Analysis Of Health Data Research Articles

Health care utilisation in Cox's Bazar district, Bangladesh, during the first year of the COVID-19 pandemic: A mixed-methods study among host communities.

To respond to the COVID-19 pandemic, countries introduced public health and social measures that had indirect societal, economic consequences. Concerns during epidemics include continuity of routine health services. We investigate how healthcare utilisation and healthcare seeking behaviour changed during the first year of the COVID-19 pandemic among host communities in Cox's Bazar, Bangladesh. This mixed-methods study combines quantitative analyses of routine health data and population-based findings about healthcare seeking behaviours. Trends in consultations changed according to facility level (higher-level facilities included Upazila Health Complexes and District Hospitals; lower-level facilities included Community Clinics and Union Health and Family Welfare Centers). At the pandemic's beginning, drops were seen at higher-level health facilities for outpatient department (OPD) consultations, respiratory infections, and antenatal care. Minor reductions or increases were seen at lower-level facilities for the same services. Half of the subdistricts reported a cumulative increase in OPD and respiratory tract infection consultations. Most subdistricts reported a cumulative decrease in antenatal care. Child vaccinations dropped in all subdistricts, half of which did not catch-up, resulting in a cumulative decrease of delivered doses. Fear of contracting COVID-19 and financial constraints were the main reasons for decreased access. Drivers of healthcare seeking behaviours should be better understood to guide preparedness and service delivery modalities at primary and secondary levels.

Oral Health Considerations for Adults Aged 18 Years or More Seeking Dental Care in the Past Year: A CDC Oral Health Data Analysis.

Oral health is an essential aspect of overall well-being, with regular dental care being fundamental to its maintenance. This study focuses on understanding dental care utilization among adults aged 18 and above who have visited a dentist or dental clinic in the past year, aiming to uncover patterns, disparities, and determinants of oral health practices within this demographic. Data from the U.S. Centers for Disease Control and Prevention (CDC)Oral Health dataset were utilized to conduct this analysis. The dataset encompasses a diverse and nationally representative sample of adults aged 18 and above. The study explored the proportion of adults who sought dental care between 2008 and 2020, further stratified by demographic variables including age, gender, income, education, and race. The analysis provides insights into the prevalence of dental care utilization and the role of demographic factors in shaping oral health behaviors. The study found that 64.8% (n =397,291; 95% CI: 64.4 - 65.2) of adults aged 18 and above visited a dentist or dental clinic in 2020. Subgroup analysis revealed variations in dental care use by age, gender, income, education, and race. Among genders, 67.4% (n = 150,510; 95% CI: 66.9 - 67.9) of females sought care in 2020, compared to 61.9% (n = 116,535; 95% CI: 61.4 - 62.4) of males. Those earning >$50,000 had the highest proportion, 75.3% (n = 13,363; 95% CI: 74.8 - 75.8), seeking care. Among racial groups, White adults had the highest proportion, 68.4% (n = 204,486; 95% CI: 68.0 - 68.8) in 2020. In education groups, college graduates or professionals had the highest, 77.3% (n = 121,800; 95% CI: 76.8 - 77.8) in 2020. Among ages, adults aged 65+ had the highest proportion, 67.1% (n = 96,012; 95% CI: 66.4 - 67.8) in 2020. However, as age decreased, dental visit proportion generally remained within the same range. This study enhances our understanding of dental care utilization patterns within the studied population, shedding light on disparities in oral health practices. Moreover, it provides insight into how demographic factors shape dental/oral healthcare-seeking behaviors. Ultimately, these insights guide efforts to improve oral health outcomes and well-being within this population.

Regulations and Funding to Create Enterprise Architecture for a Nationwide Health Data Ecosystem.

The COVID-19 pandemic highlighted the United States' lack of a nationwide infrastructure for collecting, sharing, and using health data, especially for secondary uses (e.g., population health management and public health). The federal government is taking several important steps to upgrade the nation's health data ecosystem-notably, the Centers for Disease Control and Prevention's Data Modernization Initiative and the Office of the National Coordinator for Health Information Technology's Trusted Exchange Framework and Common Agreement. However, substantial barriers remain. Inconsistent regulations, infrastructure, and governance across federal and state levels and between states significantly impede the exchange and analysis of health data. Siloed systems and insufficient funding block effective integration of clinical, public health, and social determinants data within and between states. In this analytic essay, we propose strategies to develop a nationwide health data ecosystem. We focus on providing federal guidance and incentives to develop state-designated entities responsible for the collection, integration, and analysis of clinical, public health, social determinants of health, claims, administrative, and other relevant data. These recommendations include a regulatory clearinghouse, federal guidance, model legislation and templated regulation, funding to incentive enterprise architecture, regulatory sandboxes, and a 3-pronged research agenda. (Am J Public Health. 2024;114(2):209-217. https://doi.org/10.2105/AJPH.2023.307477).

