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Dying to count: mortality surveillance methods in resource-poor settings
Umeå University, Faculty of Medicine, Public Health and Clinical Medicine, Epidemiology and Public Health Sciences.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background

Mortality data are critical to understanding and monitoring changes in population health status over time. Nevertheless, the majority of people living in the world’s poorest countries, where the burden of disease is highest, remain outside any kind of systematic health surveillance. This lack of routine registration of vital events, such as births and deaths, constitutes a major and longstanding constraint on the understanding of patterns of health and disease and the effectiveness of interventions. Localised sentinel demographic and health surveillance strategies are a useful surrogate for more widespread surveillance in such settings, but rigorous, evidence-based methodologies for sample-based surveillance are weak and by no means standardised. This thesis aims to describe, evaluate and refine methodological approaches to mortality measurement in resource-poor settings.

Methods

Through close collaboration with existing community surveillance operations in a range of settings, this work uses existing data from demographic surveillance sites and community-based surveys using various innovative approaches in order to evaluate and refine methodological approaches to mortality measurement and cause-of-death determination. In doing so, this work explores the application of innovative techniques and procedures for mortality surveillance in relation to the differing needs of those who use mortality data, ranging from global health organisations to local health planners.

Results

Empirical modelling of sampling procedures in community-based surveys in rural Africa and of random errors in longitudinal data collection sheds light on the effects of various data-capture and quality-control procedures and demonstrates the representativeness and robustness of population surveillance datasets. The development, application and refinement of a probabilistic approach to determining causes of death at the population level in developing countries has shown promise in overcoming the longstanding limitations and issues of standardisation of existing methods. Further adaptation and application of this approach to measure maternal deaths has also been successful. Application of international guidelines on humanitarian crisis detection to mortality surveillance in Ethiopia demonstrates that simple procedures can and, from an ethical perspective, should be applied to sentinel surveillance methods for the prospective detection of important mortality changes in vulnerable populations.

Conclusion

Mortality surveillance in sentinel surveillance systems in resource-poor settings is a valuable and worthwhile task. This work contributes to the understanding of the effects of different methods of surveillance and demonstrates that, ultimately, the choice of methods for collecting data, assuring data quality and determining causes of death depends on the specific needs and requirements of end users. Surveillance systems have the potential to contribute substantially to developing health care systems in resource-poor countries and should not only be considered as research-oriented enterprises.

Place, publisher, year, edition, pages
Umeå: Epidemiologi och folkhälsovetenskap , 2008. , 66 p.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1152
Keyword [en]
mortality, surveillance, verbal autopsy, survey methods
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:umu:diva-1544ISBN: 978-91-7264-500-4 (print)OAI: oai:DiVA.org:umu-1544DiVA: diva2:141346
Public defence
2008-02-29, sal 135, 9A, Norrlands Universitetssjukhus, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2008-02-14 Created: 2008-02-14 Last updated: 2010-01-11Bibliographically approved
List of papers
1. Population survey sampling methods in a rural African setting: measuring mortality
Open this publication in new window or tab >>Population survey sampling methods in a rural African setting: measuring mortality
2008 (English)In: Population Health Metrics, ISSN 1478-7954, Vol. 6, Article nr 2- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Population-based sample surveys and sentinel surveillance methods are commonly used as substitutes for more widespread health and demographic monitoring and intervention studies in resource-poor settings. Such methods have been criticised as only being worthwhile if the results can be extrapolated to the surrounding 100-fold population. With an emphasis on measuring mortality, this study explores the extent to which choice of sampling method affects the representativeness of 1% sample data in relation to various demographic and health parameters in a rural, developing-country setting.

METHODS: Data from a large community based census and health survey conducted in rural Burkina Faso were used as a basis for modelling. Twenty 1% samples incorporating a range of health and demographic parameters were drawn at random from the overall dataset for each of seven different sampling procedures at two different levels of local administrative units. Each sample was compared with the overall 'gold standard' survey results, thus enabling comparisons between the different sampling procedures.

RESULTS: All sampling methods and parameters tested performed reasonably well in representing the overall population. Nevertheless, a degree of variation could be observed both between sampling approaches and between different parameters, relating to their overall distribution in the total population.

CONCLUSION: Sample surveys are able to provide useful demographic and health profiles of local populations. However, various parameters being measured and their distribution within the sampling unit of interest may not all be best represented by a particular sampling method. It is likely therefore that compromises may have to be made in choosing a sampling strategy, with costs, logistics the intended use of the data being important considerations.

