Negative binomial mixed models for analyzing longitudinal CD4 count data

Perceived Higher Unmet Care Needs among Adults in Permanent Supportive Housing

This study is original in that it assesses various types of care needs, barriers to care, and factors associated with higher unmet needs among 308 permanent supportive housing (PSH) residents in Quebec (Canada). Data from structured interviews that featured the Perceived Need for Care Questionnaire were collected from 2020 to 2022, controlling for the COVID-19 pandemic period. Eight types of care (e.g., information, counseling) were accounted for. Based on the Behavioral Model for Vulnerable Populations, predisposing, need, and enabling factors associated with higher unmet care needs were assessed using a negative binomial regression model. The study found that 56% of adult PSH residents, even those who had lived in PSH for 5 + years, had unmet care needs. Twice as many unmet needs were due to structural (e.g., care access) rather than motivational barriers. Living in single-site PSH, in healthier neighborhoods, having better quality of life and self-esteem, and being more satisfied with housing and outpatient care were associated with fewer unmet care needs. PSH residents with co-occurring mental disorders (MD) and substance use disorders (SUD), and with moderate or severe psychological distress were likely to have more unmet needs. Better access to care, counseling and integrated treatment for co-occurring MD-SUD might be improved, as well as access to information on user rights, health and available support. Welfare benefits could be increased, with more peer support and meaningful activities, especially in single-site PSH. The quality of the neighborhoods where PSH are located might also be better monitored.

Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data

BACKGROUND

Diabetes is one of the top four non-communicable diseases that cause death and illness to many people around the world. This study aims to use an efficient count data model to estimate socio-environmental factors associated with diabetes incidences in Tanzania mainland, addressing lack of evidence on the efficient count data model for estimating factors associated with disease incidences disparities.

METHODS

This study analyzed diabetes counts in 184 Tanzania mainland councils collected in 2020. The study applied generalized Poisson, negative binomial, and Poisson count data models and evaluated their adequacy using information criteria and Pearson chi-square values.

RESULTS

The data were over-dispersed, as evidenced by the mean and variance values and the positively skewed histograms. The results revealed uneven distribution of diabetes incidence across geographical locations, with northern and urban councils having more cases. Factors like population, GDP, and hospital numbers were associated with diabetes counts. The GP model performed better than NB and Poisson models.

CONCLUSION

The occurrence of diabetes can be attributed to geographical locations. To address this public health issue, environmental interventions can be implemented. Additionally, the generalized Poisson model is an effective tool for analyzing health information system count data across different population subgroups.

Lung Cancer Prevalence in Virginia: A Spatial Zipcode-Level Analysis via INLA

BACKGROUND

Examining lung cancer (LC) cases in Virginia (VA) is essential due to its significant public health implications. By studying demographic, environmental, and socioeconomic variables, this paper aims to provide insights into the underlying drivers of LC prevalence in the state adjusted for spatial associations at the zipcode level.

METHODS

We model the available VA zipcode-level LC counts via (spatial) Poisson and negative binomial regression models, taking into account missing covariate data, zipcode-level spatial association and allow for overdispersion. Under latent Gaussian Markov Random Field (GMRF) assumptions, our Bayesian hierarchical model powered by Integrated Nested Laplace Approximation (INLA) considers simultaneous (spatial) imputation of all missing covariates through elegant prediction. The spatial random effect across zip codes follows a Conditional Autoregressive (CAR) prior.

RESULTS

Zip codes with elevated smoking indices demonstrated a corresponding increase in LC counts, underscoring the well-established connection between smoking and LC. Additionally, we observed a notable correlation between higher Social Deprivation Index (SDI) scores and increased LC counts, aligning with the prevalent pattern of heightened LC prevalence in regions characterized by lower income and education levels. On the demographic level, our findings indicated higher LC counts in zip codes with larger White and Black populations (with Whites having higher prevalence than Blacks), lower counts in zip codes with higher Hispanic populations (compared to non-Hispanics), and higher prevalence among women compared to men. Furthermore, zip codes with a larger population of elderly people (age ≥ 65 years) exhibited higher LC prevalence, consistent with established national patterns.

CONCLUSIONS

This comprehensive analysis contributes to our understanding of the complex interplay of demographic and socioeconomic factors influencing LC disparities in VA at the zip code level, providing valuable information for targeted public health interventions and resource allocation. Implementation code is available at GitHub.

cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling

MOTIVATION

The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation.

