Area based stratified random sampling using geospatial technology in a community-based survey

Evaluating an equity-focused approach to assess climate resilience and disaster priorities through a community survey

As the Duwamish Valley community in Seattle, Washington, U.S.A. and other environmental justice communities nationally contend with growing risks from climate change, there have been calls for a more community-centered approach to understanding impacts and priorities to inform resilience planning. To engage community members and identify climate justice and resilience priorities, a partnership of community leaders, government-based practitioners, and academics co-produced a survey instrument and collected data from the community using the Seattle Assessment for Public Health Emergency Response (SASPER), an approach adapted from the Centers for Disease Control and Prevention's Community Assessment for Public Health Emergency Response (CASPER). In addition, we conducted a process and outcome project evaluation using quantitative survey data collected from volunteers and qualitative semi-structured interviews with project team members. In October and November 2022, teams of volunteers from partner organizations collected 162 surveys from households in the Duwamish Valley. Poor air quality, extreme heat, and wildfires were among the highest reported hazards of concern. Most Duwamish Valley households agreed or strongly agreed that their neighborhood has a strong sense of community (64%) and that they have people nearby to call when they need help (69%). Forty-seven percent of households indicated willingness to get involved with resilience planning, and 62% of households said that they would use a Resilience Hub during an emergency. Survey volunteers evaluated their participation positively, with over 85% agreeing or strongly agreeing that they learned new skills, were prepared for the survey, and would participate in future assessments. The evaluation interviews underscored that while the SASPER may have demonstrated feasibility in a pre-disaster phase, CASPER may not meet all community/partner needs in the immediate disaster response phase because of its lack of focus on equity and logistical requirements. Future research should focus on identifying less resource intensive data collection approaches that maintain the rigor and reputation of CASPER while enabling a focus on equity.

Unmatched spatially stratified controls: A simulation study examining efficiency and precision using spatially-diverse controls and generalized additive models

Unmatched spatially stratified random sampling (SSRS) of non-cases selects geographically balanced controls by dividing the study area into spatial strata and randomly selecting controls from all non-cases within each stratum. The performance of SSRS control selection was evaluated in a case study spatial analysis of preterm birth in Massachusetts. In a simulation study, we fit generalized additive models using controls selected by SSRS or simple random sample (SRS) designs. We compared mean squared error (MSE), bias, relative efficiency (RE), and statistically significant map results to the model results with all non-cases. SSRS designs had lower average MSE (0.0042-0.0044) and higher RE (77-80%) compared to SRS designs (MSE: 0.0072-0.0073; RE across designs: 71%). SSRS map results were more consistent across simulations, reliably identifying statistically significant areas. SSRS designs improved efficiency by selecting controls that are geographically distributed, particularly from low population density areas, and may be more appropriate for spatial analyses.

Public support for policies to regulate flavoured tobacco and e-cigarette products in rural California

INTRODUCTION

Flavoured tobacco control policy exemptions and electronic cigarette products may contribute to increased youth access and tobacco use disparities.

METHODS

We assessed public support among California Central Valley residents for four policies to regulate flavoured tobacco products and e-cigarettes. The probability-based, multimode survey was conducted with English-speaking and Spanish-speaking registered voters (n=845) across 11 counties between 13 and 18 August 2020. Weighted logistic regression analyses measured odds of policy support, adjusting for predictor variables (attitudes and beliefs) and covariates.

RESULTS

The weighted sample was 50% female and predominantly Latino (30%) or non-Hispanic white (46%); 26% had a high school education or less, and 22% an annual household income

DISCUSSION

Findings add to mounting evidence of support for policies to regulate flavoured tobacco and e-cigarette products. Results on attitudes and beliefs elucidate how these factors influence support.

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Key Words

• prevention
• priority/special populations
• public opinion
• public policy

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MESH Headings

• Adolescent
• Humans
• Female
• Male
• Electronic Nicotine Delivery Systems
• Nicotiana
• Tobacco Products
• Vaping/epidemiology
• Policy
• California/epidemiology
• Flavoring Agents

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Grants

• University of California Office of the President (UCOP) Tobacco-Related Disease Research Program

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Affiliation(s)

Denise Diaz Payán Department of Public Health, University of California Merced, Merced, California, USA Nicotine & Cannabis Policy Center, University of California Merced, Merced, California, USA
Nancy J Burke Department of Public Health, University of California Merced, Merced, California, USA Nicotine & Cannabis Policy Center, University of California Merced, Merced, California, USA
Jamie Persinger American Heart Association, California, Los Angeles, California, USA
Juliette Martinez American Heart Association, California, Los Angeles, California, USA
Lisa Jones Barker American Heart Association, California, Los Angeles, California, USA
Anna V Song Department of Psychological Sciences, University of California Merced, Merced, California, USA Nicotine & Cannabis Policy Center, University of California Merced, Merced, California, USA

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Assessing Hispanic/Latino and Non-Hispanic White Social Determinants of Obesity Among a Community Sample of Residents in the Rural Southeast US

Employing an ecological approach, we sought to identify social determinants of obesity among Hispanics/Latinos and non-Hispanic whites living in the Southeast US. Data on social determinants of obesity (individual, family, community and cultural/contextual) were collected from 217 participants [106 Hispanics/Latinos; 111 non-Hispanic whites]; height and weight  were objectively measured. We compared prevalence of overweight and obese between ethnic groups and BMI values within each group by social determinants. Hispanics had a 1.9-fold increase (OR 1.93, 95% CI: 1.05-3.55) in overweight prevalence compared to non-Hispanic whites after adjusting for age and gender. We found positive estimates between unfavorable family-level determinants and BMI among Hispanic/Latinos. In contrast, non-Hispanic whites who reported unfavorable neighborhood characteristics had higher BMI's. Findings highlight the need for targeted approaches for the prevention and control of obesity.

