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      A shortened verbal autopsy instrument for use in routine mortality surveillance systems.

      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64
      BMC medicine
      Springer Nature

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          Abstract

          Verbal autopsy (VA) is recognized as the only feasible alternative to comprehensive medical certification of deaths in settings with no or unreliable vital registration systems. However, a barrier to its use by national registration systems has been the amount of time and cost needed for data collection. Therefore, a short VA instrument (VAI) is needed. In this paper we describe a shortened version of the VAI developed for the Population Health Metrics Research Consortium (PHMRC) Gold Standard Verbal Autopsy Validation Study using a systematic approach.

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          Civil registration systems and vital statistics: successes and missed opportunities.

          Vital statistics generated through civil registration systems are the major source of continuous monitoring of births and deaths over time. The usefulness of vital statistics depends on their quality. In the second paper in this Series we propose a comprehensive and practical framework for assessment of the quality of vital statistics. With use of routine reports to the UN and cause-of-death data reported to WHO, we review the present situation and past trends of vital statistics in the world and note little improvement in worldwide availability of general vital statistics or cause-of-death statistics. Only a few developing countries have been able to improve their civil registration and vital statistics systems in the past 50 years. International efforts to improve comparability of vital statistics seem to be effective, and there is reasonable progress in collection and publication of data. However, worldwide efforts to improve data have been limited to sporadic and short-term measures. We conclude that countries and developmental partners have not recognised that civil registration systems are a priority.
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            Population Health Metrics Research Consortium gold standard verbal autopsy validation study: design, implementation, and development of analysis datasets

            Background Verbal autopsy methods are critically important for evaluating the leading causes of death in populations without adequate vital registration systems. With a myriad of analytical and data collection approaches, it is essential to create a high quality validation dataset from different populations to evaluate comparative method performance and make recommendations for future verbal autopsy implementation. This study was undertaken to compile a set of strictly defined gold standard deaths for which verbal autopsies were collected to validate the accuracy of different methods of verbal autopsy cause of death assignment. Methods Data collection was implemented in six sites in four countries: Andhra Pradesh, India; Bohol, Philippines; Dar es Salaam, Tanzania; Mexico City, Mexico; Pemba Island, Tanzania; and Uttar Pradesh, India. The Population Health Metrics Research Consortium (PHMRC) developed stringent diagnostic criteria including laboratory, pathology, and medical imaging findings to identify gold standard deaths in health facilities as well as an enhanced verbal autopsy instrument based on World Health Organization (WHO) standards. A cause list was constructed based on the WHO Global Burden of Disease estimates of the leading causes of death, potential to identify unique signs and symptoms, and the likely existence of sufficient medical technology to ascertain gold standard cases. Blinded verbal autopsies were collected on all gold standard deaths. Results Over 12,000 verbal autopsies on deaths with gold standard diagnoses were collected (7,836 adults, 2,075 children, 1,629 neonates, and 1,002 stillbirths). Difficulties in finding sufficient cases to meet gold standard criteria as well as problems with misclassification for certain causes meant that the target list of causes for analysis was reduced to 34 for adults, 21 for children, and 10 for neonates, excluding stillbirths. To ensure strict independence for the validation of methods and assessment of comparative performance, 500 test-train datasets were created from the universe of cases, covering a range of cause-specific compositions. Conclusions This unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design. This dataset can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems.
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              Using verbal autopsy to measure causes of death: the comparative performance of existing methods

              Background Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability. Methods We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution. Results Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause. Conclusions Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.
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                Author and article information

