Examining underlying determinants of fertility rates in Zambia: Evidence from the 2007 Zambia Demographic and Health Survey

Limited data exits on factors influencing fertility in Zambia. This study examined underlying determinants of fertility patterns and levels in Zambia. Data extracted from the 2007 Zambia Demographic and Health Survey was analysed using bivariate and multivariate logistic regression. Of 7146 women aged 15-49 years, age group 25-29 years experienced the highest prevalence of births (28.5%). Married women accounted for 27% of all births. Women with low education recorded more births (27%) than those with higher education (9.5%) (P<0.001). Fertility was higher among the poorest (28%) compared to the richest (12%) (P<0.001). Though not statistically significant, urban areas recorded more births (25%) than rural areas (15%). Education and wealth significantly influence fertility Zambia. Fertility management strategies should consider these factors and their fertility reducing effects. Improving education and wealth status of women can contribute to fertility reduction, particularly rural women. Lower fertility, with reduced mortality and migration, would provide less pressure on distribution of the limited economic resources of the country.

Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project.

Background: Post-discharge mortality is a frequent but poorly recognized contributor to child mortality in resource limited countries. The identification of children at high risk for post-discharge mortality is a critically important first step in addressing this problem. Objectives: The objective of this project was to determine the variables most likely to be associated with post-discharge mortality which are to be included in a prediction modelling study. Methods: A two-round modified Delphi process was completed for the review of a priori selected variables and selection of new variables. Variables were evaluated on relevance according to (1) prediction (2) availability (3) cost and (4) time required for measurement. Participants included experts in a variety of relevant fields. Results: During the first round of the modified Delphi process, 23 experts evaluated 17 variables. Forty further variables were suggested and were reviewed during the second round by 12 experts. During the second round 16 additional variables were evaluated. Thirty unique variables were compiled for use in the prediction modelling study. Conclusion: A systematic approach was utilized to generate an optimal list of candidate predictor variables for the incorporation into a study on prediction of pediatric post-discharge mortality in a resource poor setting.