RESULTS

Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks.

AVAILABILITY AND IMPLEMENTATION

Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.

Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition

The amygdala processes positive and negative valence and contributes to addiction, but the cell-type-specific gene regulatory programs involved are unknown. We generated an atlas of single-nucleus gene expression and chromatin accessibility in the amygdala of outbred rats with high and low cocaine addiction-like behaviors following prolonged abstinence. Differentially expressed genes between the high and low groups were enriched for energy metabolism across cell types. Rats with high addiction index (AI) showed increased relapse-like behaviors and GABAergic transmission in the amygdala. Both phenotypes were reversed by pharmacological inhibition of the glyoxalase 1 enzyme, which metabolizes methylglyoxal-a GABAA receptor agonist produced by glycolysis. Differences in chromatin accessibility between high and low AI rats implicated pioneer transcription factors in the basic helix-loop-helix, FOX, SOX and activator protein 1 families. We observed opposite regulation of chromatin accessibility across many cell types. Most notably, excitatory neurons had greater accessibility in high AI rats and inhibitory neurons had greater accessibility in low AI rats.

Perfluorooctanoic Acid Promotes Recruitment and Exocytosis of Rodlet Cells in the Renal Hematopoietic Tissue of Common Carp

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental contaminants, with perfluorooctanoic acid (PFOA) being a prominent member. PFOA poses a risk to aquatic ecosystems and human health due to its presence in water, environmental persistence, and bioaccumulation. Since rodlet cells (RCs) have emerged as potential biomarkers for chemical stressors, this study aimed to investigate the effects of sub-chronic PFOA exposure on RCs in the renal hematopoietic tissue of common carp. Three groups of fish were used: an unexposed control group and two groups exposed to environmentally relevant (200 ng L-1) and elevated (2 mg L-1) PFOA concentrations. Light and transmission electron microscopy were employed to assess RCs' distribution patterns and exocytosis, while biometry quantified RCs in the hematopoietic tissue. The results showed that, even at environmentally relevant concentrations, PFOA significantly influenced RCs' distribution patterns, leading to increased occurrence and cluster formation, as well as heightened exocytosis activity. This research highlights PFOA's immunotoxicity in fish and suggests the potential of RCs as sentinel cells in the immunological response to environmental contaminants. These findings enhance our understanding of PFAS toxicity and emphasise the importance of monitoring their impact on fish as representative vertebrates and reliable animal models.

Machine learning and statistical models for analyzing multilevel patent data

A recent surge of patent applications among public hospitals in China has aroused significant research interest. A country's healthcare innovation capacity can be measured by its number of patents. This paper explores the link between the number of patents and ten independent variables. Multicollinearity was carefully detected and removed by using the variable selection method and LASSO regression, respectively. The Poisson model and the negative binomial model were proposed to analyze the patent data. Three goodness of fit tests, the Pearson test, the deviance test, and the DHARMa non-parametric dispersion test, were conducted to investigate if the model has a good fit. After discovering four clusters by conducting agglomerative hierarchical clustering, these two models were replaced by the negative binomial mixed model. The likelihood ratio test was used to determine which model is more appropriate and the results reveal that the negative binomial mixed model outperforms both the Poisson model and the negative binomial model. Three variables, number of health technicians per 10,000 people, financial expenditure on science and technology as well as number of patent applications per 10,000 health personnel, have a significantly positive relationship with the number of patents in Chinese tertiary public hospitals.

Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal

Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable Xj at time t-𝓁j, where t is the observation time and 𝓁j is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.

Peripheral blood CD4+ T cell count predicts recurrence of condyloma acuminatum after photodynamic therapy in HIV-positive patients

BACKGROUND

Few studies have reported postoperative relapse of condyloma acuminatum (CA) after 5-aminolevulinic acid photodynamic therapy (ALA-PDT) in human immunodeficiency virus (HIV) positive patients.

METHODS

The clinical data of HIV-positive CA patients treated with ALA-PDT from October 2018 to December 2019 were analyzed retrospectively. Univariate and multivariate Cox regression was used to analyze the variables related to postoperative recurrence. Pearson correlation test was employed to analyze the correlation between CD4⁺ T cell count and postoperative recurrence rate. Kaplan-Meier method was used to compare the CA recurrence after ALA-PDT in low CD4 group and high CD4 group.

