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Fig. 3 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 3 — Spatial observations of turtle mortality: a) Fishing vs non-fishing beaches; and b) Distance from fish landing center
Fig. 2 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 2 — Observed turtle mortality in different locations (Site codes: PP-PB: Podampeta – Puranabandha, RE – NN: Rushikulya Estuary – Nalia Nuagan, GB – MR: Golabandha – Markandi, BE – PS: Bahuda Estuary – Pati Sonapur)
Figure 5 in Entomopathogenic Fungi as Mortality Factors of Macadamia Felted Coccid, Eriococcus ironsidei (Hemiptera: Eriococcidae) in Hawaii
Figure 5. Mean mortality (±SEM) of E. ironsidei after dipping in increasing conidial concentrations of P. coccorum. Bars with different letters differ significantly (α = 0.05) by LSD.
Figure 6 in Entomopathogenic Fungi as Mortality Factors of Macadamia Felted Coccid, Eriococcus ironsidei (Hemiptera: Eriococcidae) in Hawaii
Figure 6. Mean mortality (±SEM) of E. ironsidei after dipping in increasing conidial concentrations of B. bassiana. Bars with different letters differ significantly (α = 0.05) by LSD.
Figure 2 in Entomopathogenic Fungi as Mortality Factors of Macadamia Felted Coccid, Eriococcus ironsidei (Hemiptera: Eriococcidae) in Hawaii
Figure 2. Mean (±SEM) length of time E. ironsidei continued to reproduce following treatment with different conidial concentrations of P. coccorum. Bars with different letters differ significantly (α = 0.05) by LSD.
Figure 1 in Entomopathogenic Fungi as Mortality Factors of Macadamia Felted Coccid, Eriococcus ironsidei (Hemiptera: Eriococcidae) in Hawaii
Figure 1. Mean (±SEM) length of time E. ironsidei continued to reproduce following treatment with different conidial concentrations of C. griseum. Bars with different letters differ significantly (α = 0.05) by LSD.
Figure 3 in Entomopathogenic Fungi as Mortality Factors of Macadamia Felted Coccid, Eriococcus ironsidei (Hemiptera: Eriococcidae) in Hawaii
Figure 3. Mean (±SEM) length of time E. ironsidei continued to reproduce following treatment with different conidial concentrations of B. bassiana. Bars with different letters differ significantly (α = 0.05) by LSD.
Knowledge and perceptions of risk factors for maternal mortality among postnatal mothers in Hohoe Municipality of Volta Region, Ghana
<p>This de-identified limited dataset from which the manuscript if spread. It dataset from primary research conducted to assess knowledge of postnatal mothers about clinical risk factors and perceptions about socio-cultural and health system-related factors affecting maternal mortality.</p>
Tuberculosis and HIV/AIDS-attributed mortalities and associated sociodemographic factors in Papua New Guinea: Evidence from the comprehensive health and epidemiological surveillance system
<p>Tuberculosis (TB) and HIV/AIDS are public health concerns in Papua New Guinea (PNG). This study examines TB and HIV/AIDS mortalities and associated sociodemographic factors in PNG. Method: As part of a longitudinal study, verbal autopsy (VA) interviews were conducted using the WHO 2016 VA Instrument to collect data of 926 deaths occurred in the communities within the catchment areas of the Comprehensive Health and Epidemiological Surveillance System from 2018-2020. InterVA-5 cause of deaths analytic tool was used to assign specific causes of death (COD). Multinomial logistic regression analyses were conducted to identify associated sociodemographic factors, estimate odds ratios (OR), 95% confidential intervals and p-values. Result: TB and HIV/AIDS were the leading CODs from infectious diseases, attributed to 9% and 8% of the total deaths, respectively. Young adults (25-34 years) had the highest proportion of deaths from TB (20%) and the risk of dying from TB among this age group was five times more likely than those aged 75+ years (OR: 5.5 [1.4-21.7]). Urban population were 46% less likely to die from this disease compared rural ones (OR: 0.54 [0.3-1.0]). People from middle household wealth quintile were three times more likely to die from TB than those in the richest quintile (OR: 3.0 [1.3-7.4]). Young adults also had the highest proportion of deaths to HIV/AIDS (18%) and were nearly seven times more likely to die from this disease compared with those aged 75+ years (OR: 6.7 [1.7-25.4]). Males were 48% less likely to die from HIV/AIDS than females (OR: 0.52 [0.3-0.9]). The risk of dying from HIV/AIDS in urban population was 54% less likely than their rural counterparts (OR: 0.46 [0.2-0.9]). Conclusion: TB and HIV/AIDS interventions are needed to target high-risk and vulnerable populations to reduce premature mortality from these diseases in PNG.</p>
Fig. 6 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 6 — Association of beach elevation with turtle mortality
Fig. 5 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 5 — Changes in the Rushikulya river mouth during the last two decades
Fig. 4 — A dead olive ridley with a in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 4 — A dead olive ridley with a possible hit mark on its carapace
Fig. 1 in Observations on the mortality of olive ridley sea turtles (Lepidochelys olivacea) and associated factors along Ganjam coast, east coast of India
Fig. 1 — Study area map with survey locations
Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India
