Automobile accidents: potential years of life lost.
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This paper presents a simple method for estimating a birth rate and a level of mortality for an intercensal period. The birth rate is estimated from the intercept of a line fitted to data and the level of mortality from the slope of that line. The formula that is developed is based upon a recent generalization of stable population relations. An estimate of childhood mortality level is an optional but significant piece of additional input. An important by-product of the procedure is an estimate of the true age distribution.
Patterns of variation in mortality can be studied by measuring changes in selected life table functions. A model is proposed in which the rate of change over time in the life table survivorship probability at any age has been assumed as proportional to the product of its own value and its complementary probability or the probability of dying by that age, where the proportion is the same for all ages and depends only on the time duration between successive life tables. The end result is that the logit functions of the survivorship probabilities at two points in time are linearly related with a slope of one. The projecting power of the model has been tested by using U.S. life tables for the years 1950 and 1970 as well as Coale and Demeny's regional model life tables. In the latter case, the model produced surprisingly close matches even when the expectations of life differed by as much as 20 years.
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The shape of the survivorship curve can easily be interpreted on condition that the probability of death is proportional to an exponentially rising function of ageing. According to the formation of a sum for determining of the age index by Ries it was investigated to what extent the survivorship curve may be approximated by a sum of exponentials. It follows that the difference between the pure exponential function and a sum of exponentials by using possible values is lying within the random variation. Because the probability of death for different diseases is variable, the new statement is a better one.
A regression analysis of mortality can benefit from the introduction of certain apparently redundant factors. Following a description of the method of calculating the regression coefficients, data on mortality from breast cancer in England and Wales for 1951-1970 are analysed as a function of age group, quinquennium of birth and quinquennium of death.
An inefficient, but quickly and easily calculated, estimate of a survival-time distribution is described. Conditions for the estimate to be unbiased specify bounds on the length of time a study has been in progress and the way in which people must enter a study. These appear to be frequently met when a large medical registry is examined with a view to establishing base line survival experience for a selected class of patients.
A prospective study of perinatal and neonatal mortality and morbidity at the University Teaching Hospital (UTH), Lusaka for 1976 is presented. The early neonatal mortality of the babies born in hospital was 28.7 per 1000 live-births; it was 239.96 per 1000 admissions of those born outside the hospital. The still-birth rate in the hospital-born babies was 25.2 per 1000 deliveries; the perinatal mortality was 53.3 per 1000 deliveries; and the neonatal mortality in the hospital-born was 31.1 per 1000 live-births. The cause of death were asphyxia, infections could injury, respiratory distress syndrome, congenital malformation and intracranial haemmorrhage. If the perinatal and neonatal mortality and morbidity are to be reduced, much effort and co-operation of all concerned with the health of the expectant mother, and her child are required. Records of birth-weights, stillbirth, and causes of deaths in the University Teaching Hospital (UTH) for the year 1976 have been analysed. The aim is to provide a basis for future comparisons and improvement of the care given to newborns.
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