Survival of patients in the intensive care units of referral hospitals in Amhara Region: A prospective cohort study

Background: An intensive care unit (ICU) is a place where critically ill patients are managed using life-saving interventions. Evidence regarding ICUs like average days of stay, and what caused the patients to delay in the ward were scarce in resource-limited settings such as Ethiopia. The objectives of this study were to assess the survival of patients in ICUs, the incidence density for discharge with prognosis, and the determinants of ICU stay in five referral hospitals in Amhara Region. Methods and materials: To implement the prospective cohort study design, baseline data were recorded from patients’ charts at the time of admission to the ICUs of the five referral hospitals. Patients’ status was followed every day for a maximum of nine days. Interviews and chart reviews were used to collect the data. A Kaplan–Meier curve was used to estimate the time of patients’ discharge from the ICU. A Cox proportional hazard model (Weibull) was used to identify the predictors of ICU stay. Results: A total of 2,789 patients were included; the incidence density of discharge with prognosis was 1,962/12,448 person days. The length of ICU stay was determined by patient-to-nurse ratio (AHR = 0.59 [95% CI: 0.56-0.64]), chronic illness (AHR = 0.93 [95% CI: 0.88-0.98]), hemoglobin concentration (AHR = 1.09 [95% CI: 1.05-1.14]), ICU area-to-bed ratio (AHR = 1.14 [95% CI: 1.06-1.22]), nosocomial infection (AHR = 0.47 [95% CI: 0.37-0.59]), tracheotomy (AHR =  1.12 [95% CI: 1.01-1.24]), time of admission (AHR = 0.83 [95% CI: 0.75-0.93]), and formal education (AHR = 0.72 [95% CI: 0.64-0.80]). Conclusions and recommendations: Decision makers in Ethiopia should give high priority to ICU infrastructure and to increasing the number of nurses in ICU wards. [Ethiop. J. Health Dev. 2020; 34(1):30-34] Key words: Critical care, Intensive care, predictors, resource-limited settin

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Intestinal parasitic infection among household contacts of primary cases, a comparative cross-sectional study.

BackgroundIntestinal parasitic infection affects 3.5 billion people in the world and mostly affecting the low socio-economic groups. The objectives of this research works were to estimate the prevalence and determinants of intestinal parasitic infection among family members of known intestinal parasite infected patients.Methods and materialsA comparative cross-sectional study design was implemented in the urban and rural settings of Mecha district. The data were collected from August 2017toMarch 2019 from intestinal parasite infected patient household members. Epi-info software was used to calculate the sample size, 4531 household members were estimated to be included. Data were collected using interview technique, and collecting stool samples from each household contact of intestinal parasite patients. Descriptive statistics were used to estimate the prevalence of intestinal parasites among known contacts of intestinal parasite patients/family members. Binary logistic regression was used to identify the determinant factors of intestinal parasitic infection among family members.ResultsThe prevalence of intestinal parasite among household contacts of intestinal parasite-infected family members was 86.14% [95% CI: 86.14% - 87.15%]. Hookworm infection was the predominant type of infection (18.8%). Intestinal parasitic infection was associated with sex, environmental sanitation, overcrowding, personal hygiene, residence, substandard house, role in the household, source of light for the house, trimmed fingernails, family size, regular handwashing practice. Protozoa infection was associated with habit of ingesting raw vegetable, playing with domestic animals, water source and the presence of household water filtering materials.ConclusionHigh prevalence of intestinal parasitic infection was observed among household contacts of primary cases

Clinical response of tuberculosis patients, a prospective cohort study.

Clinical response means a response to drug intake that can be detected and appreciated by a change in signs and symptoms. The objectives of this study were to assess time to clinical response, the incidence density for clinical response and determinants of clinical response of tuberculosis (TB) patients in the intensive phases of TB treatment. Prospective cohort study design was implemented. The target population for this study was all patients following the directly observed therapy. Baseline data has been collected during the start of the directly observed TB treatment strategy. We have been collected updated data after the seven days of the baseline data collection, then after every seven days updated data has been collected from each pulmonary and extra pulmonary TB patients. Kaplan Meier curve was used to estimate time to clinical response. Incidence density using person days was used to estimate incidence of clinical response. Cox proportional hazard model was used to identify the predictors of clinical responses. A total of 1608 TB patients were included with a response rate at 99.5%. The mean age of the respondents was 24.5 years [standard deviation (SD) 14.34 years]. The incidence density for clinical response was 1429/38529 person days. One fourth of the TB patients showed clinical response at day 14, 25% of at day 21 and 75% o at day 31. Predictors of clinical response for TB patients includes: age (AHR 1.007 [95% CI 1.003-1.011]), type of TB (AOR 2.3[95% CI 2.04-2.59]), Previous history of TB (AHR 0.18 [95% CI 0.11-0 .30]), Intestinal parasitic infection (AOR 0.22[95% CI 0.19-0.26]), hemoglobin (AOR 2.35 [95% CI 2.18-2.54]), weight gain (AOR 1.11 [95% CI 1.05-1.17]), Micronutrient supplementation (AOR 9.71 [95% CI 8.28-11.38]), male sex (AOR 0.87 [95% CI 0.79-0.97]).The clinical responses for extra-pulmonary TB patients were slower than pulmonary TB. Deworming and micronutrient supplementation should be considered as the additional TB treatment strategy for TB patients

Kaplan-Meir and predicted survival plot for pulmonary tuberculosis patients.

<p>Kaplan-Meir and predicted survival plot for pulmonary tuberculosis patients.</p

Socio-demographic characteristics of pulmonary tuberculosis patients (n = 804).

<p>Socio-demographic characteristics of pulmonary tuberculosis patients (n = 804).</p

Kaplan-Meier curve for pulmonary tuberculosis and extra-pulmonary tuberculosis patients.

<p>Kaplan-Meier curve for pulmonary tuberculosis and extra-pulmonary tuberculosis patients.</p

schoenfeld residuals plot for pulmonary and extra-pulmonary tuberculosis patients.

<p>schoenfeld residuals plot for pulmonary and extra-pulmonary tuberculosis patients.</p

Kaplan Meier survival estimate for extra-pulmonary tuberculosis patients.

<p>Kaplan Meier survival estimate for extra-pulmonary tuberculosis patients.</p