data presentation for environmental quality survey

What is an environmental quality survey? Made SIMPLE

So you want to know what an environmental quality survey is and how it works? Then you have come to the right place! In this article I will teach you about the purpose of an environmental quality survey and how it works. Ready to learn more? Read on…

Environmental quality surveys- what you should know

Our environment is a precious resource that requires careful monitoring and management to ensure its health and sustainability. One crucial tool in this endeavour is the Environmental Quality Survey (EQS). Have you ever wondered what an EQS is and how it helps us understand the condition of our surroundings? In this article, I will delve into the world of EQS, exploring its purpose, components, data collection methods, analysis techniques, and practical applications.

So, fasten your seatbelts, put on your environmental detective hat, and let’s explore the fascinating realm of Environmental Quality Surveys together!

What is an environmental quality survey?

An EQS is a systematic assessment conducted to evaluate the quality and condition of the environment within a specific area. It involves collecting and analysing data on various environmental parameters, such as air quality, water quality, soil conditions, biodiversity, and noise and light pollution.

The primary purpose of an EQS is to provide valuable insights into the overall health and well-being of our ecosystems, identify potential environmental issues, and guide decision-making processes related to environmental management and policy development. By conducting these surveys, we can gain a comprehensive understanding of the state of our environment, the impacts of human activities, and the effectiveness of environmental conservation measures.

Environmental Quality Surveys are conducted using a combination of data collection methods. These can include sampling techniques, where representative samples are collected from different locations within the survey area. Monitoring stations may also be set up to collect continuous data on various parameters over an extended period. Additionally, remote sensing technologies and satellite imagery can be employed to gather data on a larger scale.

Once the data is collected, it undergoes rigorous analysis and interpretation. Statistical techniques are applied to identify trends, patterns, and relationships within the data. The results are then compared to regulatory standards or benchmarks to assess compliance and measure the extent of any deviations.

The applications of EQS are wide-ranging. They play a crucial role in Environmental Impact Assessments (EIA), helping evaluate the potential environmental consequences of development projects or policy changes. The findings of an EQS also inform the development of environmental policies, regulations, and land-use planning strategies. Moreover, they guide environmental management efforts, including remediation actions to address identified issues. Sharing EQS results with the public raises awareness, promotes environmental education, and encourages active participation in environmental stewardship.

Key components of an EQS

An Environmental Quality Survey (EQS) involves the assessment of various components that collectively contribute to the overall environmental health and condition of a specific area. Let’s explore the key components typically evaluated in an EQS:

Air Quality

The evaluation of air quality is crucial in determining the presence and concentration of pollutants in the atmosphere. Parameters such as particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), and volatile organic compounds (VOCs) are measured. Monitoring air quality helps identify sources of pollution, assess human health risks, and determine compliance with air quality standards and regulations.

Water Quality

Water quality assessment focuses on the chemical, physical, and biological properties of water bodies. Parameters evaluated include pH, dissolved oxygen levels, turbidity, nutrient concentrations (such as nitrates and phosphates), heavy metal contaminants, and the presence of pathogens. Evaluating water quality helps determine ecological health, assess suitability for human use, identify pollution sources, and guide water resource management and conservation efforts.

Soil Quality

Soil quality assessment involves examining various characteristics of soil, including its texture, structure, nutrient content, organic matter levels, pH, and contamination. Evaluating soil quality helps understand its fertility, assess potential risks to ecosystems and human health, identify soil erosion or degradation, and guide agricultural practices and land management strategies.

Biodiversity and Ecological Assessment

Biodiversity assessment focuses on studying the richness, abundance, and distribution of plant and animal species within the survey area. It involves identifying and cataloging species, assessing habitat quality, and examining ecosystem dynamics. Evaluating biodiversity helps understand ecosystem health, identify threatened or endangered species, recognise the impacts of human activities, and guide conservation and restoration efforts.

Noise and Light Pollution

EQS may include an evaluation of noise levels and light pollution within the survey area. Noise pollution assessment involves measuring ambient noise levels, identifying sources of noise, and assessing their impacts on human well-being and wildlife. Light pollution assessment examines excessive artificial lighting and its effects on nocturnal ecosystems, wildlife behaviour, and human health. Understanding and mitigating noise and light pollution contribute to creating more sustainable and harmonious environments.

Data collection methods

Collecting accurate and reliable data is a crucial step in conducting an Environmental Quality Survey (EQS). Various data collection methods are employed to gather information about different environmental parameters.

Let’s explore some commonly used methods:

Sampling Techniques

Sampling involves selecting representative locations within the survey area to collect data. Random sampling, where sampling points are chosen randomly, and stratified sampling, where the survey area is divided into distinct zones and samples are collected from each zone, are commonly employed. Sampling ensures that data collected is representative of the entire area and provides a comprehensive view of environmental conditions.

Monitoring Stations

Monitoring stations are established at strategic locations within the survey area to continuously collect data on various environmental parameters. These stations are equipped with instruments and sensors that measure parameters such as air quality, water quality, weather conditions, noise levels, and more. Monitoring stations provide real-time or near-real-time data, allowing for continuous monitoring and analysis of environmental trends.

Remote Sensing

Remote sensing involves the use of satellite imagery and aerial photographs to gather information on a larger scale. Satellite sensors can capture data related to land cover, vegetation health, water bodies, and air quality. This data provides valuable insights into environmental changes over time, such as deforestation, urbanisation , or pollution patterns . Remote sensing data complements ground-based measurements and helps create a comprehensive understanding of the environment.

Laboratory Analysis

In an EQS, samples collected from the field are often subjected to laboratory analysis. These samples can include air samples, water samples, soil samples, and biological samples. Laboratory analysis involves using specialised equipment and techniques to measure various parameters, such as pollutant concentrations, nutrient levels, pH, and more. Laboratory analysis ensures precise and accurate measurement of environmental parameters, providing detailed information for assessment and interpretation.

Citizen Science and Mobile Applications

Citizen science initiatives and mobile applications play an increasingly important role in data collection for EQS. These platforms allow individuals to actively participate in environmental monitoring by reporting observations, recording measurements, and submitting data. Citizen scientists can contribute to data collection efforts related to air quality, biodiversity, weather patterns, and more. This approach enhances data collection coverage and engages the public in environmental stewardship.

Data analysis and interpretation

Once data is collected through an Environmental Quality Survey (EQS), it undergoes rigorous analysis and interpretation to derive meaningful insights and draw conclusions about the environmental conditions. Data analysis plays a crucial role in understanding the state of the environment and identifying trends, patterns, and potential environmental issues. Here are the key steps involved in data analysis and interpretation:

Data Validation and Quality Control

Before proceeding with analysis, it is important to validate and ensure the quality of the collected data. This involves checking for completeness, accuracy, and consistency. Outliers and errors are identified and corrected to maintain the integrity of the dataset.

Statistical Analysis

Statistical techniques are applied to the collected data to uncover relationships, trends, and patterns. Descriptive statistics, such as mean, median, and standard deviation, provide summaries of the data. Inferential statistics, such as hypothesis testing and regression analysis, can be used to draw conclusions and make predictions. Statistical analysis helps quantify the magnitude of environmental parameters, assess their variability, and detect significant changes over time or across different locations.

Comparison with Standards or Benchmarks

EQS data is often compared to regulatory standards, guidelines, or predefined benchmarks established by environmental authorities or organisations. This comparison helps evaluate compliance with environmental regulations and assess the extent of deviations from desired environmental conditions. It provides a basis for determining whether specific environmental parameters meet acceptable levels or require intervention.

Trend Analysis

Analysing long-term data allows for the identification of trends and changes in environmental conditions. Temporal trends can reveal gradual improvements or deteriorations in environmental quality. By examining these trends, it becomes possible to understand the effectiveness of environmental management strategies, identify emerging issues, and make predictions about future environmental scenarios.

Spatial Analysis

Spatial analysis involves examining the spatial distribution and patterns of environmental data. Geographic Information System (GIS) techniques are often used to map and analyse data geographically. This helps identify hotspots of environmental issues, understand spatial relationships, and assess the spatial extent of environmental impacts. Spatial analysis can aid in targeted interventions and management decisions.

Interpretation and Communication

The final step in data analysis is interpreting the results and communicating them effectively. Interpretation involves synthesising the findings, drawing conclusions, and identifying actionable insights. It is essential to translate complex data into understandable and meaningful information for various stakeholders, including policymakers, environmental managers, and the general public. Clear and concise communication ensures that the results of the EQS are effectively utilised for decision-making, policy formulation, and environmental awareness.

Applications of EQS

Environmental Quality Surveys (EQS) have numerous practical applications in various domains. The data and insights gathered from these surveys play a crucial role in understanding and managing our environment. Here are some key applications of EQS:

Environmental Impact Assessment (EIA)

EQS findings contribute to Environmental Impact Assessments, which evaluate the potential environmental consequences of development projects, policy changes, or industrial activities. By assessing environmental quality parameters, EQS helps identify potential impacts on air, water, soil, biodiversity, and ecosystems. This information aids in making informed decisions, mitigating adverse effects, and promoting sustainable development practices.

Policy Development and Planning

EQS results are used to inform the development of environmental policies, regulations, and land-use planning strategies. The data provides scientific evidence for policy formulation, helping policymakers understand the environmental challenges and make informed decisions. EQS data can guide the establishment of environmental standards, pollution control measures, and conservation policies to ensure sustainable resource management.

Environmental Management and Remediation

EQS serves as a tool for environmental management and remediation efforts. The data obtained from EQS helps identify environmental issues, such as pollution hotspots, degraded habitats, or contamination sources. This information enables the implementation of targeted management strategies and remediation actions to restore environmental quality. EQS supports the monitoring of ongoing environmental initiatives and helps track the effectiveness of environmental management programs.

Conservation and Biodiversity Protection

EQS plays a vital role in the conservation and protection of biodiversity. By assessing biodiversity and ecological parameters, EQS helps identify areas of high ecological value, critical habitats, and threatened or endangered species. This information aids in the development of conservation plans, restoration initiatives, and the establishment of protected areas. EQS supports the monitoring of biodiversity trends and guides conservation efforts to maintain healthy ecosystems.

Public Awareness and Education

Sharing EQS results with the public raises awareness about environmental issues, fosters environmental literacy, and promotes active participation in environmental stewardship. EQS data can be used to develop educational materials, public reports, and awareness campaigns. By disseminating the findings in an accessible manner, EQS encourages individuals and communities to take actions that contribute to environmental sustainability.

Sustainable Development and Corporate Responsibility

EQS is employed by businesses and industries to assess and manage their environmental impacts. By conducting EQS, companies can identify areas where environmental performance can be improved, implement sustainable practices, and meet regulatory compliance. EQS supports corporate responsibility initiatives, helping organisations make informed decisions to reduce their environmental footprint and contribute to sustainable development.

Environmental quality survey FAQs

Now that you have an understanding of what an environmental quality survey is and why they exist, lets answer some of the most common questions on this topic.

What is the purpose of an Environmental Quality Survey?

The purpose of an environmental quality survey is to assess and evaluate the quality and condition of the environment within a specific area. It provides valuable insights into the overall health of ecosystems, the impacts of human activities, and the effectiveness of environmental management strategies.

What parameters are typically assessed in an EQS?

An EQS assesses various parameters such as air quality, water quality, soil conditions, biodiversity, noise and light pollution, and other environmental factors relevant to the survey area.

How is data collected for an EQS?

Data for an EQS is collected using various methods, including sampling techniques, monitoring stations, remote sensing technologies, and laboratory analysis. These methods ensure comprehensive data collection for accurate assessment.

How is the collected data analysed and interpreted?

Collected data is analysed using statistical techniques to identify trends, patterns, and relationships. It is compared with regulatory standards or benchmarks to assess compliance and measure the extent of any deviations. The data is then interpreted to draw meaningful insights and conclusions.

What are the practical applications of EQS?

EQS findings have applications in Environmental Impact Assessments, policy development and planning, environmental management and remediation, conservation and biodiversity protection, public awareness and education, and corporate sustainability efforts.

How do EQS results contribute to decision-making?

