Secondary Dataset Research Articles

Nutritional risk and morbidity and mortality in intensive care unit patients with Coronavirus disease 2019

IntroductionThe Coronavirus disease 2019 (COVID-19) spread rapidly, with 37 million cases and more than 699,000 deaths. Among intensive care unit (ICU) patients with COVID-19, a high incidence of acute kidney injury (AKI) has been observed, ranging from 50-80%; furthermore, 85.9% were calculated to have high nutritional risk, which doubled their odds of death. The aim of the present study was to evaluate possible associations between nutritional risk, acute kidney injury, and morbidity and mortality in patients with COVID-19 admitted to an ICU. MethodsRetrospective cohort study of adult and older-adult patients hospitalized for >24 hours in an ICU. The exposure was diagnosis of COVID-19, while the outcomes were mortality, acute kidney injury, dialysis, mechanical ventilation, and vasopressor use. The association of nutritional risk with outcomes was evaluated. The sample consisted of two secondary datasets. Individuals aged <18 years, those with dialytic chronic kidney disease, pregnant women, and those diagnosed with brain death were excluded. ResultsThe sample consisted of 192 patients: 101 in the exposure group (positive for COVID-19) and 91 in the control group (no COVID-19 diagnosis). The COVID-19 and non-COVID-19 groups differed significantly in the variables weight, body mass index (BMI), nutritional risk, mNUTRIC-S score, and length of ICU stay. Our results suggest that the optimal mNUTRIC-S score cutoff to predict nutritional risk is < 5 points. ConclusionCOVID-19 has a significant impact on patients’ kidney function, increasing the incidence of AKI and the likelihood of death. Nutritional risk is a major factor in the mortality of patients with COVID-19. Therefore, use of the mNUTRIC-S scale could contribute to assessment of prognosis in this patient population.

Family farming in the Metropolitan Region of Fortaleza (RMF), Ceará

This article aims to deepen the discussion about agricultural activity in metropolitan cities. Our analysis focuses specifically on the Fortaleza Metropolitan Area (FMA), State of Ceará, Brazil, and allows us to further understand family farming in that area. For this purpose, we reviewed a set of primary and secondary data regarding the profile of the producers and agricultural and food processing establishments in the metropolitan area under analysis. This multifaceted approach helped us identify key issues related to production and food systems in the FMA, and showed the importance of a research agenda focused on evaluating the potential of family farming in this specific context. This study presents the production strategies adopted, the challenges faced and the opportunities identified by family farmers. Discussing agriculture in metropolitan areas help us connect socio-spatial and socio-environmental problems, in addition to enabling us to suggest the development of cross-sectoral public policies. By offering a comprehensive overview, this study aims to contribute to effective policy making, promoting the sustainability and resilience of family farming in metropolitan contexts. We hope that the reflection on agriculture developed in such areas and its characteristics in local and metropolitan scenarios will be helpful to researchers, managers and those responsible for the implementation and execution of public policies, as well as other agents and entities interested in family (and urban) agriculture.

Redefining Malaysia's printing landscape: insights from Porter's CAN Model

Malaysia's burgeoning printing industry, while showcasing potential, grapples with challenges that constrain its growth and global reach, underscoring the need for a deep and structured assessment. This pivotal study is designed to unpack the competitiveness of the Malaysian printing sector within the vast expanse of the global market, with a spotlight on the commercial printing segment. It revolves around the central inquiry: "Which critical factors mold the competitiveness of Malaysia's printing industry, and how can insights from Porter's Competitive Advantage of Nations (CAN) model illuminate them?" Anchored in the renowned Porter's CAN model, the research undertakes a multifaceted analysis of industry pillars, including intricate demand patterns, supply chain dynamics, prevailing competition, and the overarching influence of government directives. Data, meticulously sourced from 2015-2019 through national agencies and global institutions, forms the bedrock of this systemic examination. The study reveals that Malaysia stands fortified by assets like adept labor, contemporary infrastructure, and cost-effective energy provisions. Yet, it's not devoid of challenges, notably, the lag in embracing cuttingedge production tech and the absence of razor-sharp competitive strategies. However, the surge in both domestic and international demand sketches a promising trajectory. Beyond validating the robustness of the CAN model, the study weaves a tapestry of insights for industry stakeholders. It underscores the pivotal role of agile government interventions and the pressing mandate to integrate digital innovations. The findings beckon a targeted strategy to navigate and neutralize identified pitfalls, amplifying the industry's global resonance. While the study carves a panoramic landscape, its primary reliance on secondary datasets is its Achilles' heel. Still, it emerges as an invaluable compass for industry stakeholders, directing strategic investments and policy recalibrations to elevate the global stature of Malaysia's printing industry.

