Binomial Distribution Research Articles

Effect of COVID-19 on the Cultural Transition Towards the Use of Midwives Over Standard Doctors of Medicine [ID 2683504

INTRODUCTION: In recent times, the use of midwives as obstetrical providers has become increasingly popular within the United States. Unsurprisingly, the SARS-CoV-2 pandemic (COVID-19) had a large effect on societal trust of health care, including providers, and thus this study aimed to determine whether the pandemic further accelerated the cultural transition towards utilizing midwives as birth attendants. METHODS: Using the CDC Natality Database, we reviewed deliveries from 2016 to 2021 of previously healthy mothers, ages 18–35, with singleton pregnancies, measuring incidence of birth attendants including MDs, DOs, nursing midwives, and non-nursing midwives. A binomial model was used to perform logistic regressions to assess whether COVID-19 correlated with alterations in rates of transition of birthing attendants. RESULTS: Rates of deliveries attended by MDs declined at a greater extent, whereas births attended by DOs increased at a lesser rate, and nursing midwives and non-nursing midwives increased at a greater rate (change in rates pre-to-post COVID-19: −0.390%, −0.084%, 0.044%, and 0.204%, respectively; P<2.2×10−16). Although this rate of change was small, since the start of COVID-19 there has been an 11% increase in non-MD-attended deliveries, nearly double the rate seen prior. CONCLUSION: There has been a significant increase in the number of deliveries attended by DOs, nursing midwives, and non-nursing midwives over the past several decades, even more so since the start of COVID-19. With the transition in the utilization of health care professionals, further investigation needs to be conducted to assess the effects this has on health outcomes of the mother and infant.

Effect of heatwaves on daily hospital admissions in Portugal, 2000–18: an observational study

Climate change has increased the frequency, intensity, and duration of heatwaves, posing a serious threat to public health. Although the link between high temperatures and premature mortality has been extensively studied, the comprehensive quantification of heatwave effects on morbidity remains underexplored. In this observational study, we assessed the relationship between heatwaves and daily hospital admissions at a county level in Portugal. We considered all major diagnostic categories and age groups (<18 years, 18-64 years, and ≥65 years), over a 19-year period from 2000 to 2018, during the extended summer season, defined as May 1, to Sept 30. We did a comprehensive geospatial analysis, integrating over 12 million hospital admission records with heatwave events indexed by the Excess Heat Factor (EHF), covering all 278 mainland counties. We obtained data from the Hospital Morbidity Database and E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations. To estimate the effect of heatwaves on hospital admissions, we applied negative binomial regression models at both national and county levels. We found a statistically significant overall increase in daily hospital admissions during heatwave days (incidence rate ratio 1·189 [95% CI 1·179-1·198]; p<0·0001). All age groups were affected, with children younger than 18 years being the most affected (21·7% [20·6-22·7] increase in admissions; p<0·0001), followed by the working-age (19·7% [18·7-20·7]; p<0·0001) and elderly individuals (17·2% [16·2-18·2]; p<0·0001). All 25 major disease diagnostic categories showed significant increases in hospital admissions, particularly burns (34·3% [28·7-40·1]; p<0·0001), multiple significant trauma (26·8% [22·2-31·6]; p<0·0001), and infectious and parasitic diseases (25·4% [23·5-27·3]; p<0·0001). We also found notable increases in endocrine, nutritional, and metabolic diseases (25·1% [23·4-26·8]; p<0·0001), mental diseases and disorders (23·0% [21·1-24·8]; p<0·0001), respiratory diseases (22·4% [21·2-23·6]; p<0·0001), and circulatory system disorders (15·8% [14·7-16·9]; p<0·0001). Our results provide statistically significant evidence of the association between heatwaves and increased hospitalisations across all age groups and for all major causes of disease. To our knowledge, this is the first study to estimate the full extent of heatwaves' impact on hospitalisations using the EHF index over a 19-year period, encompassing an entire country, and spanning 25 disease categories during multiple heatwave events. Our data offer crucial information to guide policy makers in effectively and efficiently allocating resources to address the profound health-care consequences resulting from climate change. None.