Place, publisher, year, edition, pages
BioMed Central, 2008
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-10415 (URN)10.1186/1478-7954-6-2 (DOI)18492246 (PubMedID)
Available from: 2008-09-08 Created: 2008-09-08 Last updated: 2015-04-29Bibliographically approved
2. Demonstrating the robustness of population surveillance data: implications of error rates on demographic and mortality estimates
Open this publication in new window or tab >>Demonstrating the robustness of population surveillance data: implications of error rates on demographic and mortality estimates
2008 (English)In: BMC Medical Research Methodology, ISSN 1471-2288, Vol. 8, Article nr 13- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: As in any measurement process, a certain amount of error may be expected in routine population surveillance operations such as those in demographic surveillance sites (DSSs). Vital events are likely to be missed and errors made no matter what method of data capture is used or what quality control procedures are in place. The extent to which random errors in large, longitudinal datasets affect overall health and demographic profiles has important implications for the role of DSSs as platforms for public health research and clinical trials. Such knowledge is also of particular importance if the outputs of DSSs are to be extrapolated and aggregated with realistic margins of error and validity.

METHODS: This study uses the first 10-year dataset from the Butajira Rural Health Project (BRHP) DSS, Ethiopia, covering approximately 336,000 person-years of data. Simple programmes were written to introduce random errors and omissions into new versions of the definitive 10-year Butajira dataset. Key parameters of sex, age, death, literacy and roof material (an indicator of poverty) were selected for the introduction of errors based on their obvious importance in demographic and health surveillance and their established significant associations with mortality. Defining the original 10-year dataset as the 'gold standard' for the purposes of this investigation, population, age and sex compositions and Poisson regression models of mortality rate ratios were compared between each of the intentionally erroneous datasets and the original 'gold standard' 10-year data.

RESULTS: The composition of the Butajira population was well represented despite introducing random errors, and differences between population pyramids based on the derived datasets were subtle. Regression analyses of well-established mortality risk factors were largely unaffected even by relatively high levels of random errors in the data.

CONCLUSION: The low sensitivity of parameter estimates and regression analyses to significant amounts of randomly introduced errors indicates a high level of robustness of the dataset. This apparent inertia of population parameter estimates to simulated errors is largely due to the size of the dataset. Tolerable margins of random error in DSS data may exceed 20%. While this is not an argument in favour of poor quality data, reducing the time and valuable resources spent on detecting and correcting random errors in routine DSS operations may be justifiable as the returns from such procedures diminish with increasing overall accuracy. The money and effort currently spent on endlessly correcting DSS datasets would perhaps be better spent on increasing the surveillance population size and geographic spread of DSSs and analysing and disseminating research findings.

Place, publisher, year, edition, pages
BioMed Central, 2008
Keyword
Age Distribution, Analysis of Variance, Demography, Epidemiologic Methods, Ethiopia/epidemiology, Female, Humans, Male, Mortality, Poisson Distribution, Population Surveillance, Regression Analysis, Sex Distribution, Software
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-10416 (URN)10.1186/1471-2288-8-13 (DOI)000254661800001 ()18366742 (PubMedID)
Available from: 2008-09-08 Created: 2008-09-08 Last updated: 2015-04-29Bibliographically approved
3. Refining a probabilistic model for interpreting verbal autopsy data.
Open this publication in new window or tab >>Refining a probabilistic model for interpreting verbal autopsy data.
Show others...
2006 (English)In: Scandinavian journal of public health, ISSN 1403-4948, Vol. 34, no 1, 26-31 p.Article in journal (Refereed) Published
Keyword
Autopsy/methods, Bayes Theorem, Cause of Death, Coroners and Medical Examiners, Data Collection/methods, Data Interpretation; Statistical, Developing Countries, Humans, Models; Statistical, Reproducibility of Results, Speech, Vietnam
Identifiers
urn:nbn:se:umu:diva-14119 (URN)10.1080/14034940510032202 (DOI)16449041 (PubMedID)
Available from: 2008-09-08 Created: 2008-09-08 Last updated: 2015-04-29Bibliographically approved
4. Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies
Open this publication in new window or tab >>Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies
Show others...
2007 (English)In: Population Health Metrics, ISSN 1478-7954, Vol. 5, no 1Article in journal (Refereed) Published
Abstract [en]

Background: Substantial reductions in maternal mortality are called for in Millennium Development Goal 5 (MDG-5), thus assuming that maternal mortality is measurable. A key difficulty is attributing causes of death for the many women who die unaided in developing countries. Verbal autopsy (VA) can elicit circumstances of death, but data need to be interpreted reliably and consistently to serve as global indicators. Recent developments in probabilistic modelling of VA interpretation are adapted and assessed here for the specific circumstances of pregnancy-related death.