Do VIP medical services damage efficiency? New evidence of medical institutions' total factor productivity using Chinese panel data.

This study examines the causal impact of very important person (VIP) medical services on hospital total factor productivity in Deyang, a prefectural-level city in western China, spanning the years 2015-2020. This aims to offer empirical evidence and policy recommendations for the implementation of VIP practices in the medical field. A secondary unbalanced panel dataset of 416 observations was collected from the annual reports of the Health Commission and 92 eligible medical institutions were included. This study utilized a two-stage strategy. First, the Global Malmquist index was used to calculate the total factor productivity and its decomposition terms for hospitals from 2015 to 2020. In the second stage, two-way fixed effects models and Tobit models were used to identify the relationship between VIP medical services and hospital efficiency; instrumental variables were used to solve potential endogeneity problems in the model. The results showed that VIP medical services had a significantly negative impact on medical institutions' efficiency. The technological advances and pure technical efficiency related to VIP medical care may help explain these negative impacts, which were heterogeneous across groups divided by the nature of the hospital and the outside environment. It is imperative to prioritize the standardized provision of VIP medical services for medical institutions, optimize management and service process, enhance the training of clinical and scientific research capabilities of medical personnel, and scientifically allocate resources for both VIP and general medical services. This will help mitigate health inequality while improving the overall quality of medical services.

Historical Projection of the Rainfall Distribution in the North Region of Brazil

From a set of primary and secondary monthly data from 1991 to 2022, the annual and seasonal climatological pattern of the North region of Brazil, where most of the Amazon Biome is located, was analyzed. An incomplete Gamma distribution model was applied to the climatological precipitation data (mm); atmospheric pressure (mb); incidence of solar radiation (kJ/m²); air temperature and dew (ºC); relative humidity (%); wind intensity (m/s) with direction and maximum gust. Data normalization techniques and spatial similarity identified different patterns and trends of precipitation in the region, with significant seasonality, especially in terms of rainfall. The highest precipitation rates occurred in the N-NW axis, inside the northern Amazonian area, while the lowest values were observed in the southern equatorial zone, on the limits of the Pará State with the Cerrado Biome. Four zones and one climate range were established: southern, central, northern west and northern east zones and extreme north area, all with significant similarity in precipitation and humidity. A trend was observed in the number of days with precipitation between 25 and 50% in the southern and central zones, and an increase in precipitation of 50 to 75% for the northern zone. The wind distribution model showed a tendency towards the presence of isotropic climate phenomena, presenting the same properties or characteristics. The results obtained are relevant to support research aimed at understanding global warming and its consequences, in addition to contributing to forest management programs, and establishing more effective protocols for cultivation in agricultural areas in the Amazon.

Digital Health Needs and Preferences During Pregnancy and the Postpartum Period: Mixed Methods Study.

