Overcrowded housing increases risk for COVID-19 mortality: an ecological study

Predictors of COVID-19 infection and hospitalization in group homes for individuals with intellectual and/or developmental disabilities

BACKGROUND

More than seven million people with intellectual and/or developmental disabilities (ID/DD) live in the US and may face an elevated risk for COVID-19.

OBJECTIVE

To identify correlates of COVID-19 and related hospitalizations among people with ID/DD in group homes in Massachusetts.

METHODS

We collected data during March 1, 2020-June 30, 2020 (wave 1) and July 1, 2020-March 31, 2021 (wave 2) from the Massachusetts Department of Public Health and six organizations administering 206 group homes for 1035 residents with ID/DD. The main outcomes were COVID-19 infections and related hospitalizations. We fit multilevel Cox proportional hazards models to estimate associations with observed predictors and assess contextual home- and organizational-level effects.

RESULTS

Compared with Massachusetts residents, group home residents had a higher age-adjusted rate of COVID-19 in wave 1 (incidence rate ratio [IRR], 12.06; 95 % confidence interval [CI], 10.51-13.84) and wave 2 (IRR, 2.47; 95 % CI, 2.12-2.88) and a higher age-adjusted rate of COVID-19 hospitalizations in wave 1 (IRR, 17.64; 95 % CI, 12.59-24.70) and wave 2 (IRR, 4.95; 95 % CI, 3.23-7.60). COVID-19 infections and hospitalizations were more likely among residents aged 65+ and in group homes with 6+ resident beds and recent infection among staff and residents.

CONCLUSIONS

Aggressive efforts to decrease resident density, staff-to-resident ratios, and staff infections through efforts such as vaccination, in addition to ongoing access to personal protective equipment and COVID-19 testing, may reduce COVID-19 and related hospitalizations in people with ID/DD living in group homes.

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.

Association of race/ethnicity and severe housing problems with COVID-19 deaths in the United States: Analysis of the first three waves

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

Respiratory-related deaths and associated factors in Alicho-Weriro district, southern Ethiopia: verbal autopsy data analysis

BACKGROUND

Respiratory diseases disproportionately affect people living in resource-limited settings. However, obtaining information that explains respiratory-related deaths has been difficult, mainly due to a lack of medical certification of death and the fact that most deaths occur outside of health institutions. This study aimed to determine the proportion of respiratory-related deaths and identify associated factors in Alicho-Weriro district, southern Ethiopia, using the verbal autopsy method.

METHODS

A community-based cross-sectional study was conducted from April to June 2022. All deceased people in the study area from January 2020 to December 2021 were included in the study. Trained physicians ascertained the cause of death from verbal autopsy data that were collected using a pre-tested and modified WHO-designed questionnaire. The binary logistic regression models were used to identify factors associated with respiratory-related deaths.

RESULTS

Respiratory-related deaths accounted for 25% of the deaths from all causes, with 20.8% of male and 29.5% of female deaths. Of which, 9.7% were from tuberculosis, 8.3% were from asthma and 6.2% were from acute lower-respiratory tract infections. Moreover, being female (adjusted OR, AOR: 3.3; 95% CI: (1.75 to 6.22)), age 50-64 years (AOR: 9.3; 95% CI: (1.16 to 73.90)), age above 64 years (AOR: 8.9; 95% CI: (1.130 to 70.79)), family size of five persons or more (AOR: 1.9; 95% CI: (1.15 to 3.29)), smoking (AOR: 3.9; 95% CI: (1.86 to 8.35)), using wood and/or animal dung for household cooking (AOR: 6.6; 95% CI: (1.92 to 22.59)) and poor house ventilation (AOR: 3.1; 95% CI: (1.75 to 5.38)) were significantly associated with increased odds of dying from respiratory-related diseases.

CONCLUSION

This study has determined that about a quarter of deaths from all causes were due to respiratory diseases, mainly tuberculosis, asthma and acute lower respiratory tract infections. Therefore, interventions to reduce this burden should focus on supporting early case detection and treatment, promoting healthy lifestyles, exercising women's equality at the household level and improving housing conditions.

Overestimation of Severe Acute Respiratory Syndrome Coronavirus 2 Household Transmission in Settings of High Community Transmission: Insights From an Informal Settlement Community in Salvador, Brazil

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has spread globally. However, the contribution of community versus household transmission to the overall risk of infection remains unclear.

Methods

Between November 2021 and March 2022, we conducted an active case-finding study in an urban informal settlement with biweekly visits across 1174 households with 3364 residents. Individuals displaying coronavirus disease 2019 (COVID-19)-related symptoms were identified, interviewed along with household contacts, and defined as index and secondary cases based on reverse-transcription polymerase chain reaction (RT-PCR) and symptom onset.

