This dataset provides detailed records of patient health risk assessments, including demographic data, clinical measurements, and calculated risk factor scores for chronic disease prediction. It is ideal for population health analytics, risk stratification, and supporting proactive care management in healthcare settings.

The Financial Risk Assessment Dataset provides detailed information on individual financial profiles. It includes demographic, financial, and behavioral data to assess financial risk. The dataset features various columns such as income, credit score, and risk rating, with intentional imbalances and missing values to simulate real-world scenarios.

IntroductionIn Australia, opportunistic screening (occurring as skin checks) for the early detection of melanoma is common, and overdiagnosis is a recognised concern. Risk-tailored cancer screening is an approach to cancer control that aims to provide personalised screening tailored to individual risk. This study aimed to explore the views of key informants in Australia on the acceptability and appropriateness of risk-tailored organised screening for melanoma, and to identify barriers, facilitators and strategies to inform potential future implementation. Acceptability and appropriateness are crucial, as successful implementation will require a change of practice for clinicians and consumers.MethodsThis was a qualitative study using semi-structured interviews. Key informants were purposively selected to ensure expertise in melanoma early detection and screening, prioritising senior or executive perspectives. Consumers were expert representatives. Data were analysed deductively using the Tailored Implementation for Chronic Diseases (TICD) checklist.ResultsThirty-six participants were interviewed (10 policy makers; 9 consumers; 10 health professionals; 7 researchers). Key informants perceived risk-tailored screening for melanoma to be acceptable and appropriate in principle. Barriers to implementation included lack of trial data, reluctance for low-risk groups to not screen, variable skill level in general practice, differing views on who to conduct screening tests, confusing public health messaging, and competing health costs. Key facilitators included the perceived opportunity to improve health equity and the potential cost-effectiveness of a risk-tailored screening approach. A range of implementation strategies were identified including strengthening the evidence for cost-effectiveness, engaging stakeholders, developing pathways for people at low risk, evaluating different risk assessment criteria and screening delivery models and targeted public messaging.ConclusionKey informants were supportive in principle of risk-tailored melanoma screening, highlighting important next steps. Considerations around risk assessment, policy and modelling the costs of current verses future approaches will help inform possible future implementation of risk-tailored population screening for melanoma.

Moderator analyses.

Metadata Entry for "A population ecology- quantitative microbial risk assessment (QMRA) model for antibiotic-susceptible and antibiotic-resistant E. coli health risk in recreational water". Please contact the corresponding author to request the associated data. This dataset is not publicly accessible because: The data is non-EPA generated. It can be accessed through the following means: Please contact the corresponding author Kerry Hamilton at kerry.hamilton@asu.edu to request the data. Format: XLSX and/or CSV files. This dataset is associated with the following publication: Heida, A., M. Hamilton, J. Gambino, K. Sanderson, M. Schoen, M. Jahne, J. Garland, L. Ramirez, H. Quon, A. Lopatkin, and K. Hamilton. Population Ecology-Quantitative Microbial Risk Assessment (QMRA)Model for Antibiotic-Resistant and Susceptible E. coli in Recreational Water. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 59(9): 4266-4281, (2025).

Demographic factors at baseline CVD risk assessment among people 30–74 years with no prior CVD, by prior mental health (MH) status and gender.