Digital health is increasingly used to meet the needs of perinatal people, with estimates of pregnancy-related internet use ranging from 90% to 97% of pregnant people. As digital health takes on greater importance during the perinatal period, it is essential that providers and developers of digital health content understand why perinatal people use these resources and the features that enhance their experience. However, gaps remain in understanding the content that is most helpful and how the platforms are navigated. Learning directly from perinatal people about their needs will help ensure alignment between perinatal needs and available content. This formative study aims to identify the reasons why perinatal people use digital health resources; the features of the digital health platforms that are of greatest importance to them; and how these differ by perinatal stage (pregnancy vs post partum), mental health conditions, parity, and demographics (race and ethnicity). This mixed methods study used interviews; surveys; and secondary data on demographic, health, and pregnancy characteristics to identify the digital health needs and preferences of pregnant and postpartum people who used the Maven digital health platform in the United States during their pregnancy or postpartum period. The interviews informed the content of the surveys and provided additional insights and examples for interpreting the survey results. The surveys were used to collect data from a sample of Maven users, and the results were linked to the secondary data set. The interviews were thematically analyzed, and survey data were analyzed using descriptive statistics and stratified by parity, race, and mental health status. Overall, 13 people were interviewed (including n=4, 31% pilot interviews), and 147 pregnancy and 110 postpartum survey respondents completed the surveys and had linkable secondary data. Top reasons for using digital health resources during pregnancy were to (1) know what is normal or typical during pregnancy, (2) have access to a health care provider when needed, and (3) know how the baby is developing. Top reasons for postpartum use were to (1) help with breastfeeding, (2) know what normal baby development is, and (3) help with the baby's health issues. Top platform features during pregnancy and the postpartum period were (1) credible and trustworthy information and providers, (2) nonjudgmental information and support, and (3) no cost to the user. In general, more reasons for using digital resources were identified as extremely important during pregnancy compared with post partum. The results showed minor variations across strata. This formative research found minor differences in digital resource needs and preferences across user characteristics among perinatal people in the United States. Future work should examine whether there are variations in interests within topics by user characteristics, which may provide additional opportunities to better meet user needs.

Assessment of Primary Education System in Rural India: A Case Study on Chikana Village of Tonk District in Rajasthan, India

The case study aims to highlight the existing problems and challenges in the primary education system prevalent in Rural India, from the lens of Chikana Village, in the Tonk district of Rajasthan, India. The case study brings to light prevalent practices, concerns and beliefs that govern the process of imparting primary-level education to the children of Chikana. The points made in the course of the study, though derived from this particular village, can be used as a general idea for most schools in rural India. The researchers have taken into consideration the prevalent schemes started by state and central governments for the enhancement of the quality of education in Rural India and have built points on the impact and significance these schemes have made so far. The study is qualitative in nature and uses both secondary and primary data sets.&#x0D; Secondary data is used in the form of old schemes and impact reports on Indian primary education, while primary data is incorporated in the form of observation and interview schedules. The time frame of all secondary data is limited to the last 5 years (February 2018- February 2023). The sample size for the interview schedule is 40 participants, these included students from ranging age groups, teachers and school authorities of the ‘Rajkiya Ucch Prathmik Vidyalay, Chikana'. The study aims to raise considerable awareness among the readers of this study about the existing trends in education in rural India and to highlight the gap in urban and rural education systems. The study goes on to find major loopholes in the ongoing educational practices prevalent in rural India. It brings to light the different dimensions of the primary education system and the opinions of different stakeholders on the progression of the sector. It comments on the implementation of policies designed to cater to the needs of children. The study also offers the author's conclusion derived from the curated data sets which clearly presents the condition of the sector as more bad than good, however, stressing enough of the good and stating that progress is expected in the years to come. The study is a comprehensive work, which gives a 180-degree view of the education provided to children in rural parts of the country.