Dental decay in children and the link to weight status: A cross-sectional analysis of national school health data

Recent national reports have indicated an increasing trend of dental decay among school-aged children. National school-based data are required to guide decision-making to ensure effective public health efforts to manage dental decay. This study aimed to assess the prevalence of dental decay among school-age children in Saudi Arabia and explore the possible link between dental decay and weight status. This was a cross-sectional analysis of a national sample including 1,134,317 Saudi children in the 1st, 4th, 7th, and 10th grades who participated in a national school screening program. Data for weight, height, body mass index (BMI), dental decay prevalence, and decayed, missing, and filled teeth (DMFT) index were analysed. An independent t-test, one-way analysis of variance, and chi-square test were used to compare the means, while Pearson’s r correlation and multiple linear regression were used to examine the relationships between the study variables. The prevalence of dental decay was 24.20%, and dental decay was the highest among female students (26.5%), students in primary school (25.9%), students living in the eastern region (35.2%), in the administrative capital (27.6%), and in rural areas (23.4%). Dental decay was the highest among students who were underweight. Female sex and living in rural areas significantly predicted higher DMFT while being overweight/obese significantly predicted lower DMFT. Dental decay is highly prevalent among underweight students, female students, and students living in rural areas and the eastern regions of Saudi Arabia. To reduce the prevalence of dental decay and related health disparities, dental health screening programs should be designed to detect dental decay early among children at high risk due to abnormal BMIs and sociodemographic factors. In addition, dental health screening and management programs should utilize standardized dental decay assessment methods and ethnically representative growth charts.

Longitudinal antinuclear antibody titers in systemic lupus erythematosus and other rheumatic diseases.

Antinuclear antibodies (ANAs) are a key feature of systemic lupus erythematosus (SLE) and marker of subclinical autoimmunity. Little is known about longitudinal ANA titers in individuals from the general population or in predicting clinical disease course in persons with rheumatic diseases. We performed an exploratory analysis from an academic health system between 1999 and 2020 to assess intra-individual variation in ANAs longitudinally in persons with SLE, other ANA-associated rheumatic diseases, and ANA+ controls without rheumatic disease. Persons with SLE had a higher odds of positive ANA compared to those with other ANA-associated rheumatic diseases [OR 2.10, 95% CI (1.82, 2.43)] controlling for time and demographics (age, sex, race, ethnicity). Compared to ANA+ controls, the ANA titer strength was significantly higher for both the ANA-associated rheumatic disease (0.33 log units higher) and SLE groups (0.42 log units higher) controlling for demographics and time (p < 0.001 for both). Over time from the first positive ANA, titer strength significantly decreased for all three groups, with average monthly decreases ranging between 0.001 to 0.004 log titer units (p ≤ 0.001 for all). Based on this analysis of electronic health data spanning two decades, ANA titers may be more dynamic than previously accepted in patients with SLE and ANA-associated rheumatic diseases, with average titers tending to be higher in early disease and decreasing over time.

Health and comfort oriented automatic generative design and optimization of residence space layout: An integrated data-driven and knowledge-based approach

During the initial design phase, space layout design plays a critical role in ensuring the ultimate performance of a residence. However, physical performance like energy consumption is usually prioritized, while health and comfort performance of residence is less explored. This study proposes an integrated data-driven and knowledge-based approach for automatic generative design of residence space layout focusing on health and comfort performance. First, explicit knowledge about key parameters of the layout design is obtained through quantitative analysis of large-scale health data. Second, with health and comfort as performance oriented, adjacency preference and noise score are introduced as objective functions and automatic generative design is carried out to obtain multiple layout alternatives. Finally, multi-objective deviation analysis is conducted to determine the optimal layout scheme. This study offers a practical and efficient tool through the whole design process, including pre-parameters analysis, during-process generation, and post-scheme evaluation, which contributes to improve decision-making.