EQS results provide scientific evidence that supports informed decision-making processes. They help policymakers, environmental managers, and businesses understand environmental challenges, assess impacts, and implement targeted interventions for sustainable resource management.

Can citizen participation play a role in EQS?

Yes, citizen participation can play a valuable role in EQS. Citizen science initiatives and mobile applications allow individuals to contribute data, report observations, and actively participate in environmental monitoring, thereby enhancing data collection coverage and engaging the public in environmental stewardship.

How does EQS contribute to environmental conservation?

EQS assists in identifying environmental issues, critical habitats, and endangered species. This information guides the development of conservation plans, restoration initiatives, and the establishment of protected areas, promoting the conservation and protection of biodiversity.

How often should EQS be conducted?

The frequency of EQS depends on various factors such as the scale of the survey area, environmental dynamics, and regulatory requirements. EQS can be conducted periodically to monitor changes over time or on a project-specific basis to assess the impact of specific activities.

Can EQS help businesses comply with environmental regulations?

Yes, EQS helps businesses assess and manage their environmental impacts, ensuring compliance with environmental regulations. It provides data-driven insights that support corporate responsibility initiatives and help organisations implement sustainable practices.

What is an environmental quality survey- Key takeaways

Lastly, lets summarise the key points that we have learnt about environmental quality surveys.

• Environmental Quality Surveys (EQS) are comprehensive assessments of various environmental parameters to evaluate the quality and condition of an area’s environment.
• An environmental quality survey involves the evaluation of air quality, water quality, soil conditions, biodiversity, noise and light pollution, among other environmental factors.
• Data for EQS is collected through sampling techniques, monitoring stations, remote sensing, and laboratory analysis.
• Data analysis and interpretation involve statistical analysis, comparison with standards, trend analysis, and spatial analysis.
• An environmental quality survey has practical applications in environmental impact assessment, policy development, environmental management, conservation, public awareness, and corporate sustainability.
• EQS data supports decision-making processes, helps identify environmental issues, and guides the implementation of targeted interventions for sustainable resource management.
• Citizen participation can enhance EQS through citizen science initiatives and mobile applications, enabling public engagement in environmental monitoring.
• EQS contributes to environmental conservation by identifying critical habitats, endangered species, and supporting the development of conservation plans and protected areas.
• The frequency of conducting EQS depends on the survey area, environmental dynamics, and regulatory requirements.
• EQS helps businesses assess and manage their environmental impacts, ensuring compliance with regulations and promoting corporate responsibility.

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Environmental Data Visualization

• First Online: 01 January 2014

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• Neil Shifrin PhD 2  

Part of the book series: SpringerBriefs in Environmental Science ((BRIEFSENVIRONMENTAL))

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The graphical display of environmental data can help interpret their meaning. For example, a three-dimensional display of measurements in an environmental space shows a picture that would otherwise need to be imagined, and often incompletely, in the mind of someone reviewing otherwise tabular data. Fortunately, many tools aid in data visualization, such as database programs, GIS, and modeling, all of which are accessible through inexpensive personal computer software. Future developments may offer exciting improvements.

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Shifrin, N. (2014). Environmental Data Visualization. In: Environmental Perspectives. SpringerBriefs in Environmental Science. Springer, Cham. https://doi.org/10.1007/978-3-319-06278-5_6

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Construction of an environmental quality index for public health research

• Lynne C Messer 1 ,
• Jyotsna S Jagai 2 , 3 ,
• Kristen M Rappazzo 4 , 5 &
• Danelle T Lobdell 2  

Environmental Health volume  13 , Article number:  39 ( 2014 ) Cite this article

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A more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. An environmental quality index (EQI) for all counties in the U.S. was developed.

The EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000–2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified by four rural–urban continuum codes (RUCC). Higher values for each index were set to correspond to areas with poorer environmental quality.

Concentrations of included variables differed across rural–urban strata, as did within-domain variable loadings, and domain index loadings for the EQI. In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the domain indices. The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (−4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive scores (EQI score ranges from −5.86, 2.52).

The EQI holds promise for improving our characterization of the overall environment for public health. The EQI describes the non-residential ambient county-level conditions to which residents are exposed and domain-specific EQI loadings indicate which of the environmental domains account for the largest portion of the variability in the EQI environment. The EQI was constructed for all counties in the United States, incorporating a variety of data to provide a broad picture of environmental conditions. We undertook a reproducible approach that primarily utilized publically-available data sources.

Peer Review reports

Polluted environments have contributed to harmful exposures associated with human morbidity [ 1 – 5 ]. The empirical characterization of environmental conditions, however, is challenging because the non-residential ambient environment comprises an almost uncountable array of complex mixtures, which are difficult to quantify simultaneously. Moreover, the effect of the surrounding environment on human morbidity is more broadly understood to include exposures such as socioeconomic deprivation, access to healthy food, highway safety, etc. The complex nature of the overall environment likely contributes to the practice of using isolated exposures to represent ambient conditions.

Environment often encompasses traditional exposure like pollutants, chemicals, and water quality, as well as other non-genetic exposures such as the built environment, nutrition, and socioeconomic climate. In environmental epidemiology, ambient conditions are usually explored singly: one exposure or category of exposure at a time (e.g., ozone, pesticides, water disinfection by-products) [ 6 ]. Sometimes mixtures are used within one domain (e.g., air data) [ 1 , 7 ], and other times total environments may be characterized (e.g., exposure to healthy food environment) [ 8 , 9 ]. Still other work includes entire environmental domains to estimate non-residential ambient conditions (e.g., socioeconomic deprivation) [ 10 , 11 ]. And rarely, if ever, are multiple environmental domains combined, even though we know humans are exposed to these multiple environmental domains simultaneously.

Multiple challenges exist in combining across environmental domains or environmental types to construct one environmental measure. Much of the data we use to characterize environmental conditions are collected for administrative, regulatory and non-research purposes [ 12 ]. Measures collected at different scales would need to be meaningfully combined. They may also be measured at different units of spatial and temporal aggregation. A more complete estimation of the non-residential ambient environment may also be limited by statistical approaches and disciplinary practices. Statistical imprecision of estimates may be a concern if many variables are necessary to appropriately estimate a given domain or overall environment and a limited number of outcomes are being distributed across multiple exposure and covariate categories. From a disciplinary perspective, most research teams rarely include more than one type of exposure specialist. But many of these challenges can be readily overcome with appropriate statistical methods and interdisciplinary research teams.

Here we describe a method of constructing an environmental quality index (EQI) representing multiple domains of the non-residential ambient environment, including the air, water, land, built and sociodemographic domains. This manuscript outlines a reproducible approach to the development of the EQI that capitalizes almost exclusively on publically-available data sources.

Domain identification

A fuller description of the methods used for EQI construction is available in Additional file 1 . We initially identified three environmental domains, air, water and land, based on selected chapters from the United States (U.S.) Environmental Protection Agency (EPA) 2008 Report on the Environment (ROE) [ 13 ]. Following consultation with the ROE, the team undertook a more extensive review to complement the domains and data sources already identified, which included the following activities: 1) identifying precise literature search terms, limits and reporting format; 2) conducting a literature review on “Environment and Infant Mortality”; 3) recording findings; 4) finalizing search terms for within-domain literature review; 5) conducting a within-domain literature review; and 6) recording findings. We chose infant mortality to be the health outcome for the literature search for several reasons: 1) infant mortality is a well-researched and understood health outcome; 2) infant mortality is a general outcome, with known positive associations with other lifetime health measures such as disability-adjusted life expectancy [ 14 ]; as such, the environmental exposure–health outcome relationship would not be restricted to one organ (e.g., heart disease) or system (e.g., asthma); 3) the research team was largely composed of reproductive/perinatal researchers for whom infant mortality was an important health outcome. The literature review was conducted in PubMed for the years 1980–2008. We added the built and sociodemographic domains based on the findings of the literature review. From this broad search, and our a priori identification, five specific domains were considered: air, water, land, built, and sociodemographic environments.

Geographic level of analysis

The unit of analysis for EQI development was U.S. county. While county is a broad unit of analysis that may not allow for small-geography specificity, most national data sources are available at the county level. We wanted to construct a replicable process and product for use across the United States and we deemed the county level as the most widely generalizable. It also enables linkage to health data aggregated to the county level, such as national birth statistics from the National Center for Health Statistics (NCHS).

Data source time period

At the initiation of the EQI development, we restricted the temporal framework to 2000 to 2005. We wanted to primarily utilize publicly available data, and this six-year window was chosen based on availability of both environmental (including decennial census) and outcome data (e.g., national birth records).

Data sources

The data sources are described in detail elsewhere [ 15 ]. Briefly, data sources were considered for EQI inclusion based on temporal, spatial, and quality-related criteria. Temporal appropriateness required data to represent the 2000 to 2005 time period. Data sources were considered spatially appropriate if data were available at, or could be aggregated or interpolated to represent, the county level for all 50 states. Data quality, especially related to data source documentation, was determined by data source managers (in data reports and internal documentation), project investigators, and with the larger field of environmental research, through use and critique of the various data sources.

The air domain included two data sources: the Air Quality System (AQS) [ 16 ], which is a repository of national ambient air concentrations from monitors across the country for criteria air pollutants; and the National-Scale Air Toxics Assessment (NATA) [ 17 ], which uses emissions inventory data and air dispersion models to estimate non-residential ambient concentrations of hazardous air pollutants (HAPs).

The water domain comprised five data sources: Watershed Assessment, Tracking < Environmental Results (WATERS) Program Database [ 18 ], Estimates of Water Use in the U.S. [ 19 ], National Atmospheric Deposition Program (NDAP) [ 20 ], Drought Monitor Network [ 21 ], and National Contaminant Occurrence Database (NCOD) [ 22 ]. The WATERS Program Database is a collection of data from various EPA-conducted water assessment programs including impairment, water quality standards, pollutant discharge permits, and beach violations and closures. The Estimates of Water Use in the U.S. is calculated by the United States Geological Survey (USGS) and includes county-level estimates of water withdrawals for domestic, agricultural, and industrial uses. The NDAP dataset provides measures of chemicals in precipitation using a network of monitors located throughout the U.S. The Drought Monitor Data provides raster data on the drought status for the entire U.S. on a weekly basis. The NCOD dataset provides data from public water supplies on 69 different contaminants.

The land domain was constructed using data from five sources. The 2002 National Pesticide Use Database [ 23 ] estimates state-level pesticide usage based on pesticide ingredients and crop type. The 2002 Census of Agriculture [ 24 ] is a summary of agricultural activity, including information about crops, livestock, and chemicals used. The National Priority Site data [ 25 ] includes location of and information on sites that have been placed on the National Priority List (NPL), including indicators for major facilities (e.g., Superfund sites), large quantity generators, toxics release inventory, Resources Conservation and Recovery Act treatment, storage and disposal facilities, corrective action facilities, assessment, cleanup, and redevelopment exchange (brownfield sites), and section seven tracking system pesticide producing site locations. The National Geochemical Survey [ 26 ] contains geochemical data (e.g., arsenic, selenium, mercury, lead, zinc, magnesium, manganese, iron, etc.). The fifth source is the EPA Radon Zone Map [ 27 ], which identifies areas of the U.S. with the potential for elevated indoor radon levels.

The sociodemographic domain included two data sources: the U.S. Census [ 28 ] and Federal Bureau of Investigation (FBI) Uniform Crime Report (UCR) [ 29 ]. The U.S. Census collects population and housing data every 10 years, economic and government data every five years and the American Community Survey annually. FBI UCR rate data are available annually and by crime type (violent or property).

The built environment domain employed five data sources. Dun and Bradstreet collects commercial information on businesses and contains more than 195 million records [ 30 ]. These data are the only data used in the EQI which are not free, though they are publically available for purchase. Topographically Integrated Geocoding Encoding Reference (TIGER) [ 31 ] data provides maps and road layers for the U.S. at multiple units of census geography. The Fatality Analysis Reporting System (FARS) [ 32 ] data is a national census providing the National Highway Traffic Safety administration yearly reports of fatal injuries suffered in motor vehicle crashes. Housing and Urban Development (HUD) [ 33 ] data provide a count of low-rent and section-eight housing in each housing authority area, which corresponds to cities. The built environment domain also included the percent using public transportation variable from the census, which was not included in the sociodemographic domain; census data have been previously described.