This project addresses major gaps in knowledge on vital rates such as age to maturity, survival, sex ratios, and population size (including the males)whcih have made it difficult to conduct meaningful population and risk assessments. Although vital rates are difficult to observe directly, genetic analysis provides a practical approach to understand these processes. Understanding the proportion of males to females in any population has important consequences for population demographic studies. Using hatchling and maternal DNA fingerprints, one can deduce the paternal genotypes ? from one to many fathers per clutch. The resulting genotypes represent individual males that are actively breeding in the population. This means that males can effectively be sampled without ever having seen them or having to catch them in the field. The nesting population on St. Croix is an important US Index Population for leatherbacks that has been intensively monitored using a variety of Capture-Mark-Recapture (CMR) methods since 1981 (Dutton et al. 2005). Due to the richness and consistency of the demographic data, this population offers unique opportunities for research and development of tools & approaches for getting at vital rate parameters that are needed to improve stock assessments in sea turtles, as identified in the recent NRC Report (2010). These approaches can then be applied to other populations, e.g. the critically endangered Pacific leatherback. We have developed non-injurious in-situ techniques to mass sample large numbers of live hatchlings for genetic fingerprinting as part of a long term CMR experiment, and also demonstrated the feasibility of using hatchling genotyping and kinship analysis to determine the genotypes and number of breeding males in the population (Stewart & Dutton 2011). We have sampled a total of 17,087 hatchlings between 2009-2011 as part of this project, will continue field effort in 2012 toward the goal of a minimum sampling of 50,000 hatchlings over the next 2-4 years. At an appropriate time in the future, we will use high throughput genotyping methods currently being developed in the next 2-4 years to create a database of individual hatchling identifications (?genetic tags?) that will be compared to those first time nesters sampled annually into the future. This project will also genotype a subset of the samples collected in 2011 to assess males in two consecutive seasons for a more accurate census of the number of males in the breeding population and to determine the extent of male fidelity and breeding periodicity. Objectives include 1) mass-tagging of leatherback hatchlings for Capture-Mark-Recapture (CMR) studies to determine age at first reproduction and age-specific survival rates and 2) application of kinship approaches to reconstruct parental genotypes from mother-offspring comparison to census males, determine operational sex ratios (OSR) of the breeding population, reproductive success of males and mating system.

Ipsos Global @dvisor wave 32 was conducted on April 3 and April 17, 2012. It included the following question sections: A: Demographic Profile, B: Consumer Confidence, R: Reuters Battery, EQ: Global Retail Intended Purchase Assessment, ET: Languages Used in Business, EU: Online Dating, X: Corporate/Business Risks, C: Corporate Social Responsibility.

This dataset provides a comprehensive view of loan applications, capturing applicant demographics, financial profiles, loan details, and subsequent loan performance. Designed for credit risk modeling and decision automation, it enables in-depth analysis of risk factors, default prediction, and portfolio management in the lending industry.

Rising seas will impact millions of coastal residents in coming decades. The vulnerability of coastal populations exposed to inundation will be greater for some sub-populations due to differences in their socio-demographic characteristics. Many climate risk and vulnerability assessments, however, model current populations against future environments. We advance sea-level rise risk assessments by dynamically modeling environmental change and socio-demographic change. We project three scenarios of inundation exposure due to future sea-level rise in coastal Georgia from 2010 to 2050. We align the sea-level rise projections with five population projection scenarios of socially vulnerable sub-populations via the Hamilton-Perry method and the theory of demographic metabolism. Our combined fast sea-level rise and middle population scenarios project a near doubling of the population exposed, and a more than five-fold increase for those at risk (i.e., residing in a census tract with high social vulnerability) and most at risk (i.e., high social vulnerability and high exposure) compared to the same estimate based on 2010 population data. Of vulnerable sub-populations, women had the largest absolute increase in exposure for all scenario combinations. The Hispanic/Latinx population’s exposure increased the largest proportionally under the fast and medium sea-level rise projections and elderly people’s (65+) under the slow sea-level rise scenario. Our findings suggest that for coastal areas experiencing rapid growth (or declines) in more socially vulnerable sub-populations, estimates based on current population data are likely to underestimate (or overestimate) the proportion of such groups’ risk to inundation from future sea-level rise.

The Clinical Risk Assessment Solution market is experiencing robust growth, driven by the increasing prevalence of chronic diseases, a rising elderly population, and the growing adoption of value-based care models. These factors necessitate proactive risk stratification and personalized care interventions, fueling demand for sophisticated clinical risk assessment solutions. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching a projected market value of approximately $14 billion by 2033. This growth is further propelled by technological advancements such as artificial intelligence (AI) and machine learning (ML), which enhance predictive accuracy and enable more effective risk mitigation strategies. The integration of these technologies into electronic health records (EHRs) and other healthcare IT systems streamlines workflow and improves the overall efficiency of risk assessment processes. Furthermore, increasing government regulations and incentives focused on population health management are creating a favorable environment for market expansion. Several key segments within the Clinical Risk Assessment Solution market are contributing to this growth. These include solutions tailored for specific chronic conditions like cardiovascular disease and diabetes, as well as those offering comprehensive assessments across various health parameters. Major players like 3M, Optum Inc., and Cerner Corporation are investing heavily in research and development, expanding their product portfolios, and forging strategic partnerships to maintain their competitive edge. The market's competitive landscape is characterized by both established players and emerging innovative companies, resulting in a dynamic market with continuous product advancements and service enhancements. The adoption of cloud-based solutions is also gaining traction, offering enhanced scalability, accessibility, and cost-effectiveness. However, data security concerns and interoperability challenges remain potential restraints on the market's growth. Addressing these challenges will be crucial for sustained market expansion and wider adoption of these valuable solutions.