The Retail Food Environment Index and its association with dietary patterns, body mass index, and socioeconomic position: A multilevel assessment in Mexico.

In Mexico, 75% of the population are affected by overweight or obesity, and the availability and affordability of high-calorie-dense foods and beverages are high. This study tested the association between the retail food environment index (RFEI), dietary patterns, body mass index (BMI), and socioeconomic position (SEP) in Mexico. Cross-sectional diet, health, and sociodemographic population-based secondary data analyses were conducted. The RFEI was calculated by dividing the total number of fast-food outlets and convenience stores by the total number of supermarkets and fruit and vegetable stores per census tract area. Associations between BMI, dietary patterns, SEP and the RFEI were tested using multilevel linear regression, including interactions of the RFEI with SEP, gender, and age. Living in neighbourhoods with a higher RFEI was associated with a 0.01kg/m2 higher BMI (β = 0.01, 95%CI: 0.0005, 0.02, p = 0.04), equivalent to a mean 0.046 weight gain for a 1.60m tall person per 10% higher RFEI. Unhealthy dietary patterns were more likely in neighbourhoods with a higher RFEI (β = 0.100, 95%CI: 0.03, 0.12, p = 0.001). Multilevel linear regression showed that lower SEP households had a higher RFEI compared to higher SEP households (β = 0.020, 95% CI: -0.006 to 0.04, p = 0.10). Generalised structural equation models revealed a graded relationship between RFEI and SEP, showing that lower SEP households were exposed to a higher RFEI (β = 0.060, 95% CI: 0.05 to 0.07, p < 0.001.) The study identified significant associations between higher proportions of fast-food outlets and convenience stores, higher BMI, and unhealthy dietary patterns. It was particularly evident that low-income populations are more likely to be exposed to obesogenic food environments.

Population Neuroscience: Strategies to Promote Data Sharing While Protecting Privacy.

Population neuroscience aims to advance our understanding of how genetic and environmental factors influence brain development and brain health over the life span, by integrating genomics, epidemiology, and neuroscience at population scale. This big data approach depends on data sharing strategies at both the micro- and macro-level, as well as attention to effective data management and protection of participant privacy. At the micro-level, researchers participate in international consortia that support collaboration, standards, and data sharing. They also seek to link together cohort studies, administrative health databases, and measures of the physical, built, and social environment in creative ways. Large-scale, longitudinal, and multi-modal cohorts are being designed to support explorations of genetic and environmental impacts on the brain. At a macro-level, funding agency policies now require data across health research domains to be managed according to the FAIR (findable, accessible, interoperable, and re-useable) Data principles and made available to the research community in a timely manner to support reproducibility and re-use. Data repositories provide technical infrastructure for storing, accessing, and increasingly also analyzing rich population-level data. Federated and cloud-based approaches are being leveraged to improve the security, remote accessibility, and performance of repositories. Finally, legal frameworks are being developed to facilitate secure health data access, integration, and analysis, providing new opportunities for the field.

Domain Adaptation for Mapping LCZs in Sub-Saharan Africa With Remote Sensing: A Comprehensive Approach to Health Data Analysis

Domain Adaptation for Mapping LCZs in Sub-Saharan Africa With Remote Sensing: A Comprehensive Approach to Health Data Analysis

Comparative review of big data analytics and GIS in healthcare decision-making

This research explores the confluence of big data analytics and Geographic information systems (GIS) in healthcare decision-making. The comparative review delineates the unique strengths of each technology, showcasing potential synergies. Big data analytics harnesses advanced analytics for predictive modeling and clinical decision support, while GIS introduces a spatial context for health data analysis. Future trends suggest integrations with artificial intelligence, real-time analytics, and wearable technology. However, challenges encompass data privacy, biases, and interdisciplinary collaboration. Ethical considerations emphasize transparency, informed consent, and the responsible use of patient data. As these technologies evolve, their seamless integration holds the promise of precision health, community-oriented interventions, and proactive pandemic response, reshaping the landscape of healthcare decision-making.