Glaucoma is associated with poorer self-rated vision, vision concerns and health: Results from 59,168 questionnaire responses

PurposeThis study aimed to examine self-rated vision, vision concerns, and general health in individuals with and without glaucoma. Such assessments offer a broad view of a person's overall well-being and potential health issues and provide valuable insights into the characteristics of glaucoma patients. DesignCross-sectional. MethodsWe used questionnaire responses from Project FOREVER (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability) to evaluate self-rated vision and general health on a scale ranging from 1 (excellent), 2 (good), 3 (reasonable), 4 (poor) to 5 (very bad). Additionally, the vision concerns were rated on a scale from 1 (never), 2 (rarely), 3 (once in a while), 4 (often) to 5 (always). A glaucoma diagnosis was self-reported and obtained from the questionnaire. To investigate the strength and direction of associations, we used binomial logistic regression models including self-assessment, age, and sex as confounding variables and glaucoma status as the dependent variable. ResultsThis study analyzed questionnaire data from 59,168 participants, with an average age of 55.2 years (SD 15.0), and 62.0 % being women. Of these, 900 individuals (1.5 %) reported having a glaucoma diagnosis. Individuals having glaucoma reported poorer self-ratings in vision, vision concerns and general health. Specifically, the odds ratios for the poorest ratings were 2.46 for self-rated vision (95 % CI 1.70–3.55, p < 0.001), 8.04 for self-rated vision concerns (95 % CI 5.44–11.90, p < 0.001) and 1.86 for self-rated health (95 % CI 1.16–2.98, p = 0.010). ConclusionGlaucoma status is associated with poorer self-assessments of vision, vision concerns, and health. This suggests that individuals with glaucoma experience more vision and health challenges than people without the disease and the three assessments can be used as tools to characterize glaucoma patients in risk profiling efforts.

Inheriting housing inequality: Parental homeownership and place of origin as key factors for homeownership in Japanese metropolitan areas

The term “housing ladder” refers to the phenomenon of people moving from one housing type to another according to changing demands of different life stages. This phenomenon is observed in many countries and is known as the Jutaku Sugoroku Paradigm in the Japanese context. This study aims to clarify whether adult children's access to homeownership might be affected by their parents' homeownership and their place of origin. Questionnaire Data from a survey across Japan's two largest metropolitan areas, Tokyo and Osaka, were analyzed by combining crosstabulations and a binomial logit model. The results revealed that individuals are more likely to attain homeownership if they come from a metropolitan area and if their home of origin is an owner-occupied house. These findings suggest that housing inequality is being passed on to the next generation by parents' homeownership status and residential location. Japanese housing policies that promote private ownership of housing may reinforce these inequalities. The study concludes that Japan experiences the same intergenerational transmission of housing inequality due to homeownership as other countries, whereas place of origin was an additional factor for homeownership in the Japanese context.

Secular trends in gabapentinoid dispensing by compensated workers with low back pain: a retrospective cohort study

ObjectivesThe increase in gabapentinoid prescribing is paralleling the increase in serious harms. To describe the low back pain workers compensation population whose management included a gabapentinoid between 2010 and 2017,...

Influence of multimorbidity and socioeconomic position on long-term healthcare utilization and prognosis in patients after cardiac resynchronization therapy implantation.

We aimed to investigate the influence of socioeconomic position (SEP) and multimorbidity on cross-sectional healthcare utilization and prognosis in patients after cardiac resynchronization therapy (CRT) implantation. We included first-time CRT recipients with left ventricular ejection fraction ≤35% implanted between 2000 and 2017. Data on chronic conditions, use of healthcare services, and demographics were obtained from Danish national administrative and health registries. Healthcare utilization (in- and outpatient hospitalizations, activities in general practice) was compared by multimorbidity categories and SEP by using a negative binomial regression model. The association between SEP, multimorbidity, and prognostic outcomes was analysed using Cox proportional hazards regression. We followed 2007 patients (median age of 70 years), 79% were male, 75% were on early retirement or state pension, 37% were living alone, and 41% had low education level for a median of 5.2 [inter-quartile range: 2.2-7.3) years. In adjusted regression models, a higher number of chronic conditions were associated with increased healthcare utilization. Both cardiovascular and non-cardiovascular hospital contacts were increased. Patients with low SEP had a higher number of chronic conditions, but SEP had limited influence on healthcare utilization. Patients living alone and those with low educational level had a trend towards a higher risk of all-cause mortality [adjusted hazard ratio (aHR): 1.17, 95% confidence interval (CI) 1.03-1.33, and aHR 1.09, 95% CI 0.96-1.24). Multimorbidity increased the use of cross-sectional healthcare services, whereas low SEP had minor influence on the utilizations. Living alone and low educational level showed a trend towards a higher risk of mortality after CRT implantation.

Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model.