EQI construction

Variable construction.

Each of the data sources could plausibly give rise to hundreds of potentially relevant variables; therefore only specific variables were selected – or in some cases, constructed – from each of the data sources. A detailed listing of all the constructed variables is available in Additional file 2 .

Statistical processes common to all variables in all domains

Variable collinearity was assessed within subgrouping and when the correlation coefficients exceeded 0.7, one variable was chosen for inclusion. Similar variables with low numbers of missing values were retained over those with high numbers of missing values. If missingness was approximately equal, the decision about which variable to retain was based on exposure routes from hazard summaries [ 34 ], with routes from the appropriate domain being primary.

Variable missingness was also assessed to determine if missing data were missing or instead represented true zeros. For instance, when crime data was missing for a county we considered that missing because crime occurs most everywhere but when beach closure data were missing for a county, we considered those to be true zeros because not all counties have beaches. When more than 50 percent of all counties were missing or zero for a given variable, that variable was excluded from further consideration for the EQI.

Because of the data reduction approach used for index construction (principal components analysis (PCA), discussed in detail below), and the statistical assumptions implied by this method, variables were assessed for normality. This was done by visually comparing histograms of each variable’s distribution to a normal distribution for that variable. When violations of normality were observed, transformations were considered to enable the variable to best approximate the normal distribution. For each variable, natural-log (hereafter, log), logit, and squared-root transformations were considered and distributions were visually inspected again. In each case, log transformation resulted in the most normally-appearing distribution. For variables with true zeros, log-transformation was achieved by adding half of the non-zero minimum value to all observations and then taking the natural log of that value.

Finally, variables were assessed to determine valences for environmental quality. Valences, or the positive or negative direction of the indices, were determined based on potential for human health and ecological effects. Domains containing variables with known or suspected potential for adverse health outcomes (e.g., increased morbidity or mortality) or ecologic effects (e.g., disruption of biotic integrity) were considered to have a negative valence with higher values representing poorer environmental quality. In some cases, the valence of a given variable was unknown, in which case the valence would be empirically assigned through the data reduction/PCA process by virtue of its association with other variables in that domain.

Air domain variable construction

Daily concentrations of six criteria air pollutants were downloaded from the AQS [ 16 ] and temporally averaged to get annual mean concentrations for each monitor location from 2000 to 2005. The annual means were then temporally and spatially kriged to estimate annual concentrations at each county center point. An exponential covariance structure for the spatial covariance was implemented to represent both temporal and spatial variability. These values were then averaged for the full study period.

The 2002 NATA [ 17 ] database was used as an initial source of county-level HAP concentrations for evaluation of variables to include. Emissions estimates were retrieved from the NATAs for 1999 and 2005, and estimates for each variable from the three NATAs were averaged to get a composite emissions estimate across the study period. Air domain variables were then checked for normality of distribution and where indicated, were log-transformed. For both criteria and hazardous air pollutants, higher concentrations are negative for air quality. Therefore, the valence of the air domain is negative.

Water domain variable construction

Water impairment is determined for multiple types of water usage: agricultural, drinking, recreational, wildlife and industrial. Using the WATERS [ 18 ] database and joining the data in GIS software with measures of stream length in the Reach Address Database [ 35 ], a cumulative measure of percent of water impaired for any use was used to represent overall water quality in the county.

Water contamination is caused by several sources and we used the number of National Pollutant Discharge Elimination System (NPDES) [ 36 ] permits in a county as a proxy for general water contamination. Three composite variables were included in the EQI: a composite for number of sewage permits, a composite for industrial permits, and one for stormwater permits, all per 1000 km of stream length per county.

Recreational water quality was assessed also using the WATERS database [ 18 ], from which we created three variables for number of days of beach closure - for any event, for contamination events, and for rain events in a county.

The quality of the water used for domestic needs data was extracted from the Estimates of Water Use in the U.S. [ 19 ] database as a proxy for domestic water quality from which two variables were included in the EQI: the percent of population on self-supplied water supplies and the percent of those on public water supplies which are on surface waters.

The atmospheric deposition of chemicals can affect water quality. The NDAP [ 20 ] dataset provides measures for the concentration of nine chemicals in precipitation, calcuim, magnesium, potassium, sodium, ammonium, nitrate, chloride, sulfate, and mercury. Annual summary data from each monitoring site for each year 2000–2005 were spatially kriged, using an exponential covariance structure, to achieve national coverage and county level estimates. The annual estimates for each pollutant were then averaged over the six-year study period. The data for all pollutants, except sulfate, were skewed and therefore were log-transformed to achieve normal distributions.

We expect that drought affects the concentration of pathogens and chemicals in waters and therefore can affect water quality. The Drought Monitor [ 21 ] dataset provides raster data on six possible drought status conditions for the entire U.S. on a weekly basis. The data were spatially aggregated to the county level to estimate the percentage of the county in each drought status condition. From this data we used the percentage of the county in extreme drought (D3-D4) in the EQI.

Chemical contamination of water supplies was extracted from the NCOD [ 22 ] dataset which provides data on 69 contaminants provided by public water supplies throughout the country for the period from 1998–2005. Data for all samples in a county for each contaminant were averaged over the entire time period of the data and log-transformed to achieve normal distributions. Missing values were set to zero, with the assumption that lack of measurement for an area indicated low concern for contamination with that particular contaminant.

The majority of variables in the water domain are estimates of pollutants for which higher values are considered negative for water quality. The final valence of the water domain is negative, indicating a higher water domain score is associated with poorer environmental quality.

Land domain variable construction

Information on the agricultural environment, were obtained from the 2002 Census of Agriculture [ 24 ]. In total, eight variables representing agriculture were constructed and county-level percentages (acres applied per county total acreage) were calculated and log-transformed.

Variables specific to pesticide application were also constructed. Herbicide, insecticide, and fungicide use for each county were estimated using crop data from the 2002 Census of Agriculture and state pesticide use data from the 2002 National Pesticide Use Dataset [ 23 ]. All pesticide variables were log-transformed.

The natural geochemistry and soil contamination of an area was estimated using the National Geochemical Survey (NGS) data [ 26 ]. These data, collected for stream sediments, soils, and other media, were combined at the county level to estimate the mean values of 13 geochemical contaminants available and were log-transformed.

Large industrial facilities represent sources for pollutants released into the environment. The National Priority List [ 25 ] data from the EPA provided information on facilities for the U.S. Because many counties had at least one, but no counties had all six of the facility types present, a composite facilities data variable was constructed by summing the count of any one of the six facilities types (brownfield sites [ 37 ], superfund sites [ 38 ], toxic release inventory sites [ 39 ], pesticide producing location sites [ 40 ], large quantity generator sites [ 41 ], and treatment, storage and disposal sites [ 42 ]) across the counties. The facilities rate variable was assessed for normality and log-transformed.

Finally, the potential for elevated indoor radon levels was represented using county score from the EPA Radon Zone map [ 27 ].

As all constructs in the land domain were determined to have a negative valence, the valence of the land domain as a whole is also negative, indicating a higher land domain score represents poor environmental quality.

Sociodemographic domain

The sociodemographic environment is an important environment for human health. Eleven variables from the United States Census [ 28 ] were included in the sociodemographic domain of the EQI. The sociodemographic domain contains a mix of positive and negative features; therefore when the sociodemographic domain was constructed, positive variables were reverse-coded to ensure that a higher amount of the sociodemographic domain represented adverse environmental conditions.

The area-level crime environment was represented using the Federal Bureau of Investigation (FBI) Uniform Crime Reports (UCR) [ 29 ]. These data required some manipulation for inclusion in the EQI. Because crime reporting is voluntary and crime data are reported for less than half the U.S. counties, yet it seemed unlikely that no crimes occurred in the areas with no reported crime, crime data were spatially and temporally kriged to estimate values for counties with no reported crime. Kriging employed a double exponential covariance structure for the spatial covariance; one structure represented short-range variability and the other long-range variability. The covariance model was fit to experimental covariance values using a least squares method and demonstrated sufficient fit. Varying geographical unit sizes were not explicitly accounted for through the kriging estimates, but crime estimates were made for 57 percent of U.S. counties, mostly in rural areas. The crime variable was log-transformed for inclusion in the EQI.

Both constructs in the sociodemographic domain have a negative valence. Therefore, the final valence of the sociodemographic domain is negative, indicating a higher sociodemographic domain score is associated with poor environmental quality.

Built environment domain

Housing environments vary and features of the housing environment have the potential to influence human health and well-being. The housing environment was represented using two variables available from the HUD data source, low-rent and section-eight [ 43 ], which were summed to result in the count of any low-rent or section-eight housing in each county; the subsidized housing rate was constructed from this count. The subsidized housing rate was log-transformed.

Highway safety was represented by a traffic fatality variable. Rates for the count of fatal crashes per county were constructed. This rate was log distributed (due to many counties having zero fatal crashes) and was therefore log-transformed. The percent of county residents who use public transportation was the only U.S. Census [ 28 ] variable used in the built environment domain of the EQI. For many counties, the percent of the population who reports using public transportation is near 0, and it was therefore log-transformed.

We were interested in characterizing the relative proportions of each county that were served by highways, secondary roads and primary roads. The proportions of all roadways that were highways or primary roads were included.

Business and service environments are important predictors of human health and activity. We sought to estimate features of the economic and service environment using data from Duns and Bradstreet [ 30 ]. Nine business environment rate variables were constructed by dividing the county-level count of a business type by the county-level population count. All variables except the negative food environment were log-transformed for normality. The business and service environments contain a mix of positive and negative features; therefore when the built domain was constructed, positive variables were reverse-coded to ensure that a higher amount of the these service variables represent adverse environmental conditions. The built domain’s valence is negative indicating a higher built domain score represents poor environmental quality.

EQI temporal representation

When annual data were available, variable consistency (mean and standard deviation) was compared across each year of the six-years (2000–2005). Additionally, proto-EQIs were constructed using data from one year (2002) and from the average of all six-years. For those variables that were spatially kriged, county-level values before and after kriging were also compared. Because these county-level values were temporally consistent, the EQI was constructed based on county-level averages for the six-year period for each variable in each domain.

RUCC stratification

Recognizing that environments differ across the rural–urban continuum [ 44 ], we concluded the EQI would be most useful if it accommodated rural–urban environmental differences. Therefore, EQI construction was stratified by rural–urban continuum codes (RUCC). The RUCC is a nine-item categorization code of proximity to/influence of major metropolitan areas [ 45 ]. As has been done elsewhere, the nine-item categories were condensed into four categories for which RUCC1 represents metropolitan urbanized = codes 1 + 2 + 3; RUCC2 non-metro urbanized = 4 + 5; RUCC3 less urbanized = 6 + 7; and RUCC4 thinly populated =8 + 9 [ 46 – 49 ]. Both stratified county-specific and all-county indices were created. Loadings on the stratified and non-stratified sets of indices were assessed to determine loading heterogeneity across counties. Because these loadings differed meaningfully by RUCC level, we constructed a RUCC-stratified EQI for each county.

Data reduction

Similar to the approach employed in other research [ 10 , 50 , 51 ], principal components analysis (PCA) was chosen for data reduction in this study because the investigators sought an empirical summary of total area-level variance explained by the environmental variables, rather than a confirmation of any underlying factor structure comprised of the previously identified domains.

Because it was unclear which of the variables included in the domain-specific PCAs were irrelevant to human health, we retained all the variables for inclusion in the RUCC-stratified and overall indices.

Component extraction and index construction

The constructed variables from each dataset were merged to produce a domain-specific county-level dataset. The domain-specific variables were then combined using PCA. PCA produces variable loadings, which are roughly equivalent to the “weight” or contribution that each variable makes toward explaining the total variance. The loading associated with each variable is then multiplied by its mean value for the given geography (county, for the EQI) and these weighted mean values are summed. Although it is possible to form as many independent linear combinations as there are variables, we retained only the first principal component: the unique linear combination that accounted for the largest possible proportion of the total variability in the component measures. This process was undertaken separately for each of the four RUCC strata.