Estimated population aggregated to the building and individual address. Database containing the following information: Year of build, Level nb, Nb lgts of dwelling and commerce, Building type (House or apartment) and vocation (Habitat, Mixed and activities)

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

The COMPAS risk assessment dataset compiled by ProPublica, containing defendants' records of prison times, demographics, criminal histories, and the COMPAS risk scores.

This feature class contains the results of the exposure analysis for the risk assessment in the 2016 Louisville Metro Hazard Mitigation Plan. Exposure (assets that can be potentially exposed to a hazard) was analyzed using the 100-meter grid from the Military Grid Reference System (MGRS). Scores for each exposure variable represent relative amounts of each variable in the grid cells, based on a 0 to 1 scale, meaning the cell with the highest amount has a score of 1.00 and the cell with the lowest amount has a score of 0.00. Scores for the remaining cells represent where their amounts fall on the scale and are not a simple ranking. Scores for all six exposure variables were added and then rescored on the 0 to 1 scale to calculate Composite Exposure.

Abstract

The primary objective of the 2006 DHS is to provide to the Department of Health (DOH), Department of National Planning and Monitoring (DNPM) and other relevant institutions and users with updated and reliable data on infant and child mortality, fertility preferences, family planning behavior, maternal mortality, utilization of maternal and child health services, knowledge of HIV/AIDS and behavior, sexually risk behavior and information on the general household amenities. This information contributes to policy planning, monitoring, and program evaluation for development at all levels of government particularly at the national and provincial levels. The information will also be used to assess the performance of government development interventions aimed at addressing the targets set out under the MDG and MTDS. The long-term objective of the survey is to technically strengthen the capacity of the NSO in conducting and analyzing the results of future surveys.

The successful conduct and completion of this survey is a result of the combined effort of individuals and institutions particularly in their participation and cooperation in the Users Advisory Committee (UAC) and the National Steering Committee (NSC) in the different phases of the survey.

The survey was conducted by the Population and Social Statistics Division of the National Statistical Office of PNG. The 2006 DHS was jointly funded by the Government of PNG and Donor Partners through ADB while technical assistance was provided by International Consultants and NSO Philippines.

Geographic coverage

National level Regional level Urban and Rural

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure household members (usual residents), all women and men aged 15-50 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary focus of the 2006 DHS is to provide estimates of key population and health indicators at the national level. A secondary but important priority is to also provide estimates at the regional level, and for urban and rural areas respectively. The 2006 DHS employed the same survey methodology used in the 1996 DHS. The 2006 DHS sample was a two stage self-weighting systematic cluster sample of regions with the first stage being at the census unit level and the second stage at the household level. The 2000 Census frame comprised of a list of census units was used to select the sample of 10,000 households for the 2006 DHS.

A total of 667 clusters were selected from the four regions. All census units were listed in a geographic order within their districts, and districts within each province and the sample was selected accordingly through the use of appropriate sampling fraction. The distribution of households according to urban-rural sectors was as follows:

8,000 households were allocated to the rural areas of PNG. The proportional allocation was used to allocate the first 4,000 households to regions based on projected citizen household population in 2006. The other 4,000 households were allocated equally across all four regions to ensure that each region have sufficient sample for regional level analysis.

2,000 households were allocated to the urban areas of PNG using proportional allocation based on the 2006 projected urban citizen population. This allocation was to ensure that the most accurate estimates for urban areas are obtained at the national level.

