Mortality Rates, Prevalence of Malnutrition, and Prevalence of Lost Pregnancies among the Drought-Ravaged Population of Tete Province, Mozambique

2007 ◽  
Vol 22 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Andre M.N. Renzaho
Keyword(s):  
Mortality Rate ◽  
Mortality Rates ◽  
Sub Saharan Africa ◽  
Acute Malnutrition ◽  
Food Aid ◽  
Emergency Relief ◽  
Humanitarian Response ◽  
Crude Mortality ◽  
The Impact ◽  
Hiv Aids

AbstractBackground:Tete Province, Mozambique has experienced chronic food insecurity and a dramatic fall in livestock numbers due to the cyclic problems characterized by the floods in 2000 and severe droughts in 2002 and 2003. The Province has been a beneficiary of emergency relief programs, which have assisted >22% of the population. However, these programs were not based on sound epidemiological data, and they have not established baseline data against which to assess the impact of the programs.Objective:The objective of this study was to document mortality rates, causes of death, the prevalence of malnutrition, and the prevalence of lost pregnancies after 2.5 years of humanitarian response to the crisis.Methods:A two-stage, 30-cluster household survey was conducted in the Cahora Bassa and Changara districts from 22 October to 08 November 2004. A total of 838 households were surveyed, with a population size of 4,688 people.Results:Anthropometric data were collected among children 6–59 months of age. In addition, crude mortality rates (crude mortality rates), under five mortality rates (under 5 mortality rate), causes of deaths, and prevalence of lost pregnancies were determined among the sample population. The prevalence of malnutrition was 8.0% (95% confidence interval (CI) = 6.2–9.8%) for acute malnutrition, 26.9% (95% CI = 24.0–29.9%) for being underweight, and 37.0% (95% CI = 33.8–40.2%) for chronic malnutrition. Boys were more likely to be under-weight than were girls (odds ratio (OR) = 1.34; 95% CI = 1.00, 1.82;p <0.05) after controlling for a, household size, and food aid beneficiary status. Similarly, children 30–59 months of age were significantly less likely to suffer from acute malnutrition (OR = 0.45; 95% CI = 0.26, 0.79; p <0.01) and less likely to be underweight (OR = 0.37; 95% CI = 0.27, 0.51;p <0.01) than children 6–29 months of a, after adjusting for the other, aforementioned factors. The proportion of lost pregnancies was estimated at 7.7% (95% CI = 4.5–11.0%). A total of 215 deaths were reported during the year preceding the survey. Thirty-nine (18.1%) children <5 years of age died. The CMR was 1.23/10,000/day (95% CI = 1.08–1.38), and an under 5 mortality rate was 1.03/10,000/day (95% CI = 0.71–1.35). Diarrheal diseases, malaria, tuberculosis, and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) accounted for more than two-thirds of all deaths.Conclusions:The observed CMR in Tete Province, Mozambique is three times higher than the baseline rate for sub-Saharan Africa and 1.4 times higher than the CMR cut-off point used to define excess mortality in emergencies.The current humanitarian response in Tete Province would benefit from an improved alignment of food aid programming in conjunction with diarrheal disease control, HIV/AIDS, and malaria prevention and treatment programs. The impact of the food programs would be improved if mutually acceptable food aid program objectives, verifiable indicators relevant to each objective, and beneficiary targets and selection criteria are developed. Periodic re-assessments and evaluations of the impact of the program and evidenced-based decision-making urgently are needed to avert a chronic dependency on food aid.

Abstract P359: High Blood Pressure in African HIV+ Patients Increases Long-Term Mortality

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Gerald S Bloomfield ◽  
Joseph W Hogan ◽  
Alfred Keter ◽  
Thomas L Holland ◽  
Edwin Sang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
High Blood Pressure ◽  
Regression Models ◽  
Proportional Hazards ◽  
Mortality Rates ◽  
Sub Saharan Africa ◽  
Proportional Hazards Regression ◽  
Crude Mortality ◽  
Sub Saharan ◽  
The Impact

