Indirect methods of mortality and fertility estimation: new techniques for new realities

An estimated 287,000 women die each year from complications in pregnancy or childbirth. Millennium Development Goal 5 includes a target to reduce the maternal mortality ratio by three quarters by 2015. Accurate measurement of maternal mortality is needed to develop a greater understanding of the problem, to increase effectiveness of program planning and targeting, and to track progress toward this goal. In the absence of good quality vital statistics, a number of interim methods are used to measure maternal mortality. The purpose of this study is to document three community-based interim methods that measure maternal mortality using verbal autopsy: a post-census mortality survey in Mozambique, a sample vital registration with verbal autopsy in Zambia, and a large-scale household survey in Bangladesh. This study will summarize the measures of maternal mortality obtained from these three platforms, compare and contrast the different methodologies employed, and evaluate strengths and weaknesses of each approach. This study will show that verbal autopsy is a feasible method for collecting maternal mortality data in the absence of reliable vital registration data and that choice of an interim method is dependent on balancing the trade-offs between statistical considerations and feasibility in the local context.

A decomposition of changes in fertility in two different sub-periods over time shows that the reproductive behavior of younger and older women. Fertility behavior of women in the society can be excess through mathematical models, here in this study the fertility behavior over time is modeled using bivariate geometric distribution. The objective of this paper is to explain the changes in fertility rates, focusing in particular on the postponement of childbearing in Uttar Pradesh, India with an emphasis on socio-economic considerations. Data have been taken from the third round of NFHS. Cultural diversity in Uttar Pradesh is reflected in substantial demographic variation across the nation; therefore, fertility levels and trends are also estimated by urban-rural residence for the last two consecutive five years periods preceding the survey. The paper also presents an appropriate method for estimating the parameters involved in the model. The model also explains the proportion of childless female during both the period under consideration.

The release of the sixth population census has provided us with the authoritative information needed to quantitatively analyze the characteristics and changes regarding fertility in China in 2010. The themes of this paper include the fertility levels, fertility pattern, and the influential factors of the changes in fertility. It is revealed that with the fertility levels decreasing, structural factors (e.g., age structure and marital status) are becoming the determinative ones at present and in the future. Although the age-specific marital fertility rate has resulted in an increase of general fertility rate by 9.61% from 2000 to 2010, the age structure and marital status are more and more important among factors which resulted in the changes in fertility level. In the aspect of fertility pattern, the interval between first marriage and first childbearing is enlarging, and the parity is becoming lower. The findings are important to develop an appropriate understanding of its demographics and to scientifically plan and adjust the population policy.

Fertility rates by single year of age of mothers are not available for India. Therefore, they have to be estimated from five year age groups using appropriate statistical method. The Sample Registration System (SRS) in India has published fertility rates by five-year age groups, 15-19 to 45-49 for the twenty large states and India from 2005 to 2009. A comparison of these rates with similar estimates by the United Nations (2010) indicates that the SRS data suffer from underestimation in early reproductive ages and overestimation in the older reproductive ages. The SRS five-year fertility rates, therefore, have to be adjusted first before embarking on the estimation of single year rates. Thus, the objectives of the paper are: (1) correct the five - year data on fertility rates; (2) develop single year age-specific fertility rates by using the Pearson Curve, Types I / III/ and other interpolation methods : Beers and Quadratic Spline; and (3) discuss the rationale and application of the selected Pearson Curve for projecting the number of births. The final outcome will be a set of adjusted single year fertility rates for the large states in India for each year, 2005 to 2009.

The age-specific fertility curves normalized by total fertility can be considered as the density of the age at childbearing distribution. Generally the shape of age specific fertility rate changes from convex to concave after it reaches its maximum value. Proportion bearing children before age 35 may be interpreted as tempo of fertility and rest may be interpreted as excess fertility, which is risky for mother as well as child both. Thus, the purpose of this study is to observe the pattern of fertility over time and space keeping the above idea into mind. To experience the modest change in fertility, the estimated total fertility rate, are computed for the data through simple mathematical model. For this purpose the secondary data on age-specific fertility rate and its forward and backward cumulative distributions have been considered. Also the validity of proposed models has been checked through appropriate technique.

