scholarly journals Can routine inpatient mortality data improve HIV mortality estimates? Inpatient mortality at an urban hospital in South Africa

2018 ◽  
Vol 108 (10) ◽  
pp. 870 ◽  
Author(s):  
L C Long ◽  
D Evans ◽  
S Rosen ◽  
C Sauls ◽  
A T Brennan ◽  
...  
Keyword(s):  
South Africa ◽  
Mortality Data ◽  
Inpatient Mortality ◽  
Urban Hospital ◽  
Hiv Mortality ◽  
Mortality Estimates

scholarly journals Age, Period and Cohort Analysis of Young Adult Mortality due to HIV and TB in South Africa: 1997-2015

2021 ◽  
Author(s):  
Tshifhiwa Nkwenika ◽  
Samuel Manda
Keyword(s):  
South Africa ◽  
Young Adult ◽  
Birth Cohort ◽  
Mortality Rates ◽  
Adult Mortality ◽  
Downward Trend ◽  
Mortality Data ◽  
Hiv Mortality ◽  
Related Mortality ◽  
Hiv Aids

Abstract Background: Young adult mortality is very significant in South Africa due to the influence of HIV/AIDS, Tuberculosis (TB), Injuries and Non-Communicable Diseases (NCDs). Previous analyses have mainly focused on assessing the time effect of age and period separately. However, health outcomes often depend on three-time scales, namely age, period, and cohort, which are linearly interlinked. Using Age-Period-Cohort (APC) models, this study estimated the time effects of age, period, and cohort on HIV and TB mortality among young adults in South Africa. Methods: HIV and TB mortality data and mid population estimates were obtained from Statistics South Africa for the period 1997 to 2015. Mortality data are based on deaths reported to the Department of Home Affairs where the underlying cause of death was HIV or TB based on the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) definition. Observed HIV/AIDS deaths were adjusted for under-reporting, misclassification, and systematic proportion from ill-defined natural deaths. Three-year age, period, and birth cohort intervals for 15-64 years, 1997-2015 and 1934-2000 respectively were used. The Age-Period-Cohort (APC) analysis using the Poisson distribution was used to compute the effects of age, period, and cohort on mortality due to TB and HIV. Results: A total of 5, 825,502 adult deaths were recorded from the period 1997 to 2015 of which, 910,731 (15.6%) and 252,101 (4.3%) were attributed to TB and HIV, respectively. For both observed mortality rate and estimated relative effects, concave down associations were found between TB, HIV mortality rates and period, age with peaks, at 36-38 and 30-32 years, respectively. A downward trend and inverted V-shape between TB and HIV mortality by birth cohort was found, respectively. Conclusions: The study found an inverse U-shaped association between TB-related mortality and age, period, and general downward trend with a birth cohort for deaths reported between 1997 and 2015. A concave down relationship between HIV-related mortality and age, period, and inverted V-shaped with birth cohort was found. Our findings have shed more light on HIV and TB mortality rates across different age groups, the effect of changes in the overall TB and HIV management and care on the mortality rates, and whether the mortality rates depend on the year an individual was born.

scholarly journals Young Adult Age-Period-Cohort Analysis of HIV and TB Mortality in South Africa: 1997-2015

2020 ◽  
Author(s):  
Tshifhiwa Nkwenika ◽  
Samuel Manda
Keyword(s):  
South Africa ◽  
Young Adult ◽  
Birth Cohort ◽  
Cohort Analysis ◽  
Mortality Rates ◽  
Downward Trend ◽  
Mortality Data ◽  
Hiv Mortality ◽  
Related Mortality ◽  
Hiv Aids