Background: Patients with human immunodeficiency virus (HIV) in the modern era are at risk of developing cardiovascular diseases. High blood pressure (BP) is common in sub-Saharan Africa, however, global attention in the region has been mostly focused on HIV. The impact of BP on mortality among adults with HIV in this region has not been reported. Objective: The objective was to determine the impact of BP on mortality among HIV seropositive (+) adults without acquired immune deficiency syndrome (AIDS) in Kenya. Methods: We conducted a retrospective analysis of de-identified medical records of the Academic Model Providing Access to Healthcare HIV treatment program between 2005 and 2010. We excluded patients with AIDS, who were <16 or >80 years old, or with data out of acceptable ranges. There were 49,475 HIV+ individuals who satisfied inclusion/exclusion criteria (Figure 1). Missing data for key covariates was addressed by inverse probability weighting. We summarize crude mortality rates across BP categories, separately by gender. We used proportional hazards regression models to characterize the effect of BP on mortality, adjusting for baseline demographic and clinical factors. We subdivided the sample according to those who were clinically stable, defined as having ≥CD4 350 or WHO Stage 1. Results: Our sample was 74% (36,616 of 49,475) women. Mortality rates for men and women were 3.8/100 and 1.8/100 person-years, respectively. Crude mortality rate among clinically stable men was higher with systolic BP ≥140 mmHg (3.0, 95% CI: 1.6-5.5) than with normal systolic BP (1.1, 95% CI: 0.7-1.7). In weighted proportional hazards regression models, clinically stable men with systolic BP ≥140 mmHg carried a higher mortality risk than normotensive men (HR: 2.39, 95% CI: 0.94 to 6.08). Conclusions: Blood pressure is an important aspect of the care of HIV+ patients in sub-Saharan Africa. High systolic BP is associated with mortality among clinically stable men without AIDS. Further investigation into cause of death in warranted.

scholarly journals Mortality, malnutrition and the humanitarian response to the food crises in Lesotho

2015 ◽  
Vol 4 (4) ◽  
Author(s):  
André Renzaho
Keyword(s):  
Causes Of Death ◽  
Household Survey ◽  
Mortality Rates ◽  
Food Aid ◽  
Food Crisis ◽  
Humanitarian Response ◽  
World Vision ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
The Government

Introduction At the height of the food crisis in southern Africa, the Government of Lesotho declared a state of famine and emergency in April 2002 and launched a Famine Relief Appeal for over $137 million. World Vision, in partnership with the World Food Program, became involved in December 2002 providing food aid to affected communities. The objective was to document mortality rates, causes of death, malnutrition prevalence, and the proportion of lost pregnancies after almost three years of humanitarian response to the food crisis in Lesotho and to propose a way forward. Methods A two-stage, 30 cluster household survey was undertaken in three districts from the 16th to the 26th of May 2005, with a sample size of 3610 people. Results The crude mortality rate (CMR) of 0.8/10,000/day (95%CI: 0.7-0.9). The reported CMR was significantly lower than the CMR emergency threshold (

Endovascular Aortic Repair in Patients of Advanced Age

2021 ◽  
pp. 152660282110493
Author(s):  
Mitri K. Khoury ◽  
Micah A. Thornton ◽  
Christopher A. Heid ◽  
Jacqueline Babb ◽  
Bala Ramanan ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Survival Benefit ◽  
Elective Surgery ◽  
Endovascular Aneurysm Repair ◽  
The Elderly ◽  
Mortality Rates ◽  
Current Data ◽  
Aortic Aneurysms ◽  
Advanced Age ◽  
The Impact

Purpose: Treatment decisions for the elderly with abdominal aortic aneurysms (AAAs) are challenging. With advancing age, the risk of endovascular aneurysm repair (EVAR) increases while life expectancy decreases, which may nullify the benefit of EVAR. The purpose of this study was to quantify the impact of EVAR on 1-year mortality in patients of advanced age. Materials and Methods: The 2003–2020 Vascular Quality Initiative Database was utilized to identify patients who underwent EVAR for AAAs. Patients were included if they were 80 years of age or older. Exclusions included non-elective surgery or missing aortic diameter data. Predicted 1-year mortality of untreated AAAs was calculated based on a validated comorbidity score that predicts 1-year mortality (Gagne Index, excluding the component associated with AAAs) plus the 1-year aneurysm-related mortality without repair. The primary outcome for the study was 1-year mortality. Results: A total of 11 829 patients met study criteria. The median age was 84 years [81, 86] with 9014 (76.2%) being male. Maximal AAA diameters were apportioned as follows: 39.6% were <5.5 cm, 28.6% were 5.5–5.9 cm, 21.3% were 6.0–6.9 cm, and 10.6% were ≥7.0 cm. The predicted 1-year mortality rate without EVAR was 11.9%, which was significantly higher than the actual 1-year mortality rate with EVAR (8.2%; p<0.001). The overall rate of perioperative MACE was 4.4% (n = 516). Patients with an aneurysm diameter <5.5cm had worse actual 1-year mortality rates with EVAR compared to predicted 1-year mortality rates without EVAR. In contrast, those with larger aneurysms (≥5.5cm) had better actual 1-year mortality rates with EVAR. The benefit from EVAR for those with Gagne Indices 2–5 was largely restricted to those with AAAs ≥ 7.0cm; whereas those with Gagne Indices 0–1 experience a survival benefit for AAAs larger than 5.5 cm. Conclusion: The current data suggest that EVAR decreases 1-year mortality rates for patients of advanced age compared to non-operative management in the elderly. However, the survival benefit is largely limited to those with Gagne Indices 0–1 with AAAs ≥ 5.5 cm and Gagne Indices 2–5 with AAAs ≥ 7.0 cm. Those of advanced age may benefit from EVAR, but realizing this benefit requires careful patient selection.