Anticipating multisectoral use of services with the support of AI

Introduction: This paper presents the preliminary results of a collaborative R&amp;D project which aims at developing artificial intelligence (AI) supported anticipation of multisectoral use of services. If we were able to predict the escalation of health and social problems of clients and the use of several different services, we could plan and offer them early preventive services with the objective of improving the quality of life and reducing the costs of service provision in the longer term.&#x0D; Approach and methods: The multidisciplinary project in progress collaborates at four spheres of research and development within the elderly care of one welfare region in Finland.&#x0D; 1) Anticipatory management practices concerning risk client groups and anticipatory case management practices concerning individual clients in risk are being developed supported by predictive AI technology. The requirements concerning the information the AI based prediction model should produce to the managers and to the case managers have provisionally been defined. The current practices are co-studied, and the new practices supported by AI are co-designed by involving the “relevant” health and social care managers and practitioners in the welfare region.&#x0D; 2) A machine learning (ML) predictive model based on health and social care data is being developed. The prediction model will be developed based on retrospective analysis of health and social data of the welfare region. The available data includes clinical data recorded during healthcare encounters, decisions, and usage of social services as well as physical function assessments. The data is used to train and test potential machine learning algorithms for predicting future service use of the elderly individuals based on their current health status and past use of services.&#x0D; 3) The implementation of the AI/ML model within the IT infrastructure and information systems (IS) in use will be supported and investigated. The developed ML model would eventually be integrated into the IT/IS environment of the case organization. That entails connecting the specific data sources, running the model computations, and passing the results to the digital workflow of the care production management for knowledge management.&#x0D; &#x0D; 4) The EU and Finnish legislation that regulate the use of personal health and social data in the development and use of AI based predictive models is being analyzed. Requirements and potential limitations imposed by current legislation on the use of predictive models are identified. In particular, the strict conditions applicable to making automated risk assessments of individuals for purposes of taking anticipatory measures are analyzed and avenues for meeting them by health and social care organizations are discussed.&#x0D; Results and outputs: Working on these four spheres, the project produces a generic model on developing the anticipation practices of multisectoral use of services with the help of AI technology. The model will available to all the organizations which are developing AI based anticipation of multisectoral use of services.&#x0D; Key words: Anticipation, prediction, health and social services usage, machine learning model, AI software, legislation, elderly care

DEMENTIA DIAGNOSES PREDICT CAREGIVER EDUCATION DISCREPANCIES IN HOSPITAL SETTING

Abstract Many family caregivers report feeling unprepared to provide care for persons living with dementia (PLWD) after their hospital stay. Consequently, PLWD may experience poorer health outcomes and higher service utilization rates. Health systems can provide education to patients and their family caregivers, increasing preparedness, but these practices are less understood. This retrospective data analysis of electronic health records examines the role of diagnostic billing codes in predicting amount of education received by patients and their family caregivers. Our dataset includes 419 unique encounters of hospitalized older adult patients with a documented dementia diagnosis and a listed family caregiver between 1/1/2019 and 8/1/2022. Dementia-related ICD-10 billing codes determined unique patient diagnoses. Encounters were given designations of receiving more vs. less education if the amount of education received was above or below the sample median. Logistic regressions then determined the relationship between diagnostic billing codes and education received while controlling for demographic and contextual characteristics. We found that encounters with diagnostic codes “other degenerative disease of nervous system” and “Alzheimer’s disease” were 2.8 and 3.3 times as likely to receive more education, respectively (95% confidence interval = [1.051, 7.347], p = .0394; [1.175, 9.461], p = .0236). Conversely, encounters with the code “other frontotemporal dementia” were 3.3 times as likely to receive less education ([1.150, 9.684], p = .0266). Findings demonstrate certain dementia-related diagnoses predict differences in how likely caregivers are to receive education. Health systems should evaluate for these differences and incorporate new methods to increase caregiving preparedness.

