Skip to main content
×
×
Home

Hyak mortality monitoring system: innovative sampling and estimation methods – proof of concept by simulation

  • S. J. Clark (a1) (a2) (a3) (a4), J. Wakefield (a5) (a6), T. McCormick (a5) (a7) and M. Ross (a8)
Abstract

Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low- to middle-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low- to middle-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space – data amalgamation. Data sources amenable to this approach include sample surveys, sample registration systems, health and demographic surveillance systems, administrative records, census records, health facility records and others. We propose a new statistical framework for gathering health and population data – Hyak – that leverages the benefits of sampling and longitudinal, prospective surveillance to create a cheap, accurate, sustainable monitoring platform. Hyak has three fundamental components:

  • Data amalgamation: A sampling and surveillance component that organizes two or more data collection systems to work together: (1) data from HDSS with frequent, intense, linked, prospective follow-up and (2) data from sample surveys conducted in large areas surrounding the Health and Demographic Surveillance System (HDSS) sites using informed sampling so as to capture as many events as possible;
  • Cause of death: Verbal autopsy to characterize the distribution of deaths by cause at the population level; and
  • Socioeconomic status (SES): Measurement of SES in order to characterize poverty and wealth.

We conduct a simulation study of the informed sampling component of Hyak based on the Agincourt HDSS site in South Africa. Compared with traditional cluster sampling, Hyak's informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates of both mortality counts and mortality rates that have lower variance and small bias.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Hyak mortality monitoring system: innovative sampling and estimation methods – proof of concept by simulation
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Hyak mortality monitoring system: innovative sampling and estimation methods – proof of concept by simulation
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Hyak mortality monitoring system: innovative sampling and estimation methods – proof of concept by simulation
      Available formats
      ×
Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited
Corresponding author
*Address for correspondence: S. J. Clark, Department of Sociology, The Ohio State University, Ohio, USA. (Email: work@samclark.net)
References
Hide All
1 United Nations. Statistical Division. Principles and Recommendations for a Vital Statistics System, 3rd edn. New York: United Nations Department of Economic and Social Affairs, 2014.
2 Mathers, CD, et al. . Counting the dead and what they died from: an assessment of the global status of cause of death data. Bulletin of the World Health Organization 2005; 83: 171177.
3 AbouZahr, C, et al. . The way forward. Lancet 2007; 370: 17911799.
4 Boerma, JT, Stansfield, SK. Health Statistics 1 Health statistics now : are we making the right investments ? Tuberculosis 2007; 369: 779786.
5 Hill, K, et al. . Interim measures for meeting needs for health sector data: births, deaths, and causes of death. Lancet 2007; 370: 17261735.
6 Horton, R. Counting for health. Lancet 2007; 370: 1526.
7 Mahapatra, P, et al. . Civil registration systems and vital statistics: successes and missed opportunities. Lancet 2007; 370: 16531663.
8 Setel, PW, et al. . A scandal of invisibility: making everyone count by counting everyone. Lancet 2007; 370: 15691577.
9 AbouZahr, C, et al. . Towards universal civil registration and vital statistics systems: the time is now. Lancet 2015a; 386: 14071418.
10 AbouZahr, C, et al. . Civil registration and vital statistics: progress in the data revolution for counting and accountability. Lancet 2015b; 386: 13731385.
11 Mikkelsen, L, et al. . A global assessment of civil registration and vital statistics systems: monitoring data quality and progress. Lancet 2015; 386: 13951406.
12 Phillips, DE, et al. . Are well functioning civil registration and vital statistics systems associated with better health outcomes? Lancet 2015; 386: 13861394.