The first principal component, which we labeled the domain-specific index (e.g., air domain index), was standardized to have a mean of 0 and standard deviation (SD) of 1 by dividing the index by the square of the eigenvalue [ 52 ]. Each domain-specific index was then included in a second PCA procedure (Figure  1 ), from which we extracted the first principal component to create the EQI.

Domain-specific and overall EQI construction - conceptually.

Pearson’s product moment correlations were used to assess relationships among the indices and between the indices and other county-level variables with a cut off of 0.7.

Description of variables comprising EQI domains

The full listing and description of variables contained in the EQI can be found in Additional file 2 . Here we present exemplar variables from each domain to describe the variables that represented common patterns of variable loadings (e.g., monotonically increasing or decreasing loadings from most urban to most rural, u-shaped loading pattern from most urban to most rural, etc.). Means, standard deviations, and ranges are included.

Variables included in the air domain generally show moderate to high variability between rural–urban strata, with higher averages in the most urban stratum decreasing to the most rural stratum (Table  1 ). For example, CO has mean values of 705, 598, 472, 343 ppm for each stratum from most urban to most rural. This pattern holds true for most of the hazardous air pollutants as well, though some pollutants show higher means in the non-metro urbanized or less-urbanized strata (e.g., chlorine, dimethyl sulfate). PM 10 , PM 2.5 , and carbon tetrachloride are relatively stable across rural–urban strata.

The variables included in the water domain also demonstrate moderate variability across the rural–urban strata. The metropolitan-urbanized and non-metro urbanized strata both have higher overall impaired stream length (14.00% and 14.20%, respectively) compared to the less-urbanized and thinly populated strata (8.79% and 6.54% respectively) (Table  2 ). The urban strata also demonstrated a higher number of discharge permits per stream length than the rural strata. The thinly-populated stratum had the highest percentage of population on self-supplied sources (35.61%) and the lowest percentage of population on surface water sources (21.94%). While most chemical contaminants demonstrated similar concentrations across the rural–urban strata, there were a few differences. Fluoride and Di(2-ethylhexyl)adipate (DEHA) were present in higher concentrations on the metropolitan-urbanized stratum. There was little variability across rural–urban strata for atmospheric deposition of chemicals and percent of land in extreme drought.

In the land domain, the metropolitan-urbanized counties have higher averages of soil contaminants, more facilities, and lower agricultural-related variables (% harvested,% irrigated) than non-metro urbanized, less-urban, and thinly-populated counties (Table  3 ). Pesticides and animal units show no clear pattern in variation across the strata. For example, average pounds of fungicides applied are 1820, 4030, 2740, and 2140 for most urban to most rural strata, respectively. There is little variation in the distribution of radon zones or agricultural chemicals applied across rural–urban strata.

Socioeconomic variables included in the sociodemographic domain indicate that rural counties are generally more deprived than more urban counties (Table  4 ), having the lowest household income ($30,350) and highest percent of persons in poverty (16.1%). From the crime perspective, however, rural areas are at an advantage compared to more urban areas; the mean violent crime rate for rural counties was 352.5 compared with 390.9 for the most urban and 398.1 for the non-metropolitan urbanized counties.

Contributing to the built environment domain (Table  5 ), the most rural counties have the smallest proportion of highways and significantly higher rate of traffic fatalities compared with more urban areas. Urban counties had fewer education-related businesses, positive food establishments, recreation-related resources and subsidized housing units per person compared with more rural counties.

Variable loadings on EQI domains

Variable loadings are a function of the county-level prevalence of a variable and its association with the other variables contributing to the total county-level variability for a given domain. The full listing variable loadings across RUCC strata and on the overall EQI can be found in Additional file 3 . Here we present exemplar variables from each domain to describe the variables that represented common patterns of variable loadings (e.g., monotonically increasing or decreasing loadings from most urban to most rural; u-shaped loading pattern from most urban to most rural, etc.).

The loadings for the variables that comprise the air domain varied by RUCC strata, though not extensively (Table  6 ). Direction of loadings were similar across rural–urban strata. Criteria air pollutants were less influential in the metropolitan-urban stratum compared to the other strata, while influence of hazardous air pollutants varied. The first principal component explained 47% of the total air variability and the domain was approximately normally distributed.

The loadings for the variables that comprise the water domain varied by RUCC and also by construct, suggesting that some constructs were more influential in urban areas and others in rural areas (Table  7 ). Variables representing overall water quality loaded positively in the two urban RUCC and negatively in the rural RUCC strata. The loadings for variables representing general water contamination and recreational water quality varied by RUCC though they were overall quite low. Loadings for variables representing domestic water quality and drought varied by RUCC, though they were all positive. The loadings for variables representing the atmospheric deposition construct varied by RUCC and did not demonstrate any clear patterns. Variables in the chemical contamination construct demonstrated little variability by RUCC with loadings of similar values for all variables across all RUCC. The first principal component explained 46% of the total variability for the water variables, and while each of the variables contributing to the water domain were normally distributed, the water domain itself was not. This may have resulted from so many regions of the U.S. lacking water quality information; there was considerable data for some counties and almost no data for others. In light of its non-normal distribution, the water domain itself and its contribution to the overall EQI should be interpreted with caution.

The loadings for variables in the land domain varied considerably (Table  8 ). For mercury, lead, titanium, and aluminum, loading magnitudes were much lower in the most urban stratum, while the loadings across all other strata were comparable. Some variables had the highest loading in the most-urban and most-rural strata (e.g., herbicides), while others remained stable across strata (e.g., arsenic, iron, harvested acreage). Direction of loadings was consistent across strata and the first principal component accounted for 32% of the total variability. This domain was approximately normally distributed with just a few counties having significantly lower land-domain values. These outlying counties were retained, however, to enable the EQI’s construction for all U.S. counties.

The loadings for the variables that comprise the sociodemographic domain also varied by RUCC code (Table  9 ), indicating some variables were more influential in urban settings while others exerted more of an effect on the domain score in rural counties. The patterns of association within the socioeconomic construct were fairly consistent, however, meaning the variables that loaded negatively in the urban counties also loaded negatively in the least urban counties. For instance, renter occupation and vacant units were negatively associated with median household value and median household income across rural–urban status. The one socioeconomic variable for which this was not the case was for the percentage of persons who worked outside the county; for this variable, working outside the county in less urbanized and thinly populated was inversely associated with more than a high school education, but was positively associated in metropolitan urbanized and non-metropolitan urbanized counties. The first principal component accounted for 32% of all county-level variability and was normally distributed.

The variables that comprised the built environment domain loaded much less consistently across the rural–urban categories (Table  10 ). In general, there were more inverse or negative variable loadings in the most urban counties compared with the less urban counties, and the most rural counties had fairly consistent positive variable loadings. Given this variability, the first principal component accounted for only 23% of the total county-level variability in the built environment, but was also normally distributed.

Domain-specific index description for overall EQI

The means, standard deviations, and ranges for each domain-specific index are presented in Table  11 . In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the indices. Mean values for the land domain index did not vary substantially by RUCC strata and mean values for the built environment indices were below zero, or in the direction of better built environment quality.

Correlations among the domain specific indices were modest (Table  12 ), ranging from 0.08 (air and water domain) to 0.40 (air and built domain). The correlations between the overall EQI and each of the domain specific indices reflected the relative importance of that domain to overall environmental variability, and ranged from 0.75 (overall EQI and the sociodemographic domain) to 0.37 (overall EQI and the water domain).

Domain-specific loadings on overall EQI

The first principal component accounted for 39% of the total county-level non-residential ambient environmental variability. The pattern of association for the domain-specific loadings differed by rural–urban status (Table  13 ). As constructed, the index loadings on the overall EQI index are mean (0) and standard deviation (1); the index is normally distributed with a very slight left skew. In the most urban areas, RUCC 1, the built environment domain was most influential as indicated by its highest loading value (0.52) followed by the air domain (0.51). For the non-metropolitan urbanized areas (RUCC 2), the sociodemographic and land domains loaded similarly on the overall EQI (0.60 and 0.55, respectively), followed by the built environment domain. For this particular grouping of counties, the water domain was least influential, based on its low PCA coefficient (0.30). The air domain was the least influential for the less-urbanized counties ((RUCC 3) 0.16), followed by the water domain (0.30). In the most thinly populated counties, the air and water domain were characterized by the lowest loadings (0.03 and 0.13, respectively) while the sociodemographic and land domains were the most influential (loadings of 0.63 and 0.58, respectively).

Description of EQI

The distribution of the RUCC-stratified EQI scores is displayed in Figure  2 . For these scores, higher values tend toward poorer environments while negative values are associated with more positive domain attributes. By virtue of their standardization, just under half of the EQI score across all RUCC strata were at the negative end of the distribution. The metropolitan urbanized (RUCC 1) and non-metropolitan urbanized (RUCC 2) counties had approximately the same heterogeneity of EQI score (−4.39, 2.53 and −4.74, 2.20, respectively). The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (−4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive environments (EQI score ranges from −5.86, 2.52).

Distribution of overall EQI scores across rural–urban categories for years 2000-2005*.

Correlations with other sociodemographic features

Environmental quality is only modestly associated with age, sex and racial sociodemographic characteristics in the United States (Table  14 ). The lowest positive correlations are between the percent under five years of age and high values on the EQI in both the overall and in the most urban counties (0.05 and 0.02, respectively). The highest correlations, 0.60 and 0.54, were for the relationship between percent white non-Hispanic and EQI values in the non-metro and less urban counties.

We developed an Environmental Quality Index for all counties in the United States incorporating data for five environmental domains: air, water, land, built, and sociodemographic. For each environmental domain, variables were constructed to represent exposures within that domain; indices for each domain and for environmental quality as a whole were developed by stratifying by rural–urban continuum codes. Variable loadings varied by domain and rural–urban designation, suggesting that environmental quality is driven by different domains in rural and urban areas. By virtue of the standardization used to construct the indices, approximately equal numbers of counties were at the positive end of the environmental quality spectrum as were at the negative end of the environmental quality spectrum.

The EQI is not the only index available for environmental estimation. The Environmental Performance Index (EPI), produced by a team at Yale University, is a country-level index that uses 22 performance indicators for which countries can be held accountable for environmental sustainability [ 53 ]. Both the EQI and the EPI rely on similar data sources (official statistics, monitoring data, modeled data, spatial data), prepare data similarly for variable construction (e.g., use of population denominators to construct standardized weights), and employ weighting and aggregation in construction. These similarities support the approach undertaken to construct the EQI. The EPI differs from the EQI in important ways, however. The EPI includes a substantially different set of environmental domains than the EQI, focusing on water effects (human and ecological health), air effects (human and ecological health), biodiversity and habitat, forests, fisheries, agriculture, climate change and energy. It is also constructed using target-based indicators for assessing performance on environmental health indicators rather than being purely an environmental representation. Finally, the EPI is aggregated at the country level to accommodate its international scope, while the EQI, though solely for the United States, gets at much finer detail at the county level.

Another index for natural environment vulnerability was developed by the South Pacific Applied Geoscience commission, the United Nations Environment Programme and their partners. The Environmental Vulnerability Index (EVI) [ 54 ] was developed through collaboration with countries, institutions, and experts across the globe and was designed for use with other economic and social vulnerability indices to provide insights into the processes that can negatively influence the sustainable development of countries. The EVI is based on 50 indicators for estimating country-level environmental vulnerability. Unlike the EQI, it is constructed by averaging the various measures. One limitation of the EVI is that it does not reflect environments dominated by human systems (e.g., cities, farms).

Most other environmental quality indices focus on one environmental domain (e.g., Air Quality Index [ 55 ]) or a specific type of activity (e.g., Pedestrian Environmental Quality Index [ 56 ]) or vulnerability (e.g., Cumulative Environmental Vulnerability Assessment [ 57 ], heat vulnerability index [ 58 ]). State-specific indices also exist, (e.g., CalEnviro Screen 1.0 [ 59 ], Virginia Environmental Quality Index [ 60 ] and Michigan Environmental Quality Index [ 61 ]) but their comparability across states is limited by their respective data sources and construction. A major strength of the EQI is that it encompasses multiple environmental domains, and all U.S. states and counties.