Results

In 61 households, we detected a total of 94 RT-PCR-positive cases. Of 69 sequenced samples, 67 cases (97.1%) were attributed to the Omicron BA.1* variant. Among 35 of their households, the secondary attack rate was 50.0% (95% confidence interval [CI], 37.0%-63.0%). Women (relative risk [RR], 1.6 [95% CI, .9-2.7]), older individuals (median difference, 15 [95% CI, 2-21] years), and those reporting symptoms (RR, 1.73 [95% CI, 1.0-3.0]) had a significantly increased risk for SARS-CoV-2 secondary infection. Genomic analysis revealed substantial acquisition of viruses from the community even among households with other SARS-CoV-2 infections. After excluding community acquisition, we estimated a household secondary attack rate of 24.2% (95% CI, 11.9%-40.9%).

Conclusions

These findings underscore the ongoing risk of community acquisition of SARS-CoV-2 among households with current infections. The observed high attack rate necessitates swift booster vaccination, rapid testing availability, and therapeutic options to mitigate the severe outcomes of COVID-19.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India

The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Aboriginal peoples' lived experience of household overcrowding in the Kimberley and implications for research reciprocity in COVID-19 recovery

OBJECTIVE

Household overcrowding was identified early in the COVID-19 pandemic as a risk factor increasing transmission and worsening outcomes. Nirrumbuk Environmental Health and Services designed this project to deepen understanding of Aboriginal peoples' experiences of overcrowding in social housing.

METHODS

Our household survey explored overcrowding, capacity to respond to COVID-19 directives and the Canadian National Overcrowding Standard (CNOS).

RESULTS

For 219 participating Aboriginal households, usual number of residents per household ranged from 1 to 14, increasing with short- and long-term visitors. 17.8% had occupants who themselves were on waiting lists for their own home. Nearly one-third of houses had three generations under one roof. 53.4% indicated isolation of COVID-19 cases as 'extremely' difficult. 33.8% indicated their community could not manage COVID-19 at scale. Overcrowding was defined by interpersonal dynamics or consequences such as plumbing blockages or conflict rather than the number or people or ratio of people to bedrooms. 64.8% welcomed CNOS to determine acceptable and healthy occupancy levels. Participants encouraged research about environmental health in Aboriginal hands.

CONCLUSIONS

Cultural obligations, poverty and social housing waitlist management impose extreme demand on remote housing. CNOS relevance was endorsed but tempered by lived experience.

IMPLICATIONS FOR PUBLIC HEALTH

Aboriginal-led research is directly accountable to communities through reciprocity and kinship. Nirrumbuk has already modified service planning.

Individual and structural determinants of COVID-19 vaccine uptake in a marginalized community in the United States

Socially and medically vulnerable groups (e.g., people 65 years or older, minoritized racial groups, non-telework essential workers, and people with comorbid conditions) experience barriers to COVID-19 prevention and treatment, increased burden of disease, and increased risk of death from COVID-19. Researchers are paying increased attention to social determinants of health (SDH) in explaining inequities in COVID-19-related health outcomes and rates of vaccine uptake. The purpose of the present manuscript is to identify clinically significant predictors of COVID-19 vaccine uptake among people who were socially and medically vulnerable to SARs-CoV-2 infection. Analysis was informed by the SDH framework and included a sample of 641 baseline surveys from participants in a clinical trial designed to increase COVID-19 testing. All participants were at high risk of developing COVID-19-related complications or dying from COVID-19. Following community-based participatory research principles, a well-established community collaborative board conducted every aspect of the study. Multiple logistic regressions were conducted to examine the relationships between individual and structural factors and COVID-19 vaccine uptake. In the final time adjusted model, we found that vaccine uptake was only predicted by specific individual-level factors: being 65 years and older, living with HIV/AIDS, and having previously received a flu vaccine or a COVID-19 test. Those reporting to believe in COVID-19-conspiracy theories were less likely to get the COVID-19 vaccine. More research is needed to identify predictors of vaccine uptake among people with comorbidities that make them more vulnerable to COVID-19 complications or death.

Predicting busulfan exposure in patients undergoing hematopoietic stem cell transplantation using machine learning techniques

PURPOSE

This study aimed to establish an optimal model to predict the busulfan (BU) area under the curve at steady state (AUCss) by using machine learning (ML).

PATIENTS AND METHODS

Seventy-nine adult patients (age ≥18 years) who received BU intravenously and underwent therapeutic drug monitoring from 2013 to 2021 at Fujian Medical University Union Hospital were enrolled in this retrospective study. The whole dataset was divided into a training group and test group at the ratio of 8:2. BU AUCss were considered as the target variable. Nine different ML algorithms and one population pharmacokinetic (pop PK) model were developed and validated, and their predictive performance was compared.