RESULTS

A total of 38 HIV-positive patients with CA were included in the study. Among them, 26 patients experienced CA recurrence within 6 months, and the recurrence rate was 68.4%. CD4⁺ T cell count was 187.0 (79.0-596.0) cells/μl in relapsed patients and 406.0 (89.0-612.0) cells/μl in non-relapsed patients, showing a statistically significant difference (p = .005). Pearson correlation coefficient analysis revealed a negative correlation between CD4⁺ T cell count and postoperative recurrence rate (p = .005, r = -.443). Univariate regression analysis showed that CD4⁺ T cell count was correlated with postoperative recurrence, hazard ratio (HR) was 0.99 [95% Confidence interval (CI) = 0.99-1.0, p = .012]. Multivariate Cox regression analysis showed that with the low CD4⁺ T cell count as the reference, the high CD4⁺ T cell count was negatively correlated with postoperative recurrence (HR = 0.09, 95% CI 0.01-0.87, p = .038).

CONCLUSIONS

Peripheral blood CD4⁺ T cell count can predict the CA recurrence rate after ALA-PDT in HIV-positive patients.

A Novel Approach to Tally Aculops cannabicola (Acari: Eriophyidae) for Field and Laboratory Studies

The hemp russet mite, Aculops cannabicola (Farkas) is a key pest of hemp (Cannabis sativa L.). Given its microscopic size, estimating the size of its populations can be a major limitation to control this pest. Here, we describe a method to count A. cannabicola using photographs taken with a handheld digital microscope. We compared the consistency and strength of the relationship between photographic- and microscope-based counts of mites from infested plants. Among the three sections of the leaflet, the maximum consistency levels were observed in the proximal (50%) and middle photographs (50%). However, it reached from 64% to 100% when the three sections were considered. Photographic and a microscope-based counts were positively correlated (>0.7). A single photograph can be a good predictor of the total mites per leaflet, however, using two pictures (proximal and middle sections) will increase the consistency of the abundance of A. cannabicola per leaflet. A minimum of 22 leaflets per sampling event can support a strong correlation between the microscope and photographic counts. Our method requires low budget and training and takes short time (0.4 to 1.3 min per leaflet) to count mites per sample. Additionally, photographs can be stored on a smartphone, computer, or tablet, allowing users to share, store and process the photos. This method simplifies counts of A. cannabicola on hemp for research purposes and provides a practical tool for growers to assess mite populations for management decisions. In addition, it may be useful for monitoring eriophyid mites on cultivated plants.

The Longitudinal Association between Co-Residential Care Provision and Healthcare Use among the Portuguese Population Aged 50 and Over: A SHARE Study

Co-residential care is associated with poor caregiver health and a high burden. Although Portugal relies heavily on co-residential care by individuals aged 50 and over, studies on the impact of co-residential care provision on Portuguese caregivers' healthcare use are lacking. This study aims to analyze the impact of co-residential care (spousal and non-spousal care) on healthcare use of the Portuguese population aged 50 plus. Data from waves 4 (n = 1697) and 6 (n = 1460) of the Survey of Health, Ageing and Retirement in Europe (SHARE) were used. Negative Binomial Generalized Linear Mixed Models with random (individual level) and fixed (covariates) effects were performed. The results show that the number of visits to the doctor decrease significantly over time for the co-residential spousal caregivers as compared to the non-co-residential caregivers. This result highlights the fact that the Portuguese co-residential spousal caregiver group is at a higher risk of not using healthcare, thus jeopardizing their own health and continuity of care. Promoting more accessible healthcare services and implementing public policies adjusted to the needs of informal caregivers are important to improve the health and healthcare use of Portuguese spousal co-residential caregivers.

Road environment characteristics and fatal crash injury during the rush and non-rush hour periods in the U.S: Model testing and cluster analysis

This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day - rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.

Role of small acute hyperintense lesions in long-term progression of cerebral small vessel disease and clinical outcome: a 14-year follow-up study

BACKGROUND

Small hyperintense lesions are found on diffusion-weighted imaging (DWI) in patients with sporadic small vessel disease (SVD). Their exact role in SVD progression remains unclear due to their asymptomatic and transient nature. The main objective is to investigate the role of DWI+lesions in the radiological progression of SVD and their relationship with clinical outcomes.