<p>This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India".</p> <p>Data</p> <p>The data is organized in the form of tables.</p> <p>hypothesis-test-data</p> <p>This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.</p> <pre><code>* Region * Male_Deaths - Number of male COVID-19 deaths in region. * Female_Deaths - Number of female COVID-19 deaths in region. * Male_cases - Number of male COVID-19 positive in region. * Female_cases - Number of female COVID-19 positive in region. </code></pre> <p>lasso-covid19India</p> <p>This table contains data used for analysis on cases throughout India.</p> <p>Columns from COVID-19 India data</p> <pre><code>* State_Code * State * District * Confirmed * Active * Recovered * Deceased </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_ratio_of_the_total_population_females_per_1000_males * Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>lasso-KA+TN-bulletin</p> <p>This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.</p> <p>Data from Media Bulletin</p> <pre><code>* District * Total_Positives * total_deaths * male_deaths * female_deaths * Male_cases_in_data * Female_cases_in_data </code></pre> <p>Calculated Data</p> <pre><code>* Estimated_Male_cases - Estimated male cases using total positives column and existing case data * Estimated_Female_Cases - Estimated female cases using total positives column and existing case data * Male_Mortality - Estimated Male Cases / male_deaths * Female_Mortality - Estimated Female Cases / female_deaths </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_Ratio_females_every_1000_males * State Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>Code</p> <p>The code is available at this <a href="https://github.com/harishpb26/Sex-disaggregated-Analysis-of-Risk-Factors-of-COVID-19-Mortality-Rates-in-India">Github Repository</a>.</p>
Factors Affecting Mortality in Critical Patients Admitted to Intensive Care Unit Due to Coronavirus Disease 2019
ClinicalTrials.gov study NCT04659876. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Tuberculosis and HIV/AIDS-attributed mortalities and associated sociodemographic factors in Papua New Guinea: Evidence from the comprehensive health and epidemiological surveillance system
Open the record for dataset details and reuse information.
Aetiology and prognostic risk factors of mortality in pneumonia patients receiving glucocorticoids alone or glucocorticoids and other immunosuppressants: a retrospective cohort study
<p><b>Objectives:</b> Long-term use of high-dose glucocorticoids can lead to severe immunosuppression and increased risk of treatment-resistant pneumonia and mortality. We investigated the aetiology and prognostic risk factors of mortality in hospitalised patients who developed pneumonia while receiving glucocorticoid therapy alone or glucocorticoid and other immunosuppressant therapies.</p> <p><b>Design:</b> Retrospective cohort study</p> <p><b>Setting: </b>Six secondary and tertiary academic hospitals in China</p> <p><b>Participants: </b>Patients receiving glucocorticoids who were hospitalised with pneumonia between 1<sup>st</sup> January 2013 and 31<sup>st</sup> December 2019.</p> <p><b>Main Outcomes: </b>We analysed<b> </b>the prevalence of comorbidities, microbiology, antibiotic susceptibility patterns, 30-day and 90-day mortality rates, and prognostic risk factors.</p> <p><b>Results</b>: A total of 716 patients were included, with pneumonia pathogens identified in 69.8% of patients. Significant morbidities occurred, including respiratory failure (50.8%), intensive care unit (ICU) transfer (40.8%), and mechanical ventilation (36%), with a 90-day mortality rate of 26.0%. Diagnosis of pneumonia occurred within 6 months of glucocorticoid initiation for 69.7% of patients with <i>Cytomegalovirus</i> (CMV) pneumonia and 79.0% of patients with <i>Pneumocystis jirovecii</i> pneumonia (PCP). Pathogens, including <i>Pneumocystis</i>, CMV, and multidrug-resistant bacteria, were identified more frequently in patients with persistent lymphocytopenia and high-dose glucocorticoid treatment (≥ 30 mg/day of prednisolone or equivalent within 30 days before admission). The 90-day mortality rate was significantly lower for non-CMV viral pneumonias than for PCP (<i>P</i> < 0.05), with a similar mortality rate as CMV pneumonias (24.2% vs 38.1% vs 27.4%, respectively).Cox regression analysis indicated <a name="_Hlk30339725"></a><a name="_Hlk31278800">several independent negative predictors for mortality in this patient population, including septic shock, respiratory failure, </a>persistent lymphocytopenia, interstitial lung disease, and high-dose glucocorticoid use.</p> <p><b>Conclusions</b>: Patients who developed pneumonia while receiving glucocorticoid therapy experienced high rates of opportunistic infections, with significant morbidity and mortality. These findings should be carefully considered when determining treatment strategies for this patient population.</p>
The U-shaped pattern of size-dependent mortality and its driving factors in a subtropical monsoon evergreen forest