Abstract Background: Young adult mortality is very significant in South Africa due to the influence of HIV/AIDS, Tuberculosis (TB), Injuries and Non-Communicable Diseases (NCDs). Previous analyses have mainly focused on assessing time effect of age and period separately. However, health outcomes often depend on three-time scales of age, period and cohort, which are linearly interlinked. Using Age-Period-Cohort (APC) models, this study estimated the time effects of age, period and cohort on HIV and TB mortality among young adults in South Africa.Methods: HIV and TB mortality data and mid population estimates were obtained from Statistics South Africa for the period 1997 to 2015. Mortality data are based on deaths reported to the Department of Home Affairs where the underlying cause of death was HIV or TB based on the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) definition. Observed HIV/AIDS deaths were adjusted for under-reporting, misclassification and systematic proportion from ill-defined natural deaths. Three-year age, period, and birth cohort intervals for 15–64 years, 1997–2015 and 1934–2000 respectively were used. The Age-Period-Cohort (APC) analysis using the Poisson distribution was used to compute the effects of age, period and cohort on mortality due to TB and HIV.Results: A total of 5, 825,502 adult deaths were recorded from the period 1997 to 2015 of which, 910,731 (15.6%) and 252,101 (4.3%) were attributed to TB and HIV, respectively. For both observed mortality rate and estimated relative effects, concave down associations were found between TB, HIV mortality rates and period, age with peaks, at 36–38 and 30–32 years, respectively. A downward trend and inverted V-shape between TB and HIV mortality by birth cohort was found, respectively.Conclusions: The study found an inverse U-shaped association between TB-related mortality and age, period, and general downward trend with birth cohort for deaths reported between 1997 and 2015. A concave down relationship between HIV-related mortality and age, period, and inverted V-shaped with birth cohort was found. Our findings have shed more light on HIV and TB mortality rates across different age groups, effect of changes in the overall TB and HIV management and care on the mortality rates and whether or not the mortality rates depended on the year an individual was born.

scholarly journals Trends of HIV Mortality between 2001 and 2018: An Observational Analysis

2021 ◽  
Vol 6 (4) ◽  
pp. 173
Author(s):  
Chinmay Jani ◽  
Kripa Patel ◽  
Alexander Walker ◽  
Harpreet Singh ◽  
Omar Al Omari ◽  
...  
Keyword(s):  
South Africa ◽  
World Health ◽  
Mortality Data ◽  
Observational Analysis ◽  
Standard Population ◽  
Hiv Mortality ◽  
The World ◽  
World Standard ◽  
Males And Females ◽  
Health Organization

Since the beginning of the epidemic in the early 1980s, HIV-related illnesses have led to the deaths of over 32.7 million individuals. The objective of this study was to describe current mortality rates for HIV through an observational analysis of HIV mortality data from 2001 to 2018 from the World Health Organization (WHO) Mortality Database. We computed age-standardized death rates (ASDRs) per 100,000 people using the World Standard Population. We plotted trends using locally weighted scatterplot smoothing (LOWESS). Data for females were available for 42 countries. In total, 31/48 (64.60%) and 25/42 (59.52%) countries showed decreases in mortality in males and females, respectively. South Africa had the highest ASDRs for both males (467.7/100,000) and females (391.1/100,000). The lowest mortalities were noted in Egypt for males (0.2/100,000) and in Japan for females (0.01/100,000). Kyrgyzstan had the greatest increase in mortality for males (+6998.6%). Estonia had the greatest increase in mortality for females (+5877.56%). The disparity between Egypt (the lowest) and South Africa (the highest) was 3042-fold for males. Between Japan and South Africa, the disparity was 43,454-fold for females. Although there was a decrease in mortality attributed to HIV among most of the countries studied, a rising trend remained in a number of developing countries.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

scholarly journals Resistome of a carbapenemase-producing novel ST232 Klebsiella michiganensis isolate from urban hospital effluent in South Africa

2021 ◽  
Vol 24 ◽  
pp. 321-324
Author(s):  
T.L. King ◽  
S. Schmidt ◽  
S. Thakur ◽  
P. Fedorka-Cray ◽  
S. Keelara ◽  
...  
Keyword(s):  
South Africa ◽  
Urban Hospital ◽  
Hospital Effluent

scholarly journals Tuberculosis Mortality by Occupation in South Africa, 2011–2015

2018 ◽  
Vol 15 (12) ◽  
pp. 2756 ◽  
Author(s):  
Tahira Kootbodien ◽  
Kerry Wilson ◽  
Nonhlanhla Tlotleng ◽  
Vusi Ntlebi ◽  
Felix Made ◽  
...  
Keyword(s):  
South Africa ◽  
Smoking Status ◽  
Direct Method ◽  
Multivariate Logistic Regression Analysis ◽  
World Health ◽  
Health Concern ◽  
Mortality Data ◽  
Middle Income ◽  
Risk Of Death ◽  
Increased Risk

