International Journal of Population Research

Volume 2016 (2016), Article ID 5236351, 7 pages

http://dx.doi.org/10.1155/2016/5236351

## Proximate Determinants of Fertility in Zambia: Analysis of the 2007 Zambia Demographic and Health Survey

Department of Public Health, University of Zambia School of Medicine, P.O. Box 50110, 10101 Lusaka, Zambia

Received 25 August 2015; Revised 18 March 2016; Accepted 23 March 2016

Academic Editor: Jonathan Haughton

Copyright © 2016 Mumbi Chola and Charles Michelo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The role of proximate determinants in influencing fertility has been well documented worldwide. Bongaarts’ aggregate model of the proximate determinants (which focuses on marriage, contraception, abortion, and sterility) has been widely used to analyse the influence of proximate determinants on fertility. In Zambia, however, there is limited understanding of their effects. Therefore, the aim of this study was to examine the effect of proximate determinants of fertility in Zambia using Bongaarts’ model. This was a cross-sectional analysis of women’s data from the 2007 Zambia Demographic and Health Survey (ZDHS). A total of 7,146 women aged 15 to 49 years participated in the ZDHS. Bongaarts’ model was employed in the data analysis. Results showed that, overall, mean age was 27.8 years and rural-urban distribution was 56% and 44%, respectively. Marriage (40%) and postpartum infecundity (22%) accounted for the largest inhibiting effect on natural fertility from its biological maximum of 19.10. Contraception use accounted for only 3%. Therefore, in order to manage fertility in Zambia, policies and programmes should consider the effects of marriage, postpartum infecundity, and contraception on fertility. Without such targeted interventions, managing and maintaining population growth will remain a challenge in Zambia.

#### 1. Introduction

The 2010 Census of Population and Housing reported that the average population growth rate of Zambia is 2.8%, which is an increase from 2.4% recorded during the 1990–2000 intercensal period [1]. This population growth rate has been driven by a high fertility rate of 6.2 births per woman [2]. This means that a Zambian woman could give birth to an average of about 6.2 children during her reproductive life cycle [2]. Studies by Bongaarts and Potter [3] and Boermaa and Weir [4], among others, have shown that fertility rates are influenced by various factors among which are proximate determinants. Proximate determinants of fertility include those factors that directly influence fertility [3–5]. This implies that if one proximate variable changes, holding all others constant, fertility will change.

Bongaarts [6] argued that there were eight proximate determinants of fertility categorised into three broad categories:(I)Exposure factors:(1)Proportion married.(II)Deliberate marital fertility control factors:(2)Contraception.(3)Induced abortion.(III)Natural marital fertility factors:(4)Lactational infecundability.(5)Frequency of sexual intercourse.(6)Sterility.(7)Spontaneous intrauterine mortality.(8)Duration of the fertile period.

These were the factors that directly influenced fertility. Their influence reduced fertility from the total fecundity rate which is the expected level of fertility or “natural fertility” in the absence of any deliberate controls. Bongaarts devised a quantitative framework and his analysis indicated that variations in four major factors, namely, marriage, contraception, lactation, and induced abortion, were the primary proximate causes of differences among populations [6]. This framework has since been used in various studies to analyse these four proximate determinants [4, 7].

Various studies employing this framework, such as studies by Letamo and Mekonnen and Worku, have shown the effect and influence that proximate determinants have on fertility [5, 8–11]. For example, an analysis of factors affecting fertility in Bangladesh showed that contraception emerged as the highest fertility reducing factor [5]. It was also shown that although the fertility reducing marriage was increasing, its effect was offset by the declining trend in the lactational infecundability period [5].

An application of Bongaarts’ model to vital statistics, population census, and survey data of Peninsular Malaysia revealed that marriage postponement and contraception were the two most important proximate determinants of fertility [12]. Analysis by Mahjabeen and Khan in 2011 in Bangladesh found that contraception had the highest fertility reducing effect accounting for 51.1% [13]. Lubaale and Kayizzi [7] analysed the 1995 and 2001 Uganda Demographic and Health Surveys using Bongaarts’ model in an effort to explain fertility decline in urban areas of Uganda [7]. Their findings established that the change in the proportion of married women and postpartum infecundability due to breastfeeding had the greatest inhibiting effect on fertility in urban areas of Uganda, while contraception use contributed the least [7]. Similar results have been found in Zambia [14]. Analysis of the 1992 Zambia Demographic and Health Survey data using Bongaarts’ model showed that, at that time, postpartum infecundity (37%) contributed most towards reducing fertility followed by contraception (7%) and marriage (5%) [14].

Although the effects of proximate determinants have been documented in Zambia, no study has been conducted recently to show what the current scenario is with regard to proximate determinants and how this understanding interacts or informs prevailing or previously conducted interventions as they related to maternal and child health survival strategies. Previous studies, for example, by Dzekedzeke [14], were conducted prior to 1996. With the campaigns on sexual abstinence, contraception use, and breastfeeding that have been conducted since the early 1990s, it is necessary to determine how these activities may have influenced changes in the effects of proximate determinants on fertility. The effect of these factors may have changed from what was observed previously and thus may provide more recent information on the current effects of proximate determinants on fertility levels in Zambia.

Therefore, this study aimed to examine the proximate determinants of fertility in Zambia using data from the 2007 Zambia Demographic and Health Survey (ZDHS). This is the most recent Demographic and Health Survey done in Zambia. Findings from this research will provide information on proximate determinants of fertility in Zambia and how they are associated or interact with health promotion activities for maternal survival programmes relevant to fertility management. The following sections describe the methods used, the results obtained, the discussion, and conclusions drawn.