METHODS

Participants with SVD were included from the Radboud University Nijmegen Diffusion tensor MRI Cohort. DWI+lesions were assessed on four time points over 14 years. Outcome measures included neuroimaging markers of SVD, cognitive performance and clinical outcomes, including stroke, all-cause dementia and all-cause mortality. Linear mixed-effect models and Cox regression models were used to examine the outcome measures in participants with a DWI+lesion (DWI+) and those without a DWI+lesion (DWI-).

RESULTS

DWI+lesions were present in 45 out of 503 (8.9%) participants (mean age: 66.7 years (SD=8.3)). Participants with DWI+lesions and at least one follow-up (n=33) had higher white matter hyperintensity progression rates (β=0.36, 95% CI=0.05 to 0.68, p=0.023), more incident lacunes (incidence rate ratio=2.88, 95% CI=1.80 to 4.67, p<0.001) and greater cognitive decline (β=-0.03, 95% CI=-0.05 to -0.01, p=0.006) during a median follow-up of 13.2 (IQR: 8.8-13.8) years compared with DWI- participants. No differences were found in risk of all-cause mortality, stroke or dementia.

CONCLUSION

Presence of a DWI+lesion in patients with SVD is associated with greater radiological progression of SVD and cognitive decline compared with patients without DWI+lesions.

Impaired SARS-CoV-2 specific T-cell response in patients with severe COVID-19

Cellular immune responses are of pivotal importance to understand SARS-CoV-2 pathogenicity. Using an enzyme-linked immunosorbent spot (ELISpot) interferon-γ release assay with wild-type spike, membrane and nucleocapsid peptide pools, we longitudinally characterized functional SARS-CoV-2 specific T-cell responses in a cohort of patients with mild, moderate and severe COVID-19. All patients were included before emergence of the Omicron (B.1.1.529) variant. Our most important finding was an impaired development of early IFN-γ-secreting virus-specific T-cells in severe patients compared to patients with moderate disease, indicating that absence of virus-specific cellular responses in the acute phase may act as a prognostic factor for severe disease. Remarkably, in addition to reactivity against the spike protein, a substantial proportion of the SARS-CoV-2 specific T-cell response was directed against the conserved membrane protein. This may be relevant for diagnostics and vaccine design, especially considering new variants with heavily mutated spike proteins. Our data further strengthen the hypothesis that dysregulated adaptive immunity plays a central role in COVID-19 immunopathogenesis.

Characteristics and general practice resource use of people with comorbid cancer and dementia in England: a retrospective cross-sectional study

Background

Cancer and dementia are common in older people and management of the conditions as comorbidities can be challenging, yet little is known about the size or characteristics of this group. We aimed to estimate the prevalence, characteristics and general practice resource usage of people living with both conditions in England.

Methods

Anonymised electronic healthcare records from 391 National Health Service general practices across England using the TPP SystmOne general practice system were obtained from ResearchOne. Data included demographic and clinical characteristics, and general practice healthcare useage (appointments, prescriptions, referrals and secondary care contacts) for people aged 50 and over with a cancer and/or dementia diagnosis consistent with the Quality and Outcomes Framework between 2005 and 2016. Multi-level negative binomial regression was used to analyse the association between having cancer and/or dementia and the number of general practice appointments.

Results

Data from 162,371 people with cancer and/or dementia were analysed; 3616 (2.2%) people were identified as having comorbid cancer and dementia. Of people with cancer, 3.1% also had dementia, rising to 7.5% (1 in 13 people) in those aged 75 and over. Fewer people with both conditions were female (50.7%) compared to those with dementia alone (65.6%) and those with comorbid cancer and dementia were older than those with cancer alone [mean ages 83 (sd = 7), 69 (sd = 12) respectively]. Those with both conditions were less likely to have lung cancer than those with cancer alone (7.5% vs. 10.3%) but more likely to have prostate cancer (20.9% vs. 15.8%). Additional comorbidities were more prevalent for those with both conditions than those with cancer or dementia alone (68.4% vs. 50.2% vs. 54.0%). In the year following the first record of either condition, people with cancer and dementia had 9% more general practice appointments (IRR:1.09, 95% CI:1.01–1.17) than those with cancer alone and 37% more appointments than those with dementia alone (IRR: 1.37, 95% CI: 1.28–1.47).