The EQI holds substantial promise for improving environmental estimation for public health. One important limitation of prior environmental health work has been the inability to control for the multiple environments to which people are simultaneously exposed. If these multiple human activity spaces occur within the same county, using the EQI will provide an estimate of the non-residential ambient county-level conditions to which residents are exposed, whether they are at home, at school, or at work. In addition to the EQI, each of the domain-specific indices is informative. The domain-specific loadings on the EQI indicate which of the environmental domains accounts for the largest portion of the variability in the EQI; in essence, these loadings answer the question as to which domain is making the biggest contribution to the total environment. Because most environmental health practice occurs at the domain level, this domain-specific information may be even more important to policy makers and environmental health activists than the overall EQI. Drilling down further, the variable loadings on each of the domains are also informative for the same reason. In the land environment, for instance, it might be important to know if pesticides or superfund sites seem to be contributing the largest share of variability to the land index. This information has obvious implications for public health intervention. The RUCC-stratified domains and EQI indices will also make an important public health contribution. We know urban and rural areas differ in important ways and these RUCC-stratified indices help us disentangle what domains may be driving some of the observed rural–urban differences in public health outcomes. While the total amount of environmental variability accounted for by any given EQI domain or the overall index may be modest, they contribute more control for or explanation of non-residential environmental conditions than has heretofore been possible.

While the process and product reported here makes a clear contribution to the environmental health literature, this work is not without limitations. Despite the large number of variables used for the EQI, data scarcity – in terms of spatial and temporal coverage – represents an important limitation to this work. Many of the data sources required spatial or temporal kriging to construct county level estimates. For example, even with extensive air monitoring networks, the measured spatial coverage of the U.S. is incomplete, particularly in rural areas. Many data sources are disproportionately located in urban areas (e.g., crime data), whereas others are found in rural areas (e.g., industrial livestock operations). The nonrandom distribution of environmental risk means that virtually all interpolated data are inaccurate, and our ability to draw inference for data-sparse rural areas is impaired.

Another potential limitation of the EQI is its construction at the county level. While the county may be too diffuse a unit to enable specific exposure assessment, it is a fair representation of the non-residential ambient environment. By explicitly describing the EQI construction process, we provide the necessary tools for interested investigators to apply at smaller units of aggregation with more specific data sources. Further, we plan to provide access to the data used to construct the EQI publically on the U.S. EPA website. A third limitation results from the data that were available for EQI construction. One aspect of our literature review identifying data sources used “infant mortality and environment” as search terms. While we contend we obtained adequate representation of the five environmental domains, it is possible our use of infant mortality precluded us from finding an environmental domain. Despite this possibility, however, the index is so broadly representative of the non-residential ambient environment it should be widely applicable to other health outcomes. Most of the EQI data were collected for non-research purposes; therefore, the data collection methodology, quality control and reporting varied across data source, domain and variable. We endeavored to include comparable data whenever possible, but data-quality differences are important to recognize. Because we relied on available data, and not all sources of environmental quality are measured at the county level, not all potentially relevant data are represented in the EQI. However, we attempted to capture as much as available for each of the five domains. Further, more data are collected in urban areas, which likely results in a more valid estimate of urban compared with rural environments. We have little information for Native American reservations and National Parks, for instance, which limits our ability to comment on those county spaces. In addition, the use of the EQI as a measure of exposure assumes exposure to “environment” is consistent for all individuals, but the extent of environmental exposure was not assessed. The EQI is focused mostly on the outside environment, which may not be the most relevant exposure in relation to human health and disease. Finally, population-level analyses offer little predictive utility for individual-level risk. Therefore, while the index may be useful at identifying lower quality environments that may predict population-level health outcomes, it cannot be used to predict adverse outcomes for individuals. We believe the EQI, and the approach taken for its development, represents a promising step and we encourage others to contribute additional work to this endeavor.

Conclusions

The Environmental Quality Index was constructed for all counties in the United States and incorporates a wide variety of data to provide a broad picture of environmental conditions in the United States. The approach we undertook was based on a reproducible methodology that accesses mostly publically-available data sources. Future development of the EQI includes assessing the consequences of the variable choices through sensitivity analyses, updating for 2006–2010, and exploring other levels of spatial aggregation. In this manuscript we present a valid, easily replicable methodology that can be broadly applied at different units of aggregation. As environmental public health researchers, we are fundamentally interested in the environmental contribution to human health. The EQI may aid us in developing knowledge on connections between the overall environment and human health outcomes.

Abbreviations

Air quality system

Environmental performance index

Environmental quality index

U. S. environmental protection agency

Environmental vulnerability index

Fatality annual reporting system

Federal bureau of investigation

Federal information processing standards

Hazardous air pollutants

Housing urban development

National-scale air toxics assessment

National center for health statistics

National contaminant occurrence database

National atmospheric deposition program

National geochemical survey

National pollutant discharge elimination system

National priority list

Principal components analysis

Particulate matter under 2.5 micrometers in aerodynamic diameter

Particulate matter under 10 micrometers in aerodynamic diameter

Report on the environment

Rural urban continuum code

United States

Topologically integrated geographic encoding and referencing

United states geological survey

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Acknowledgements

The Office of Research and Development (ORD), U.S. Environmental Protection Agency (EPA), partially funded the research with Innovate!, Inc. and L. C. Messer (Contracts WCF DP26H0001 and EP09D000003) and under EPA Cooperative Agreement with the University of North Carolina at Chapel Hill (CR83323601) and an appointment to the Research Participation Program for the U.S. Environmental Protection Agency, Office of Research and Development, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA.

The authors would like to thank the following people for their contributions to the development of the EQI: Kyle Messer, Genee Smith, Shannon Grabich, Christine Gray, Suzanne Pierson, Barbara Rosenbaum, and Mark Murphy. The authors also wish to thank the following people for their insightful review of the EQI: Lisa Vinikoor-Imler, Jane Gallagher, Tom Brody, and Lisa Smith.

The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

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Lynne C Messer

National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Chapel Hill, NC, USA

Jyotsna S Jagai & Danelle T Lobdell

School of Public Health, Division of Environmental and Occupational Health Sciences, University of Illinois, Chicago, Chicago, IL, USA

Jyotsna S Jagai

Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

Kristen M Rappazzo

Oak Ridge Institute for Science and Education, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, Oak Ridge, NC, USA

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The authors declare that they have no competing interests.

Authors’ contributions

LCM contributed to the study conception, constructed the EQI and drafted the manuscript. JSJ contributed to the EQI construction and manuscript revisions. KMR contributed to the EQI construction and manuscript revisions. DTL conceived of the study, participated in its design and coordination, and manuscript revisions. All authors read and approved the final manuscript.

Electronic supplementary material

Additional file 1:methods appendix.(pdf 61 kb), 12940_2013_747_moesm2_esm.xlsx.

Additional file 2:Overall and rural-urban continuum codes (RUCC)-stratified domain variable means, standard deviations and ranges.(XLSX 58 KB)

Additional file 3:Overall and rural-urban continuum codes (RUCC)-stratified loading for all variables.(XLSX 28 KB)

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Messer, L.C., Jagai, J.S., Rappazzo, K.M. et al. Construction of an environmental quality index for public health research. Environ Health 13 , 39 (2014). https://doi.org/10.1186/1476-069X-13-39

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Published : 22 May 2014

DOI : https://doi.org/10.1186/1476-069X-13-39

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Google My Maps Project – Environmental Quality Survey in My Local Area

In this lesson, you will investigate environmental quality in your local area. To do this you need a Google account. If you don’t have one through school, simply head over to Google.com and click the sign-in button. Then click create an account. Go through the process of setting up an account. You will also use Google Streetview to complete this investigation.

What is an environmental quality survey?

Environmental Quality Surveys are used to measure the ‘look and feel’ of a location. The technique is very subjective, which means people will have different views about an area. Some will find a location unattractive, while others may find the same place very attractive.

Below is the environmental quality survey you will use in this investigation. You are welcome to edit it and add more characteristics. You can download the table in MS Word .

• Click to share
• Print Friendly

Environmental quality survey

The Challenge

Your challenge is to complete environmental quality surveys in your local area and present the findings on Google Maps. You can either visit the locations and complete the environmental quality surveys if it is safe to do so. However, this guide will take you through the steps to complete the environmental quality surveys using Google Street. There are limitations to this such as the images may have been taken some time ago, however, it will be good practice for completing environmental quality surveys.

• Go to Google My Maps
• Click Create A New Map

Naming your Google Map

• Next, find your local area by typing in the name of your street in the search box. When it is displayed, click it, then the map will zoom into your local area.
• You can share your map and work with other students at the same time. To do this click Share, in the Add people box add the email address of the person you want to share the map with. Then click send.
• You need to identify the locations where you will complete your environmental quality surveys. You could choose the locations by using random, systematic or stratified sampling . In this example, we are going to use random sampling.

Identifying sites randomly

To do this click on Site 1. Then click the edit button (pencil) to add the information.

Recording your environmental quality surveys

Below is a completed example:

Setting up markers for data

• The next step is to complete the environmental quality survey for each location. If you are not visiting the locations you can use the satellite view on your map along with Google Street View to complete your environmental quality surveys.

Changing your base map

You can then zoom into your first location and view it in more detail. This could provide some help with completing your environmental quality survey, but you should also use Google Street View.

Extracting latitude and longitude data

Return to Google Maps and paste the latitude and longitude data into the seach box. As you ocan see below Google Maps has dropped a marker.

Searching by latitude and longitude in Google Maps

Opening Street View

You will now go to Street View and will be able to complete your environmental quality survey. Remember to add your data to your marker in Google My Maps. Repeat this for each site.

Taking it further

Compare environmental quality in your local area. This could include adding presenting your data in appropriate graphs and describing and explaining the differences. You could also complete environmental quality surveys between wealthy and less wealth areas within and between countries.

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Measuring the Quality of the Enabling Environment: Global Data Regulation Survey

5/25/2021 08:55:00 AM

Vivien Foster,Rong Chen

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DEC-Policy-Research-Talk-WDR-FINAL-Global-Data-Regulation-Survey-May-2021.pdf

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MINI REVIEW article

This article is part of the research topic.

Towards Standards for Marine Environment Impact Assessment

Marine exploration and its environmental impact assessment: Insights from international standards Provisionally Accepted

• 1 Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, China
• 2 Second Institute of Oceanography, Ministry of Natural Resources, China
• 3 Institute of Geology and Geophysics, Chinese Academy of Sciences (CAS), China

The final, formatted version of the article will be published soon.

Rising demand for marine resources has led to a great interest in seabed exploration and mining, while deep-sea environments are faced with cumulative effects of many human activities. Currently, conducting an environmental impact assessment for deep-sea exploration and mining is challenging due to the dynamic nature and a lack of high-quality data. The International Seabed Authority (ISA), which charges with regulating human activities on the seabed beyond the continental shelf, requires contractors to establish both geological and environmental baselines. Also, the ISA provides a general environmental guideline for exploring various seabed resources. However, standardization of its implementation would need to be addressed with specified technical international standards. The marine technology subcommittee of International Standardization Organization (ISO) contributes to the study of standards on deep-sea geological, geophysical and biological surveys, also on marine environmental protection. In this review, we explore two broad aspects of ISO standards: (1) the development of marine geological and geophysical exploration standard, which may help to establish geological map in the seabed area.(2) the current state of development of a series of Marine Environmental Impact Assessment (MEIA) standards, which could standardize the environmental surveys and monitoring activities in the seabed area. We also consider the standardization gap between MEIA and seabed mining, and propose future focus on coordination relationship between marine exploration and environmental protection.

Keywords: Marine exploration 1, marine environment impact assessment 2, seabed mining 3, International Standardization Organization (ISO) 4, Standard 5

Received: 22 Feb 2024; Accepted: 09 May 2024.

Copyright: © 2024 Ma, Li, Feng, Hao and Nan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Letian Ma, Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, Hangzhou, Jiangsu Province, China

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A template Environmental Quality Survey for students to use in the field. The first page has predefined fields and on the second page students can create their own survey categories.