#### 2. Materials and Methods

The research was based on analysis of population data from the 2007 Zambia Demographic and Health Survey (ZDHS). This is a nationally representative survey of Zambian households with a stratified representative sample of 8,000 households. All women aged between 15 and 49 years and all men aged between 15 and 59 years that were either permanent residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed. The survey collected various data including levels, patterns, and trends in both current and cumulative fertility and sexual activity as well as family planning which included aspects of contraception including knowledge of specific contraceptive methods, attitudes, and behaviour regarding contraceptive use and sources of and cost of methods [2].

The research focused on analysing female fertility data. The outcome variable was live births recorded in the year preceding the survey, that is, whether the woman gave birth to a live baby in year before the ZDHS was conducted. This was a dichotomous variable with those who gave birth coded as 0 and those who did not give birth coded as 1. Analysis of the data focused on proximate determinants as collected in the ZDHS. This included marriage, contraception, sexual activity, abstinence, postpartum amenorrhea, abortion, and menopause.

Data analysis was done by applying Bongaarts’ proximate determinants model for analysing proximate determinants of fertility to female fertility data from the 2007 ZDHS. The model of Bongaarts and Potter [3] quantifies the contribution of four proximate determinants of fertility, namely, marriage, contraception, abortion, and postpartum infecundity [3]. The basic structure of the model is summarized by relating the fertility measures to the proximate determinants. The equations are shown as follows: (see [15]). * * (2). * * (3).TFR is the total fertility rate, TM is the total marital fertility rate, TN is the total natural marital fertility rate, TF is the total fecundity rate, and , , , and are the indices of marriage, contraception, induced abortion, and postpartum fecundability, respectively. The indices can only take values between 0 and 1. When there is no fertility inhibiting effect of a given intermediate fertility variable, the corresponding index equals 1; if the fertility inhibition is complete, the index equals 0. These indices can be estimated from measures of the proximate variables and these estimates are given below.

##### 2.1. Estimation of the Index of Marriage ()

The index of marriage measures the inhibiting effect of marriage on fertility in the population. It has to be noted that the higher the level of marriage in the population the less the inhibiting effect on fertility and the reverse is true. The index of marriage is estimated using the following formula:where is index of marriage, is age specific proportions of married females, and is gotten by dividing the number of married women of a particular age group by the number of women in the same age group. is age specific marital fertility rates; is gotten by dividing the births of a particular age group by the number of women in the same age group.

##### 2.2. Estimation of the Index of Contraception ()

The index of contraception measures the inhibiting effect of contraception on fertility in the population. The higher the level of contraception in the population the higher the inhibiting effect due to contraception and the lower the level of contraception the lower the inhibiting effect. The index of contraception is estimated using the following formula:where is proportion using contraception among married women of reproductive age (15–49 years); is average use effectiveness of contraception; the coefficient 1.08 represents an adjustment for the fact that women do not use contraception if they know that they are sterile. The indices of use effectiveness proposed for particular contraceptives are pill = 0.90, IUD = 0.95, sterilisation = 1.00, and others = 0.70 [3].

##### 2.3. Estimation of the Index of Abortion ()

The index of abortion measures the inhibiting effect of abortion on fertility in the population. In this research, the index of abortion was set at 1.0 due to lack of data. Abortion data in the ZDHS include still births and miscarriages; therefore, it was difficult to isolate the abortion data. The index of abortion is estimated using the following formula: where is prevalence of contraceptive use; is average number of births averted per induced abortion and (). when and when . TA is total abortion (average number of induced abortions per woman at the end of the reproductive period if induced abortion rates remain at prevailing levels throughout the reproductive period). if the TA is 0. Therefore, the total abortion rate in this study is 1.0.

##### 2.4. Estimation of the Index of Postpartum Infecundability ()

The index of postpartum infecundability measures the inhibiting effect of breastfeeding or abstinence on fertility in the population. The index of postpartum infecundability in the model is estimated using the effect of breastfeeding (lactational amenorrhea) or postpartum abstinence. The index of postpartum infecundability () is estimated aswhere is the index of postpartum infecundability and is average duration of postpartum infecundability caused by breastfeeding or postpartum abstinence. In this research, the index of postpartum infecundability was estimated using the mean duration of breastfeeding.

The indices in Bongaarts’ proximate determinants model were computed using a Microsoft Excel spreadsheet developed by the Futures Group [16] containing the necessary formulas needed to compute the indices. The spreadsheet illustrates the proximate determinants of fertility using the model developed by John Bongaarts. Information on the major proximate determinants described above was entered into the spreadsheet. Based on this information, the spreadsheet calculated the total fecundity or the biological maximum of fertility. The effects of each of the proximate determinants in reducing fertility from the total fecundity rate, or biological maximum, to the actual total fertility rate were then computed and displayed using graphs and tables. This was also done for selected background characteristics such as residence, education, and wealth quintiles. Below is the presentation of the results obtained.

#### 3. Results and Discussion

Overall (,146) the mean age of the female participants was 27.8 years and about 60% were reported as married, whereas only 26% were single [2]. This percentage was considered as proportion married in computing the indices of marriage. Duration of postpartum infecundability, as reported in the ZDHS report, was 13 months [2] and this is what was used in the computations to represent the average duration of postpartum infecundability. With regard to abortion data, 12.5% indicated having had a pregnancy terminated. However, this included still births and spontaneous abortions such as miscarriages. As such, determining the actual abortion figures proved problematic because of difficulties in isolating abortion data. Therefore, in the computations, abortion index was indicated as 1 to indicate absence of abortion data. Sterility contraceptive prevalence was reported as 1.3%. Based on this information, total fecundity was calculated and the effects of each proximate determinant in reducing fertility from the total fecundity rate to the actual total fertility rate are displayed graphically in Figure 1.