Abstract:ObjectivesWe aim to analyse the trends and causes of mortality among adults in Addis Ababa.SettingThis analysis was conducted using verbal autopsy data from the Addis Ababa Mortality Surveillance in Addis Ababa, Ethiopia.ParticipantsAll deceased adults aged 15 years and above between 2007–2012 and 2015–2017 were included in the analysis.Outcome measuresWe collected verbal autopsy and conducted physician review to ascertain cause of death.ResultA total of 7911 data were included in this analysis. Non-communicab… Show more
“…The 2016 DHS in Ethiopia is an important example of such a data source because it is the only nationally representative source of information on road traffic injury mortality. Instead, GBD’s estimates for road traffic injuries in Ethiopia are informed by cause-of-death data from verbal autopsy surveillance in a primarily rural setting (Kilite Awlaelo in Northern Ethiopia),32 33 data from burial site surveillance in the capital city of Addis Ababa and morbidity and mortality data from seven health and demographic surveillance sites 34. However, publications from the Addis Ababa surveillance team suggest that traffic death rates are much higher than GBD estimates.…”
Section: Discussionmentioning
confidence: 99%
“…However, publications from the Addis Ababa surveillance team suggest that traffic death rates are much higher than GBD estimates. For instance, Fenta et al 34 report that 4.53% of all deaths in the 15–49 age group in the surveillance data during 2007–2017 were due to transport accidents. In contrast, GBD estimates for Addis in this age group and for this period range from 2.1% to 3.0%.…”
BackgroundThere are large discrepancies between official statistics of traffic injuries in African countries and estimates from the Global Burden of Disease (GBD) study and WHO’s Global Status Reports on Road Safety (GSRRS). We sought to assess the magnitude of the discrepancy in Ethiopia, its implications and how it can be addressed.MethodsWe systematically searched for nationally representative epidemiological data sources for road traffic injuries and vehicle ownership in Ethiopia and compared estimates with those from GBD and GSRRS.FindingsGBD and GSRRS estimates vary substantially across revisions and across projects. GSRRS-2018 estimates of deaths (27 326 in 2016) are more than three times GBD-2019 estimates (8718), and these estimates have non-overlapping uncertainty ranges. GSRRS estimates align well with the 2016 Demographic and Health Survey (DHS-2016; 27 838 deaths, 95th CI: 15 938 to 39 738). Official statistics are much lower (5118 deaths in 2018) than all estimates. GBD-2019 estimates of serious non-fatal injuries are consistent with DHS-2016 estimates (106 050 injuries, 95th CI: 81 728 to 130 372) and older estimates from the 2003 World Health Survey. Data from five surveys confirm that vehicle ownership levels in Ethiopia are much lower than in other countries in the region.InterpretationInclusion of data from national health surveys in GBD and GSRRS can help reduce discrepancies in estimates of deaths and support their use in highlighting under-reporting in official statistics and advocating for better prioritisation of road safety in the national policy agenda. GBD methods for estimating serious non-fatal injuries should be strengthened to allow monitoring progress towards Sustainable Development Goal target 3.6.
“…The 2016 DHS in Ethiopia is an important example of such a data source because it is the only nationally representative source of information on road traffic injury mortality. Instead, GBD’s estimates for road traffic injuries in Ethiopia are informed by cause-of-death data from verbal autopsy surveillance in a primarily rural setting (Kilite Awlaelo in Northern Ethiopia),32 33 data from burial site surveillance in the capital city of Addis Ababa and morbidity and mortality data from seven health and demographic surveillance sites 34. However, publications from the Addis Ababa surveillance team suggest that traffic death rates are much higher than GBD estimates.…”
Section: Discussionmentioning
confidence: 99%
“…However, publications from the Addis Ababa surveillance team suggest that traffic death rates are much higher than GBD estimates. For instance, Fenta et al 34 report that 4.53% of all deaths in the 15–49 age group in the surveillance data during 2007–2017 were due to transport accidents. In contrast, GBD estimates for Addis in this age group and for this period range from 2.1% to 3.0%.…”
BackgroundThere are large discrepancies between official statistics of traffic injuries in African countries and estimates from the Global Burden of Disease (GBD) study and WHO’s Global Status Reports on Road Safety (GSRRS). We sought to assess the magnitude of the discrepancy in Ethiopia, its implications and how it can be addressed.MethodsWe systematically searched for nationally representative epidemiological data sources for road traffic injuries and vehicle ownership in Ethiopia and compared estimates with those from GBD and GSRRS.FindingsGBD and GSRRS estimates vary substantially across revisions and across projects. GSRRS-2018 estimates of deaths (27 326 in 2016) are more than three times GBD-2019 estimates (8718), and these estimates have non-overlapping uncertainty ranges. GSRRS estimates align well with the 2016 Demographic and Health Survey (DHS-2016; 27 838 deaths, 95th CI: 15 938 to 39 738). Official statistics are much lower (5118 deaths in 2018) than all estimates. GBD-2019 estimates of serious non-fatal injuries are consistent with DHS-2016 estimates (106 050 injuries, 95th CI: 81 728 to 130 372) and older estimates from the 2003 World Health Survey. Data from five surveys confirm that vehicle ownership levels in Ethiopia are much lower than in other countries in the region.InterpretationInclusion of data from national health surveys in GBD and GSRRS can help reduce discrepancies in estimates of deaths and support their use in highlighting under-reporting in official statistics and advocating for better prioritisation of road safety in the national policy agenda. GBD methods for estimating serious non-fatal injuries should be strengthened to allow monitoring progress towards Sustainable Development Goal target 3.6.