Zika, a viral disease transmitted to humans by Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported over 72,000 Zika cases between 2015 and 2016. Using national surveillance data from 1121 municipalities over 70 weeks, we identified sociodemographic and environmental factors associated with Zika's emergence, re-emergence, persistence, and transmission intensity in Colombia. We fitted a zero-state Markov-switching model under the Bayesian framework, assuming Zika switched between periods of presence and absence according to spatially and temporally varying probabilities of emergence/re-emergence (from absence to presence) and persistence (from presence to presence). These probabilities were assumed to follow a series of mixed multiple logistic regressions. When Zika was present, assuming that the cases follow a negative binomial distribution, we estimated the transmission intensity rate. Our results indicate that Zika emerged/re-emerged sooner and that transmission was intensified in municipalities that were more densely populated, at lower altitudes and/or with less vegetation cover. Warmer temperatures and less weekly-accumulated rain were also associated with Zika emergence. Zika cases persisted for longer in more densely populated areas with more cases reported in the previous week. Overall, population density, elevation, and temperature were identified as the main contributors to the first Zika epidemic in Colombia. We also estimated the probability of Zika presence by municipality and week, and the results suggest that the disease circulated undetected by the surveillance system on many occasions. Our results offer insights into priority areas for public health interventions against emerging and re-emerging Aedes-borne diseases.

The Impact of Primary Care Clinic and Family Physician Continuity on Patient Health Outcomes: A Retrospective Analysis From Alberta, Canada.

Continuity of care is broadly associated with better patient health outcomes. The relative contributions of continuity with an individual physician and with a practice, however, have not generally been distinguished. This retrospective observational study examined the impact of continuity of care for patients seen at their main clinic but by different family physicians. We analyzed linked health administrative data from 2015-2018 from Alberta, Canada to explore the association of physician and clinic continuity with rates of emergency department (ED) visits and hospitalizations across varying levels of patient complexity. Physician continuity was calculated using the known provider of care index and clinic continuity with an analogous measure. We developed zero-inflated negative binomial models to assess the association of each with all-cause ED visits and hospitalizations. High physician continuity was associated with lower ED use across all levels of patient complexity and with fewer hospitalizations for highly complex patients. Broadly, no (0%) clinic continuity was associated with increased use and complete (100%) clinic continuity with decreased use, with the largest effect seen for the most complex patients. Levels of clinic continuity between 1% and 50% were generally associated with slightly higher use, and levels of 51% to 99% with slightly lower use. The best health care outcomes (measured by ED visits and hospitalizations) are associated with consistently seeing one's own primary family physician or seeing a clinic partner when that physician is unavailable. The effect of partial clinic continuity appears complex and requires additional research. These results provide some reassurance for part-time and shared practices, and guidance for primary care workforce policy makers.

Risk factors for asthma-related hospital and intensive care admissions in children, adolescents and adults: a cohort study using primary and secondary care data

BackgroundAsthma remains a common cause of hospital admissions across the life course. We estimated the contribution of key risk factors to asthma-related hospital and intensive care unit (ICU) admissions in...

Addiction-Centered Pregnancy Care: Bridging the Gap Between Addiction Treatment and Prenatal Care to Improve Outcomes for Pregnant People and Infants [ID 2683418

INTRODUCTION: Opioid use disorder (OUD) is a leading cause of maternal mortality. This study evaluated a new model of care for pregnant people with OUD, addiction-centered pregnancy care (ACPC), which provides assessment and support for the social, economic, and personal challenges linked to OUD within prenatal care. Unlike integrated models of care providing medication for OUD, ACPC is easier to implement and rapidly scalable. This study compared risk of preterm delivery and infant length of stay for patients receiving ACPC versus standard prenatal care. METHODS: In this retrospective cohort study approved by the Dartmouth Hitchcock IRB, data were abstracted from charts of patients with OUD who received ACPC (n=53) or standard prenatal care (n=41) and delivered at an academic medical center in the Northeast between 2018 and 2021. Risk of preterm delivery was examined using multivariate logistic regressions, and infant length of stay was assessed with negative binomial models. RESULTS: Women in the ACPC cohort had reduced odds of preterm delivery (n=3, 5.7%) compared to the standard prenatal cohort (n=11, 26.8%; odds ratio [OR] 0.40; 95% CI, 0.21, 0.80). Compared to standard prenatal care, infants of patients receiving ACPC stayed in the hospital fewer days after delivery (incidence risk ratio, 0.77; 95% CI, 0.61, 0.97). CONCLUSION: Among a cohort of pregnant patients with OUD in the Northeast, receiving ACPC was associated with reduced risk for preterm delivery and shorter length of infant hospitalization compared to standard prenatal care. Future research should assess if these differences are due to this model’s focus on supporting psychosocial and socioeconomic challenges linked to OUD.