13 Abouzahr, C, Gollogly, L, Stevens, G. Better data needed: everyone agrees, but no one wants to pay. The Lancet 2010; 375: 619621.
14 Bchir, A, et al. . Better health statistics are possible. Lancet 2006; 367: 190193.
15 Mathers, CD, Boerma, T, Ma Fat, D. Global and regional causes of death. British Medical Bulletin 2009; 92: 732.
16 Rudan, I, et al. . Gaps in policy-relevant information on burden of disease in children: a systematic review. Lancet 2000; 365: 20312040.
17 World Health Organization. Strengthening civil registration and vital statistics for births, deaths and causes of death: resource kit. Technical Report, World Health Organization, 2013.
18 World Health Organization. Strengthening civil registration and vital statistics systems through innovative approaches in the health sector: guiding principles and good practices. Technical Report, World Health Organization, 2013.
19 World Health Organization. Improving mortality statistics through civil registration and vital statistics systems: strategies for country and partner support. Technical Report, World Health Organization, 2014.
20 United Nations. Sustainable Development Goals, 2014. (http://sustainabledevelopment.un.org/owg.html).
21 Commission on Population and Development. Strengthening the demographic evidence base for the post-2015 development agenda: report of the secretary-general. Technical report, United Nations, 2016.
22 Data Revolution Group: The UN Secretary General's Independent Expert Advisory Group on a Data Revolution for Sustainable Development. A world that counts: Mobilising the data revolution for sustainable development. Technical Report, United Nations, 2014.
23 United Nations. Resolution 2016/1: Strengthening the demographic evidence base for the 2030 agenda for sustainable development, 2016. (http://undocs.org/E/CN.9/2016/1).
24 United Nations. Data Revolution for Sustainable Development, 2014a. (http://www.un.org/apps/news/story.asp?NewsID=48594#.VEVQpoctuvJ).
25 Office of the Registrar General & Census Commissioner, India. India's Sample Registration System, 2012. (http://censusindia.gov.in/Vital_Statistics/SRS/Sample_Registration_Sys tem.aspx).
26 Jha, P, et al. Prospective study of one million deaths in India: rationale, design, and validation results. PLoS Medicine 2006; 3: e18.
27 Lopez, AD, et al. . Verbal autopsy: innovations, applications, opportunities – improving cause of death measurement (article collection). Population Health Metrics 2011; 9 (https://www.biomedcentral.com/collections/verbal-autopsy).
28 MEASURE Evaluation. SAVVY: Sample Vital Registration with Verbal Autopsy, 2012. (http://www.cpc.unc.edu/measure/tools/monitoring-evaluation-systems/sav vy).
29 Measure DHS. Demographic and Health Surveys, 2012. (http://www.measuredhs.com).
30 UNICEF – Statistics and Monitoring. Multiple Indicator Cluster Surveys (MICS), 2012. (http://www.unicef.org/statistics/index_24302.html).
31 Naghavi, M, et al. . Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the global burden of disease study 2013. The Lancet 2015; 385: 117171.
32 Gething, PW, et al. . A new world malaria map: Plasmodium falciparum endemicity in 2010. Malaria Journal 2011; 10: 378.
33 Kabaghe, AN, et al. . Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural malawi. PLoS ONE 2017; 12: e0172266.
34 Chipeta, MG, et al. . Adaptive geostatistical design and analysis for prevalence surveys. Spatial Statistics 2016; 15: 7084.
35 Thompson, SK, Seber, GAF. Adaptive sampling. New York, NY: John Wiley and Sons, 1996.
36 Bryce, J, Steketee, R. Continuous surveys and quality management in low-income countries: a good idea. The American Journal of Tropical Medicine and Hygiene 2010; 82: 360; author reply 361–362.
37 Rowe, AK. Potential of integrated continuous surveys and quality management to support monitoring, evaluation, and the scale-up of health interventions in developing countries. The American Journal of Tropical Medicine and Hygiene 2009; 80: 971979.
38 Turner, AG. Sampling frames and master samples, 2003. (https://unstats.un.org/unsd/demographic/meetings/egm/Sampling_1203/doc s/no_3.pdf).
39 Bryce, J, et al. . The multi-country evaluation of the integrated management of childhood illness strategy: lessons for the evaluation of public health interventions. American Journal of Public Health 2004; 94: 406415.