scholarly journals Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Joshua J. Levy ◽  
Rebecca M. Lebeaux ◽  
Anne G. Hoen ◽  
Brock C. Christensen ◽  
Louis J. Vaickus ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Mortality Rate ◽  
Satellite Images ◽  
The United States ◽  
Mortality Rates ◽  
Image Features ◽  
County Level ◽  
Crude Mortality Rate ◽  
Crude Mortality

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

scholarly journals On Dynamics of Gender-Specific Mortality Among the Population of the Republic of Belarus in the Second Half of the XX – the Beginning of the XXI Centuries

2017 ◽  
Vol 24 (3) ◽  
Author(s):  
A Romanova ◽  
O Krasko
Keyword(s):  
Mortality Rate ◽  
Environmental Changes ◽  
Research Work ◽  
Mortality Rates ◽  
Female Mortality ◽  
Female Population ◽  
Male And Female ◽  
Crude Mortality ◽  
The Republic ◽  
Population Mortality

Aim of the study: to evaluate the dynamics and to make a comparative analysis of male and female mortality among the population of Belarus Republic during 1959 – 2015.Materials and methods. The data on natural population movement in the Republic of Belarus during 1959 – 2015 have been analyzed in the research work. Crude and standardized mortality rates have been calculated using the direct standardization according to the world standard (Standard “World”), approved by WHO. JoinPoint software was used to investigate time trends as well as office suite MSEXCEL 2010.Results of the study. The minimum values of male and female crude and standardized mortality rates were established in 1964. Throughout the study period, the male population mortality rate grew 1.8-fold (based on crude rates – 2.4-fold), the female population mortality rate – 1.6-fold (based on crude rates – 2.2-fold). During 1985 – 2005, the differences in crude mortality rates among men and women grew 1.2-fold, and during 1962 – 2011, the differences in standardized rates increased 1.8-fold. Since 2003, the mortality rate among men and since 1999, the death rate among women has declined with an annual decrease rate to be more than twice as high as compared to an annual mortality increase registered during its growth.Conclusion. Since the 1960s, the changes in population age structure of the male and female population affected the crude mortality rates. The male and female mortality growth is due to an increased unfavorable impact of combined environmental factors. The adaptive capacity of women to sustain environmental changes contributed to their later entry into the period of mortality growth, as compared to men. The mortality rate reduction in men since 2003 and the excess of a decrease over an increase rate is associated with a set of state measures aimed at protecting and strengthening the public health in the republic.

Does economic freedom improve health outcomes in sub-Saharan Africa?

2020 ◽  
Vol 47 (12) ◽  
pp. 1633-1649
Author(s):  
Anand Sharma
Keyword(s):  
Mortality Rate ◽  
Life Expectancy ◽  
Health Outcomes ◽  
Economic Freedom ◽  
Sub Saharan Africa ◽  
African Countries ◽  
Content Type ◽  
The World ◽  
Sub Saharan ◽  
The Impact

