Poster session on Demographic methods and data

Different surveys were giving different results about malnutrition of young children. The objective of this paper are: (i) to assess the quality of anthropometric and age data of children of the Multiple Cluster Indicator Survey (MICS) of Somalia 2006, and (ii) to identify the sources of errors and (iii) to suggest recommendations for improving data. Consistency checking of the indices, deviation from the expected pattern of age distribution, age specific malnutrition, and distribution of digits of weight and height were used as measuring tools of errors. Some qualitative data were collected by interviewing key survey persons and checking the filled in questionnaire and data files. National Nutrition Survey (NNS) and DHS data were used for comparison. Some errors in weight and height data were present. But mainly error in age makes the estimate of malnutrition unexpectedly high. Different sources of errors were identified. Error in age, weight and height data need serious attention to make estimate of malnutrition meaningful. Good training, supervision, and equipment are essential for obtaining good quality weight and height data. In addition to all of these, necessary methods and tools including a calendar of local events should be developed, tested and used for good age data.

This paper aims to discuss the implementation of contemporary ICT application , called short messaging service (SMS), in supporting the monitoring process during the field enumeration period. This technology was remarkable useful, effective and cheap to overcome the field monitoring in Timor Leste which has geographical and infrastructure barriers. Best practices in the management along with field monitoring using SMS reports were believed to have positive impact on the result of the 2010 Timor Leste population and Housing Census. The differences of total population in Timor Leste between preliminary results (reported on October 2010, one month after field works completed) and final results (reported on April 2011) is only 173 persons out of 1.07 million.

Keywords: population and housing census, ICT implementation, SMS reporting.

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.

The Human Fertility Collection (HFC) is a joint project of the Max Planck Institute for Demographic Research (MPIDR) and the Vienna Institute of Demography (VID). It is designed to supplement the Human Fertility Database (www.humanfertility.org) and provide a variety of fertility data collected from various external sources. In contrast to the HFD, the collection comprises data not only from official statistics but also published by individual researchers and research organizations and may involve additional dimensions (regions, ethnic groups, etc.). The relevant documentation is also offered to the users. The HFC aims at including a wide range of countries and covering the longest possible time periods. The data in the HFC are provided in the standardized format and can be easily and openly accessed. The HFC website is expected to be launched in August 2013 (www.fertilitydata.org ). The database will contain unconditional fertility rates and selected aggregate indicators for a number of national and subnational populations. Whenever possible, order-specific data will be also made accessible to the users. This paper describes the HFC, computational procedures used for data harmonization as well as its structure.

In this paper, we develop a Bayesian cohort population projection model that incorporates model uncertainty. We first argue that a Bayesian approach is a more natural framework for incorporating various forms of uncertainty in probabilistic projections. Second, we demonstrate the differences that arise from choosing different Lee-Carter type models for fertility, mortality, immigration and emigration in terms of forecasted age patterns and their associated measures of uncertainty. Third, we incorporate this information into a cohort component projection model and use Bayesian model averaging techniques to produce a model-averaged population forecast for the United Kingdom by age and sex. We end the paper by discussing the merits and flexibility of a Bayesian cohort component projection model and highlight some areas where this work could be extended.

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.

Short Abstract: Age is an important variable in the anthropometric investigation of nutritional status of children. Bias and random error are common in children’s age data. We assess the effect of bias and random error in age on (i) estimates of malnutrition; and on (ii) the mortality discriminating power of age dependent anthropometric indicators. Accurate age, weight, height and mortality data were collected for 1,571 Bangladeshi children under five years of age. We introduce systematic bias and random error into the age variable and compute anthropometric indicators using both the correct original data and the data with errors. Results from different data sets are compared. Positive age bias over estimates while negative bias under estimates malnutrition. It is shown statistically and empirically in the paper that if malnutrition in the population is less than 50%, random error in age overestimates malnutrition; if it is more than 50%, it underestimates malnutrition; and if it is 50%, there will be no effect. Both bias and random error affect the mortality discriminating power of anthropometric indices. Errors in age data of young children deserve serious attention to make anthropometric investigation of young children meaningful and useful.

During last two decades India has moderate reduction of child mortality which is an important measure of population health. It is frequently watched by demographers to determine how rapidly moving. However, most of its projection techniques are still based on conventional approach which ignores prior information. This suggests performing Bayesian analysis for projecting Indian child mortality rate to incorporate prior information. Our method of illustration includes dynamic linear models based on state space prior and known demographic information from National Family Health Survey (NFHS) data. In order to evaluate the accuracy of estimates, we compare our estimates with conventional estimates.

We present a methodology based on Cubic Splines to fit the general mortality curve in Mexico by sex at all ages for the years of 2000, 2005 and 2010. This method consists in fitting a polynomial of degree three in the first four ages (fitting them perfectly); between ages 4 and 80 we fit a cubic spline using five nodes; we fit another third-degree polynomial beginning at age 80. We employ 24 parameters to estimate the mortality curve in the full age range. This model presents an excellent goodness of fit in men, which increases over the years; for women, the goodness of fit is, in general, better than men’s. This methodology is considered to be a relatively parsimonious estimation of mortality; it maintains the mortality curve’s changes in concavity; and it allows the realization of mortality projections in different contexts and causes of death.

An American demographer George Chandler Whipple (1866–1924) gives the Whipple’s index of digit preference shows systematic heaping on ages ending with the digit 0 and 5. Thomas Spoorenberg (2009) states that “the original Whipple’s index does not account well for the quality of age reporting once it is improving and reaches better levels”. He further states that “the total modified Whipple’s Index (Wtot) suggested by him offers however a simple alternative which fully accounts for the changes in the attraction/repulsion on all age-digits”. In present study an attempt is made to apply the newly developed indices to single year age data by sex of 26 states and 601 districts in India of District Level Household and Facility Surveys-3 (DLHS-3). An analysis of 26 states and 601 districts in India is done by using GIS software and is shown by means of maps of India.