Performance analysis of various deep learning models for detecting rice diseases

&lt;p&gt;A major portion of the world’s population relies on rice as a staple diet, hence rice is essential to maintaining food security worldwide. Unfortunately, rice crops are susceptible to a number of illnesses that, if detected and treated promptly, can result in significant output losses. Expert visual inspection is a time-consuming and arbitrary part of the conventional procedures for diagnosing diseases in rice. An effective method for automated illness diagnosis in agriculture has evolved in recent years: deep learning, a branch of artificial intelligence. The objective of this research is to compare AlexNet, DCNN, MobileNet, GoogleNet, VGG16, ResNet50 and Xception, these are various deep learning models in order to choose the one that would produce the highest levels of accuracy, precision recall, specificity, and F1-score for detecting rice diseases. In this study we train the model for nine different types of rice diseases named as Rice Blast (Pyricularia oryzae), Rice Sheath Blight (Rhizoctonia Solani), Bacterial Leaf Blight (Xanthomonas oryzae pv. oryzae), Tungro Disease, Rice Grassy Stunt Virus (RGSV), Rice Yellow Mottle Virus (RYMV), Bakanae Disease (Fusarium moniliforme), Brown Spot (Cochliobolus miyabeanus) and Rice Tungro Bacilliform Virus (RTBV) with 30,000 images. For this we used the secondary dataset for analyzing the performance of models. We trained the model for both normalized and non-normalized dataset. After comparing the various models we get the better result from ResNet50 model with accuracy of 97.50%.&lt;/p&gt;

Diabetes Prediction Using Machine Learning Ensemble Model

Malaysia National Health and Morbidity Survey revealed that one-fifth of Malaysian adults are diagnosed with Diabetes. It exists in different age groups and is hardly discovered especially among youths as the test could only be performed in certain places which require special equipment. It is essential to develop a tool that is capable to generate high accuracy predictions. This research underwent features selection of a secondary dataset which contains seventeen attributes, with no irrelevant data and missing values, and fed it into an AdaBoost with Decision Tree as Base Algorithm Model, Support Vector Machine (SVM), and an ensemble model developed by the machine learning knowledge. The first five most influenced features in the dataset were selected using SelectKBest for each model to conduct training and testing on the dataset and higher accuracy prediction results were achieved. The predictions from the three models were compared and the results from AdaBoost and SVM were combined in the ensemble model. A diabetes prediction prototype was developed to compare the accuracy of the three methods using the observed dataset. This research concludes the ensemble model gives the highest accuracy for Diabetes prediction and might be considered the most suitable method applied in Diabetes prediction tools.

SDG 2 – ending hunger: the effect of Nigeria’s land titling on production output and food security of farming households

Achieving zero hunger is intricately linked to sustainable agricultural production and the workings of a farming system. With land being the most important resource in smallholder agriculture in developing countries; including Nigeria; the effect of legal land ownership is a sine qua non for the process of achieving zero hunger in Nigeria. This study examined Nigeria’s attempt at making agricultural land both accessible and utilizable for smallholders in Nigeria through the Presidential Technical Steering Committee on Land Reforms in 2009. The aim was to explore the extent to which land ownership had become legalized; so that it had positive investment incentives for smallholder farmers to improve their investment portfolio on their holdings. A secondary dataset of 4,032 respondents obtained from the Living Standard Survey Integrated Survey on Agriculture (LSMS/ISA) of the 2018/2019 season, was used for the study. Data analysis included descriptive statistics and a 2-stage least square model. The apriori being that legal land ownership provided a perception of security that would enhance farm-level investment; which would therefore increase production output and hence food security. The findings revealed that although land titling was low (~12%) within the agricultural system; its return on production output was highly significant (5.3; p &amp;lt; 0.05). The possession of land title would also increase per capita food consumption expenditure (0.35; p &amp;lt; 0.05) among the farming households. On average, households with access to land titles had a significantly higher (at p &amp;lt; 0.01) food expenditure (N9, 868.00) than those without land titles (N6171.72). Other farm-level investment variables such as credit use, and mechanization through tractor use were significant in improving food security amidst the security presented by possession of land title. Thus, expediting the implementation and coverage of formal land registration and titling may be a first step to contributing meaningfully to the food security drive of the SDGs in Nigeria.