Analisis Kepuasan Generasi Z terhadap Layanan E-Commerce: Implementasi Distribusi Binomial

This research aims to determine the probability of success in testing the quality of e-commerce services. The variables in this research consider the quality of e-commerce services and customer satisfaction, which are then grouped again based on dimensions. The dimensions of e-commerce services are product availability, delivery, and information and complaints. A binomial distribution is used to estimate the indicators in that dimension. Indicators in this dimension include product availability, delivery time, price and reliability, product quality and condition, customer complaints and return policies, and information quality. Success and samples were taken with n equal to 14 respondents as one representative of each study program. The population N is modeled using a binomial distribution by conducting n experiments. Each trial has two possible outcomes, namely success or failure. The probabilities of success and failure are expressed jointly by p and 1-p. The instrument used to collect data was a questionnaire with a Likert scale. The results of this research are p (strongly agree) = 0.191, p (agree) = 0.466, p (somewhat agree) = 0.269, p (disagree) = 0.046 and p (strongly disagree) = 0.027. Strongly agree, agree, and so on, state the respondent's answer to the statement about Shopee's service quality. The probability of success will increase when the sample size is smaller than a considerable probability value based on trials with varying n and x values.

The influence of app function evolution on transport SuperApp use behaviour over time

AbstractIn the past few decades, there has been a significant increase in smartphone apps that are designed to help users optimise their daily activities. As a result, there has been a noticeable impact on travel demand. Some of these apps have evolved with the incorporation of additional functions in a gradual transformation into multi-function apps or SuperApps, thereby providing users with more integrated and personalised services for a wider range of activities. Focusing on Transport SuperApps (TSA) in Indonesia, this study aims to investigate how app usage behaviour interacts with the evolving functions of these apps over time. The study further examines the influence of personality traits, socio-demographic factors, and residential location on app usage patterns. In this study, longitudinal data on TSA usage from 2015–2022 was collected from users in four Indonesian cities. The Latent Markov (LMM) and Negative Binomial (NBM) Models were used to analyse the transition of behaviours, app types, and the number of apps used. The findings reveal that transport and shopping services are the most popular and consistently utilised services by users. The results suggest that the introduction of new services has a positive impact on the number of TSA services used. However, some services were found to be used only temporarily, primarily serving as alternatives to support users’ daily needs and desires. Initial higher service usage was observed among educated users with sociable and disorganised personalities, while discontinuation of usage is associated with older users and affluent households. Higher transition and continuation to use more services are also observed in larger cities like Jakarta compared to smaller cities like Cianjur.

Mental health service use and barriers to help-seeking among LGBTQ+ first-year college students in Chile

This study aimed to describe university students’ use of mental health services and the barriers to help-seeking by sexual orientation and gender identity and to examine the factors related to these variables. A total of 7,136 first-year students from five Chilean universities participated. They answered an online survey on mental health service use, barriers to help-seeking, 12-month mental disorders, and sociodemographic variables. Data were analyzed using logistic and negative binomial regression models. Lesbian, gay, bisexual, trans, questioning, and other sexual and gender minority (LGBTQ+) students, especially trans and gender non-conforming participants, reported higher mental health service use than non-LGBTQ+ students. Sexual minority students were less likely to report the help-seeking barriers "prefer to handle on one’s own" and "talk with friends/family", but were more likely to report the barriers "cost", "unsure where to go", and "time, transportation, or scheduling problems". Some variables were associated with service use and barriers in both groups (e.g. lower parental education was associated with lower service use), while others were only associated with one group (e.g. non-LGBTQ+ women reported higher service use). These results suggest that initiatives aimed at promoting access to mental health services should be tailored to different subpopulations of university students.

Association Between Dementia, Change in Home-Care Use, and Depressive Symptoms During the COVID-19 Pandemic: A Longitudinal Study Using Data from Three Cohort Studies.

