Design options for sample-based mortality surveillance.
BACKGROUND
Reliable information on causes of death is a fundamental component of health development strategies, yet globally only about one-third of countries have access to such information. For countries currently without adequate mortality reporting systems there are useful models other than resource-intensive population-wide medical certification. Sample-based mortality surveillance is one such approach. This paper provides methods for addressing appropriate sample size considerations in relation to mortality surveillance, with particular reference to situations in which prior information on mortality is lacking.


METHODS
The feasibility of model-based approaches for predicting the expected mortality structure and cause composition is demonstrated for populations in which only limited empirical data is available. An algorithm approach is then provided to derive the minimum person-years of observation needed to generate robust estimates for the rarest cause of interest in three hypothetical populations, each representing different levels of health development.


RESULTS
Modelled life expectancies at birth and cause of death structures were within expected ranges based on published estimates for countries at comparable levels of health development. Total person-years of observation required in each population could be more than halved by limiting the set of age, sex, and cause groups regarded as 'of interest'.


CONCLUSIONS
The methods proposed are consistent with the philosophy of establishing priorities across broad clusters of causes for which the public health response implications are similar. The examples provided illustrate the options available when considering the design of mortality surveillance for population health monitoring purposes.
Begg
Stephen
S
School of Population Health, University of Queensland, Brisbane, QLD, Australia.
Rao
Chalapati
C
Lopez
Alan D
AD
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002423:Cause of Death; D002648:Child; D002675:Child, Preschool; D017281:Cost of Illness; D005260:Female; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D008017:Life Expectancy; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D018401:Sample Size; D017678:Sex Distribution