Review of the impact of menopause symptoms on longevity

AbstractBackgroundEmerging evidence suggests that experiencing specific symptoms during menopause can heighten risks of developing conditions which can impact lifespan and long‐term vitality including dementia, cardiovascular disease, psychiatric conditions and cancer. In addition, it is now recognised that, while women live on average about 5 years longer than men, they often have poorer health which could be linked to this increased risk. The aim of the present review was to assess past literature to understand how distinct menopausal symptom presentations can impact lifespan and propensity for developing life‐limiting conditions.MethodThe following databases were searched: Google Scholar, EMBASE, MEDLINE, Cochrane Library, Scopus, PubMed, and Web of Science. Keys words aimed to capture factors relating to menopause, menopausal symptoms, mortality, and life limiting conditions such as Alzheimer’s disease, dementia, cardiovascular disease, stroke, cancer, liver disease, and suicide susceptibility.ResultFindings suggested that cognitive symptoms experienced during menopause were related to heightened risks of developing Alzheimer’s disease, mild cognitive impairment, dementia, and premature mortality. Vasomotor symptoms were related to increased risks of stroke and cardiovascular disease mortality. Psychological symptoms during the menopause transition were related to increased suicide risks and cardiovascular disease. Sleeping problems during menopause were related to heightened risks of cardiovascular mortality, cancer‐related mortality, and premature death from all causes.ConclusionThese findings warrant further investigation through robust analyses of public health data, to evaluate whether these outcomes are replicated within the general population. Should these findings endure, these outcomes suggest a need to improve the health of women during midlife, as doing so may prolong wellness in later life and also have consequences for socioeconomic costs. Furthermore, clarifying these associations may enable women and their health providers to recognise whether they are at risk of developing life‐limiting conditions, thus enhancing their vitality by increasing their likelihood of accessing preventative or early interventions and treatments.

Perceived barriers and facilitators to preventing hospital-acquired pressure injury in paediatrics: A qualitative analysis.

This qualitative study aimed to identify nurses' and allied health professionals' perceptions and experiences of providing hospital-acquired pressure injury (HAPI) prevention in a paediatric tertiary hospital in Australia, as well as understand the perceived barriers and facilitators to preventing HAPI. A qualitative, exploratory study of hospital professionals was undertaken using semi-structured interviews between February 2022 and January 2023. Two frameworks, the Capability, Opportunity and Motivation Model of Behaviour (COM-B) and the Theoretical Domains Framework (TDF), were used to give both theoretical and pragmatic guidance. Participants included 19 nursing and allied health professionals and data analysis was informed by the framework approach. Analysis revealed nine core themes regarding professionals' beliefs about the barriers and facilitators to HAPI prevention practices across seven TDF domains. Themes included HAPI prevention skills and education, family-centred care, automated feedback and prompts, allocation and access to equipment, everybody's responsibility, prioritizing patients and clinical demands, organizational expectations and support, integrating theory and reality in practice and emotional influence. These findings provide valuable insights into the barriers and facilitators that impact paediatric HAPI prevention and can help identify and implement strategies to enhance evidence-based prevention care and prevent HAPI in paediatric settings. Overcoming barriers through evidence-based interventions is essential to reduce HAPI cases, improve patient outcomes, and cut healthcare costs. The findings have practical implications, informing policy and practice for improved preventive measures, education, and staffing in paediatric care, ultimately benefiting patient well-being and reducing HAPIs. No patient or public contribution. The focus of the study is on healthcare professionals and their perspectives and experiences in preventing HAPIs in paediatric patients. Therefore, the involvement of patients or the public was not deemed necessary for achieving the specific research objectives.

Sistem Informasi Kesehatan Dan Telemedicine: Narrative Review

HIS has a major role in managing health data and information applied through Electronic Medical Records (EMR). EMR facilitates the analysis of health data in patients, decision-making by health care providers, disease mapping, helps identify disease patterns, and maps the spread of the disease. HIS supports the creation of telemedicine services through the concept of telemedicine. This study uses the Narrative Literature Review (NLR) method on the concepts of Health Information Systems (HIS) and Telemedicine. Integrating HIS and Telemedicine provides promising opportunities for healthcare improvement, offering data accessibility, and efficient remote consultation. However, addressing privacy, security, infrastructure, and regulatory challenges is critical to the successful implementation of such integrations. Collaborative efforts between healthcare providers, policymakers, and technology developers are critical to ensuring the ethical and effective implementation of integrated systems.

Bayesian safety surveillance with adaptive bias correction.

