Forecasting Survival, Health, and Disability: Report on a Workshop

Stoto, Michael A.; Durch, Jane S.

doi:10.2307/2938466

“…Model misspecification and the presence of leading indicators (where changes in one cause systematically precede changes in another cause) can also result in reduced forecast accuracy from decomposition (Alho, 1991). Further, the assumption that discrete diseases drive overall mortality is questionable, especially at the older ages, and the appropriate means of combining correlated causespecific measures is unclear (Stoto & Durch, 1993). Additional limitations arise because available time series of cause-specific data are often too short for forecasting using ARIMA methods, and the parameters to be forecast are often less stable for causes of death than for overall mortality (Wilmoth, 1995).…”

Section: ð Decomposition By Cause Of Deathmentioning

confidence: 99%

“…Dimensionality is also increased by decomposition. Alho (1991), Stoto & Durch (1993), Tuljapurkar (1998) and Wilmoth (2005) discuss these and other relevant issues.…”

Section: ð Decomposition By Cause Of Deathmentioning

confidence: 99%

Mortality Modelling and Forecasting: a Review of Methods

Booth¹,

Tickle²

2008

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Continuing increases in life expectancy beyond previously-held limits have brought to the fore the critical importance of mortality forecasting. Significant developments in mortality forecasting since 1980 are reviewed under three broad approaches: expectation, extrapolation and explanation. Expectation is not generally a good basis for mortality forecasting, as it is subjective; expert expectations are invariably conservative. Explanation is restricted to certain causes of death with known determinants. Decomposition by cause of death poses problems associated with the lack of independence among causes and data difficulties. Most developments have been in extrapolative forecasting, and make use of statistical methods rather than models developed primarily for age-specific graduation. Methods using two-factor models (age-period or age-cohort) have been most successful. The two-factor Lee–Carter method, and, in particular, its variants, have been successful in terms of accuracy, while recent advances have improved the estimation of forecast uncertainty. Regression-based (GLM) methods have been less successful, due to nonlinearities in time. Three-factor methods are more recent; the Lee–Carter age-period-cohort model appears promising. Specialised software has been developed and made available. Research needs include further comparative evaluations of methods in terms of the accuracy of the point forecast and its uncertainty, encompassing a wide range of mortality situations.

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Public Health Surveillance

Stoto

¹

2008

Encyclopedia of Quantitative Risk Analysis and Assessment

1

0

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Public health surveillance seeks to assess and track the health of the public, define public health priorities, evaluate programs, and stimulate research. It includes case surveillance focuses on individuals, or sometimes groups of individuals, to identify individuals with certain diseases and take action to stop spread of disease. Statistical surveillance, on the other hand, focuses on populations to identify differentials and trends that can inform public health policymaking, including the allocation of resources. Surveillance systems rely on a diversity of data sources. Surveillance of infectious disease relies primarily on the system of notifiable diseases put in place in the nineteenth century. Statistical surveillance systems use data from vital statistics, population‐based sample surveys, registries, and health care records. The introduction and growth of syndromic surveillance following 9/11 has focused on a statistical approach to detecting bioterrorist attacks. Despite many accomplishments, the value of syndromic surveillance for detecting bioterrorist attacks has not yet been demonstrated. Ultimately, the most important contribution of syndromic surveillance systems to public health may be for natural disease outbreaks, such as seasonal and pandemic influenza, and for other public health purposes beyond simple detection.

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Public Health Surveillance

Stoto

¹

2014

Wiley StatsRef: Statistics Reference Online

1

0

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Public health surveillance seeks to assess and track the health of the public, define public health priorities, evaluate programs, and stimulate research. It includes case surveillance focuses on individuals, or sometimes groups of individuals, to identify individuals with certain diseases and take action to stop spread of disease. Statistical surveillance, on the other hand, focuses on populations to identify differentials and trends that can inform public health policymaking, including the allocation of resources. Surveillance systems rely on a diversity of data sources. Surveillance of infectious disease relies primarily on the system of notifiable diseases put in place in the nineteenth century. Statistical surveillance systems use data from vital statistics, population‐based sample surveys, registries, and health care records. The introduction and growth of syndromic surveillance following 9/11 has focused on a statistical approach to detecting bioterrorist attacks. Despite many accomplishments, the value of syndromic surveillance for detecting bioterrorist attacks has not yet been demonstrated. Ultimately, the most important contribution of syndromic surveillance systems to public health may be for natural disease outbreaks, such as seasonal and pandemic influenza, and for other public health purposes beyond simple detection.

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Forecasting Survival, Health, and Disability: Report on a Workshop

Cited by 16 publications

References 26 publications

Mortality Modelling and Forecasting: a Review of Methods

Mortality Modelling and Forecasting: a Review of Methods

Public Health Surveillance

Public Health Surveillance

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