All households in the selected census units were listed in a separate field operation from June to July 2006. From the list of households, 16 households were selected in the rural census units and 12 in the urban census units using systematic sampling. All women and men age 15-50 years who were either usual residents of the selected households or visitors present in the household on the night before the survey were eligible to be interviewed. Further information on the survey design is contained in Appendix A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2006 DHS namely; the Household Questionnaire (HHQ), the Female Individual Questionnaire (FIQ) and the Male Individual Questionnaire (MIQ). The planning and development of these questionnaires involved close consultation with the UAC members comprising of the following line departments and agencies namely; Department of Health (DOH), Department of Education (DOE), Department of National Planning and Monitoring (DNPM), National Aids Council Secretariat (NACS), Department of Agriculture and Livestock (DAL), Department of Labour and Employment (DLE), University of Papua New Guinea (UPNG), National Research Institute (NRI) and representatives from Development partners.

The HHQ was designed to collect background information for all members of the selected households. This information was used to identify eligible female and male respondents for the respective individual questionnaires. Additional information on household amenities and services, and malaria prevention was also collected.

The FIQ contains questions on respondents background, including marriage and polygyny; birth history, maternal and child health, knowledge and use of contraception, fertility preferences, HIV/AIDS including new modules on sexual risk behaviour and attitudes to issues of well being. All females age 15-50 years identified from the HHQ were eligible for interview using this questionnaire.

The MIQ collected almost the same information as in the FIQ except for birth history. All males age 15-50 years identified from the HHQ were eligible to be interviewed using the MIQ.

Two pre-tests were carried out aimed at testing the flow of the existing and new questions and the administering of the MIQ between March and April 2006. The final questionnaires contained all the modules used in the 1996 DHS including new modules on malaria prevention, sexual risk behaviour and attitudes to issues of well being.

Cleaning operations

All questionnaires from the field were sent to the NSO headquarters in Port Moresby in February 2007 for editing and coding, data entry and data cleaning. Editing was done in 3 stages to enable the creation of clean data files for each province from which the tabulations were generated. Data entry and processing were done using the CSPro software and was completed by October 2008.

Response rate

Table A.2 of the survey report provides a summary of the sample implementation of the 2006 DHS. Despite the recency of the household listing, approximately 7 per cent of households could not be contacted due to prolonged absence or because their dwellings were vacant or had been destroyed. Among the households contacted, a response rate of 97 per cent was achieved. Within the 9,017 households successfully interviewed, a total of 11, 456 women and 11, 463 of men age 15-49 years were eligible to be interviewed. Successful interviews were conducted with 90 per cent of eligible women (10, 353) and 88 per cent of eligible men (10,077). The most common cause of non-response was absence (5 per cent). Among the regions, the rate of success among women was highest in all the regions (92 per cent each) except for Momase region at 86 per cent. The rate of success among men was highest in Highlands and Islands region and lowest in Momase region. The overall response rate, calculated as the product of the household and female individual response rate (.97*.90) was 87 per cent.

Sampling error estimates

Appendix B of the survey report describes the general procedure in the computation of sampling errors of the sample survey estimates generated. It basically follows the procedure adopted in most Demographic and Health Surveys.

Data appraisal

Appendix C explains to the data users the quality of the 2006 DHS. Non-sampling errors are those that occur in surveys and censuses through the following causes: a) Failure to locate the selected household b) Mistakes in the way questions were asked c) Misunderstanding by the interviewer or respondent d) Coding errors e) Data entry errors, etc.

Total eradication of non-sampling errors is impossible however great measures were taken to minimize them as much as possible. These measures included: a) Careful questionnaire design b) Pretesting of survey instruments to guarantee their functionality c) A month of interviewers’ and supervisors’ training d) Careful fieldwork supervision including field visits by NSOHQ personnel e) A swift data processing prior to data entry f ) The use of interactive data entry software to minimize errors

Population Health Management Market Outlook

According to our latest research, the global population health management market size reached USD 34.7 billion in 2024, reflecting a robust expansion driven by technological integration and evolving healthcare needs. The market is expected to grow at a CAGR of 12.8% from 2025 to 2033, reaching a projected value of USD 102.3 billion by 2033. This impressive growth rate is primarily attributed to the increasing prevalence of chronic diseases, the shift toward value-based care models, and the rising adoption of digital health solutions by healthcare providers and payers worldwide. As per our latest research, the market is witnessing a significant transformation, with a strong emphasis on data-driven decision-making and patient-centric care models.