PurposeThe purpose of this study is to examine the impact of economic freedom on four key health indicators (namely, life expectancy, infant mortality rate, under-five mortality rate and neonatal mortality rate) by using a panel dataset of 34 sub-Saharan African countries from 2005 to 2016.Design/methodology/approachThe study obtains data from the World Development Indicators (WDI) of the World Bank and the Fraser Institute. It uses fixed effects regression to estimate the effect of economic freedom on health outcomes and attempts to resolve the endogeneity problems by using two-stage least squares regression (2SLS).FindingsThe results indicate a favourable impact of economic freedom on health outcomes. That is, higher levels of economic freedom reduce mortality rates and increase life expectancy in sub-Saharan Africa. All areas of economic freedom, except government size, have a significant and positive effect on health outcomes.Research limitations/implicationsThis study analyses the effect of economic freedom on health at a broad level. Country-specific studies at a disaggregated level may provide additional information about the impact of economic freedom on health outcomes. Also, this study does not control for some important variables such as education, income inequality and foreign aid due to data constraints.Practical implicationsThe findings suggest that sub-Saharan African countries should focus on enhancing the quality of economic institutions to improve their health outcomes. This may include policy reforms that support a robust legal system, protect property rights, promote free trade and stabilise the macroeconomic environment. In addition, policies that raise urbanisation, increase immunisation and lower the incidence of HIV are likely to produce a substantial improvement in health outcomes.Originality/valueExtant economic freedom-health literature does not focus on endogeneity problems. This study uses instrumental variables regression to deal with endogeneity. Also, this is one of the first attempts to empirically investigate the relationship between economic freedom and health in the case of sub-Saharan Africa.

Withdrawal of Care in a Trauma Intensive Care Unit: The Impact on Mortality Rate

2014 ◽  
Vol 80 (8) ◽  
pp. 764-767 ◽  
Author(s):  
Leonard J. Weireter ◽  
Jay N. Collins ◽  
Rebecca C. Britt ◽  
T. J. Novosel ◽  
L. D. Britt
Keyword(s):  
Intensive Care Unit ◽  
Decision Making ◽  
Mortality Rate ◽  
Trauma Center ◽  
Mortality Rates ◽  
Active Aging ◽  
Neurologic Injury ◽  
Trauma Service ◽  
The Impact ◽  
Overall Mortality

Withdrawal of care has increased in recent years as the population older than 65 years of age has increased. We sought to investigate the impact of this decision on our mortality rate. We retrospectively reviewed a prospectively collected database to determine the percentage of cases in which care was actively withdrawn. Neurologic injury as the cause for withdrawal, age of the patient, number of days to death, number of cases thought to be treatment failures, and the reason for failure were analyzed. Between January 2008 and December 2012, there were 536 trauma service deaths; 158 (29.5%) had care withdrawn. These patients were 67 (6 18.5) years old and neurologic injury was responsible in 63 per cent (6 5.29%). Fifty-two per cent of the patients died by Day 3; 65 per cent by Day 5; and 74 per cent Day 7. A total of 22.7 per cent (6 7.9%) could be considered a treatment failure. Accounting for cases in which care was withdrawn for futility would decrease the overall mortality rate by approximately 23 per cent. Trauma center mortality calculation does not account for care withdrawn. Treating an active, aging population, with advance directives, requires methodologies that account for such decision-making when determining mortality rates.

scholarly journals Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Neighborhood Features

2020 ◽  
Author(s):  
Joshua J. Levy ◽  
Rebecca M. Lebeaux ◽  
Anne G. Hoen ◽  
Brock C. Christensen ◽  
Louis J. Vaickus ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Mortality Rate ◽  
Satellite Images ◽  
The United States ◽  
Mortality Rates ◽  
Image Features ◽  
County Level ◽  
Crude Mortality Rate ◽  
Crude Mortality

AbstractWhat is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?BackgroundFollowing a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.MethodsSatellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.ResultsPredicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g. sidewalks, driveways and hiking trails) associated with lower mortality.ConclusionsThe application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

scholarly journals A Comprehensive Analysis of COVID-19 Transmission and Fatality Rates at the County level in the United States considering Socio-Demographics, Health Indicators, Mobility Trends and Health Care Infrastructure Attributes

2020 ◽  
Author(s):  
Tanmoy Bhowmik ◽  
Sudipta Dey Tirtha ◽  
Naveen Chandra Iraganaboina ◽  
Naveen Eluru
Keyword(s):  
Health Care ◽  
Mortality Rate ◽  
Health Indicators ◽  
Mortality Rates ◽  
County Level ◽  
Model Framework ◽  
Transmission Rates ◽  
Health Care Infrastructure ◽  
The Impact ◽  
At Home

Background: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. Method: We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Findings: Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 30% in daily cases. Interpretation: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available). Funding: None.

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