| 1. |
- Anderson, Ian, et al.
(författare)
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Indigenous and tribal peoples' health (The Lancet-Lowitja Institute Global Collaboration) : a population study
- 2016
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Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 388:10040, s. 131-157
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Tidskriftsartikel (refereegranskat)abstract
- Background: International studies of the health of Indigenous and tribal peoples provide important public health insights. Reliable data are required for the development of policy and health services. Previous studies document poorer outcomes for Indigenous peoples compared with benchmark populations, but have been restricted in their coverage of countries or the range of health indicators. Our objective is to describe the health and social status of Indigenous and tribal peoples relative to benchmark populations from a sample of countries.Methods: Collaborators with expertise in Indigenous health data systems were identified for each country. Data were obtained for population, life expectancy at birth, infant mortality, low and high birthweight, maternal mortality, nutritional status, educational attainment, and economic status. Data sources consisted of governmental data, data from non-governmental organisations such as UNICEF, and other research. Absolute and relative differences were calculated.Findings: Our data (23 countries, 28 populations) provide evidence of poorer health and social outcomes for Indigenous peoples than for non-Indigenous populations. However, this is not uniformly the case, and the size of the rate difference varies. We document poorer outcomes for Indigenous populations for: life expectancy at birth for 16 of 18 populations with a difference greater than 1 year in 15 populations; infant mortality rate for 18 of 19 populations with a rate difference greater than one per 1000 livebirths in 16 populations; maternal mortality in ten populations; low birthweight with the rate difference greater than 2% in three populations; high birthweight with the rate difference greater than 2% in one population; child malnutrition for ten of 16 populations with a difference greater than 10% in five populations; child obesity for eight of 12 populations with a difference greater than 5% in four populations; adult obesity for seven of 13 populations with a difference greater than 10% in four populations; educational attainment for 26 of 27 populations with a difference greater than 1% in 24 populations; and economic status for 15 of 18 populations with a difference greater than 1% in 14 populations.Interpretation: We systematically collated data across a broader sample of countries and indicators than done in previous studies. Taking into account the UN Sustainable Development Goals, we recommend that national governments develop targeted policy responses to Indigenous health, improving access to health services, and Indigenous data within national surveillance systems.
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| 2. |
- Desai, Nikita, et al.
(författare)
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Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries
- 2014
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Ingår i: BMC Medicine. - : BioMed Central. - 1741-7015. ; 12:1, s. 20-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other.METHODS: We compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level.RESULTS: The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%).CONCLUSIONS: On an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.
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| 3. |
- Leitao, Jordana, et al.
(författare)
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Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries : systematic review
- 2014
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Ingår i: BMC Medicine. - : BioMed Central. - 1741-7015. ; 12:1, s. 22-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.METHODS: The reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.RESULTS: The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.CONCLUSIONS: There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.
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