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• Published: 13 May 2024

Nurse perceptions of practice environment, quality of care and patient safety across four hospital levels within the public health sector of South Africa

• Immaculate Sabelile Tenza 1 ,
• Alwiena J. Blignaut 1 ,
• Suria M. Ellis 2 &
• Siedine K. Coetzee 1  

BMC Nursing volume  23 , Article number:  324 ( 2024 ) Cite this article

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Improving the practice environment, quality of care and patient safety are global health priorities. In South Africa, quality of care and patient safety are among the top goals of the National Department of Health; nevertheless, empirical data regarding the condition of the nursing practice environment, quality of care and patient safety in public hospitals is lacking.

This study examined nurses’ perceptions of the practice environment, quality of care and patient safety across four hospital levels (central, tertiary, provincial and district) within the public health sector of South Africa.

This was a cross-sectional survey design. We used multi-phase sampling to recruit all categories of nursing staff from central ( n  = 408), tertiary ( n  = 254), provincial ( n  = 401) and district ( n  = 244 [large n  = 81; medium n  = 83 and small n  = 80]) public hospitals in all nine provinces of South Africa. After ethical approval, a self-reported questionnaire with subscales on the practice environment, quality of care and patient safety was administered. Data was collected from April 2021 to June 2022, with a response rate of 43.1%. ANOVA type Hierarchical Linear Modelling (HLM) was used to present the differences in nurses’ perceptions across four hospital levels.

Nurses rated the overall practice environment as poor (M = 2.46; SD = 0.65), especially with regard to the subscales of nurse participation in hospital affairs (M = 2.22; SD = 0.76), staffing and resource adequacy (M = 2.23; SD = 0.80), and nurse leadership, management, and support of nurses (M = 2.39; SD = 0.81). One-fifth (19.59%; n  = 248) of nurses rated the overall grade of patient safety in their units as poor or failing, and more than one third (38.45%; n  = 486) reported that the quality of care delivered to patient was fair or poor. Statistical and practical significant results indicated that central hospitals most often presented more positive perceptions of the practice environment, quality of care and patient safety, while small district hospitals often presented the most negative. The practice environment was most highly correlated with quality of care and patient safety outcomes.

There is a need to strengthen compliance with existing policies that enhance quality of care and patient safety. This includes the need to create positive practice environments in all public hospitals, but with an increased focus on smaller hospital settings.

Peer Review reports

Improving the nurse practice environment, quality of healthcare and patient safety has become a global priority [ 1 ]. This is because countries worldwide are striving to provide universal health coverage (UHC) to their citizens, and quality and safe care has been prioritised in the agenda to achieve UHC [ 2 , 3 ]. Recently there has been an increase in scholarly attention on the relationship between the nurse practice environment, quality of healthcare and patient safety, with global consensus that a positive nurse practice environment contributes positively to these [ 4 ].

The nurse practice environment is defined as the organisational characteristics of a work context that facilitate or constrain professional nursing practice [ 5 ]. Quality of care is the degree to which health services for individuals and populations increase the likelihood of the desired health outcomes [ 6 ], and patient safety is a dimension of quality of care and is defined as the avoidance of unintended or unexpected harm to people during the provision of healthcare [ 1 ].

Studies on the nurse practice environment have focused on nurse participation in organisational affairs, staffing and resource adequacy, and nurse leadership, management, and support of nurses, nurse-physician collegial relations, and foundations of quality of care [ 7 , 8 , 9 ]. A recent meta-analysis [ 10 ] found consistent and significant associations between the practice environment and quality of care and patient safety, based on data from 1,368,420 patients in 22 countries (including South Africa), 141 nursing units, 165,024 nurses, and 2677 hospitals. Ten years ago, a South African article—the only one from Africa included in this meta-analysis—showed the following trends: 52.3% of nurses assessed their practice environment as either poor or fair, 20.7% rated the quality of care as either poor or fair, and 5.5% rated patient safety as inadequate or failing [ 11 ]. In all cases, the public sector had worse outcomes than the private sector; and the study concluded that the nurse practice environment was significantly associated with better nurse and patient outcomes [ 11 ]. No national study has since followed this, with most studies focusing on small-scale or single-site qualitative and quantitative descriptive studies. Furthermore the variables of interest were explored separately from each other, such as the influence of the nurse practice environment on nurse outcomes [ 12 , 13 ], professional nurses’ understanding of quality nursing care [ 14 ], with a primary focus on patient safety culture [ 13 , 15 , 16 , 17 ]. Quality of care and patient safety studies in South Africa reported negative experiences of health providers, but these were not linked with the practice environment, even with ample evidence of its influence. One significant issue is the existence of policy documents that govern quality of care and patient safety in the nation. These include the following: the Patient Rights Charter, the Batho Pele principles, the National Core Standards framework [ 18 ], the National Guideline for Patient Safety Incident Reporting [ 19 ], and the Ideal Facility Framework [ 20 ]. Despite the aforementioned governmental obligations, achieving quality in healthcare continues to be a struggle [ 21 ]. This has been evidenced by the reports of litigations experienced by public health hospitals [ 22 ]. A major concern of the National Department of Health is the sudden increase in expenditure related to medico-legal claims. In the 2020/2021 financial year, more than ZAR6.5 billion (US $343,496.02) was awarded in medicolegal claims in the public sector [ 23 ].

Nurses as frontline, street-level bureaucrats in the implementation of the policies related to quality of care and patient safety in healthcare have critical experience of the nurse practice environment, quality of care and patient safety, and their views could contribute to future improvements [ 5 ]. Given existing evidence that the nurse practice environment influences quality of care and patient safety, it is important to understand the current situation. While there are existing policies directing quality of care and patient safety, it is not known how having these policies in place shapes the nurse practice environment, perceived quality of care and patient safety. This article expands on the findings of a previous national study [ 11 ], which demonstrated that the public sector had a more negative nurse practice environment, quality of care and patient safety. To add to the body of knowledge, this study examines the public sector and four hospital levels: central, tertiary, provincial, and district (small, medium, and large) hospitals. Hence this national study sought to examine nurses' perceptions of the practice environment, quality of care and patient safety across four hospital levels within the public health sector of South Africa.

Theoretical framework

This study is based on the theoretical framework of Tvedt et al. [ 24 ], which is a system perspective based on the model of Donabedian and modified by Battles (2006) to show how hospital structures and practice environment features improve quality of care and patient safety [ 24 ]. These outcomes are specifically identified as quality of care, patient safety (work-related outcome measures), and low-frequency adverse events and self-care ability (patient-related outcome measures).

Study context

This study was conducted in all nine provinces of South Africa, namely, Northern Cape, Western Cape, Eastern Cape, Free State, North West, Gauteng, Limpopo, Mpumalanga, and KwaZulu-Natal. South Africa has a two-tier healthcare system, with a public and a private sector [ 18 , 25 ]. The public sector is state-funded and caters to the majority – 71% – of the population [ 19 , 25 ]. The private sector is largely funded through individual contributions to medical aid schemes or health insurance, and serves a minority of the population [ 20 , 25 ]. This study focused on the public sector hospitals as they cater for the majority of the population. There are five categories of hospitals in the public sector, including district, regional, tertiary, central, and specialised hospitals, which are categorised according to the nature and extent of services provided and size [ 26 ]. The first point of entry to the South African health system is through primary healthcare (PHC) facilities, often referred to as clinics. Patients are referred from PHC facilities to district hospitals, regional, tertiary and central hospitals or specialised hospitals [ 26 ]. District hospitals are categorised into small, medium, and large district hospitals. Small district hospitals have between 50 and 150 beds; medium district hospitals have between 150 and 300 beds; and large district hospitals have between 300 and 600 beds [ 26 ]. These hospitals serve a defined population within a health district and support PHC facilities, providing services that include in-patient, ambulatory health services as well as emergency health services [ 26 ]. A regional hospital has between 200 and 800 beds and receives referrals from several district hospitals. Regional hospitals provide health services on a 24-h basis to a defined regional population, limited to provincial boundaries [ 26 ]. A tertiary hospital has between 400 and 800 beds and receives referrals from regional hospitals not limited to provincial boundaries, and also provides specialist level services [ 26 ]. A central hospital has a maximum of 1200 beds, receives patients referred from more than one province, and provides tertiary hospital services; they may also provide national referral services, including conducting research. A central hospital is attached to a medical school as the main teaching platform [ 26 ].

Study design

This study had a cross-sectional descriptive design. The STROBE checklist of items that should be included in reports of cross-sectional studies was used to guide the study and the reporting thereof.

Population and sampling

Multi-phase sampling was applied in the public sector. Purposive sampling was applied to the selection of hospitals in the public sector. A total of 27 hospitals were included by selecting the largest central or tertiary hospital in every province, and the provincial and district hospital in closest proximity to the selected central or tertiary hospital. The district hospitals were further stratified into large ( n  = 2), medium ( n  = 3), and small ( n  = 4) hospitals. Specialist hospitals were excluded. All in-patient medical and surgical units were included. Total population sampling was applied to all categories of nursing staff (registered nurses, community service nurses, enrolled nurses [2-year diploma], and enrolled nursing auxiliaries [1-year certificate]), including temporary staff, in these selected units. Nurses had to have worked in the respective unit for at least three months, and student nurses were excluded. The total sample of participants was as follows: central n  = 408; tertiary, n  = 254; provincial, n  = 401; and district, n  = 244 [large n  = 81; medium n  = 83 and small n  = 80]). Data were collected from April 2021 to June 2022. A sample size calculation was performed in g-power using the F-tests as the Test Family and the ANOVA: Fixed effects, special, main effects and interactions as the Statistical test in order to take the structure of the data into account. The parameters were specified as follow: Effect size f as and large (0.4) and medium (0.25), α err prob as 0.05, Power (1-β err prob) as 0.95, Numerator df as 10, Number of groups 6. The total sample sizes calculated were 162 and 400, which is well below the realised sample size of 1307. Total population sampling was used and not a random sample, thus no generalisations are made beyond the study population of nurses from these hospitals.

Instruments

In accordance with the theoretical framework of Tvedt et al., the variables measured included practice environment, quality of care, self-care ability, patient safety, and adverse events [ 24 ] . The practice environment was measured using the Practice Environment Scale of the Nurse Work Index Revised (PES-NWI-R). It consists of 32 questions and is divided into five subscales measuring nurse participation in hospital affairs; nursing foundations for quality of care; nurse manager ability, leadership, and support of nurses; staffing and resource adequacy; and collegial nurse-physician relations. The questions are measured on a Likert scale from 1 to 4, where 1 represents strongly disagree and 4 strongly agree. A mean score of 2.5 or more is indicative of a positive practice environment. This tool was found to be valid and reliable in many countries, including South Africa [ 27 ].

Quality of care was measured using the following question: In general, how would you describe the quality of nursing care delivered to patients on your unit/ward? The question was measured on a Likert scale from 1 to 5, where 1 represented excellent and 5 poor. Self-care ability was measured using one question (How confident are you that your patients and their caregivers can manage their care after discharge?), measured on a Likert scale from 1 to 4, where 1 represented very confident and 5 not at all confident.

Patient safety was measured using the following question: Please give your current practice setting an overall grade on patient safety. This was measured on a Likert scale from 1 to 5, where 1 represented excellent and 5 represented failing. The other eight items came from the Hospital Survey on Patient Safety Culture (HSOPSC) [ 28 ]. They were answered on a Likert scale from 1 to 5, where one represented strongly agree and five strongly disagree.

Finally, adverse events were measured by five questions on a five-point scale, where 1 represented never and 5 represented daily. These questions have been employed in multi-country research in South Africa [ 29 ], Europe [ 30 ], the United States of America [ 31 ], and Asia [ 32 ]. The specific outcomes have also been used in a meta-analysis [ 33 ]. The authors tried to control for response bias and subjectivity by asking neutrally worded questions, using anonymous surveys, ensuring that answer options were not leading, and that the order of the answers was randomised. i.e. the range for the practice environment was 1 = Strongly disagree.