One of the most significant growth factors propelling the population health management market is the surging incidence of chronic diseases such as diabetes, cardiovascular disorders, and respiratory illnesses. As populations age and lifestyle-related health risks escalate globally, healthcare systems are under mounting pressure to deliver more effective and coordinated care. Population health management solutions offer a holistic approach by integrating clinical, financial, and operational data, enabling healthcare stakeholders to identify at-risk populations, implement targeted interventions, and monitor health outcomes in real-time. This proactive approach not only reduces the overall cost of care but also improves patient outcomes, making it a critical component in the transition from fee-for-service to value-based care models.

Another crucial driver for the population health management market is the rapid advancement and adoption of digital health technologies. The proliferation of electronic health records (EHRs), wearable health devices, telemedicine platforms, and artificial intelligence-powered analytics tools has revolutionized how healthcare data is collected, shared, and analyzed. These technologies empower healthcare providers to gain deeper insights into population health trends, personalize care plans, and enhance patient engagement. Furthermore, government initiatives and regulatory mandates supporting interoperability and data sharing are accelerating the adoption of population health management software and services, especially in developed regions. The integration of advanced analytics and machine learning further amplifies the ability to predict disease outbreaks and manage resource allocation efficiently.

A third major growth factor is the increasing focus on preventive healthcare and wellness programs by both public and private sector stakeholders. Employers, insurers, and government bodies are investing heavily in population health management solutions to reduce long-term healthcare expenditures and improve workforce productivity. Preventive health initiatives, such as vaccination programs, health risk assessments, and wellness coaching, are being seamlessly integrated into population health platforms. These efforts are supported by favorable reimbursement policies and incentives for adopting value-based payment models, which reward healthcare organizations for improving population health metrics. As a result, the market is experiencing widespread adoption across various end-user segments, including healthcare providers, payers, employer groups, and government organizations.

From a regional perspective, North America continues to dominate the population health management market, accounting for the largest share in 2024. This dominance is driven by the presence of advanced healthcare infrastructure, high healthcare IT adoption rates, and supportive government policies such as the Affordable Care Act in the United States. Europe follows closely, benefiting from strong regulatory frameworks and increasing investments in digital health transformation. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rising healthcare expenditure, expanding insurance coverage, and the growing burden of chronic diseases. Latin America and the Middle East & Africa are also witnessing gradual adoption, although challenges such as limited healthcare IT infrastructure and regulatory complexities persist. Overall, the global market landscape is characterized by rapid technological advancements, evolving care delivery models, and a growing emphasis on population health outcomes.

Introduction and translocation programs require thoughtful planning to increase the likelihood of success and to understand the benefits, risks, and constraints of such programs. A risk assessment was completed for bull trout introduction into the Sullivan Lake and Harvey Creek watershed, northeastern Washington. The risk assessment was designed to evaluate potential risks to resident fish species, to bull trout introduced into Sullivan Lake, and to bull trout donor source populations. The risk assessment describes the potential risks associated with pathogens, genetics, and ecological interactions. Literature reviews were completed for fish species composition and abundance in Sullivan Lake watershed to assess potential ecological interactions and risks to these populations and to the introduced bull trout. A resident species table was designed to summarize general information to aid in assessing the type (such as predation, competition, prey) and frequency of interactions between resident species and introduced bull trout. Population status metric scores were assigned by U. S. Geological Survey (USGS) on the basis of a review of the data on past fish surveys and discussions with regional biologist. Population status metrics included: abundance, trend, and distribution, and were assigned ranking scores between 1 and 5 for the relative species composition. Species specific pathogen concerns were identified. Literature reviews were used in conjunction with discussions among regional biologists to identify and compile potential donor source populations and their population attributes into a donor source table. A decision framework was developed by USGS in collaboration with Kalispel Tribe of Indians biologists that identified desirable population attributes (life history behavior, abundance, population viability, feasibility of collection, and environmental match) associated with donor source populations and established ranking criteria. The population attribute information was used with the (1) decision framework, (2) established ranking criteria, and (3) expert opinion of regional biologists, to assign scores for overall ranking of donor source populations.

Abstract

The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.

The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.

The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.

Response rate

Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).

Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Zambia Demographic and Health Survey (ZDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 ZDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018

Note: Data quality tables are presented in APPENDIX C of the report.