RESULTS

All ML models were superior to the pop PK model (R2 = 0.751, MSE = 0.722, 14 and RMSE = 0.830) in model fitting and had better predictive accuracy. The ML model of BU AUCss established through support vector regression (SVR) and gradient boosted regression trees (GBRT) had the best predictive ability (R2 = 0.953 and 0.953, MSE = 0.323 and 0.326, and RMSE = 0.423 and 0.425).

CONCLUSION

All the ML models can potentially be used to estimate BU AUCss with the aim of facilitating rational use of BU on the individualized level, especially models built by SVR and GBRT algorithms.

Modifiable risk factors of COVID-19 in patients with multiple sclerosis: a single-centre case-control study

BACKGROUND

Disease and treatment-associated immune system abnormalities may confer higher risk of Coronavirus disease 2019 (COVID-19) to people with multiple sclerosis (PwMS). We assessed modifiable risk factors associated with COVID-19 in PwMS.

METHODS

Among patients referring to our MS Center, we retrospectively collected epidemiological, clinical and laboratory data of PwMS with confirmed COVID-19 between March 2020 and March 2021 (MS-COVID, n = 149). We pursued a 1:2 matching of a control group by collecting data of PwMS without history of previous COVID-19 (MS-NCOVID, n = 292). MS-COVID and MS-NCOVID were matched for age, expanded disability status scale (EDSS) and line of treatment. We compared neurological examination, premorbid vitamin D levels, anthropometric variables, life-style habits, working activity, and living environment between the two groups. Logistic regression and Bayesian network analyses were used to evaluate the association with COVID-19.

RESULTS

MS-COVID and MS-NCOVID were similar in terms of age, sex, disease duration, EDSS, clinical phenotype and treatment. At multiple logistic regression, higher levels of vitamin D (OR 0.93, p < 0.0001) and active smoking status (OR 0.27, p < 0.0001) emerged as protective factors against COVID-19. In contrast, higher number of cohabitants (OR 1.26, p = 0.02) and works requiring direct external contact (OR 2.61, p = 0.0002) or in the healthcare sector (OR 3.73, p = 0.0019) resulted risk factors for COVID-19. Bayesian network analysis showed that patients working in the healthcare sector, and therefore exposed to increased risk of COVID-19, were usually non-smokers, possibly explaining the protective association between active smoking and COVID-19.

CONCLUSIONS

Higher Vitamin D levels and teleworking may prevent unnecessary risk of infection in PwMS.

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate

Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease

In countries with a high incidence of tuberculosis, the typical clinical features of Crohn's disease (CD) may be covered up after tuberculosis infection, and the identification of atypical Crohn's disease and intestinal tuberculosis (ITB) is still a dilemma for clinicians. Least absolute shrinkage and selection operator (LASSO) regression has been applied to select variables in disease diagnosis. However, its value in discriminating ITB and atypical Crohn's disease remains unknown. A total of 400 patients were enrolled from January 2014 to January 2019 in second Xiangya hospital Central South University.Among them, 57 indicators including clinical manifestations, laboratory results, endoscopic findings, computed tomography enterography features were collected for further analysis. R software version 3.6.1 (glmnet package) was used to perform the LASSO logistic regression analysis. SPSS 20.0 was used to perform Pearson chi-square test and binary logistic regression analysis. In the variable selection step, LASSO regression and Pearson chi-square test were applied to select the most valuable variables as candidates for further logistic regression analysis. Secondly, variables identified from step 1 were applied to construct binary logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed on these models to assess the ability and the optimal cutoff value for diagnosis. The area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy rate, together with their 95% confidence and intervals (CIs) were calculated. MedCalc software (Version 16.8) was applied to analyze the ROC curves of models. 332 patients were eventually enrolled to build a binary logistic regression model to discriminate CD (including comprehensive CD and tuberculosis infected CD) and ITB. However, we did not get a satisfactory diagnostic value via applying the binary logistic regression model of comprehensive CD and ITB to predict tuberculosis infected CD and ITB (accuracy rate:79.2%VS 65.1%). Therefore, we further established a binary logistic regression model to discriminate atypical CD from ITB, based on Pearsonchi-square test (model1) and LASSO regression (model 2). Model 1 showed 89.9% specificity, 65.9% sensitivity, 88.5% PPV, 68.9% NPV, 76.9% diagnostic accuracy, and an AUC value of 0.811, and model 2 showed 80.6% specificity, 84.4% sensitivity, 82.3% PPV, 82.9% NPV, 82.6% diagnostic accuracy, and an AUC value of 0.887. The comparison of AUCs between model1 and model2 was statistically different (P < 0.05). Tuberculosis infection increases the difficulty of discriminating CD from ITB. LASSO regression showed a more efficient ability than Pearson chi-square test based logistic regression on differential diagnosing atypical CD and ITB.