Here in this study an attempt has been made to suggest a new methodology, based on regression approach to estimate the life expectancy at birth and also the life expectancy at age one for districts of EAG states and Assam in India using the following four simple demographic indicators namely: the crude death rate, the crude birth rate, the infant mortality rate and the proportion (or percent) of population aged 65 and above from annual reports of Sample Registration System (SRS) data. The basis of this methodology is adopted from a methodology which is earlier suggested by Bourgeois-Pichat (1983) for some other purpose but this has not been tried before for Indian situation.
This methodology is applied to estimate the life expectancy at birth for 2011 to the districts of EAG states and Assam in India utilizing the Annual Health Survey (AHS-2011) published results data under the Office of Registrar General of India.
The consistency and acceptability of the results obtained through this new method have been tested by comparing them with that of LEB that can be obtained using other methods which have also used the similar input in their development and result is quite acceptable.

The level of maternal mortality in Burkina Faso presents huge differences according to the sources of estimate. While the last census estimated a maternal mortality ratio to be 307 in 2006, United Nation agencies and the Demographic and Health survey provided for the year 2010, the numbers of 300 and 341 respectively. These fluctuations and inconsistencies in the estimates create confusion among policy makers and authorities. This study went through the contradictions and divergences to establish the good quality of census data compare to the others in terms of maternal mortality estimates. However, findings also highlighted the crucial need of improving the adjustment method use during census 2006. Furthermore, this study provided projected levels of maternal mortality ratio for the period 2006 to 2050.

Important demographic regularities between fertility and child mortality rates in Indian states are strong positive correlation and roughly parallel decline over time. This suggests developing a dynamic relationship between them by using National Family Health Survey (NFHS) data. Vector Auto regressions (VAR) with exogenous variable methodology are employed to estimate their relationship. Our model estimated a fertility equation in which child mortality rate is an endogenous variable in simultaneous equations system and vice versa. The model yields implication for the number of children born per thousand reproductive periods of women and died before reaching age five per thousand live births during a particular period of time. In order to evaluate the accuracy of estimates, we compare our estimates with observed estimates which provide consistent result.

We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works in a very wide variety of data quality contexts. To date, it has been shown to work only in a single case (i.e., Burkina Faso). Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates and net international migration flows from fragmentary data while formally accounting for measurement error. As inputs, Bayesian reconstruction takes initial bias-reduced estimates of age-specific population counts, fertility rates, survival proportions and net international migration. Here, we show that the method performs well when applied in a range of data quality contexts by reconstructing the female populations of Laos, a country with little vital registration data where population estimation depends largely on surveys, Sri Lanka, a country with some vital registration data, and New Zealand, a country with a highly developed statistical system and high-quality vital registration data. In addition, we extend the method to apply to countries without censuses at regular intervals. We also develop a method for using it to assess the consistency between model life tables and available census data, and hence to compare different model life table systems.

Old-age mortality accounts for large fractions of death. Yet for many developing countries, old-age mortality are often only referred by model life tables using mortality data at young ages, or sometimes at young and adult ages; and reliable estimates of old-age mortality using data collected from old-age population can hardly be found. Based on the fact that migration is rare and death risk is high at old ages, this paper proposed a method, namely the Census method, to estimate old-age mortality, using census data on old-age population. The Census method aims to eliminate the effects of age-reporting errors, and is composed of three models. The first model is the variable-r method that converts the census populations into the person-years of the underlying stationary population. The second is an adjustment model, which uses a common relationship between the survival ratios that is found in model life tables to eliminate the effects of age-reporting errors in censuses. And the third is the extended Gompertz model, which estimates the number of survivors at exact ages of the underlying stationary population based on the commonly observed mortality pattern. Examples are provided using census data from developing countries in Africa and Asia.