Work-related tuberculosis (TB) remains a public health concern in low- and middle-income countries. The use of vital registration data for monitoring TB deaths by occupation has been unexplored in South Africa. Using underlying cause of death and occupation data for 2011 to 2015 from Statistics South Africa, age-standardised mortality rates (ASMRs) were calculated for all persons of working age (15 to 64 years) by the direct method using the World Health Organization (WHO) standard population. Multivariate logistic regression analysis was performed to calculate mortality odds ratios (MORs) for occupation groups, adjusting for age, sex, year of death, province of death, and smoking status. Of the 221,058 deaths recorded with occupation data, 13% were due to TB. ASMR for TB mortality decreased from 165.9 to 88.8 per 100,000 population from 2011 to 2015. An increased risk of death by TB was observed among elementary occupations: agricultural labourers (MORadj = 3.58, 95% Confidence Interval (CI) 2.96–4.32), cleaners (MORadj = 3.44, 95% CI 2.91–4.09), and refuse workers (MORadj = 3.41, 95% CI 2.88–4.03); among workers exposed to silica dust (MORadj = 3.37, 95% CI 2.83–4.02); and among skilled agricultural workers (MORadj = 3.31, 95% CI 2.65–4.19). High-risk TB occupations can be identified from mortality data. Therefore, TB prevention and treatment policies should be prioritised in these occupations.

scholarly journals Changing effect of the numerator–denominator bias in unlinked data on mortality differentials by education: evidence from Estonia, 2000–2015

2020 ◽  
pp. jech-2020-214487
Author(s):  
Domantas Jasilionis ◽  
Mall Leinsalu
Keyword(s):  
High Education ◽  
Type Inequality ◽  
Mortality Data ◽  
Data Set ◽  
Inequality Measures ◽  
Death Record ◽  
Education Group ◽  
Range Type ◽  
Mortality Estimates ◽  
Mortality Inequality

BackgroundThis study highlights changing disagreement between census and death record information in the reporting of the education of the deceased and shows how these reporting differences influence a range of mortality inequality estimates.MethodsThis study uses a census-linked mortality data set for Estonia for the periods 2000–2003 and 2012–2015. The information on the education of the deceased was drawn from both the censuses and death records. Range-type, Gini-type and regression-based measures were applied to measure absolute and relative mortality inequality according to the two types of data on the education of the deceased.ResultsThe study found a small effect of the numerator–denominator bias on unlinked mortality estimates for the period 2000–2003. The effect of this bias became sizeable in the period 2012–2015: in high education group, mortality was overestimated by 23–28%, whereas the middle education group showed notable underestimation of mortality. The same effect was small for the lowest education group. These biases led to substantial distortions in range-type inequality measures, whereas unlinked and linked Gini-type measures showed somewhat closer agreement.ConclusionsThe changing distortions in the unlinked estimates reported in this study warn that this type of evidence cannot be readily used for monitoring changes in mortality inequalities.

Trends in Melanoma Mortality in the Population Groups of South Africa

Dermatology ◽  
2019 ◽  
Vol 235 (5) ◽  
pp. 396-399 ◽  
Author(s):  
Caradee Yael Wright ◽  
Thandi Kapwata ◽  
Elvira Singh ◽  
Adele C. Green ◽  
Peter Baade ◽  
...  
Keyword(s):  
South Africa ◽  
Regression Models ◽  
Population Group ◽  
Mortality Rates ◽  
Mortality Data ◽  
African Countries ◽  
South Africans ◽  
Awareness Campaigns ◽  
Increasing Trend ◽  
Join Point

The incidence of cutaneous melanoma (CM) is increasing in countries around the world. However, little is known about melanoma trends in African countries by population group. We studied CM mortality in South Africa from 1997 to 2014 to partly address this knowledge gap. Unit record mortality data for all South Africans who died from CM (n = 8,537) were obtained from Statistics South Africa. Join-point regression models were used to assess whether there was a statistically significant change in the direction and/or magnitude of the annual trends in CM mortality. A significant increasing trend of 11% per year was observed in age-adjusted mortality rates in men between 2000 and 2005 (p < 0.01), rising from 2 to 3 per 100,000. There was also a statistically significant increase of 180% per year among White South Africans from 1997 to 1999 (p < 0.05) and of 3% from 1999 to 2014 (p < 0.01). These results may be used to inform CM awareness campaigns and will motivate efforts to improve the collection and analysis of relevant statistics regarding the present burden of CM in South Africa.

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