4 = Strongly agree (ascending order), while quality of care and patient safety ranged from 1 = Excellent; 4 = Poor (descending order).

Data collection

Data collection took place between April 2021 and June 2022 after ethics approval and obtaining permission from relevant health departments. A team of trained field workers visited the hospitals to administer a paper-based survey to all of the consenting nurses in the hospitals, according to participation criteria. Upon arrival at each hospital, each unit manager was approached and a discussion was held between researcher, manager and staff regarding permission to do a survey among nurses in the unit. The discussion gave detailed information about the study, including the voluntary nature of participation, with an invitation to participate. The survey forms were given to the participants and they were allowed to complete them at a time convenient to them. The survey was completed anonymously, and participants were requested to return them in a sealed envelope via a sealed box with a post-box split, which was placed in all departments in the participating hospitals. The contents of these boxes were emptied by the researcher at the end of each day and removed a week later upon completion of data collection at the selected hospital.

Quantitative data analysis

Data was analysed using SPSS [ 34 ]. Descriptive statistics were used to analyse the demographic data, and data from each subscale representing the practice environment, quality of care and patient safety. These described frequencies, percentages, means and standard deviations. ANOVA type Hierarchical Linear Modelling (HLM), with p -values for all effects and interactions were calculated to present the differences in nurses’ perceptions of the practice environment, quality of care and patient safety across four hospital levels within the public health sector of South Africa, as the means of the different hospital levels and not the regression coefficients were important in the interpretation of the results. After the ANOVA type HLM, pair wise post-hoc comparisons were done to determine the statistically significant differences between the groups. Additionally, effect sizes were computed to determine which of these differences were important in practice. Where significant p - values lead to generalisations of results, effect sizes only indicate whether the differences in the sample groups were important in practice and are not used for generalisation if the p -values are not significant. Effect sizes were calculated and the magnitude of difference between the groups indicated as 0.2 = small, 0.5 = medium, 0.8 = large. Correlations between aspects of the nurse practice environment, quality of care and patient safety were also explored for the entire sample with 0.1. = small; 0.3 = medium and 0.5 = large relationships. Normality of the data was tested using the Kolmogorov–Smirnov test, but due to the unlikelihood of non-significant p -values in such a large sample size, more significance was ascribed to results from Q-Q plots. The points in the Q-Q plot lies close enough to the straight line to retain the assumption that the data distribution is normal for all variables [ 35 ].

Demographic data

We obtained a 43.1% response rate. As indicated in Table  1 , the majority of the participants were female ( n  = 1159; 88.7%), working on a full-time basis ( n  = 1158; 89.35%) and in the registered nurse/midwifery category ( n  = 593; 45.58%). Most nurses worked in the surgical units ( n  = 483; 36.95%), and we received most participation from the central level hospitals ( n  = 408; 31.22%).

• Nurse practice environment

The overall practice environment was not considered to be positive (M = 2.46; SD = 0.65), especially with regard to the subscales of nurse participation in hospital affairs (M = 2.22, SD = 0.76), staffing and resource adequacy (M = 2.23; SD = 0.80), and nurse manager ability, leadership, and support of nurses (M = 2.39; SD = 0.81), see Table  2 .

Table 3 provides an overview of responses to items on quality of care, patient safety, and adverse events.

• Quality of care

When asked about their perception of the quality of nursing care delivered to patients in their work setting, a third of participants (38.45%; n  = 486) indicated a negative outcome, and more than half of the nurses reported that they lacked confidence in patient or caregiver post-discharge care abilities (52.22%; n  = 658).

• Patient safety

As indicated in Table  3 , the overall grade for patient safety was rated as poor or failing by 19.59% ( n  = 248) of participants, and 430 participants (35.95%) agreed that there was a high reliance on temporary staff in their hospitals. In addition, more than half of the participants strongly agreed that their mistakes were held against them (64.38%; n  = 770), and that there was a lack of support for staff involved in patient safety errors (63.15%; n  = 749). Close to half felt that they could not question the decisions or actions of those in authority when related to patient safety issues (42.22%; n  = 505).

Adverse events

The subscale on adverse events examined the weekly and daily occurrence of adverse events. At least 21.32% ( n  = 252) of the participants experienced complaints weekly or daily, while 9.29% ( n  = 108) reported a weekly or daily incidence of hospital-acquired infections, and 7.77% ( n  = 93) weekly or daily medication errors.

Table 4 shows several effect sizes between the different levels of hospitals; however, only medium effect sizes will be reported on. Regarding the practice environment, there were medium practical effects between central hospitals and the small district hospitals for nurse participation in hospital affairs ( r  = 0.40; p  = 0.291), nursing foundations for quality of care ( r  = 0.44; p  = 0.469), and nurse manager ability, leadership, and support of nurses ( r  = 0.45; p  = 0.484), where central hospitals reported a more positive perception of these elements. There were also medium practical effects between provincial hospitals and the small district hospitals for nurse participation in hospital affairs ( r  = 0.40; p  = 0.211) and nursing foundations for quality of care ( r  = 0.43; p  = 0.398), where provincial hospitals reported a more positive perception of these elements of the practice environment.

Regarding the quality of care, there were medium practical effects with statistical significance between central hospitals and tertiary hospitals ( r  = 0.54; p  = 0.015) and small district hospitals ( r  = 0.51; p  = 0.061), where central hospitals reported better quality of care. Regarding patients’ self-care ability, there were medium practical effects between central hospitals and tertiary hospitals ( r  = 0.42; p  = 0.042) as well as medium district hospitals ( r  = 0.41; p  = 0.110) and small district hospitals ( r  = 0.56; p  = 0.007), where central hospitals reported more confidence in patients’ ability to manage their own care after discharge.

Regarding patient safety, there were medium practical effects between central hospitals and tertiary hospitals ( r  = 0.45; p  = 0.178), and also between medium district hospitals ( r  = 0.44; p  = 0.399), and small district hospitals ( r  = 0.51; p  = 0.178), where central hospitals reported higher grades of patient safety. Regarding staff feeling that their mistakes are held against them, there was a medium practical effect between small and medium district hospitals ( r  = 0.42; p  = 0.681), where small district hospitals reported that mistakes were held against them more often. There was also a medium practical effect between medium and large district hospitals regarding lack of support for staff involved in patient safety errors ( r  = 0.44; p  = 0.572), where small district hospitals reported less support for staff involved in patient safety errors. Finally, there was a medium practical effect between large and small district hospitals regarding the actions of hospital management showing that patient safety is a top priority ( r  = 0.40; p  = 0.856), where small district hospitals felt that the actions of hospital management showed that patient safety is a top priority.

Complaints were the only adverse event that had a medium practical effect, these effects being between provincial hospitals and large district hospitals ( r  = 0.57; p  = 0.056), and large district hospitals and small district hospitals ( r  = 0.60; p  = 0.114), where large district hospitals had a greater incidence of complaints.

As shown in Table  5 , all practice environment subscales showed medium to large negative correlations with the quality of nursing care delivered ( r  = -3.20 to r  = -4.28; p  = 0.00) and that patients and their caregivers can manage care after discharge ( r  = -0.282 to r  = -0.327; p  = 0.00). When considering the correlations of the practice environment on overall grade of patient safety, the practice environment had a large negative correlation ( r  = -0.405; p  = 0.00), especially regarding nurse foundations of quality of care ( r  = -0.411; p  = 0.00). Furthermore, medium negative correlations were noted between overall grade of patient safety and staffing and resources ( r  = -0.347; p  = 0.00) and nurse management, leadership, and support of nurses ( r  = -0.340; p  = 0.00), nurse participation ( r  = -0.323; p  = 0.00) and collegial nurse-physician relationships ( r  = -0.299; p  = 0.00). This shows that the more that participants agreed with positive statements about the nurse practice environment, the better they rated their quality of care, the more confidence they had in their patients’ post-discharge management, and the better they rated their overall grade on patient safety.

All practice environment items, except for collegial nurse-physician relationships, had medium negative correlations with the AHRQ item that the unit regularly reviews work processes to determine if changes are needed to improve patient safety ( r  = -0.221 to r  = -0.275; p  = 0.00). Furthermore, foundations of quality of care showed a medium negative correlation with staff speaking up when they see something that may negatively impact patient care ( r  = -0.226; p  = 0.00). Nurse participation ( r  = 0.235; p  = 0.00), leadership ( r  = 0.278; p  = 0.00), collegial nurse- physician relationship ( r  = 0.200; p  = 0.00), and the total practice environment scale ( r  = 0.259; p  = 0.00) all showed medium positive correlations with the AHRQ item ‘Staff feel like their mistakes are held against them’. All practice environment subscales exhibited medium correlations with the lack of support for staff involved in patient safety errors ( r  = 0.239 to r  = 0.315; p  = 0.00). Foundations of quality of care ( r  = -0.222; p  = 0.00), leadership, management, and support of nurses ( r  = -0.223; p  = 0.00), and the overall practice environment scale ( r  = -0.219; p  = 0.00) had negative medium correlations with discussing ways to prevent errors from happening again. All except the collegial nurse-physician relationship subscale of the practice environment showed medium negative correlations with staff feeling free to question the decisions or actions of those in authority ( r  = -0.222 to r  = -0.314; p  = 0.00). All practice environment subscales had medium correlations with the actions of hospital staff showing that patient safety is a top priority ( r  = -0.222 to r  = -0.362; p  = 0.00). To explain, the more that nurses agreed with positive practice environment items, the more they would agree to positive patient safety (AHRQ) items and the more they would disagree with negative patient safety (AHRQ) items.

Overall patient safety correlated positively and strongly with quality of nursing care delivered ( r  = 0.563; p  = 0.00), with a medium positive correlation with confidence in patients’ and caregivers’ post-discharge management ( r  = 0.357; p  = 0.00). Overall grade of patient safety also revealed a medium positive correlation with the unit regularly reviewing work processes ( r  = 0.258; p  = 0.00), staff feeling free to question the actions of those in authority ( r  = 0.222; p  = 0.00), and the actions of hospital management showing that patient safety is a top priority ( r  = 0.372; p  = 0.00). Regarding adverse events, overall grade of patient safety showed medium correlations with medication errors ( r  = 0.208; p  = 0.00) and patient falls ( r  = 0.223 p  = 0.00). This indicates that, as nurses rated overall patient safety more positively, they would also rate quality of care, confidence in post-discharge management, and positive items on patient safety (AHRQ) better, while at the same time leaning towards a lower incidence of adverse events occurring.

Another strong positive correlation was observed between quality of nursing care, and confidence that patients and their caregivers can manage care after discharge ( r  = 0.438; p  = 0.00), while medium positive correlations were noted between quality of nursing care and the unit reviewing work processes regularly ( r  = 0.273; p  = 0.00), staff speaking up if they see something that may negatively impact patient care ( r  = 0.209; p  = 0.00), staff feeling free to question the actions of those in authority ( r  = 0.210; p  = 0.00), and the actions of hospital management showing that patient safety is a top priority ( r  = 0.305; p  = 0.00). Regarding adverse events, quality of nursing care was correlated positively with medication errors ( r  = 0.230; p  = 0.00), patient falls ( r  = 0.237; p  = 0.00), and complaints ( r  = 0.249; p  = 0.00). This shows that the nurses rating the quality of care in their units as more positive would also have more confidence in their patients’ post-discharge management and agree more with positive patient safety items (AHRQ), while indicating a lower incidence of adverse events.

Confidence in post-discharge care and the actions of hospital management showing that patient safety is a top priority were also correlated positively on a medium level ( r  = 0.216; p  = 0.00). Regarding adverse events, confidence in post-discharge care was correlated positively with patient falls ( r  = 0.202; p  = 0.00), healthcare-associated infections ( r  = 0.206; p  = 0.00), and complaints ( r  = 0.211; p  = 0.00). To explain, this indicates that nurses with a higher rating in confidence in post-discharge management would also have a higher rating of their hospital management’s actions showing that patient safety is a top priority, while also rating the incidence of patient falls, healthcare-associated infections and complaints as occurring less often.

This national study sought to examine nurses' perceptions of the practice environment, quality of care and patient safety across four hospital levels within the public health sector of South Africa. In the participating hospitals we found that there was a negative nurse practice environment, and reports of poor quality of care and patient safety.