“…Cerebrovascular disease (12.8%), diabetes mellitus (8.1%), and chronic liver disease (6.3%) were the leading causes of death in Addis Ababa, Ethiopia. 16 In case of Kersa, Eastern Ethiopia, 32.4% of deaths occurred due to infectious and parasitic diseases, 11.4% due to circulatory diseases, and 9.2% due to gastrointestinal disorders. 17 In Uganda, non-TB pneumonia (28.8%), tuberculosis (27.1%), stroke (26.8%), malignancy (26.1%), and HIV/AIDS (25%) were the major causes of mortality among adults patients.…”
Section: Discussionmentioning
confidence: 99%
“… 13 , 14 Moreover, different mortality rates were reported in different SSA countries. 13 , 15 Likewise, while cerebrovascular disease and diabetes mellitus are predominant causes of death in some SSA settings, 16 infectious and parasitic diseases are predominant in other settings. 13 , 17 , 18 …”
“…Ethiopia ranks 153 rd out of 167 countries in the Prosperity Index [ 32 ]. In Ethiopia, HIV/AIDS is the 6 th and 13 th leading cause of mortality among females and males, respectively [ 33 ]. Women are at a higher risk of HIV infection, with approximately 0.36 million women and 0.22 million men living with HIV in Ethiopia [ 34 ].…”
Socioeconomic inequality in comprehensive knowledge about HIV/AIDS can hinder progress towards ending the epidemic threat of this disease. To address the knowledge gap, it is essential to investigate inequality in HIV/AIDS services. This study aimed to investigate socioeconomic inequality, identify contributors, and analyze the trends in inequality in comprehensive knowledge about HIV/AIDS among adults in Ethiopia. A cross-sectional study was conducted using 2005, 2011, and 2016 population-based health survey data. The sample size was 18,818 in 2005, 29,264 in 2011, and 27,261 in 2016. Socioeconomic inequality in comprehensive knowledge about HIV/AIDS was quantified by using a concentration curve and index. Subsequently, the decomposition of the concentration index was conducted using generalised linear regression with a logit link function to quantify covariates’ contribution to wealth-based inequality. The Erreygers’ concentration index was 0.251, 0.239, and 0.201 in 2005, 2011, and 2016, respectively. Watching television (24.2%), household wealth rank (21.4%), ever having been tested for HIV (15.3%), and education status (14.3%) took the significant share of socioeconomic inequality. The percentage contribution of watching television increased from 4.3% in 2005 to 24.2% in 2016. The household wealth rank contribution increased from 14.6% in 2005 to 21.38% in 2016. Education status contribution decreased from 16.2% to 14.3%. The percentage contribution of listening to the radio decreased from 16.9% in 2005 to -2.4% in 2016. The percentage contribution of residence decreased from 7.8% in 2005 to -0.5% in 2016. This study shows comprehensive knowledge about HIV/AIDS was concentrated among individuals with a higher socioeconomic status. Socioeconomic-related inequality in comprehensive knowledge about HIV/AIDS is woven deeply in Ethiopia, though this disparity has been decreased minimally. A combination of individual and public health approaches entangled in a societal system are crucial remedies for the general population and disadvantaged groups. This requires comprehensive interventions according to the primary health care approach.
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