Conclusions

A significant number of people are living with comorbid cancer and dementia in England. This group have additional comorbidity and higher general practice usage than those with cancer/dementia alone. The needs of this group should be considered in future general practice care planning and research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-022-01882-w.

The Impact of Cost-Containment Schemes on Outpatient Services for Schoolchildren with Refractive Errors in Taiwan—A Population-Based Study

Objectives: Extant research on cost-sharing finds no impact on health care utilization when the amount is insubstantial. This research investigates the effects on nonacute outpatient services for schoolchildren with refractive errors in Taiwan and discusses the potential harm caused by cost sharing and relevant cost containment policies. Methods: Longitudinal claims data from the National Health Insurance database are employed. District demographic information is also used for aggregate-level analyses. Interventional modeling is conducted on pooled individual-level data with a Poisson model and negative binomial models. Generalized least square modeling is performed on aggregate district-level data to elucidate the impacts of cost sharing and the reimbursement rate with controls for patient and institutional characteristics, district socioeconomic factors, and competitiveness among institutions. Results: The findings of this study show that cost sharing does not significantly affect children’s utilization of outpatient services in the patient-level analyses. However, it significantly decreases the service volume based on the results of district aggregate analyses. There are potentially marginal patients in society, and they are more likely to be girls in poorer families, whose chances of seeking medical care significantly decrease when cost sharing increases. Conclusions: The gap in health inequity can be widened when stringent cost-containment policies are implemented. The offset effect caused by delayed care may also result in higher health care expenditures later. Cost sharing for children should be separately and prudently designed to better protect them from deprivations caused by changes in health policies.

Determinants and Temporal Dynamics of Cerebral Small Vessel Disease: 14-Year Follow-Up

BACKGROUND

The aim of this study is to investigate the temporal dynamics of small vessel disease (SVD) and the effect of vascular risk factors and baseline SVD burden on progression of SVD with 4 neuroimaging assessments over 14 years in patients with SVD.

METHODS

Five hundred three patients with sporadic SVD (50-85 years) from the ongoing prospective cohort study (RUN DMC [Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort]) underwent baseline assessment in 2006 and follow-up in 2011, 2015, and 2020. Vascular risk factors and magnetic resonance imaging markers of SVD were evaluated. Linear mixed-effects model and negative binomial regression model were used to examine the determinants of temporal dynamics of SVD markers.

RESULTS

A total of 382 SVD patients (mean [SD] 64.1 [8.4]; 219 men and 163 women) who underwent at least 2 serial brain magnetic resonance imaging scans were included, with mean (SD) follow-up of 11.15 (3.32) years. We found a highly variable temporal course of SVD. Mean (SD) WMH progression rate was 0.6 (0.74) mL/y (range, 0.02-4.73 mL/y) and 13.6% of patients had incident lacunes (1.03%/y) over the 14-year follow-up. About 4% showed net WMH regression over 14 years, whereas 38 out of 361 (10.5%), 5 out of 296 (2%), and 61 out of 231 (26%) patients showed WMH regression for the intervals 2006 to 2011, 2011 to 2015, and 2015 to 2020, respectively. Of these, 29 (76%), 5 (100%), and 57 (93%) showed overall progression across the 14-year follow-up, and the net overall WMH change between first and last scan considering all participants was a net average WMH progression over the 14-year period. Older age was a strong predictor for faster WMH progression and incident lacunes. Patients with mild baseline WMH rarely progressed to severe WMH. In addition, both baseline burden of SVD lesions and vascular risk factors independently and synergistically predicted WMH progression, whereas only baseline SVD burden predicted incident lacunes over the 14-year follow-up.

CONCLUSIONS

SVD shows pronounced progression over time, but mild WMH rarely progresses to clinically severe WMH. WMH regression is noteworthy during some magnetic resonance imaging intervals, although it could be overall compensated by progression over the long follow-up.

Application of quantile mixed-effects model in modeling CD4 count from HIV-infected patients in KwaZulu-Natal South Africa

BACKGROUND

The CD4 cell count signifies the health of an individual's immune system. The use of data-driven models enables clinicians to accurately interpret potential information, examine the progression of CD4 count, and deal with patient heterogeneity due to patient-specific effects. Quantile-based regression models can be used to illustrate the entire conditional distribution of an outcome and identify various covariates effects at the respective location.