The findings on the perceived poor nurse practice environment in the public hospitals is of great concern. The recent South African Human Resources for Health Strategy 2030 advocates for the support of health personnel to deliver quality services [ 36 ]. The findings are worrying given that nurses are the backbone of the health system. A negative practice environment contributes to increased staff turnover and mental health challenges due to a stressful environment [ 37 ]. The challenges of unavailability of resources are common in public hospitals [ 38 ], but when coupled with poor leadership support could worsen the nurse outcomes. The finding on poor participation in hospital affairs indicates a lack of prioritisation of frontline nurses’ voices in hospital affairs; previous studies also found poor involvement of frontline nurses in policy decision making [ 39 , 40 ]. This could also mean that nurse managers may need empowerment on how to be supportive to staff and in improving prioritisation of frontline nurses’ voices in decision making. The implication of poor involvement of staff in hospital affairs usually includes retaliation, lack of a sense of belonging, and demotivation [ 41 ], and it also indicates weak leadership [ 42 ]. System level improvements, including relational leadership focused on prioritising staff involvement in decision making, could improve nursing practice environments in these hospitals [ 43 , 44 , 45 ]. Nurses’ perceptions of a negative practice environment have been reported in the literature. A study in Menoufia University Hospital, Egypt, reported that about 66.3% of nurses had a poor perception of the work environment [ 46 ]. Equally, a rapid review of literature on positive practice environment in the United Kingdom reported that most articles revealed a negative practice environment [ 47 ]. Nurse practice environment scholars concur that improving the nurse practice environment saves resources while building a culture of safety [ 48 ], and that prudent nurse managers should prioritise creating practice environments that are conducive to providing quality nursing care, as well as that managers should take the lead in impacting the elements of a positive practice environment [ 49 ]. In the context of our study, managers could be advocates for resources, nurse inclusion in hospital affairs, and provide strong leadership support.

The finding that almost half of the participating nurses rated the quality of care in their ward as poor is not new. A study in KwaZulu-Natal province in South Africa that explored nurses’ attitudes in providing care to patients revealed that they reported a poor incidence of nursing care to patients and deliberate disregard of essential patient care [ 50 ]. The study also noted that nurses attributed poor quality of nursing care to the attitudes of patients’ relatives or patients themselves, including unsupportive management behaviour. Maphumulo and Bhengu (2019) also report on poor quality of care [ 51 ]. As mentioned earlier, there are existing national policies to support quality and safety. For example, the Office of Health Standards Compliance set standards on quality in health care; additionally, in 2013 there was a nationwide quality improvement initiative called ideal facility [ 52 ]. Such initiatives focused on setting standards to assess each facility regarding compliance with set criteria for quality care in the facilities, started with the PHC clinics [ 53 ] and rolled out to hospital level. These results could mean that hospitals are not adhering to the set standards. Major challenges reported in the South African literature associated with poor quality of patient care include poor infrastructure [ 54 , 55 ], unavailability of medicine [ 56 , 57 ], shortage of staff, increased workload, shift work and long working hours [ 58 , 59 ]. These have contributed to an ongoing cycle of high staff turnover [ 60 ]. In line with international evidence, it would appear that the practice environment is closely linked to the quality of care [ 11 ].

The findings that nurses rated patient safety in their hospitals as poor could be due to their perceived patient safety culture. Evidence suggests that to achieve patient safety, strong leadership and a culture supportive of learning from errors (rather than a punitive approach) are critical ingredients [ 61 ]. In this study nurses reported that they experience a punitive reaction when reporting errors, and that they lacked support from their managers, and this is of great concern. Management response to errors is a critical determinant of patient safety culture; positive reactions to reported errors have been cited to encourage health providers to report them and subsequently improve patient care [ 62 ]. The findings resonate with recent South African studies that also found a poor patient safety culture in public hospitals [ 13 , 16 ]. A punitive reaction to reporting of adverse events remains a global challenge. A qualitative study conducted in South Korea on nurses’ experiences with disclosure of patient safety incidents found that nurses often prefer not to report patient safety incidents [ 63 ] due to the reaction expected from managers. An integrative review of literature from January 2010 to December 2020 using 31 papers revealed that a non-punitive reaction to patient safety incident reporting could improve patient safety and learning from errors [ 64 ]. Such strategies should be adopted in these participating hospitals.

The findings on weekly and daily reported adverse events and complaints resonate with the perceived poor patient safety. The finding that nurses perceived reactions to reported incidents as punitive could mean that more adverse events are not reported, for fear of the management reaction. Adverse events are a critical indicator of patient safety, hence weekly adverse events reported in a hospital should be a major concern. A qualitative study conducted in Palestine on nurses’ experience of the most common medical errors in the intensive care unit and coronary care unit demonstrated that they usually experience events like medication errors, nursing procedure errors, equipment errors, patient monitoring errors, intravenous medication errors, and resuscitation errors [ 65 ]. Similarly, in their study in Ghana Alhassan et al. (2019) noted that the type of errors nurses experience were wrong documentation, wrong intravenous fluid, and blood transfusion [ 66 ]. Furthermore, a study in Tehran, Iran on the types and causes of medication errors from nurses’ viewpoint indicated that about 64.55% of nurses reportedly made medication errors, and approximately 31.4% nearly experienced medication errors [ 67 ]. In the South African context reporting of adverse events remains a challenge, due to similar fear of reporting patient safety incidents, and evidence suggests that health providers often classify adverse events as minor to avoid reporting them [ 68 ]. A need to emphasise a just culture in the nursing environment will improve reporting of adverse events, and learning from these events will further reduce occurrences.

We also observed that when looking at comparison of effect sizes across hospitals, larger hospitals most often revealed better practice environments, quality of care and patient safety outcomes, while small district hospitals had the worst. These findings are not uncommon, as a Korean study also confirmed a strong relationship between practice environment and hospital sizes, concluding that the nurse practice environment varies with hospital size [ 69 ]. A distinct difference in the hospital categories compared is bed capacity, and complexity of conditions treated in each category, with more complex conditions seen in higher levels of hospitals. In the South African context, hospital categories also influence decisions on allocation and prioritisation of resources among the hospital categories, with more resources given to the larger hospitals [ 26 ]. Availability of resources plays a significant role in improving the nursing practice environment [ 70 ], and this is a possible contributor to a negative practice environment in small hospitals, since they often have fewer staff and resources [ 49 ]. We also found that central hospitals reported more confidence in patients’ ability to manage care after discharge than the smaller hospitals did; this could mean that central hospitals have prioritised teaching of their patients, thereby empowering them for post-discharge care. A 2020 study in the United Kingdom also reported that hospital size is a good predictor of efficient discharge processes [ 71 ].

The finding that patient safety in central hospitals was better than in small district hospitals is contrary to the assumption that large hospitals are busy and likely to be attending to complex patient conditions which could make them more prone to errors and unsafe practice [ 72 ]. In our study better safety in tertiary hospitals could be related to the fact that they are operated mostly by specialised health professionals who may be more knowledgeable than those in non-specialised hospitals, and that tertiary hospitals are teaching hospitals, often with ongoing training related to practice [ 26 , 73 ]. In the South African context to our knowledge this is the first study to link nurse practice environment, quality of care and patient safety in four hospital levels, and showing definite differences in nurses’ perceptions at these different levels of care. It followed a quantitative approach, and it would be interesting to further explore the reasons for the perceived practice environment, quality of care and patient safety using qualitative approaches in these hospitals. The findings of this study, specifically the variations in perceived nurse practice environment, quality of care and patient safety across hospital levels, imply an urgent need for mindfulness in resource allocation so as not to compromise care in the smaller hospitals.

The finding of strong correlations between the nurse practice environment, quality of care and patient safety is similar to those of other studies that also emphasised that a negative practice environment is associated with perceived poor quality of care and patient safety [ 48 , 49 , 74 , 75 ]. For our study it implies a need to intentionally improve the nurse practice environment, in order to influence quality of care and patient safety. It also means that quality of care and patient safety policies should deliberately consider the practice environment. This could start by involving nurses in policy development, so they can contribute to hospital affairs and the feasibility of the policies. This will also make them feel included as important role-players in the health system. Patient safety policies could also not focus on reporting of errors but consider system level contributors to errors; such practice will also address the reported challenges of punitive reactions to reported errors.

Limitations and strengths

Since this was a cross-sectional, self-reported survey, one of the limitations could be that the nurses may have had social-desirable bias in their responses, although the authors did try to control for this by asking neutrally worded questions, using anonymous surveys, ensuring that the answer options were not leading and that the order of the answers was randomised. There are several strengths of this study: firstly a contribution to knowledge of a link between nurse practice environment, quality in health care and patient safety in the South African context; often studies investigating these concepts are isolated. An additional strength is that we included a large sample size, representing all nine provinces of South Africa. To our knowledge this is the first study examining nurses' perceptions of the practice environment, quality of care and patient safety across four hospital levels within the public health sector of South Africa.

Recommendations

There are several recommendations from this study which could contribute to improvement to nurse practice environment, quality of care and patient safety. For example, there is a need to improve organisational culture with a focus on empowering leaders on leading compliance to existing policies, and on supportive leadership; this would lead to an improved nurse practice environment, quality of care and patient safety. A specific focus should be placed on support and empowerment of nurses working in more rural and smaller hospitals. Resource allocation to smaller hospitals should be reviewed, considering the added expenditure associated with remote locations and the added challenges in achieving economies of scale. Enabling positive nursing practice environments by means of enhanced nurse participation, non-punitive strategies of enhancing quality of care, leadership championing and better resource auditing will create environments nurses can thrive in, while also maximising patient outcomes in terms of quality of care and patient safety. In addition, there is an urgent need to review existing policies to identify how the nurse practice environment is enhanced or negatively affected by such policies, and intentionally examine and improve the link between nurse practice environment, quality of care and patient safety in the existing policies.

Nurses perceived the practice environment, quality of care and patient safety to be poor across four hospital levels within the public health sector of South Africa. Since there is a strong correlation between nurse practice environment, quality of care and patient safety, there is a need to review the existing policies on quality of care and patient safety and if and to what extent they enhance the nursing practice environment. In addition, strengthening compliance with existing policies that enhance quality of care and patient safety remains important, including the creation of a culture that supports a positive nurse practice environment characterised by manager support, nurse participation in hospital affairs and increased supply of resources, especially in smaller and more rural hospital settings.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Hospital Survey on Patient Safety Culture

National Department of Health

Practice Environment Scale of the Nurse Work Index Revised

Primary healthcare

Universal Health Coverage

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Acknowledgements

We would like to thank all the participants of this study; without their participation, this study would not be in existence.

Open access funding provided by North-West University. This work is based on the research supported in part by the National Research Foundation of South Africa (Grant Number 123541). All opinions, findings expressed, and conclusions arrived at, are solely those of the author and are not attributed to the National Research Foundation in any way.

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Immaculate Sabelile Tenza, Alwiena J. Blignaut & Siedine K. Coetzee

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IST; AJB; SME and SKC, conceptualised the manuscript. SKC, AJB, and SME analysed the data. IST wrote the original draft of the manuscript. All authors reviewed and edited the manuscript. SKC is a primary investigator, project administrator and a recipient of funding of this study.

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This study has been performed following the Declaration of Helsinki [ 76 ] and has obtained ethical approval from the North-West University Health Research Ethics Committee, with ethics number (NWU-00033–19-A1). We also obtained permission to conduct the study from the health authorities and individual public health facilities in the nine provinces of South Africa. All participants were given a detailed information sheet, as well as a verbal explanation of the study. We also informed study participants of the voluntary and confidential nature of participation in the study. All participants signed an informed consent to participate in the study.

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Tenza, I.S., Blignaut, A.J., Ellis, S.M. et al. Nurse perceptions of practice environment, quality of care and patient safety across four hospital levels within the public health sector of South Africa. BMC Nurs 23 , 324 (2024). https://doi.org/10.1186/s12912-024-01992-z

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Received : 15 December 2023

Accepted : 06 May 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s12912-024-01992-z

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