The emotional impact of the coronavirus disease 2019 (COVID-19) pandemic on people with dementia has been quantified. However, little is known about the impact of change in home-care use owing to the pandemic. To determine the longitudinal association between dementia, change in home-care use, and depressive symptoms during the pandemic. We included data of 43,782 home-dwelling older adults from the English Longitudinal Study of Ageing (ELSA), Study of Health, Ageing and Retirement in Europe (SHARE), and National Health and Aging Trends Study (NHATS). This study considered the latest main wave survey prior to the pandemic as the baseline, and the COVID-19 survey as follow-up. In a series of coordinated analyses, multilevel binomial logistic regression model was used to examine the association between baseline dementia, change in home-care use at follow-up, and presence of depressive symptoms. Dementia, using the ELSA, SHARE, and NHATS datasets, was identified in 2.9%, 2.3%, and 6.5% of older adults, and home-care use reduced in 1.7%, 2.8%, and 1.1% of individuals with dementia, respectively. Dementia was significantly associated with the increased risk of depressive symptoms in all three cohorts. However, the interaction between dementia and period (follow-up) was non-significant in SHARE and NHATS. Across all three cohorts, home-care use during the pandemic, regardless of change in amount, was significantly associated with increased depressive symptoms, compared to the non-use of home care. These results highlight the need for tailoring dementia care at home to promote independence and provide sustainable emotional support.

Differences in end-of-life care patterns between types of hospice used for cancer patients: a retrospective cohort study.

In response to the rapid aging population and increasing number of cancer patients, discussions on dignified end-of-life (EoL) decisions are active around the world. Therefore, this study aimed to identify the differences in EoL care patterns between types of hospice used for cancer patients. In this population-based cohort study, the Korean National Health Insurance Service cohort data containing all registered cancer patients who died between 2017 and 2021 were used. A total of 408,964 individuals were eligible for analysis. The variable of interest, the type of hospice used in the 6months before death, was classified as follows: (1) Non-hospice users; (2) Hospital-based hospice single users; (3) Home-based hospice single users; (4) Combined hospice users. The outcomes were set as patterns of care, including intense care and supportive care. To identify differences in care patterns between hospice types, a generalized linear model with zero-inflated negative binomial distribution was applied. Hospice enrollment was associated with less intense care and more supportive care near death. Notably, those who used combined hospice care had the lowest probability and frequency of receiving intense care (aOR: 0.18, 95% CI: 0.17-0.19, aRR: 0.47, 95% CI: 0.44-0.49), while home-based hospice single users had the highest probability and frequency of receiving supportive care (Prescription for narcotic analgesics, aOR: 2.95, 95% CI: 2.69-3.23, aRR: 1.45, 95% CI: 1.41-1.49; Mental health care, aOR: 3.40, 95% CI: 3.13-3.69, aRR: 1.35, 95% CI: 1.31-1.39). Our findings suggest that although intense care for life-sustaining decreases with hospice enrollment, QoL at the EoL actually improves with appropriate supportive care. This study is meaningful in that it not only offers valuable insight into hospice care for terminally ill patients, but also provides policy implications for the introduction of patient-centered community-based hospice services.

Internalization of External Cost in the Thermal Power Generation on Social Welfare Maximization

For decades, Kenya has incorporated thermal power technology into its grid to generate electrical energy using fossil fuels such as petroleum, natural gas, and coal. The burning of fossil fuels has become a major source of air pollutants and is associated with several undesirable side effects on the environment and human health. However, the impact of pollutants on environmental sustainability and public welfare has yet to be evaluated. Therefore, the purpose of this study is to evaluate the external cost of electricity generated by thermal power plants in Kenya. Both survey data and secondary data were used. The analysis was conducted using externality valuation and welfare maximization approaches, and the research hypotheses were tested using a negative binomial regression model. The annual external cost ($/2022) was determined to be $ 1,333,904,970.76, with the following distribution: environmental at $ 993,488,336.26, Public health at $ 86,760,038.01, and socio-economic at $ 253,656,596.49. Equally, the thermal power generation marginal social cost ($/2022) was determined to be 1.22 $cents/kWh with the following distribution: Marginal Private Cost (MPC) at 0.01 $cents/kWh and Marginal External Cost (MEC) at 1.21 $cents/kWh. The established marginal social cost (MSC) (i.e. Σ MPC+MEC) was 1.22 ($cents/kWh). Thus, MSC is significantly greater than the established social marginal benefit (SMB) of 0.089 ($ cents/kWh); hence, we conclude that the burden of social welfare loss is highly significant, making thermal power a non-sustainable and economic energy source.

ScTPC: a novel semisupervised deep clustering model for scRNA-seq data.

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging. This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework. scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.

