :------------------------------------------------------------------------- | | Poverty | Following up with SWAP about this | | StuntingRate | Rate, following up with SWAP about this | | Infant\_MR | Probability of a child born dying before age 1 per 1000 live births | | Under\_5\_MR | Probability of a child dying before age 5 per 1000 live births | | AdultMR\_Male | Probability of dying between age 15 and 60 per 1000 population for males | | AdultMR\_Female | Probability of dying between age 15 and 60 per 1000 population for females | | ElderlyMR\_Male | Probability of dying after age 60 per 1000 population for males | | Elderly\_MR\_Female | Probability of dying after age 60 per 1000 population for females | | CIDP\_Total\_Score | Total score from CIDP evaluation | | CIDP\_Group | Low, medium or high connection groups based on CIDP total | | Density\_of\_doctors\_nurses\_and\_clinicalofficers | Number of all health professionals per 10,000 people | | Density\_of\_doctors | Number of doctors per 10,000 | | Density\_of\_nurses | Number of nurses per 10,000 | | Density\_of\_clinicalofficers | Number of clinical officers per 10,000 | **Weather Data:** | **Variable** | **Description** | | :-------------- | :---------------------------------------------------------- | | Std\_pre | Standard deviation in precipitation in mm | | std\_tmax | Standard deviation in max temperature | | std\_tmin | Standard deviation in minimum temperature | | averagetempdiff | Average daily difference in minimum and maximum temperature | | avg\_pre | Average daily precipitation in mm | | avg\_Tmax | Average daily maximum temperature in C | | avg\_Tmin | Average daily minimum temperature in C | | freq\_eh | Average number of extreme heat days  | | freq\_ec | Average number of extreme cold days | | freq\_er | Average number of extreme rain days | 
 
 \** ** ## Code/software SAS Studio ## Access information Other publicly
 accessible locations of the data: * NA Data was derived from the following
 sources: * Kenya National Bureau of Statistics * Kenya Meterological
 Department * Kenya Ministry of Health * Food and Agriculture Organization
 of the United Nations * National Imagery and Mapping Agency of the United
 States * Published Literature