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

Comparison of new computational methods for spatial modelling of malaria

BackgroundGeostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes. The appeal of faster methods is particularly great as the size of the region and number of spatial locations being modelled increases.MethodsThis work presents an applied comparison of four proposed ‘fast’ computational methods for spatial modelling and the software provided to implement them—Integrated Nested Laplace Approximation (INLA), tree boosting with Gaussian processes and mixed effect models (GPBoost), Fixed Rank Kriging (FRK) and Spatial Random Forests (SpRF). The four methods are illustrated by estimating malaria prevalence on two different spatial scales—country and continent. The performance of the four methods is compared on these data in terms of accuracy, computation time, and ease of implementation.ResultsTwo of these methods—SpRF and GPBoost—do not scale well as the data size increases, and so are likely to be infeasible for larger-scale analysis problems. The two remaining methods—INLA and FRK—do scale well computationally, however the resulting model fits are very sensitive to the user’s modelling assumptions and parameter choices. The binomial observation distribution commonly used for disease prevalence mapping with INLA fails to account for small-scale overdispersion present in the malaria prevalence data, which can lead to poor predictions. Selection of an appropriate alternative such as the Beta-binomial distribution is required to produce a reliable model fit. The small-scale random effect term in FRK overcomes this pitfall, but FRK model estimates are very reliant on providing a sufficient number and appropriate configuration of basis functions. Unfortunately the computation time for FRK increases rapidly with increasing basis resolution.ConclusionsINLA and FRK both enable scalable geostatistical modelling of malaria prevalence data. However care must be taken when using both methods to assess the fit of the model to data and plausibility of predictions, in order to select appropriate model assumptions and parameters.

Seasonal malaria chemoprevention in a context of high presumed sulfadoxine-pyrimethamine resistance: malaria morbidity and molecular drug resistance profiles in South Sudan

BackgroundSeasonal malaria chemoprevention (SMC) using sulfadoxine-pyrimethamine plus amodiaquine (SP-AQ), is a community-based malaria preventive strategy commonly used in the Sahel region of sub-Saharan Africa. However, to date it has not been implemented in East Africa due to high SP resistance levels. This paper is a report on the implementation of SMC outside of the Sahel in an environment with a high level of presumed SP-resistance: five cycles of SMC using SPAQ were administered to children 3–59 months during a period of high malaria transmission (July–December 2019) in 21 villages in South Sudan.MethodsA population-based SMC coverage survey was combined with a longitudinal time series analysis of health facility and community health data measured after each SMC cycle. SMC campaign effectiveness was assessed by Poisson model. SPAQ molecular resistance markers were additionally analysed from dried blood spots from malaria confirmed patients.ResultsIncidence of uncomplicated malaria was reduced from 6.6 per 100 to an average of 3.2 per 100 after SMC administration (mean reduction: 53%) and incidence of severe malaria showed a reduction from 21 per 10,000 before SMC campaign to a mean of 3.3 per 10,000 after each cycle (mean reduction: 84%) in the target group when compared to before the SMC campaign. The most prevalent molecular haplotype associated with SP resistance was the IRNGE haplotype (quintuple mutant, with 51I/59R/108N mutation in pfdhfr + 437G/540E in pfdhps). In contrast, there was a low frequency of AQ resistance markers and haplotypes resistant to both drugs combined (< 2%).ConclusionsThe SMC campaign was effective and could be used as an additional preventive tool in seasonal malaria settings outside of the Sahel, especially in areas where access to health care is unstable. Malaria case load reduction was observed despite the high level of resistance to SP.

Association of long-term benzodiazepine hypnotic use and prediabetes in US population: A cross-sectional analysis of national health and nutrition examination survey data.

Benzodiazepine hypnotics' effects on glucose metabolism are seldom reported, and the association between long-term (≥4 weeks) benzodiazepine usage and prediabetes has not been studied. This study was aimed to investigate the association between benzodiazepine hypnotic usage for ≥ 3 months and the prevalence of prediabetes. We analyzed cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) during 2005 to 2008, selecting adult participants without diabetes who used benzodiazepine hypnotics for at least 3 months or did not take any hypnotics. Individuals taking other hypnotics, antipsychotics, glucocorticoids, or hypoglycemic drugs were excluded. We defined prediabetes as an hemoglobin A1C (HbA1C) 5.7-6.4%, as suggested by the American Diabetes Association. Prescribed drug information was self-reported and checked by official interviewers, and HbA1C data in NHANES was recognized by the National Glycohemoglobin Standardization Program. We calculated the propensity score according to the covariates and adjusted it using multivariate logistic regression. Lower thresholds of HbA1C ≥ 5.5% or ≥ 5.3% were also analyzed. Among 4694 eligible participants, 38 received benzodiazepine hypnotics; using these hypnotics for ≥ 3 months was not significantly associated with the prevalence of prediabetes, as well as HbA1C ≥ 5.5% or ≥ 5.3%. Adjusted for propensity score, the respective odds ratios for prediabetes, HbA1C ≥ 5.5%, and HbA1C ≥ 5.3% were 1.09 (95% confidence interval [CI] 0.19-6.32), 0.83 (95% CI 0.22-3.13), and 1.22 (95% CI 0.3-4.93). No significant association was found between benzodiazepine hypnotic usage ≥ 3 months and the prevalence of prediabetes.