Livelihood transitions in rural Vietnam under climate change effects in the period of 2008–2018

The goal of this research is assess the variation in livelihood choices of households in Vietnam rural areas under the effects of climate change. Sustainable livelihood analysis framework and previous studies show that climate change has positive significant impacts on household livelihood transitions. Base on secondary dataset (Vietnam Access to Resources Household Survey) from 2008 to 2018 and applying multinomial logit model, the authors have indicated that climate change not only directly affects livelihood choices of inhabitans in rural areas but also indirectly influcences them through livelihood capitals, including: human capital, natural capital, material capital, financial capital, and social capital. In addion, authors have shown that the wage-based and nonagricultural strategy preferred under the effects of climate change. This study has also determined the effects of climate change on rural households via livelihood capitals, particularly human and social capital. Finally, the authors suggested some policy implications about quickly policy implement from government, enhancing social capital as well as social networks of households, improving human capital for climate change adaptation.

The formulation of community empowerment model based on dairy cattle business in Plaosan district, Magetan regency

This study aimed to formulate community empowerment model based on dairy cattle business as an effort to support the development of village milk tourism in Plaosan District, Magetan Regency. A survey method was used in this study along with quantitative descriptive analysis. Furthermore, the location and samples used were selected using a purposive sampling method. Data were then collected using interviews, observation, documentation, and literature study. Primary data were derived from interviews with dairy farmers, while secondary datasets were obtained from related agencies. The data obtained were evaluated using descriptive analysis and needs analysis. The results showed that the formulation of community empowerment model aimed to improve the ability of farmers. This was achieved through counseling and training activities based on dairy cattle business and the existence of support from financial and supporting institutions. These concerted efforts were carried out to support the development of village milk tourism in Plaosan District, Magetan Regency.

Manipur's Journey Towards the Health SDGS: What's Achieved and what more is Needed?

India and its constituent states are committed to achieving universal health coverage (UHC) as a part of the Sustainable Development Goals (SDGs) 2030 by strengthening primary healthcare (PHC). The National Health Mission (NHM) has been a landmark effort at health systems strengthening, aiming to reboot both the architecture and service delivery. Manipur ranks fourth out of the eight states in the North East Region in the National Institution for Transforming India (NITI) Aayog's ranking for SDGs. The available evidence, drawing upon secondary datasets and available primary data, aims to unpack the progress made in health systems strengthening to interpret the journey toward achieving UHCs and SDGs. The progress made and the gaps in terms of human resources, infrastructure, service utilization, and reduction of out-of-pocket expenditure are interpreted in terms of critical social determinants such as geographic and ethnic divides. Newer policies, such as the Act East Policy and medical tourism, are examined in the context of current disparities and the potential to accelerate the journey towards achieving SDGs.

Comparative Study of Iconnet Jabodetabek and Banten Using Linear Regression and Support Vector Regression

PT PLN Indonesia Comnets Plus has little information regarding future customer growth, making it difficult to take steps to meet customer needs. This research aims to predict customer growth in the future based on quantitative data from the previous year, where the output provided produces data in the form of numbers that are analyzed using statistical methods. The hope is to provide information to maximize customer growth with the minimum area or bandwidth used by the company. This research uses linear regression and support vector regression (SVR) algorithms using a company secondary dataset of 252 data points with 5 attributes. Data was collected during the last one-year period, from January to December 2021. The results of the research show that predictions using both algorithms have increased, customer growth when viewed from the number of customers, bandwidth, and regional data has increased significantly. This can be seen from the value of the number of customers, which continues to increase, while the highest number of customers falls in December, the most requested bandwidth is 20 mbps, and the largest customer area is in the Depok area. The results of the research show that the SVR algorithm is superior in terms of mean absolute percentage error (MAPE): 0.02% MAPE, 0.10 MAE, and 0.99 RMSE, while for linear regression, the MAPE values were 36.28%, MAE 201, and RMSE 0.80.