<p>1. Tree mortality is an important ecological process influencing multiple functions of forest ecosystems. Previous studies have shown two basic size-mortality patterns, including a competition-driven declining and a disturbance-driven increasing mortality rate with tree size. Subtropical forests, which have a high species diversity and subject to frequent monsoon disturbances, are widely distributed in eastern Asia. However, the tree size-mortality pattern in the mature subtropical forests remains unclear.</p> <p>2. Here we analyzed patterns of size-dependent mortality from tree species to forest community using a 5-year inventory data from 117 species and 163,612 individuals in a 20-ha forest dynamic plot in a mature subtropical monsoon evergreen forest in eastern China. To explain the spatial variability in mortality patterns, two major biotic drivers (competition and tree size) and multiple local-scale environmental factors were further analyzed.</p> <p>3. Our results showed that tree size was the best predictor of tree mortality at the scales of both species and community. A species-level analysis identified four size-mortality patterns that are shaped by species-specific attributes such as maximum size and life form. For 27 out of 92 species that comprised 59% of tree individuals, the relationship between size and mortality exhibited a U-shaped pattern of a first decline followed by an increase. An overall community-scale size-dependent mortality also showed a U-shaped pattern.</p> <p>4. Tree mortality was also influenced by the competition and environmental conditions, but the relative importance varied widely across tree sizes and species. The competition showed significant correlations with the mortality of small trees, while the effect of environmental conditions on mortality was strongest for large trees. A principal component analysis showed that a combination of biotic and abiotic factors explained 42.3% of the spatial variation in mortality at large sizes.</p> <p><i>Synthesis.</i> Our results reveal four identifiable size-dependent mortality patterns that differ across diverse species, jointly leading to a U-shaped mortality size pattern at the community level. This finding calls for the need to establish the details of every potential size-mortality pattern with consideration of the different effects of biotic and abiotic factors on tree mortality of specific size.</p>
Data from: Influence of mortality factors and host resistance on the population dynamics of emerald ash borer (Coleoptera: Buprestidae) in urban forests
The success of emerald ash borer (Agrilus planipennis Fairmaire) in North America is hypothesized to be due to both the lack of significant natural enemies permitting easy establishment and a population of trees that lack the ability to defend themselves, which allows populations to grow unchecked. Since its discovery in 2002, a number of studies have examined mortality factors of the insect in forests, but none have examined the role of natural enemies and other mortality agents in the urban forest. This is significant because it is in the urban forest where the emerald ash borer has had the most significant economic impacts. We studied populations in urban forests in three municipalities in Ontario, Canada, between 2010 and 2012 using life tables and stage-specific survivorship to analyze data from a split-rearing manipulative experiment. We found that there was little overall mortality caused by natural enemies; most mortality we did observe was caused by disease. Stage-specific survivorship was lowest in small and large larvae, supporting previous observations of high mortality in these two stages. We also used our data to test the hypothesis that mortality and density in emerald ash borer are linked. Our results support the prediction of a negative relationship between mortality and density. However, the relationship varies between insects developing in the crown and those in the trunk of the tree. This relationship was significant because when incorporated with previous findings, it suggests a mechanism and hypothesis to explain the outbreak dynamics of the emerald ash borer.
Data from: Disparities in influenza mortality and transmission related to sociodemographic factors within Chicago in the pandemic of 1918
Social factors have been shown to create differential burden of influenza across different geographic areas. We explored the relationship between potential aggregate-level social determinants and mortality during the 1918 influenza pandemic in Chicago using a historical dataset of 7,971 influenza and pneumonia deaths. Census tract-level social factors, including rates of illiteracy, homeownership, population, and unemployment, were assessed as predictors of pandemic mortality in Chicago. Poisson models fit with generalized estimating equations (GEEs) were used to estimate the association between social factors and the risk of influenza and pneumonia mortality. The Poisson model showed that influenza and pneumonia mortality increased, on average, by 32.2% for every 10% increase in illiteracy rate adjusted for population density, homeownership, unemployment, and age. We also found a significant association between transmissibility and population density, illiteracy, and unemployment but not homeownership. Lastly, analysis of the point locations of reported influenza and pneumonia deaths revealed fine-scale spatiotemporal clustering. This study shows that living in census tracts with higher illiteracy rates increased the risk of influenza and pneumonia mortality during the 1918 influenza pandemic in Chicago. Our observation that disparities in structural determinants of neighborhood-level health lead to disparities in influenza incidence in this pandemic suggests that disparities and their determinants should remain targets of research and control in future pandemics.
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