Measuring effects of ivermectin-treated cattle on potential malaria vectors in Vietnam: A cluster-randomized trial.

Malaria elimination using current tools has stalled in many areas. Ivermectin (IVM) is a broad-antiparasitic drug and mosquitocide and has been proposed as a tool for accelerating progress towards malaria elimination. Under laboratory conditions, IVM has been shown to reduce the survival of adult Anopheles populations that have fed on IVM-treated mammals. Treating cattle with IVM has been proposed as an important contribution to malaria vector management, however, the impacts of IVM in this One Health use case have been untested in field trials in Southeast Asia. Through a randomized village-based trial, this study quantified the effect of IVM-treated cattle on anopheline populations in treated vs. untreated villages in Central Vietnam. Local zebu cattle in six rural villages were included in this study. In three villages, cattle were treated with IVM at established veterinary dosages, and in three additional villages cattle were left as untreated controls. For the main study outcome, the mosquito populations in all villages were sampled using cattle-baited traps for six nights before, and six nights after a 2-day IVM-administration (intervention) period. Anopheline species were characterized using taxonomic keys. The impact of the intervention was analyzed using a difference-in-differences (DID) approach with generalized estimating equations (with negative binomial distribution and robust errors). This intervention was powered to detect a 50% reduction in total nightly Anopheles spp. vector catches from cattle-baited traps. Given the unusual diversity in anopheline populations, exploratory analyses examined taxon-level differences in the ecological population diversity. Across the treated villages, 1,112 of 1,523 censused cows (73% overall; range 67% to 83%) were treated with IVM. In both control and treated villages, there was a 30% to 40% decrease in total anophelines captured in the post-intervention period as compared to the pre-intervention period. In the control villages, there were 1,873 captured pre-intervention and 1,079 captured during the post-intervention period. In the treated villages, there were 1,594 captured pre-intervention, and 1,101 captured during the post-intervention period. The difference in differences model analysis comparing total captures between arms was not statistically significant (p = 0.61). Secondary outcomes of vector population diversity found that in three villages (one control and two treatment) Brillouin's index increased, and in three villages (two control and one treatment) Brillouin's index decreased. When examining biodiversity by trapping-night, there were no clear trends in treated or untreated vector populations. Additionally, there were no clear trends when examining the components of biodiversity: richness and evenness. The ability of this study to quantify the impacts of IVM treatment was limited due to unexpectedly large spatiotemporal variability in trapping rates; an area-wide decrease in trapping counts across all six villages post-intervention; and potential spillover effects. However, this study provides important data to directly inform future studies in the GMS and beyond for IVM-based vector control.

The impact of the COVID-19 pandemic on HIV treatment gap lengths and viremia among people living with HIV British Columbia, Canada, during the COVID-19 pandemic: Are we ready for the next pandemic?

The SARS-CoV-2 (COVID-19) pandemic has impacted the care of people living with HIV (PLWH). This study aims to characterize the impact of the pandemic on the length of HIV treatment gap lengths and viral loads among people living with HIV (PLWH) in British Columbia (BC), Canada, with a focus on Downtown Eastside (DTES), which is one of the most impoverished neighbourhoods in Canada. We analyzed data from the HIV/AIDS Drug Treatment Program from January 2019 to February 2022. The study had three phases: Pre-COVID, Early-COVID, and Late-COVID. We compared results for individuals residing in DTES, those not residing in DTES, and those with no fixed address. Treatment gap lengths and viral loads were analyzed using a zero-inflated negative binomial model and a two-part model, respectively, adjusting for demographic factors. Among the 8982 individuals, 93% were non-DTES residents, 6% were DTES residents, and 1% had no fixed address during each phase. DTES residents were more likely to be female, with Indigenous Ancestry, and have a history of injection drug use. Initially, the mean number of viral load measurements decreased for all PLWH during the Early-COVID, then remained constant. Treatment gap lengths increased for all three groups during Early-COVID. However, by Late-COVID, those with no fixed address approached pre-COVID levels, while the other two groups did not reach Early-COVID levels. Viral loads improved across each phase from Pre- to Early- to Late-COVID among people residing and not residing in DTES, while those with no fixed address experienced consistently worsening levels. Despite pandemic disruptions, both DTES and non-DTES areas enhanced HIV control, whereas individuals with no fixed address encountered challenges. This study offers insights into healthcare system preparedness for delivering HIV care during future pandemics, emphasizing community-driven interventions with a particular consideration of housing stability.

Fast Fusion Clustering via Double Random Projection.

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.