Early-life mortality clustering in families: A literature review

Research on early-life mortality in contemporary and historical populations has shown that infant and child mortality tend to cluster in a limited number of high-mortality families, a phenomenon known as ‘mortality clustering’. This paper is the first to review the literature on the role of the family in early-life mortality. Contemporary results, methodological and theoretical shortfalls, recent developments, and opportunities for future research are all discussed in this review. Four methodological approaches are distinguished: those based on sibling deaths, mother heterogeneity, thresholds, and excess deaths in populations. It has become clear from research to date that the death of an older child harms the survival chances of younger children in that family, and that fertility behaviour, earlier stillbirths, remarriages, and socio-economic status all explain mortality clustering to some extent.     

Child Obesity and the Interaction of Family and Neighborhood Socioeconomic Context

Population Research and Policy Review - The literature on neighborhoods and child obesity links contextual conditions to risk, assuming that if place matters, it matters in a similar way for...     

PREFERENCE MAPPING OF PERCEIVED INFORMATION STRUCTURE: IMPLICATIONS FOR DECISION SUPPORT SYSTEMS DESIGN

In a recent issue of this journal, Watkins [13] presented an approach for discovery of decision-maker perceptions of the complexity (dimensionality) of information items that might be supplied by a decision support system. Through use of multidimensional scaling and cluster analysis, relatively homogeneous groups of decision makers, sharing common perceptions of various information items, were formed. This prior research was referred to as a first step in suggesting that information reports could be tailored to groups of decision makers classified on the basis of common perceptions of information. The current research extends the prior study by evaluating decision maker preferences for information in a variety of decision-making scenarios in relation to the previously identified perceptions of the information. Based on the results of the study, conclusions are made which suggest that the tailoring of information to groups of decision makers should be based on both perceptions and preferences for information. Even so, it is demonstrated that the decision tasks have an impact on the preferences for information which may affect the attempt to tailor information to groups of decision makers.     

ASSESSING ENVIRONMENTAL SCANNING AT THE SUBUNIT LEVEL: A MULTITRAIT-MULTIMETHOD ANALYSIS*

Environmental scanning activities of over 400 top management subunits in 108 European manufacturing firms provided the data base for this research. Four traits (constructs) of scanning using three methods (interest, frequency, and time) were examined. A confirmatory factor analysis approach to multitrait-multimethod (MTMM) data was employed. Verification of the reliability as well as convergent and discriminant validity of the scanning scales are reported and discussed. The viability of confirmatory factor analysis in providing a precise analysis of partitioning variance according to trait, method, and error is demonstrated.     

DEVIL'S ADVOCACY AND DIALECTICAL INQUIRY EFFECTS ON PREDICTION PERFORMANCE: TASK INVOLVEMENT AS A MEDIATING VARIABLE

Critics of previous laboratory experiments comparing devil's advocacy (DA) to dialectical inquiry (DI) have suggested that these experiments produced misleading results because (1) they used subjects who had low levels of task involvement and (2) the DI treatment used was confusing to subjects and required further explanation to be useful. The present study examines the effects of four inquiry methods—expert (E), DA, DI, and DI with explanatory statement (DI+)—on subjects' performance at a financial prediction task. Results show that DA, DI, and DI + were superior to E when the state of the world differed significantly from assumptions underlying the expert's plan. For subjects with high task involvement, DI and DI + were more effective than E and DA. The results support some of the criticisms of previous laboratory research and suggest that future research on these decision aids should include task involvement as a factor.     

THE EFFECT AND COST OF FORECAST ERROR BIAS FOR MULTIPLE-STAGE PRODUCTION-INVENTORY SYSTEMS

This paper examines forecast error with an intention not to minimize forecast error so much as to bias the resultant error in order to achieve better managerial results. Much work has been accomplished by other researchers toward the goal of minimizing forecast error—sometimes by the use of sophisticated forecasting techniques. The results of this paper indicate that manipulating forecast error bias may be the better managerial strategy as opposed to going to great lengths to minimize forecast error. Subject Areas: Forecasting, Aggregate Planning, Capacity Planning, and Production/Operations Management.     

A MINIMUM-COST AUDIT SAMPLING METHODOLOGY UNDER CONDITIONS OF PREDETERMINED BETA RISK

This paper presents a minimum-cost methodology for determining a statistical sampling plan in substantive audit tests. In this model, the auditor specifies β, the risk of accepting an account balance as correct when it is not, according to audit evidence requirements. Using β as a constraint, the auditor then selects a sampling plan to optimize the trade-off between sampling costs and the costs of follow-up audit procedures. Tables to aid in this process and an illustration are provided.     

Mortality attributable to obesity among middle-aged adults in the united states

Obesity is considered a major cause of premature mortality and a potential threat to the longstanding secular decline in mortality in the United States. We measure relative and attributable risks associated with obesity among middle-aged adults using data from the Health and Retirement Study (1992–2004). Although class II/III obesity (BMI _ 35.0 kg/m2) increases mortality by 40% in females and 62% in males compared with normal BMI (BMI = 18.5-24.9), class I obesity (BMI = 30.0-34.9) and being overweight (BMI = 25.0-29.9) are not associated with excess mortality. With respect to attributable mortality, class II/III obesity (BMI _ 35.0) is responsible for approximately 4% of deaths among females and 3% of deaths among males. Obesity is often compared with cigarette smoking as a major source of avoidable mortality. Smoking-attributable mortality is much larger in this cohort: about 36% in females and 50% in males. Results are robust to confounding by preexisting diseases, multiple dimensions of socioeconomic status (SES), smoking, and other correlates. These findings challenge the viewpoint that obesity will stem the long-term secular decline in U.S. mortality.     

The End of “Lowest‐Low” Fertility?

Total fertility rates fell to previously unseen levels in a large number of countries beginning in the early 1990s. The persistence of TFRs below 1.3 raised the possibility of rapid population aging and decline. We discuss the recent widespread turnaround in so‐called lowest‐low‐fertility countries in Europe and East Asia. The number of countries with TFRs below 1.3 fell from 21 in 2003 to five in 2008. Moreover, the upturn in the TFR was not confined to lowest‐fertility countries, but affected the whole developed world. We explore the demographic explanations for the recent rise in TFRs stemming from fertility timing effects as well as economic, policy, and social factors. Although the current economic downturn may suppress TFRs in the short run, we conclude that formerly lowest‐low‐fertility countries will continue to see increases in fertility as the transitory effects of shifts to later childbearing become less important.     

Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.