                Journal
                BMC Med
                BMC medicine
                Springer Nature
                1741-7015
                1741-7015
                Dec 16 2015
                : 13
                Affiliations
                [1 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. ptserina@uw.edu.
                [2 ] University of Queensland, School of Public Health, Level 2 Public Health Building School of Public Health, Herston Road, Herston, QLD, 4006, Australia. i.riley@sph.uq.edu.au.
                [3 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. andrea.leigh.stewart@gmail.com.
                [4 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. abie@uw.edu.
                [5 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. rafael.lozano@insp.mx.
                [6 ] National Institute of Public Health, Av. Universidad 655, Buena Vista, 62100, Cuernavaca, Morelos, Mexico. rafael.lozano@insp.mx.
                [7 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. megham2@uw.edu.
                [8 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. rluning@uw.edu.
                [9 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. bhp3@uw.edu.
                [10 ] Institute for International Programs, Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, 21205, USA. rblack1@jhu.edu.
                [11 ] Community Empowerment Lab, Shivgarh, India. kgmcice@sancharnet.in.
                [12 ] The INCLEN Trust International, New Delhi, India. kgmcice@sancharnet.in.
                [13 ] International Center for Diarrhoeal Disease Research, Dhaka, Bangladesh. nalam@icddrb.org.
                [14 ] International Center for Diarrhoeal Disease Research, Dhaka, Bangladesh. saidul@icddrb.org.
                [15 ] Public Health Laboratory-IdC, P.O.BOX 122, Wawi, Chake Chake, Pemba, Zanzibar, Tanzania. saidmali2003@yahoo.com.
                [16 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. atkinsct@uw.edu.
                [17 ] Institute for International Programs, Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, 21205, USA. abaqui@jhsph.edu.
                [18 ] University of Melbourne, School of Population and Global Health, Building 379, 207 Bouverie St., Parkville, 3010, VIC, Australia. hafiz.chowdhury@unimelb.edu.au.
                [19 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. dandona@uw.edu.
                [20 ] Public Health Foundation of India, Plot 47, Sector 44, Gurgaon, 122002, National Capital Region, India. dandona@uw.edu.
                [21 ] Public Health Foundation of India, Plot 47, Sector 44, Gurgaon, 122002, National Capital Region, India. Rakhi.dandona@phfi.org.
                [22 ] Malaria Consortium Cambodia, 113 Mao Tse Toung, Phnom Penh, Cambodia. emilydantzer@gmail.com.
                [23 ] Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94304, USA. gdarmsta@stanford.edu.
                [24 ] CSM Medical University, Shah Mina Road, Chowk Lucknow, Uttar Pradesh, 226003, India. das_lko@yahoo.com.
                [25 ] Institute for International Programs, Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, 21205, USA. udhingra@jhu.edu.
                [26 ] Public Health Laboratory-IdC, P.O.BOX 122, Wawi, Chake Chake, Pemba, Zanzibar, Tanzania. udhingra@jhu.edu.
                [27 ] Institute for International Programs, Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, 21205, USA. adutta@cphealthkinetics.org.
                [28 ] Public Health Laboratory-IdC, P.O.BOX 122, Wawi, Chake Chake, Pemba, Zanzibar, Tanzania. adutta@cphealthkinetics.org.
                [29 ] Harvard School of Public Health, 677 Huntington Avenue, Boston, MA, 02115-6018, USA. mina@hsph.harvard.edu.
                [30 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. mikefree@uw.edu.
                [31 ] WHO Collaborating Centre for Public Health Workforce Development, National Institute of Health Sciences, Kalutara, Sri Lanka. samanhattotuwa@yahoo.com.
                [32 ] Iipas, Chapel Hill, NC, USA. saraegomez@gmail.com.
                [33 ] WHO Collaborating Centre for Public Health Workforce Development, National Institute of Health Sciences, Kalutara, Sri Lanka. hensmand@SEARO.WHO.INT.
                [34 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. spencj@gmail.com.
                [35 ] The George Institute for Global Health, Sydney, Australia. rjoshi@george.org.au.
                [36 ] Institute for International Programs, Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, 21205, USA. hkalter1@jhu.edu.
                [37 ] Community Empowerment Lab, Shivgarh, India. aarti.kumar@shivgarh.org.
                [38 ] The INCLEN Trust International, New Delhi, India. aarti.kumar@shivgarh.org.
                [39 ] Community Empowerment Lab, Shivgarh, India. vishwajeet.kumar@shivgarh.org.
                [40 ] The INCLEN Trust International, New Delhi, India. vishwajeet.kumar@shivgarh.org.
                [41 ] Research Institute for Tropical Medicine, Corporate Ave., Muntinlupa City, 1781, Philippines. grandchallenge13@yahoo.com.
                [42 ] Cornell University, Division of Nutritional Sciences, 314 Savage Hall, Ithaca, NY, 14853, USA. smehta@cornell.edu.
                [43 ] The George Institute for Global Health, University of Sydney and Royal Prince Albert Hospital, Sydney, Australia. bneal@georgeinstitute.org.au.
                [44 ] Imperial college, London, London, UK. bneal@georgeinstitute.org.au.
                [45 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. summerlockett9@yahoo.com.
                [46 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. davidp6@uw.edu.
                [47 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. kpierce2@uw.edu.
                [48 ] CSM Medical University, Shah Mina Road, Chowk Lucknow, Uttar Pradesh, 226003, India. rprasad2@sancharnet.in.
                [49 ] The George Institute for Global Health, Hyderabad, India. dpraveen@georgeinstitute.org.in.
                [50 ] Muhimbili University of Health and Allied Sciences, United Nations Rd., Dar es Salaam, Tanzania. zulpremji688@gmail.com.
                [51 ] National Institute of Public Health, Av. Universidad 655, Buena Vista, 62100, Cuernavaca, Morelos, Mexico. mdolores@insp.mx.
                [52 ] University of Queensland, School of Public Health, Level 2 Public Health Building School of Public Health, Herston Road, Herston, QLD, 4006, Australia. rampatige@gmail.com.
                [53 ] Research Institute for Tropical Medicine, Corporate Ave., Muntinlupa City, 1781, Philippines. britt_ph11@yahoo.com.
                [54 ] National Institute of Public Health, Av. Universidad 655, Buena Vista, 62100, Cuernavaca, Morelos, Mexico. mpromero@insp.mx.
                [55 ] Muhimbili University of Health and Allied Sciences, United Nations Rd., Dar es Salaam, Tanzania. mwana77@gmail.com.
                [56 ] Research Institute for Tropical Medicine, Corporate Ave., Muntinlupa City, 1781, Philippines. diozele_sanvictores@yahoo.com.
                [57 ] Institute for International Programs, Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, 21205, USA. sazawal@jhu.edu.
                [58 ] Public Health Laboratory-IdC, P.O.BOX 122, Wawi, Chake Chake, Pemba, Zanzibar, Tanzania. sazawal@jhu.edu.
                [59 ] International Center for Diarrhoeal Disease Research, Dhaka, Bangladesh. pkstreatfield@icddrb.org.
                [60 ] Research Institute for Tropical Medicine, Corporate Ave., Muntinlupa City, 1781, Philippines. veronica.tallo2015@gmail.com.
                [61 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. alvahdat@microsoft.com.
                [62 ] WHO Collaborating Centre for Public Health Workforce Development, National Institute of Health Sciences, Kalutara, Sri Lanka. nmwijesekara@yahoo.com.
                [63 ] Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA. cjlm@u.washington.edu.
                [64 ] University of Melbourne, School of Population and Global Health, Building 379, 207 Bouverie St., Parkville, 3010, VIC, Australia. alan.lopez@unimelb.edu.au.
                Article
                10.1186/s12916-015-0528-8
                10.1186/s12916-015-0528-8
                4681088
                26670275
                0a6dcbcf-97c2-413d-868f-749dae914b70
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