METHODS

This study uses the quantile mixed-effects model that assumes an asymmetric Laplace distribution for the error term. The model also incorporated multiple random effects to consider the correlation among observations. The exact maximum likelihood estimation was implemented using the Stochastic Approximation of the Expectation-Maximization algorithm to estimate the parameters. This study used the Centre of the AIDS Programme of Research in South Africa (CAPRISA) 002 Acute Infection Study data. In this study, the response variable is the longitudinal CD4 count from HIV-infected patients who were initiated on Highly Active Antiretroviral Therapy (HAART), and the explanatory variables are relevant baseline characteristics of the patients.

RESULTS

The analysis obtained robust parameters estimates at various locations of the conditional distribution. For instance, our result showed that baseline BMI (at [Formula: see text] 0.05: [Formula: see text]), baseline viral load (at [Formula: see text] 0.05: [Formula: see text] [Formula: see text]), and post-HAART initiation (at [Formula: see text] 0.05: [Formula: see text]) were major significant factors of CD4 count across fitted quantiles.

CONCLUSIONS

CD4 cell recovery in response to post-HAART initiation across all fitted quantile levels was observed. Compared to HIV-infected patients with low viral load levels at baseline, HIV-infected patients enrolled in the treatment with a high viral load level at baseline showed a significant negative effect on CD4 cell counts at upper quantiles. HIV-infected patients registered with high BMI at baseline had improved CD4 cell count after treatment, but physicians should not ignore this group of patients clinically. It is also crucial for physicians to closely monitor patients with a low BMI before and after starting HAART.

Thermal Conditions and Hospital Admissions: Analysis of Longitudinal Data from Cyprus (2009-2018)

The state of the thermal environment can affect human health and well-being. Heat stress is associated with a wide range of health outcomes increasing morbidity and mortality and is recognized as an important health risk posed by climate change. This study aims at examining the effect of thermal conditions on the daily number of hospital admissions in Cyprus. Data from eight public hospitals located in five districts of Cyprus were analyzed from 2009 to 2018. Meteorological hourly gridded data were extracted by the ERA-5 Land reanalysis database with a spatial horizontal resolution of 0.1° × 0.1°. The Physiologically Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI) were calculated as measures of the integrated effect of meteorological variables. Negative binomial regression was fitted to examine associations between the daily number of hospital admissions and meteorological variables, PET, and UTCI. The results showed that the mean daily temperature (Tair) was positively associated with hospital admissions from any cause. Hospital admissions increased by 0.6% (p < 0.001) for each 1 °C increase of Tair and by 0.4% (p < 0.001) for each 1 °C increase of PET and UTCI. Ozone and nitrogen oxides act as confounding factors. An effect of particulate matter (less than 10 μm in diameter) was observed when the analysis focused on April to August. Thresholds above which hospital admissions are likely to increase include daily mean Tair = 26.1 °C, PET = 29 °C, and UTCI = 26 °C. Studies on heat-related health effects are necessary to monitor health patterns, raise awareness, and design adaptation and mitigation measures.

Additive quantile mixed effects modelling with application to longitudinal CD4 count data

Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. Quantile regression for mixed-effects models has become practical for longitudinal data analysis due to the recent computational advances and the ready availability of efficient linear programming algorithms. Recently, quantile regression has also been extended to additive mixed-effects models, providing an efficient and flexible framework for nonparametric as well as parametric longitudinal forms of data analysis focused on features of the outcome beyond its central tendency. This study applies the additive quantile mixed model to analyze the longitudinal CD4 count of HIV-infected patients enrolled in a follow-up study at the Centre of the AIDS Programme of Research in South Africa. The objective of the study is to justify how the procedure developed can obtain robust nonlinear and linear effects at different conditional distribution locations. With respect to time and baseline BMI effect, the study shows a significant nonlinear effect on CD4 count across all fitted quantiles. Furthermore, across all fitted quantiles, the effect of the parametric covariates of baseline viral load, place of residence, and the number of sexual partners was found to be major significant factors on the progression of patients' CD4 count who had been initiated on the Highly Active Antiretroviral Therapy study.