Using Patient-Reported Outcome Measures to Identify Factors Associated with Impaired Physical Function in Younger and Older Adults Living with Sickle Cell Disease

Background Improvements in disease management techniques have led to increases in the life expectancy of individuals with sickle cell disease (SCD). Despite the increasing number of older adults with SCD (defined as age ≥ 40 years), there is limited data on how symptoms experienced by this population such as pain, sleep, depression, and fatigue impact how they function day-to-day. In this study, we sought to determine which modifiable patient-reported factors most strongly correlate with patient reported physical function in adults with SCD. Understanding factors associated with impaired physical function will provide targets for future interventional studies to improve function. Methods We conducted a cross-sectional study at a single academic sickle cell center. Participants completed the Adult Sickle Cell Quality of Life Measurement Information System (ASCQ-Me) Stiffness Impact and Sleep Impact surveys and the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function, Pain Intensity, Pain Interference, Fatigue, Cognitive Function, and Emotional Distress measures. For ASCQ-Me, higher scores imply better self-reported health. For PROMIS, higher scores mean higher levels of the concept being measured. Relationships between the variables thought to influence SCD experience were explored using analytical networks and centrality measures (INSIGHTS, Nanbar Health's data analysis and 3D visualization tool) that built upon calculated pairwise Spearman correlations of the non-normal variable set. Results Surveys were administered to 92 participants with 97% of participants completing all surveys. The mean score for each item was obtained across all participants. Scores were then stratified by age group and t-tests were performed to compare scores on results between groups for each ASCQ-Me and PROMIS measure. Across those who completed both surveys, the mean age was 43 (SD 13.7). The mean age for older adults was 54 (SD 8.6) and the mean age for younger adults was 30 (SD 5.5). More participants were female (58%) than male (42%). Genotypes included HbSS (58%), HbSβ 0-thalassemia (5%), HbSC, (24%), and HbSβ +-thalassemia (13%). The mean score on PROMIS Physical Function for all participants was 48.7 with 18% of responders reporting impaired physical function (score ≤ 40). Older adults had lower mean scores on PROMIS Physical Function (46.4) vs. for younger adults (51.6, p &amp;lt;0.05). The strongest correlations were between PROMIS Physical Function and ASQME Stiffness Impact (r s= 0.66; p&amp;lt;0.001), PROMIS Pain Intensity (r s= -0.61; p&amp;lt;0.001), PROMIS Pain Interference, (r s= -0.61; p&amp;lt;0.001), and ASCQ-Me Fatigue (r s = -0.55; p&amp;lt;0.001). There was a moderate positive correlation between PROMIS Physical Function and ASCQ-Me Sleep Impact (r s= 0.40; p&amp;lt;0.001) and PROMIS Cognitive Function (r s=0.36 p &amp;lt; 0.001) implying better sleep and better cognitive function were associated with better patient reported physical function. Lastly, better physical function had a low-moderate association with PROMIS Emotional Distress (r s= - 0.24; p &amp;lt; 0.05). We assessed relationships between all of the patient-reported outcome measures and found that the strongest relationships were between Pain Intensity and Pain Interference (r s= 0.69), Pain Intensity and Stiffness (r s= -0.67), and Fatigue and Cognitive Function (r s= -0.69). There was no central node identified (Figure). Conclusion Each factor evaluated in this study showed a statistically significant and clinically meaningful relationship to patient reported physical function. One of our major findings was that when mapped against each other, there was no central node explaining the identified correlations between the variables assessed. These results suggest that physical functioning is multifactorial and that there exist multiple modifiable factors that may influence function in people with SCD. Factors associated with physical function will serve as targets for future interventional studies to maintain and improve function in adults with SCD.