Comprehensive knowledge and positive attitude of married men regarding HIV/ AIDS in Pakistan Evidence from Demographic and Health Survey 2017-18

Acquired Immunodeficiency Syndrome (AIDS) is a life-threatening condition due to Human Immunodeficiency Virus (HIV) infection, and remains a major public health issue, mostly in under-developing countries. Pakistan is the second largest country in South Asia with a significant increase in HIV cases. This research explores the comprehensive knowledge regarding HIV/AIDS, dispelling myths and their positive attitude toward people living with HIV/ AIDS (PLWHA) in Pakistan. The study is based on the secondary dataset of married men of the Pakistan Demographic and Health Survey (PDHS) 2017-18. The sample size was comprised of 3,690 married men, within the age bracket between 15 to 49 years. The descriptive analysis, cross-tabulation, and logistics regression were applied. The results illustrated that more than 67 percent of Pakistani married men have heard about HIV/ AIDS, however, among these men, 50.6 percent have comprehensive knowledge regarding HIV/AIDS, and 54.1 percent have a positive attitude toward PLWHA. The result of logistic regression analysis highlighted that married men who have lower knowledge regarding HIV/AIDS belong to the provinces of Khyber Pakhtunkhwa, and Sindh, aged between 20 -24 years, having primary level education and working in the sector of agriculture. Further, logistic regression analysis found that there are lower odds of positive attitude among those married men, who belong to the region of Gilgit Baltistan, Federal Administrated Tribal Area, aged between 20-24 years, having a primary level of education and working in the sector of agriculture. Finally, this research concluded that individuals’ socio-demographic traits are critical to understanding and addressing the prevailing myths to combat HIV/AIDS in Pakistan.

CBR Correlation with Index and Compaction Properties of Soil

California Bearing Ratio (CBR) is widely used in the design of roads to indicate the bearing capacity of subgrade as well as each layer of the flexible pavement system. However, the procedure for obtaining the CBR value is often considered complicated and time-consuming, thus; some correlations were developed by previous researchers between the CBR value and index properties such as fine content (w), liquid limit (LL), and plasticity index (PI) of the soil, as well as between the CBR value and compaction properties i.e., optimum moisture content (OMC) and maximum dry density (MDD). This study was conducted to evaluate the applicability of the published correlation on the soil obtained in Palembang and the surrounding areas. In this case, 30 sets of secondary data were collected and selected from past projects. In addition, laboratory tests were conducted on five sets of samples for verification purposes. The laboratory tests include a wet sieve to obtain fine content, Atterberg limits to determine soil’s plasticity, and a standard Proctor test to obtain OMC and MDD. Analysis shows that with modifications, some published correlations could be used to predict unsoaked and soaked CBR values for soils in Palembang. Furthermore, two correlations were developed between the CBR values and compaction properties of the soil based on statistical analysis of the collected data.

Economic Complexity, Institutions, and Property Rights

Objectives/Background: Property rights play a fundamental role in economics through rights to asset and resource owners, thereby enhancing their economic efficiency. The study investigates the relationship between property rights and economic complexity across countries classified by income levels, including high, middle, and low-income nations. Methods: The study utilized Robust Least Square (RLS) to obtain results from a secondary data set. Results: Our analysis reveals a positive relation between economic complexity and property rights across all income groups. It is suggested to have potential developmental paths for countries based on their income status such as those of low income and middle income groups. More specifically, it is proposes that low-income countries could benefit from the institutional improvements observed in high middle-income countries group. Whereas, high middle-income countries may find help in modeling their institutions after those of high-income countries. The approaches may help sustain and enhance institutions and foster an environment conducive to economic complexity. Conclusion: This research contributes valuable insights into the interplay between economic complexity and property rights across diverse income contexts, offering potential strategies for policymakers to enhance property rights and drive inclusive economic development.