Unlocking the Entrepreneurial State of Mind for Digital Decade: SMEs and Digital Marketing

Given the current technological changes, entrepreneurs need to transform their businesses and become more and more digitalized. The process of digital transformation through digital marketing tool adoption is decisive for SMEs’ economic development. It may lead to an increase in revenues, may contribute to the transformation of business models, and may contribute to the increase in the sustainability of SMEs. Aspiring to understand what entrepreneurs think and perceive about their digital knowledge and tools to promote their business online, the authors performed a study to determine and quantify the perceived usefulness of digital tools used by SME owners, by using two different methods of research. They are represented by quantitative analysis and multi-criteria decision analysis, used to analyse the motivations, intentions, and characteristics of 333 entrepreneurs and the businesses they run. As a consequence, a matrix with 12 typologies of entrepreneurs was generated according to their behaviour towards digital marketing tools implemented for business. The obtained outcomes show the incremental potential interest of specialists and academia for SME entrepreneurs with intermediate digital knowledge. In this context, they might want to improve their skills to achieve entrepreneurial resilience with self-learning opportunities.

Performance Assessment of Medical Professionals in Prevention of Ventilator Associated Pneumonia in Intensive Care Units

Purpose

Ventilator-associated pneumonia (VAP) is one of the most common infections in intensive care units (ICU) with a 6–52% incidence. The VAP mortality rate is 50% to 70%. Medical professionals (MPs) working in the ICU are expected to follow the guidelines to prevent VAP. The study aimed to assess the performance of MPs in preventing VAP and to associate the performance with the baseline information.

Methods

An observational cross-sectional study was conducted in the ICUs of selected hospitals in eastern Saudi Arabia. A total of 152 MPs were selected by random sampling. A structured questionnaire including baseline information, knowledge and performance-related questions was used to collect the data. Frequency, mean, and chi-square tests were used for analysis.

Results

Out of 152 MPs, 40.8% had adequate and 7.9% had inadequate knowledge. A high mean score of 12.9 ± 2.2 was obtained by physicians, followed by 11.3 ± 1.6 by nurses, 9.8 ± 2.2 by RTs, and 8.6 ± 2.1 by interns. Overall, 52.6% had satisfactory performance. Approximately 57.9% and 67.8% of MPs cleaned their hands before touching the patient and the ventilator, respectively. Many (79.6%) MPs used personal protective equipment in the ICU. Some (47.4%) of the MPs changed the patient’s position regularly. About 77.6% of MPs followed the sterile technique when suctioning the airway. There was a significant association found between the performance of MPs on the prevention of VAP with age (p < 0.001), designation (p < 0.05), professional experience (p < 0.05), managing chronic obstructive pulmonary disease conditions (p < 0.05) and training attended (p < 0.001).

Conclusion

Although some of the MPs had satisfactory performance regarding VAP prevention in the ICU, more attention should be paid to training them on clinical guidelines to improve health care quality and reduce the rate of VAP.

The Mediating Role of Eco-Friendly Artwork for Urban Hotels to Attract Environmental Educated Consumers

The adoption and implementation of environmental marketing strategies is the hotel industry’s new approach to maintaining a competitive advantage and attracting more green consumers. Indeed, hotels with more sustainable practices and eco-friendly artwork generate more trust and make green consumers more loyal and satisfied. However, there is little prior research which has suggested the mediating role of green artwork between customers’ levels of green perception and their hotel satisfaction. For this reason, the current authors obtained a total of 659 responses from South Korean consumers and conducted the structural equation analysis (SEM) to identify the indirect effect explaining how green arts boosts green hotel consumers’ satisfaction. Our statistical findings offer vital insights regarding the relationship between customers’ green perceptions and their hotel satisfaction, with eco-friendly artwork in the hotel interior design as the mediating variable. Finally, the current study provides a detailed understanding of art infusion to urban hotels by highlighting the impact of art and its spillover effects on consumer satisfaction.

Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

Purpose

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

Design/methodology/approach

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

Findings

The authors got very satisfactory classification results.

Originality/value

DDPML system is specially designed to smoothly handle big data mining classification.

Developing Climate Information Services for Aquaculture in Bangladesh: A Decision Framework for Managing Temperature and Rainfall Variability-Induced Risks

Climate information services (CIS) are increasingly in demand to assist farmers in managing risks associated with climate variability and extremes experienced in food production. However, there are significant gaps in the availability and accessibility of these services, especially in aquatic food production in developing countries. In response, this study aims to generate the background knowledge for developing climate information and decision support services tailored for aquaculture farmers in Bangladesh. We surveyed 800 fish-farming households, interviewed 30 key informants, and conducted a systematic literature review to identify climate-sensitive operations and management decisions in aquaculture and to document fish-farmers' awareness of the relationships between climate variability and aquatic food production systems. We also sought to identify the lead time and communication method(s) needed to deploy forecasts effectively and prepare aquaculture farmers to act in response to the forecasts. A fish-farming activity calendar was developed that identified high temperature, cold spell, heavy rainfall, and dry spell events as key climatic phenomena affecting year-round aquaculture operations, including pond preparation and maintenance, fingerling stocking, grow-out management, and harvesting. We also identified five climate-sensitive management decision points and 26 potential advisories in line with specific climate variability to manage induced risks in the day-to-day operations of fish farmers. Finally, the research team developed a decision framework based on the temperature and rainfall thresholds for the grow-out phase of four widely cultivated and economically important fish species in Bangladesh. This innovative decision support approach is to our knowledge the very first endeavor to develop CIS using species-specific temperature and rainfall thresholds to reduce climate risks and ensure resilience capacity for South Asian aquaculture system.