Methods: A preliminary version of the InterVA-M probabilistic VA interpretation model was developed and refined with adult female VA data from several sources, and then assessed against 258 additional VA interviews from Burkina Faso. Likely causes of death produced by the model were compared with causes previously determined by local physicians. Distinction was made between free-text and closed-question data in the VA interviews, to assess the added value of free-text material on the model's output.

Results: Following rationalisation between the model and physician interpretations, cause-specific mortality fractions were broadly similar. Case-by-case agreement between the model and any of the reviewing physicians reached approximately 60%, rising to approximately 80% when cases with a discrepancy were reviewed by an additional physician. Cardiovascular disease and malaria showed the largest differences between the methods, and the attribution of infections related to pregnancy also varied. The model estimated 30% of deaths to be pregnancy-related, of which half were due to direct causes. Data derived from free-text made no appreciable difference.

Conclusion: InterVA-M represents a potentially valuable new tool for measuring maternal mortality in an efficient, consistent and standardised way. Further development, refinement and validation are planned. It could become a routine tool in research and service settings where levels and changes in pregnancy-related deaths need to be measured, for example in assessing progress towards MDG-5.

Place, publisher, year, edition, pages
BioMed Central, 2007
National Category
Public Health, Global Health, Social Medicine and Epidemiology Obstetrics, Gynecology and Reproductive Medicine
Identifiers
urn:nbn:se:umu:diva-14131 (URN)10.1186/1478-7954-5-1 (DOI)17288607 (PubMedID)
Available from: 2008-09-08 Created: 2008-09-08 Last updated: 2015-04-29Bibliographically approved
5. Identifying humanitarian crises in population surveillance field sites: simple procedures and ethical imperatives.
Open this publication in new window or tab >>Identifying humanitarian crises in population surveillance field sites: simple procedures and ethical imperatives.
2009 (English)In: Public Health, ISSN 0033-3506, E-ISSN 1476-5616, Vol. 123, no 2, 151-155 p.Article in journal (Refereed) Published
Abstract [en]

OBJECTIVES: Effective early warning systems of humanitarian crises may help to avert substantial increases in mortality and morbidity, and prevent major population movements. The Butajira Rural Health Programme (BRHP) in Ethiopia has maintained a programme of epidemiological surveillance since 1987. Inspection of the BRHP data revealed large peaks of mortality in 1998 and 1999, well in excess of the normally observed year-to-year variation. Further investigation and enquiry revealed that these peaks related to a measles epidemic, and a serious episode of drought and consequent food insecurity that went undetected by the BRHP. This paper applies international humanitarian crisis threshold definitions to the BRHP data in an attempt to identify suitable mortality thresholds that may be used for the prospective detection of humanitarian crises in population surveillance sites in developing countries.

STUDY DESIGN: Empirical investigation using secondary analysis of longitudinal population-based cohort data.

METHODS: The daily, weekly and monthly thresholds for crises in Butajira were applied to mortality data for the 5-year period incorporating the crisis periods of 1998-1999. Days, weeks and months in which mortality exceeded each threshold level were identified. Each threshold level was assessed in terms of prospectively identifying the true crisis periods in a timely manner whilst avoiding false alarms.

RESULTS: The daily threshold definition is too sensitive to accurately detect impending or real crises in the population surveillance setting of the BRHP. However, the weekly threshold level is useful in identifying important increases in mortality in a timely manner without the excessive sensitivity of the daily threshold. The weekly threshold level detects the crisis periods approximately 2 weeks before the monthly threshold level.

CONCLUSION: Mortality measures are highly specific indicators of the health status of populations, and simple procedures can be used to apply international crisis threshold definitions in population surveillance settings for the prospective detection of important changes in mortality rate. Standards for the timely use of surveillance data and ethical responsibilities of those responsible for the data should be made explicit to improve the public health functioning of current sentinel surveillance methodologies.

Keyword
Humanitarian crisis; Famine; Mortality surveillance; Demographic surveillance sites
National Category
Environmental Health and Occupational Health
Identifiers
urn:nbn:se:umu:diva-21768 (URN)10.1016/j.puhe.2008.10.032 (DOI)19157467 (PubMedID)
Available from: 2009-04-20 Created: 2009-04-20 Last updated: 2015-04-29Bibliographically approved

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