40 Victora, CG, et al. . Measuring impact in the Millennium Development Goal era and beyond: a new approach to large-scale effectiveness evaluations. Lancet 2011; 377: 8595.
41 Ye, Y, et al. . Health and demographic surveillance systems: a step towards full civil registration and vital statistics system in sub-Sahara Africa?. BMC Public Health 2012; 12: 741.
42 Byass, P, et al. . Lessons from history for designing and validating epidemiological surveillance in uncounted populations. PLoS ONE 2011; 6: e22897.
43 Jha, P. Counting the dead is one of the world's best investments to reduce premature mortality. Hypothesis 2012; 10: 16.
44 Alkema, L, Raftery, A, Brown, T. Bayesian melding for estimating uncertainty in national hiv prevalence estimates. Sexually Transmitted Infections 2008; 84(Suppl. 1): i11i16.
45 Alkema, L, Raftery, A, Clark, S. Probabilistic projections of hiv prevalence using Bayesian melding. The Annals of Applied Statistics 2007; 1: 229248.
46 Lanjouw, P, Ivaschenko, O. A new approach to producing geographic profiles of hiv prevalence. Technical Report 5207, World Bank - Policy Research Working Paper Series, 2010.
47 Kahn, K, et al. . Profile: Agincourt health and socio-demographic surveillance system. International Journal of Epidemiology 2012; 41: 9881001.
48 Kahn, K, et al. . Research into health, population and social transitions in rural South Africa: data and methods of the Agincourt health and demographic surveillance system1. Scandinavian Journal of Public Health 2007; 35(69 Suppl.): 820.
49 Clark, SJ, et al. . Cardiometabolic disease risk and hiv status in rural South Africa: establishing a baseline. BMC Public Health 2015; 15: 135.
50 Clark, SJ, et al. . Young children's probability of dying before and after their mother's death: a rural South African population-based surveillance study. PLoS Medicine 2013; 10: e1001409.
51 Gómez-Olivé, FX, et al. . Prevalence of hiv among those 15 and older in rural South Africa. AIDS Care 2013; 25: 11221128.
52 Houle, B, et al. . The unfolding counter-transition in rural South Africa: mortality and cause of death, 1994–2009. PLoS ONE 2014; 9: e100420.
53 Houle, B, et al. . Household context and child mortality in rural South Africa: the effects of birth spacing, shared mortality, household composition and socio-economic status. International Journal of Epidemiology 2013; 42: 14441454.
54 Kabudula, CW, et al. . Assessing changes in household socioeconomic status in rural South Africa, 2001–2013: a distributional analysis using household asset indicators. Social Indicators Research 2017; 133: 10471073.
55 Diggle, PJ, Tawn, JA, Moyeed, RA. Model-based geostatistics (with discussion). Applied Statistics 1998; 47: 299350.
56 Besag, J, York, J, Molliè, A. Bayesian image restoration, with two applications in spatial statistics (with discussion). Annals of the Institute of Statistical Mathematics 1991; 43: 159.
57 Rue, H, Martino, S, Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2009; 71: 319392.
58 Denison, D, Holmes, C. Bayesian partitioning for estimating disease risk. Biometrics 2001; 57: 143149.
59 Wakefield, J, Simpson, D, Godwin, J Comment: getting into space with a weight problem. discussion of, “model-based geostatistics for prevalence mapping in low-resource settings”, by P. J. Diggle and E. Giorgi. Journal of the American Statistical Association 2016; 111: 11111119.
60 Diggle, PJ, Menezes, R, Su, T-L. Geostatistical inference under preferential sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2010; 59: 191232.
61 Lohr, SL. Sampling: design and analysis (2nd edition). Boston, MA: Brooks/Cole, 2010.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Global Health, Epidemiology and Genomics
  • ISSN: -
  • EISSN: 2054-4200
  • URL: /core/journals/global-health-epidemiology-and-genomics
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Type Description Title
PDF
Supplementary materials

Clark et al. supplementary material
Appendix

 PDF (370 KB)
370 KB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 19
Total number of PDF views: 125 *
Loading metrics...

Abstract views

Total abstract views: 333 *
Loading metrics...

* Views captured on Cambridge Core between 5th February 2018 - 18th July 2018. This data will be updated every 24 hours.