Machine Learning Modelling for Imbalanced Dataset: Case Study of Adolescent Obesity in Malaysia

Obesity among adolescent is a public health issue with increasing burden of disease. Predicting imbalanced health data with Machine Learning may introduce bias and lead to diminished model performance. Misclassification in healthcare data could lead to misdiagnosing a patient or failing to detect a health issue when it is present. The purpose of this study is to predict adolescent obesity using machine learning along with implementation of multiple approaches on the imbalanced dataset. This study used secondary dataset from National Health and Morbidity Survey 2017. Samples 13 – 17 years were selected for the classification. SPSS V26 was used for data pre-processing, data cleaning, and data analysis. Meanwhile, Python language used for prediction and evaluation of the models. Approaches on the imbalanced dataset including resampling method (Random Oversampling, Random Under-sampling) and hybrid method (SMOTE and ADASYN) were implemented. This dataset was used for the formation of predictive models on ML algorithm including Artificial Neural Network, Decision Tree, K-Nearest Neighbour, Logistic Regression, Naïve Bayes, Random Forest and Support Vector Machine. The performance of each model was evaluated and compared using accuracy, precision, recall, F- score and Area under the Curve (AUC). Random Oversampling approached with Decision Tree Algorithm performs the best with accuracy (91.35%), precision (0.93), recall (0.91), F- score (0.91) and AUC (0.91) for the prediction of obesity among adolescent in Malaysia. The presented ML model development workflow along with the imbalanced techniques can be adapted to other health survey-based studies and may be valuable for developing other clinical prediction models.

OPPORTUNITIES AND CHALLENGES IN LARGE QUALITATIVE DATASETS IN GERONTOLOGICAL RESEARCH

Abstract Qualitative research focuses on in-depth data to understand people, processes, and phenomena. It often involves smaller datasets that do not intend to produce generalizable results but instead illuminate nuances, counterexamples, or underrepresented voices. Qualitative data can produce theory and allow for in-depth analyses. Recent methodological developments have created opportunities for large mixed methods projects to produce voluminous amounts of qualitative data that can and sometimes are shared as secondary datasets. The growing use of big data has also produced new possibilities for large qualitative datasets. Additionally, longitudinal and comparative qualitative research can produce significant data. This symposium presents four studies that examine opportunities and challenges in large qualitative datasets in gerontology. The first article uses multiple qualitative datasets to examine how long-term care staff balance resident rights of safety and autonomy. The second article uses multiple qualitative methods in a larger mixed methods Friendship Study to examine meaningful connections among people living with dementia in long-term care. Through a multi-site community university partnership, the third study extracts qualitative data from over 500 patient records to examine experiences of terminally ill hospice patients from residential homes for the dying. The fourth study examines nearly ten years of qualitative data from over 20 communities to examine successful aging from an Alaska Native perspective. Altogether, these four studies illuminate diverse strategies for identifying qualitative data, using data software, managing data, addressing ethical dilemmas, and employing research teams for coding and analyzing large qualitative datasets in gerontological research. This is a Qualitative Research Interest Group Sponsored Symposium.

CARDIOVASCULAR DISEASE AND FRAILTY IN OLDER ADULTS FROM COLOMBIA

Abstract Cardiovascular disease (CV) is a leading cause of death worldwide. Frailty is a growing geriatric syndrome. Limited research has explored the relationship between cardiovascular disease and frailty. This study assesses the association between a cardiovascular disease history and older adults’ frailty. We used data from the SABE (Salud, Bienestar y Envejecimiento) Colombia Study, a cross-sectional nationwide population-based survey (2015) including community-dwelling adults ≥60 years. A secondary dataset of 5,242 participant responses was analyzed. Frailty was assessed according to the Fried Frailty Phenotype (weakness, low speed, low physical activity, exhaustion, and weight loss) and categorized as frail (3 or more components) vs. not frail or pre-frail. We performed logistic regression analysis to assess the association between a history of cardiovascular disease (hypertension, stroke, heart disease) and frailty, adjusting for socio-demographics, other comorbidities, and functional status. The prevalence of cardiovascular disease was 59.4%, and frailty was 20.0%. Compared to those who do not have CV disease, those having 1 CV disease (OR=1.23, 95% CI 1.04-1.46) or ≥2 CV diseases (OR=1.55, 95% CI 1.24-1.94) were associated with higher odds for frailty. A higher number of CV diseases were independently associated with frailty among older Colombian adults. In future studies, preventing CV disease might help to decrease frailty in older adults.